Neuroimaging and neuromodulation approaches to study eating

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Neuroimaging and neuromodulation approaches to study

eating behavior and prevent and treat eating disorders

and obesity

David Val-Laillet, E. Aarts, B. Weber, M. Ferrari, V. Quaresima, L.E.

Stoeckel, M. Alonso-Alonso, M. Audette, Charles-Henri Malbert, E. Stice

To cite this version:

David Val-Laillet, E. Aarts, B. Weber, M. Ferrari, V. Quaresima, et al.. Neuroimaging and neuro-

modulation approaches to study eating behavior and prevent and treat eating disorders and obesity.

Neuroimage-Clinical, 2015, 8, pp.1-31. �10.1016/j.nicl.2015.03.016�. �hal-01417725�

Review

Neuroimaging and neuromodulation approaches to study eating

behavior and prevent and treat eating disorders and obesity

D. Val-Laillet

⁎

, E. Aarts

,B.Weber

,M.Ferrari

, V. Quaresima

,L.E.Stoeckel

,M.Alonso-Alonso

,M.Audette

C.H. Malbert

,E.Stice

INRA, UR1341 ADNC, France

Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands

Department of Epileptology, University Hospital Bonn, Germany

Department of Life, Health and Environmental Sciences, University of L3Aquila, Italy

Massachusetts General Hospital, Harvard Medical School, USA

Beth Israel Deaconess Medical Center, Harvard Medical School, USA

Old Dominion University, USA

INRA, US1395 Ani-Scans, France

Oregon Research Institute, USA

abstractarticle info

Article history:

Received 1 December 2014

Received in revised form 18 March 2015

Accepted 19 March 2015

Available online 24 March 2015

Keywords:

Brain

Neuroimagin g

Neuromodulation

Obesity

Eating disorders

Human

Functional, molecular and genetic neuroimaging has highlighted the existence of brain anomalies and neural vul-

nerability factors related to obesity and eating disorders such as binge eating or anorexia nervosa. In particular,

decreased basal metabolism in the prefrontal cortex and striatum as well as dopaminergic alterations have

been described in obese subjects, in parallel with increased activation of reward brain areas in response to palat-

able food cues. Elevated reward region responsivity may trigger food craving and predict future weight gain. This

opens the way to prevention studies using functional and molecular neuroimaging to perform early diagnostics

and to phenotype subjects at risk by exploring different neurobehavioral dimensions of the food choices and mo-

tivation processes. In the ﬁrst part of this review, advantages and limitations of neuroimaging techniques, such as

functional magnetic resonance imaging (fMRI), positron emission tomography (PET), single photon emission

computed tomography (SPECT), pharmacogenetic fMRI and functional near-infrared spectroscopy (fNIRS) will

be discussed in the context of recent work dealing with eating behavior, with a particular focus on obesity. In

the second part of the review, non-invasive strategies to modulate food-related brain processes and functions

will be presented. At th e leading edge of non-invasive brain-based technologies is real-time fMRI (rtfMRI)

neurofeedback, which is a powerful tool to better understand the complexity of human brain–behavior relation-

ships. rtfMRI, alone or when combined with other techniques and tools such as EEG and cognitive therapy, could

be used to alter neural plasticity and learned behavior to optimize and/or restore healthy cognition and eating

behavior. Other promising non-invasive neuromodulation approaches being explored are repetitive transcranial

magnetic stimulation (rTMS) and transcranial direct-current stimulation (tDCS). Converging evidence points at

the value of these non-invasive neuromodulation strategies to study basic mechanisms underlying eating behav-

ior and to treat its disorders. Both of these approaches will be compared in light of recent work in this ﬁeld, while

addressing technical and practical questions. The third part of this review will be dedicated to invasive

neuromodulation strategies, such as vagus nerve stimulation (VNS) and deep brain stimulation (DBS). In combi-

nation with neuroimaging approaches, these techniques are p romising experimental tools to unravel the

NeuroImage: Clinical 8 (2015) 1–31

Abbreviations: 5-HT, serotonin; aCC,anterior cingulate cortex; ADHD, attention deﬁcit hyperactivity disorder; AN, anorexia nervosa; ANT, anterior nucleus of the thalamus; BAT, brown

adipose tissue; BED, binge eating disorder; BMI, body mass index; B N, bulimia nervosa; BOLD,blood oxygenation level dependent; BS, bariatric surgery; CBF, cerebral blood ﬂow; CCK,cho-

lecystokinin; Cg25, subgenual cingulate cortex; DA, dopamine; daCC, dorsal anterior cingulate cortex; DAT, dopamine transporter; DBS, deep brain stimulation; DBT, deep brain therapy;

dlPFC,dorsolateralprefrontalcortex;DTI, diffusion tensorimaging;dTMS, deep transcranial magneticstimulation;ED, eatingdisorders;EEG,electroencephalography;fMRI, functional mag-

netic resonance imaging; fNIRS, functional near-infrared spectroscopy; GP, globuspallidus; HD-tDCS, high-deﬁnition transcranial direct current stimulation; HFD, high-fat diet; HHb, deox-

ygenated-hemoglobin; LHA, lateral hypothalamus; lPFC, lateral prefrontal cortex; MER, microelectrode recording; MRS, magnetic resonance spectroscopy; Nac, nucleus accumbens; OCD,

obsessive–compulsive disorder; OFC, orbitofrontal cortex; O

Hb, oxygenated-hemoglobin; pCC, posterior cingulate cortex; PD, Parkinson3s disease; PET, positron emission tomography;

PFC, prefrontal cortex; PYY, peptide tyrosine tyrosine; rCBF, regional cerebral blood ﬂow; rtfMRI, real-time functional magnetic resonance imaging; rTMS, repetitive transcranial magnetic

stimulation;SPECT,single photon emissioncomputed tomography;STN,subthalamic nucleus;tACS,transcranial alternatecurrentstimulation;tDCS,transcranial directcurrentstimulation;

TMS,transcranial magneticstimulation;TRD, treatment-resistant depression;tRNS,transcranialrandom noisestimulation; VBM, voxel-basedmorphometry; vlPFC, ventrolateralprefrontal

cortex; vmH, ventromedial hypothalamus; vmPFC, ventromedial prefrontal cortex; VN, vagus nerve; VNS, vagus nerve stimulation; VS, ventral striatum; VTA, ventral tegmental area

* Corresponding author at: INRA UR1341 ADNC F-35650, France.

E-mail address: david.val-laillet@rennes.inra.fr (D. Val-La illet).

http://dx.doi.org/10.1016/j.nicl.2015.03.016

Contents lists available at ScienceDirect

NeuroImage: Clinical

journal homepage: www.elsevier.com/locate/ynicl

intricate relationships between homeostatic and hedonic brain circuits. Their potential as additional therapeutic

tools to combat pharmacorefractory morbid obesity or acute eating disorders will be discussed, in terms of tech-

nical challenges, applicability and ethics. In a general discussion, we will put the brain at the core of fundamental

research, prevention and therapy in the context of obesity and eating disorders. First, we will discuss the possi-

bility to identify new biological markers of brain functions. Second, we will highlight the potential of neuroimag-

ing and neuromodulation in individualized medicine. Third, we will introduce the ethical questions that are

concomitant to the emergence of new neuromodulation therapies.

(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Contents

1. Introduction............................................................... 2

2. Utility of neuroimaging to investigate eating behavior and elucidate risk and maintenance factors for weight gain and eating disorders: towards new phenotyping

andpreventionstrategies ......................................................... 3

2.1. Predictingfutureweightgainandmaintenanceonthebasisofneuralresponsivityandfunctioning .................... 3

2.1.1. Rewardsurfeitandincentivesensitizationtheoriesofobesity ................................. 3

2.1.2. Reward deﬁcittheoryofobesity .............................................. 4

2.1.3. Inhibitorycontrol ..................................................... 4

2.1.4. Theoreticalimplicationsandfutureresearchdirections .................................... 5

2.2. Dopaminergicimaging....................................................... 5

2.2.1. Nucleartomographicimaging ............................................... 5

2.2.2. GeneticfMRI ....................................................... 7

2.2.3. Futuredirectionsfordopaminergicimaging ......................................... 7

2.3. Thecontributionoffunctionalnear-infraredspectroscopy(fNIRS) ................................... 7

2.3.1. Briefoverviewoftheprinciples,advantagesandlimitationsoffNIRS .............................. 7

2.3.2. ApplicationoffNIRSformappinghumancorticalresponsesinthecontextoffoodstimuli/intakeandeatingdisorders........10

3. Non-invasiveneuromodulationapproaches:recentdevelopmentsandcurrentchallenges............................10

3.1. Real-timefMRIneurofeedbackandcognitivetherapy .........................................10

3.1.1. Introductiontoneurofeedbackincognitivereappraisal ....................................10

3.1.2. Cognitivereappraisal,obesity,andeatingdisorders......................................10

3.1.3. Proof-of-conceptfortheuseofrtfMRIneurofeedbackwithcognitivereappraisalfortheregulationoffoodintakebehavior .....12

3.1.4. ConsiderationforrtfMRIneurofeedbackexperimentstargetingdisordersofingestivebehavior ..................12

3.2. Transcranialmagneticstimulation(TMS)andtranscranialdirect-currentstimulation(tDCS) .......................13

3.2.1. IntroductiontoTMSandtDCS ...............................................13

3.2.2. Summaryofclinicalstudiestomodifyeatingbehaviorandeatingdisorders...........................15

3.2.3. Futureneeds:fromempirically-drivenstudiestorationalandmechanisticapproaches......................15

4. Invasiveneuromodulationstrategies:recentdevelopmentsandcurrentchallenges...............................16

4.1. Overviewoftheperipheralneuromodulationstrategiesinthecontextoffoodintakeandweightcontrol..................16

4.1.1. Changesinvagalsignalingduringobesity ..........................................16

4.1.2. Effectsofvagalstimulation.................................................16

4.1.3. Effectsofvagalblockade..................................................17

4.2. Stateoftheartofdeepbrainstimulation(DBS)anditspotentialfortacklingobesityandeatingdisorders .................17

4.2.1. OverviewonthestateoftheartinDBS ...........................................17

4.2.2. RecentDBSinnovationsandemergingDBStherapies .....................................18

4.2.3. ApplicabilityofDBSinthecontextofobesityandeatingdisorders ...............................18

5. Generaldiscussionandconclusions:thebrainatthecoreofresearch,preventionandtherapyinthecontextofobesityandeatingdisorders.....19

5.1. Towardsnewbiologicalmarkers?..................................................19

5.2. Neuroimagingandneuromodulationinthescopeofpersonalizedmedicine...............................20

5.3. Ethicsrelatedtonoveldiagnosticandtherapeutictools ........................................22

5.4. Conclusion ............................................................22

Acknowledgments...............................................................22

References ..................................................................23

1. Introduction

A recent study estimated the number of overweight adults in the

world as roughly 2.1 billion in 2013 (Ng et al., 2014). In the United

States alone, obese individuals have 42% higher health care costs than

those with healthy-weight (Finkelstein et al., 2009). Obesity is on the

rise, with severe obesity rising at a particularly alarming rate (Flegal

et al., 2010; Finkelstein et al., 2012). Because obesity is a multifactorial

condition with a complex etiology, and because success of interventions

is subject to a large interindividual variability, there is no panacea or

“one-ﬁt-all” treatment for obesity. Bariatric surgery (BS) is the treat-

ment of choice for severe obesity due to its effectiveness compared to

behavioral and pharmacologic al interventions (Buchwald and Oien,

2013). Its utility and success rate is widely accepted. However, 20–40%

of those who undergo BS fail to lose sufﬁcient weight (Christou et al.,

2006; Livhits et al., 2012) or regain signiﬁcant weight after treatment

(Magro et al., 2008; DiGiorgi et al., 2010; Adams et al., 2012), and can

experience a number of complications during and after surgery or med-

ical and psychiatric comorbidities (Shah et al., 2006; Karlsson et al.,

2007; DiGiorgi et al., 2010; Bolen et al., 2012; Chang et al., 2014). In ad-

dition to existing methods such as BS, which annually helps thousands

of people worldwide, there is a clear need for novel approaches to obe-

sity prevention and treatment, including the development of novel di-

agnostic and phenotyping methods, as well as adjunctive therapies

2 D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

that may lead to better treatment outcomes for patients who may

require invasive procedures such as BS. In comparison to the rising

obesity epidemic, eating disorders (ED) are scarcer but also certainly

underestimated and increasing at a s tartling state (Makino et a l.,

2004). In the United States, up to 24 million people across all ages and

genders suffer from ED (anorexia — AN, bulimia — BN and binge eating

disorder — BED) (Renf rew Center Foundation for Eating Disorders, 2003),

and only 1 in 10 people with ED receives treatment (Noordenbox, 2002),

even though ED have the highest mortality rate of any mental illness

(Sullivan, 1995). Epidemiology of ED was described in details (including

risk factors, incidence, prevalence, and morbidity) in recent reviews

(see Smink et al., 2012; Mitchison and Hay, 2014).

In the ﬁght against obesity and eating disorders, improved knowl-

edge about the pathophysiological and neurobehavioral mechanisms

underlying these diseases is needed to better prevent risky behaviors,

diagnose and treat patients, and develop new therapies that are safer

and adjustable to each patient. As noted by Schmidt and Campbell

(2013), treatment of eating disorders cannot remain ‘brainless’,and

the same applies to obesity when we consider the growing amount of

literature highlighting the behavioral and brain changes/plasticity

induced by obesit y (Wang et al., 2009b; Burger and Berner, 2014),

effective bariatric surgery (Geliebter, 2013; Scholtz et al., 2014), and

neuromodulatory interventions (McClelland et al., 2013a ; Gorgulho

et al., 2014) in animal models and human subjects.

Although several excellent review papers on this subject exist (see

McClelland et al. , 2013a; Sizonenko et al., 2013; Burger and Berner,

2014; Gorgulho et al., 2014), a comprehensive work comparing a large

spectrum of exploratory and therapeutic strategies using neuroimaging

and neuromodulation technologies, in terms of advantages and limita-

tions, degree of invasiveness, and applicability to individualized medi-

cine from prevention to treatment is missing and can help provide a

road map for future research and applications. Predictive and preven-

tion studies beneﬁting from neuroimaging are emerging thanks to the

characterization of neural vulnerability factors that increase ri sk for

weight gain and risky eating behaviors. The ﬁrst part of our review

will be dedicated to this question, as well as to the role of functional, nu-

clear, and genetic neuroimaging in fundamental research and preven-

tion programs. A particular focus will be put on obesity, because it is

the number one concern, though references to speciﬁc ED will be in-

cluded when relevant. In this ﬁrst part we will also review for the ﬁrst

time the contribution of a less costly and more portable cortical func-

tional neuroimaging tool (i.e. fNIRS) in the context of research on eating

behavior. The second part of our review will provide an overview of the

non-invasive neuromodulatory approaches to combat weight problems

and ED, including a pres entation of real-time fMRI neurofeedback

coupled with cognitive therapy, as well as a comparison between trans-

cranial magnetic stimulation (TMS) and transcranial direct current

imulation (tDCS). The third section will be dedicated to more invasive

neuromodulatory approaches to modulate homeostatic and hedonic

mechanisms through the stimulation of the vagus nerve or deep-brain

structures. Finally, we will discuss all the data presented in the perspec-

tive of obesity/ED phenotyping and individualized medicine, while ad-

dressing the ethical questions raised by new therapeutic approaches

and their promise.

2. Utility of neuroimaging to investigate eating behavior and elucidate

risk and maintenance factors for weight gain and eating disorders:

towards new phenotyping and prevention strategies

2.1. Predicting future weight gain and maintenance on the basis of neural

responsivity and functioning

An improved understanding of the risk processes that give rise to ex-

cess weight gain should guide the design of more effective preventive

programs and treatments, which is vital because extant interventions,

with the possible exception of bariatric surgery, have limited efﬁcacy.

Theorists have focused on the reward circuitry because eating palatable

food increases activation in regions implicated in reward in both

humans and other animals, including the ventral and dorsal striatum,

midbrain, amygdala, and orbitofrontal cortex (OFC: Small et al., 2001;

Avena et al., 2006; Berridge, 2009; Stice et al., 2013) and causes dopa-

mine (DA) release in the dorsal striatum, with the amount released cor-

relating with meal pleasantness (Small et al., 2003) and caloric density

of the food (Ferreira et al., 2012) in humans. Both the orosensory prop-

erties of palatable food consumption (gustatory stimulation) and direct

intragastric infusion of high calorie food induce striatal DA release in re-

ward regions in human and animal studies (Avena et al., 2006; Tellez

et al., 2013).

2.1.1. Reward surfeit and incentive sensitization theories of obesity

The reward surfeit model holds that individuals with greater reward

region responsivity to food intake are at elevated risk for overeating

(Stice et al., 2008b). The incentive sensitization model posits that re-

peated intake of palatable foods results in an elevated responsivity of re-

ward regions to cues that are associated with palatable food intake via

conditioning, prompting elevated food intake when these cues are en-

countered (Berridge et al., 2010). According to animal studies, ﬁrin g of

striatal and ventral pallidum DA neurons initially occurs in response to

receipt of a novel palatable food, but after repeated pairings of palatable

food intake and cues that signal impending receipt of that food, DA neu-

rons begin ﬁring in response to reward-predictive cues and no longer

ﬁre in response to food receipt (Schultz et al., 1997; Tobler et al.,

2005). Elevated reward-related responses to food intake and cues puta-

tively override homeostatic processes of satiety, promoting excess

weight gain.

The present review focuses on prospective studies because cross-

sectional data ca nnot differentiate precursors from consequenc es of

overeating, with a focus on human studies unless otherwise indicated.

Hyper-responsivity of reward regions (striatum, amygdala, OFC) to pal-

atable food images (Demos et al., 2012), palatable food television com-

mercials (Yokum et al., 2014), geometric cues that signa l impending

palatable food image presentation (Yokum et al., 2011), palatable food

odors that predict impending palatable food receipt (Chouinard-

Decorte et al., 2010; Sun et al. , 2013), and pictorial cues that predict

impending palatable food receipt (Stice et al., 2015) predicted future

weight gain. Humans who show elevated dorsal striatum responsivity

to palatable food images show greater futu re weight gain, but only

if they are at genetic risk for higher DA signaling capacity due to

possessing an A2/A2 genotype of the TaqIA

polymorphism or a 6-

epeat or shorter of the 48-base pair exon 3 variable number tandem re-

peat (VNTR) polymorphis m of the DRD4 gene (Stice et al., 2010b),

which are both associated with greater DA signaling and reward region

responsivity (Jonsson et al., 1999; Bowirrat and Oscar-Berman, 2005).

The evidence from independent laboratories that elevated reward re-

gion responsivity to various food cues, including those that predict

impending palatable food receipt, predicted future weight gain provides

behavioral support for the incentive sensitization theory.

Elevated midbrain, thalamus, hypothalamus, and ventral striatum

responsivity to milk shake taste also predicted future weight gain

(Geha et al., 2013; Sun et al., 2013). Further, individuals who show ele-

vated dorsal striatum responsivity to palatable food intake show greater

future weight gain, but only if they are at genetic risk for elevated DA

signaling capacity by virtue of possessing an A2/A2 genotype of the

TaqIA polymorphism (Stice et al., 2008a; Stice et al., 2015 ). The evidence

that individuals who show elevated reward region responsivity to palat-

able food intake are more likely to enter a prolonged period of positive

energy balance and gain weight provides behavioral data in support of

the reward surfeit theory.

Although extant data provide support for both the incentive sensiti-

zation and reward surfeit theories of obesity, which are not mutually ex-

clusive, future studies should simultaneously examine individual

differences in neural response to palatable food taste, cues that signal

3D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

impending palatable food taste, and palatable food images to provide a

more comprehensive investigation of neural vulnerability factors that

predict future weight gain. Results imply that prevention programs

that reduce habitual intake of high-calorie foods should attenuate the

conditioning process that eventually leads to elevated reward region

responsivity to food cues, which may reduce future weight gain. Yet,

the fa ct that be havioral weig ht loss programs typically result in a tran -

sient reduction of high-calorie food intake, but do not produce sustained

weight loss implies that it is very difﬁcult to reduce reward region

hyper-responsivity to food cues once it has emerged. An uncontrolled

study suggested th at humans who have been able to sustain their

weight loss over long periods of time carefully limit intake of high -

calorie foods, exercise daily, and monitor their weight (Wing a nd

Phelan, 2005). These observations imply that it would be useful to test

whether interventions that increase executive control, either by direct

modiﬁcation of brain-behavior function or indirectly by modiﬁcation

of the environment (which could offset the risk from elevated reward

region responsivity) result in more lasting weight loss.

2.1.2. Reward deﬁcit theory of obesity

The reward deﬁcit model of obesity posits that indiv iduals with

lower sensitivity of DA-based reward regions overeat to compensate

for this deﬁciency (Wang et al., 2002). There have only been a few pro-

spective fMRI studies that could have potentially determined whether

reduced reward region responsivity preceded weight gain, and there

have not been any prospective studies that assessed with DA function-

ing (e.g. assessed with PET) predicted future weight change. Ou t of

the six prospective studies that examined the relation of BOLD response

to palatable food images, cues that signal impending palatable food re-

ceipt, and actual palatable food receipt to future weight gain reviewed

above (Chouinard-Decorte et al., 2010; Yokum et al., 2011; Demos

et al., 2012; Geha et al., 201 3; Yokum et al., 2014; Stice et al., 2015),

none found a relation between reduced reward region responsivity to

these food stimuli and greater future weight gain. Interestingly, howev-

er, a prospective study found that young adults who showed lower re-

cruitment of striatal regions in response to milk shake receipt (Stice

et al., 2008b, 2015) and palatable food images (Stice et al., 2010b)

showed greater future weight gain if they had a genetic propensity for

reduced DA signaling capacity. The interactive effects imply that there

may be qualitatively distinct reward surfeit and rewar d deﬁcit path-

ways to obesity, which should be investigated further.

Obese versus lean adults have shown lower striatal DA D2 receptor

availability (Volkow et al., 2008; de Weijer et al., 2011; Kessler et al.,

2014) and less striatal responsivity to high-calo rie beverage taste

(Stice et al., 2008b). I nterestingly, Guo et al. (2014) also suggested

that obese people have alterations in the DA neurocircuitry that may in-

crease their susceptibility to opportunistic overeating while at the same

time making food intake less rewarding, less goal directed and more

habitual. Whether the observed neurocircuitry alterations pre-exist or

occur as a result of obesity development is still controversial, but consid-

erable evidence suggests that overeating contributes to a down-

regulation of the DA-based reward circuitry. Lean younger subjects at

risk for future obesity due to parental obesity show hyper- rather than

hypo-responsivity of reward regions to palatable food receipt (Stice

et al., 2011). Women who gained weight over a 6-month period showed

a reduction in striatal responsivity to palatable food receipt relative to

baseline and to women who remained weight stable (St

ice et al.,

2010a). Rats randomized to overeating conditions that result in weight

gain versus control conditions show a down-regulation of post-synaptic

D2 receptors, and reduced D2 sensitivity, extracellular DA levels in the

nucleus accumbens and DA turnover, and lower sensitivit y of DA re-

ward circuitry (Kelley et al., 2003; Davis et al., 2008; Geiger et al.,

2009; Johnson and Ken ny, 2010). Minipigs randomized to a weight

gain intervention versus a stable weight condition showed red uced

prefrontal cortex, midbrain and nucleus accumbens resting activity

(Val-Laillet et al., 2011). The reduced DA signaling capacity appears to

occur because habitual intake of high-fat diets causes decreased synthe-

sis of ol eoylethanolamine, a gastrointestinal lipid messenger (Tellez

et al., 2013). Interestingly, people who report elevated intake of a partic-

ular food show reduced striatal response during intake of that food, in-

dependent of BMI (Burger and Stice, 2012; Green and Murphy, 2012;

Rudenga and Small, 2012).

Geiger et al. (2009) hypothesized that diet-induced down-regulation

of the DA circuitry may prompt overeating to increase DA signaling. Yet,

mice in which reduced striatal DA signaling from food intake was exper-

imentally induced through chronic intragastric infusion of fat worked less

for acute intragastric infusion of fat and consumed less rat chow ad lib

than control mice (Tellez et al., 2013). Further, genetically engineered

DA-deﬁcient mice are unable to sustain appropriate levels of feeding

(Sotak et al., 2005). These dat a seem incompatible with the notion that

an induced down-regulation of DA reward circuitry leads to compensato-

ry overeating. The Tellez et al. (2013) study also provided further evi-

dence that intake of fat can result in reduced DA response to food

intake, independent of weight gain per se.

2.1.3. Inhibitory control

Vulnerabilities in reward sensitivity, habit, and inhibitory control

appear to interact to produce prolonged hyperphagia of highly palatable

foods leading to the development and maintenance of obesity

(Appelhans et al., 2011). By extension, lower activation of prefrontal-

parietal brain regions implicated in inhibitory c ontrol, may le ad to

greater s ensitivity to the rewarding effects of highly palatable foods

and greater susceptibility to the pervasive temptation of appetizing

foods in our environment, which increases overeating in the absence

of meeting homeostatic energy needs (Nederkoorn et al., 2006). In

fact, this pattern of food intake behavior appears to occur with only a

limited role for homeostatic input in modulating obesogenic food intake

behavior (Ha ll et al., 2014). Inefﬁcient or underdeveloped inhibitory

control function may increase the risk for obesity in early childhood at

a time when rapid development is occurring in subcortical and

prefrontal–parietal brain systems that support reward and inhibitory

control functions (see Reinert et al., 2013; Miller et al., 2015 for re-

cent reviews). In addition, obesity-related alterations in adipokines,

inﬂammatory cytokines, and gut hormones may l ead to further dis-

ruption in neurodevelopment, especially in reward and inhibitory

control functions, which may increase the risk for poor academic perfor-

mance and even dementia risk in later life (Miller et al., 2015). For exam-

ple, obese versus lean teens sh owed less activation of prefrontal regions

(dorsolateral prefrontal cortex [dlPFC], ventral lateral prefrontal cortex

[vlPFC]) when trying to inhibit responses to high-calorie food images

and behavioral evidence of reduced inhibitory control (Batterink et al.,

2010)

and adults who had greater dlPFC activation when instructed to

“resist craving” while viewing food images had better weight loss success

following gas tric bypass surgery (Goldma n et al., 2013). Another study

found that participants who showed less recruitment of inhibitory control

regions (inferior, middle, and superior frontal gyri) during difﬁcult versus

easy choices on a delay discounting task showed elevated future weight

gain (Kishinevsky et al., 2012; r = 0.71); however, individual differences

in delay discounting behavior did not explain weight outcomes (Stoeckel

et al., 2013b). These results converge with evidence that obese versus lean

adults showed reduced gray mater volume in the prefrontal cortex

(Pannacciulli et al., 2006), a region that modulates inhibitory control,

and with a marginal trend for reduced gray matter volume in the prefron-

tal cortex to predict weight gain over 1-year follow-up (Yokum et al.,

2011). Interestingly, obese versus lean humans also showed less recruit-

ment of inhibitory regions (ventral medial prefrontal cortex [vmPFC]) in

response to high-calorie food images (Silvers et al., 2014) and high-

calorie food TV commercials (Gearhardt et al., 2014). Further, lower

dlPFC response to high-calorie food images predicted greater ad lib food

intake over the next 3 days (Cornier et al., 2010). These ﬁnd ing s are note-

worthy because all but the results from the Batterink, Kishinevsky, and

Stoeckel studies emerged in paradigms lacking a behavioral response

4 D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

component. In some instances (Kishinevsky et al., 2012; Stoeckel et al .,

2013b), the neuroimaging data were a better predictor of weight out-

comes than the behavioral measure. This example highlights the future

potential for “neuromarkers” to improve outcome prediction and individ-

ualize intervention strategies to improve weight outcomes (Gabrieli et al.,

2015). Finally, it may also be possible to directly target and normalize

these brain systems using several of the neuromodulator y tools and tech-

niques described throughou t this article, such as transcrania l stimulation,

to enhance treatment outcomes (Alonso-Alonso and Pascual-Leone,

2007).

2.1.4. Theoretical implications and future research directions

Thus, most prospective and experimental studies have not provided

support for the reward deﬁcit theory of obesity, and whereas available

data suggest that the reduced DA signaling capacity of the reward cir-

cuitry may largely result from overeating, extent data provide little sup-

port for the notion that this contributes to compensatory overeating.

Yet, there is emerging evidence that there may be qualitatively distinct

reward surfeit and reward deﬁcit pathways to obesity that are based on

individual differences in genes that affect DA signaling and reward re-

gion responsivity to palatable food receipt, implying that it might be

useful to reﬁne our working model regarding neural vulnerability

factors that contribute to obesity. According to what might be referred

to as the dual pathway model of obesity, we posit that individuals in

the reward surfeit pathway initially show hyper-responsivity of reward,

gustatory, and oral somatosensory regions to palatable food in take,

which increases habitual intake of energy dense foods. The reward sur-

feit pathway might be more likely for those at genetic risk for greater DA

signaling capacity. Habitual intake of palatable foods theoretically leads

to the development of hyper-responsivity of attention and reward valu-

ation regions to cues that predict food reward through conditioning

(Berr idge, 200 9), which maintains overeating because exposure to

ubiquitous food cues results in craving that prompts eating. Data sug-

gest that the hyper-responsivity of reward regions to palatable food in-

take contributes to more pr onounced cue-reward learning, whi ch

increases risk for future weight gain (Burger and Stice, 2014). We fur-

ther submit that overeating results in a down-regulation of DA-based

reward regions, producing a blunted striatal response to food intake

that emerges with obesity, but that this may not contribute to further

escalation in eating. We also theorize deﬁcits in inhibitory control in-

crease the risk for overeating, and further that overeating leads to a sub-

sequent reduction in inhibit ory response to food stimuli, which may

also contribute to future escalation in overeating. This prediction is

based on evidence that individuals exhibit greater inhibitory control

deﬁcits in response to frequently versus infrequently experienced re-

wards; obese versus lean individuals show a greater immediate reward

bias to food stimuli but not monetary reward (Rasmussen et al., 2010).

In contrast, individuals in the reward deﬁcit pathway, which may be

more likely for those with a genetic propensity for lower DA-signaling

capacity, might consume more calories per eating episode because the

weaker DA-signaling may attenuate feelings of satiety, as reward re-

gions project to the hypothalamus. It is possible that the weaker DA-

signaling of reward regions attenuates the effects of gut peptides that

relay satiety. It is also possible that the lower DA signaling and reward

region responsivity operates through a completely different process,

such as by reducing physical activity because these individuals might

ﬁnd exercise less rewarding, contributing to a positive energy balance.

More broadly, data imply that too much or too little reward circuitry

responsivity, which is referred to as the Goldilocks Principle, serves to

disrupt homeostatic processes that have evolved to promote sufﬁcient,

but not excessive caloric intake. This notion would be consistent with

an allostatic load model.

With

regard to future research, additional large prospective brain

imagi ng studies should seek to identify neural vulnerability factors

that predict future weight gain. Second, environmental, social, and bio-

logical factors, including genotypes, that moderate the effects of these

vulnerability factors on future weight gain should be examined in

more detail. Third, additional prospective repeated-measures studies

should attempt to capture the plasticity of reward region responsivity

to food images/cues and food receipt, which appears to results from

overeating. Randomized controlled experiments could be used to ad-

dress these research questions, allowing much stronger inferences re-

garding these etiologic processes. It will also be important to expand

research into other relevant neuropsychological functions (e.g. motiva-

tion, working memory, multisensory processing and integration, execu-

tive function), the neural systems that mediate these functions, their

interaction with reward and homeostatic (i.e. hypothalamic, brainstem)

brain systems, and how dysfunction in these neural systems and cogni-

tive functions may impact reward and homeostatic functions in order to

have a more uniﬁed bra in–behavior model of food intake behavior

(Berthoud, 2012; Hall et al., 2014). For example, inhibitory control and

the fronto-parietal brain systems that mediate this function have been

studied; however, there are other aspects of executive function (e.g.

mental set shifting, information updating and monitoring; Miyake

et al., 2000) that are mediated by dissociable, but overlapping regions

of the fronto-parietal “executive” network and are understudied in the

context of their relationship to food intake behavior. Finally, investiga-

tors should continue to translate ﬁndings from brain imaging studies

into more effective obesity prevention and treatment interventions.

2.2. Dopaminergic imaging

As reviewed above, dopamine (DA) plays an important role in eating

behavior. Understanding the neurocognitive mechanisms by which DA

inﬂuences eating behavior is crucial for prediction, prevention and

(pharmacologic al) treatment of ob esity. To infer the in volvement of

the dopaminergic system, it is important to actually measure DA pro-

cessing. Findings of increased me tabolism or blood ﬂow in a dopaminer-

gic target region do not necessarily imply that DA is directly involved.

For example, activation in the striatum could reﬂect opioid modulation

of hedonic ‘liking’ instead of dopaminergic modulation of ‘wanting’

(Berridge, 2007 ). Here, we will go into more detail about results of stud-

ies directly investigating DA.

2.2.1. Nuclear tomographic imaging

Nuclear imaging techniques such as positron emission tomography

(PET) and single photon emission computed tomography (SPECT) use

radioactive tracers and detection of gamma rays to image tissue concen-

trations of molecules of interest (e.g. DA receptors). PET and SPECT have

a very low temporal resolution (tens of seconds to minutes), usually re-

quiring one imaging session for one data point, limiting the kind of re-

search questions that ca n be targeted with the se methods.

Table 1 provides an overview of dopaminergic PET and SPECT stud-

ies that have assessed differences as a function of BMI in humans. In line

with a downregulation of dopamine signaling with obesity is the rela-

tion between lower dopamine synthesis capacity in the dorsal striatum

and an elevated BMI (Wilcox et al., 2010; Wallace et al., 2014)andlower

striatal DA D2/D 3 re ceptor binding in obese versus le an in dividuals

(Wang et al., 2001; Haltia et al., 2007; Volkow et al., 2008; de Weijer

et al., 2011; Kessler et al., 2014;

van de Giessen et al., 2014).

However,

others have found positive associations between striatal D2/D3 receptor

binding and BMI (Dunn et al., 2012; Caravaggio et al., 2015), or no asso-

ciation (Eisenstein et al., 2013). From the above-mentioned studies it is

also unclear whether differences in DA processing reﬂect a cause or a

consequence of an increased BMI. Some have touched upon this ques-

tion by assessing changes in DA D2/D3 receptor binding after bariatric

surgery and signiﬁcant weight loss. While one study found increases

and the other found decreases in receptor binding after surgery (Dunn

et al., 2010; Steele et al., 2010), a study with a larger sample did not

ﬁnd any signiﬁcant changes (de Weijer et al., 2014).

Another way to investigate the involvement of DA in obesity is to as-

sess changes in extracellular DA levels induced by a psychostimulant or

5D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

Table 1

Summary of studies using SPECT or PET for dopaminergic imaging in lean, overweight or obese human subjects.

Subjects, status Radioligand Marker for Challenge Main ﬁndings References

SPECT studies

n = 15 obese (BMI 43 ± 5) vs.

n = 15 non-obese (BMI 22 ± 2)

[123I] iodobenzamide (IBZM) DA D2/3R 2 sessions

: after amphetamine

vs. baseline

Obese individuals had lower striatal DA D2/3R binding than

controls at baseline; increases in extracellular dopamine were

correlated with enhanced trait food craving in obese individuals

van de Giessen

et al. (2014)

n = 19 bariatric surgery patients

(BMI 46 ± 6 before and 41 ± 6

after)

[123I] iodobenzamide (IBZM) DA D2/3R None No signiﬁcant changes in striatal DA D2/3R binding before vs.

6 weeks after bariatric surgery were found; and no correlation

with BMI before or after surgery

de Weijer et al.

(2014)

n = 123 (BMI 18–41) [(123)I]FP-CIT DAT None No association between striatal DAT binding and BMI was found van de Giessen

et al. (2013)

n = 33 (BMI 21–50) [123I] PE2I DAT None No association between striatal DAT binding and BMI was found Thomsen et al.

(2013)

n = 15 obese (BMI 47 ± 7) vs.

n = 15 non-obese (BMI 22 ± 2)

[123I] iodobenzamide DA D2/3R None Obese individuals had lower striatal DA D2/3R binding than

controls

de Weijer et al.

(2011)

n = 50 (BMI 19–31) [99mTc]-TRODAT-1 DAT None Lower DAT binding in the striatum was correlated with a higher

BMI

Chen et al. (2008)

PET studies

n = 13 obese (BMI 37–49)

n = 24 non-obese (BMI 23 ± 3)

[11C] carfentanil

[11C] raclopride

μ-Opioid R

DA D2/3R

None No difference in D2/D3R availability between obese and

non-obese women, but signiﬁcantly reduced μ-opioid R

availability in obese women

Karlsson et al.

(2015)

n = 19 (BMI 21–35) [11C] raclopride DA D2/3R 2 sessions

: glucose (caloric) vs.

sucralose (non-caloric)

Calorie-induced increases in extracellular dopamine in ventral

striatum were correlated with a lower BMI

Wang et al. (2014)

n = 16 (BMI 20–33) 6-[18F]-Fluoro-

L-m-Tyrosine (FMT) AADC, DA synthesis None Lower dopamine synthesis in caudate nucleus was correlated

with 1) greater BMI and 2) greater preference for perceived

“healthy”, but not actual healthy, foods (independent of BMI)

Wallace et al.

(2014)

n =33(n = 16) (BMI 19–35) [18F] fallypride DA D2/3R 2 sessions (n = 16)

: after

amphetamine vs. baseline

Lower DA D2/3R binding in caudate and amygdala was correlated

with a higher BMI at baseline; amphetamine-induced increases in

extracellular dopamine in putamen and substantia nigra were

correlated with a higher BMI

Kessler et al. (2014)

n = 15 obese (BMI 33 − 47) vs.

n = 15 non-obese (BMI 19–28)

(N-[(11)C] methyl) benperidol

([(11) C] NMB)

DA D2R-speciﬁc,

non-displaceable

None No association between striatal DA D2 binding and BMI was

found

Eisenstein et al.

(2013)

n = 26 vs. n = 35 (BMI 19–28) [11C]-(+)-PHNO vs. [11C]

raclopride

DA D2/3R (agonist vs.

antagonist)

None Higher DA D2/3R binding in ventral striatum, as measured with

[11C]-(+)-PHNO, was correlated with a higher BMI

Caravaggio et al.

(2015)

n = 14 obese (BMI 40 ± 5) vs.

n = 8 non-obese (BMI 23 ± 2)

[18F] fallypride DA D2/3R None Lower DA D2/3R binding in caudate was correlated with a lower

BMI

Dunn et al. (2012)

n = 15 (BMI 25) 6-[18F]-Fluoro-

L-m-Tyrosine (FMT) AADC, DA synthesis None Lower DA synthesis capacity in the dorsal striatum was

correlated with a higher BMI (caudate) and increased weight loss

attempts (putamen)

Wilcox et al. (2010)

n = 5 bariatric surgery patients

(45 ± 6 before and 38 ± 7 after)

[11C] raclopride DA D2/3R None Four out of ﬁve patients showed an increase in DA D2/3R binding

in the striatum 6 weeks after bariatric surgery

Steele et al. (2010)

n = 5 bariatric surgery patients (BMI

43 ± 3 before and 38 ± 3 after)

[18F] fallypride DA D2/3R None DA D2/3R binding in striatum, (hypo) thalamus, substantia nigra

(corrected for multiple comparisons) and amygdala decreased

7 weeks after bariatric surgery

Dunn et al. (2010)

n = 10 obese (BMI 51 ± 5) vs.

n = 12 non-obese (BMI 25 ± 3)

[11C] raclopride and [18F]

ﬂudeoxyglucose (FDG)

DA D2/3R; glucose None In obese individuals striatal D2/3R binding was lower than

controls and was positively correlated with glucose metabolism

in frontal and somatosensory cortices

Volkow et al.

(2008)

n = 12 obese (BMI 33 ± 5) vs.

n = 12 non-obese (BMI 22 ± 1)

[11C] raclopride DA D2/3R 2 sessions

:after i.v. glucose vs.

after i.v. placebo

Obese individuals had lower striatal DA D2/3R binding than

controls; glucose increased extracellular striatal dopamine in

men and reduced it in women

Haltia et al. (2007)

n = 10 obese (BMI 42–60) vs.

n = 10 non-obese (BMI 21–28)

[11C] raclopride DA D2/3R None Obese individuals had lower striatal DA D2/3R binding than

controls; lower striatal DA D2/3R binding was correlated with a

higher BMI in obese individuals

Wang et al. (2001)

BMI: body mass index (kg/m

); “x–x” reﬂects the range, and“x±x” reﬂects the average ± standard deviation; PET: positron emission tomography; DA: dopamine; D2/3R: D2/D3 receptor;

Increases in extracellular dopamine were observed as reductions in binding potential; i.v.: intravenous; SPECT: single photon emission tomography; DAT: dopamine transporter; AADC: aromatic l-amino acid decarboxyla se.

6 D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

a food challenge (see Table 1). In such challenge studies, lower receptor

binding is interpreted as greater release of endogenous DA leading to

greater competition with the radioligand at the receptors. Challenge

studies have observed that food- or psychostimulant-induced increases

in extracellular striatal DA are associated with a lower BMI (Wang et al.,

2014), a higher BMI (Kessler et al., 2014), or have found no differences

between BMI groups (Haltia et al., 2007).

In sum, ﬁndings from nuclear imaging studies investigating differ-

ences in the striatal DA system as a function of BMI are very inconsis-

tent. In an attempt to converge on one theory of dopaminergic hypo-

activation in obesity, different authors have used different explanations

for their results. For example, DA D2/D3 receptor binding has been

interpreted to reﬂect DA receptor availability (e.g. Wang et al., 2001;

Haltia et al., 2007; Volkow et al., 2008; de Weijer et al., 2011; van de

Giessen et al., 2014), DA receptor afﬁnity (Caravaggio et al., 2015), or

competition with endogenous DA (Dunn et al., 2010; Dunn et al.,

2012). Based on the data, it is often unclear whether such differences

in interpretation are valid. In addition, a very recent study by Karlsson

and colleagues showed a signiﬁcant reduced μ-opioid receptor availabil-

ity in obese compared to normal-weight women, without changes in

D2-receptor availability, whic h might be an additional chan nel that

might explain the inconsisten t ﬁndings in a lo t of other studies

(Karlsson et al., 2015).

2.2.2. Genetic fMRI

By investigating the effects of common variations in DA genes the

role of predisposed vulnerability can be determined. To date, there

have only been a few studies that have combined genetics with neuro-

imaging in the domain of food reward. Most of them are functional mag-

netic resonance imaging (fMRI) studies.

Most genetic fMRI studies investigating food reward have taken into

account a common variation (i.e. polymorphism) referred to as TaqIA, of

which the A1 allele has been positively associated with BMI in several

early genetic studies (Noble et al., 1994; Jenkinson et al., 2000; Spitz

et al., 2000; Thomas et al., 2001; Southon et al., 2003). The TaqIA poly-

morphism is located in the ANKK1 ge ne, ~10 kb downstream of the

DRD2 gene (Neville et al., 2004). A1-allele carriers of the TaqIA poly-

morphism show reduced striatal D2R expression (Laruelle et al., 1998;

Pohjalainen et al., 1998; Jonsson et al., 1999). Genetic fMRI studies

have demonstrated that A1-carriers show decreased blood-oxygen-

level-dependent (BOLD) responses in DA-rich regions in the brain (dor-

sal striatum, midbrain, thalamus, orbitofrontal cortex) when consuming

a milk shake versus a tasteless solution relative to non-carriers (Stice

et al., 2008a;

Felsted et al., 2010)

. Importantly, these decreased re-

sponses for food reward consumption, as well as for imagined food in-

take, predicted future weight gain in the A1 risk allele carriers (Stice

et al., 2008a; Stice et al., 2010b). This is in line with the idea that DA

modulates the blunted response to food reward in obesity. In contrast,

when anticipating a milk shake versus a tasteless solution, A1-carriers

have demonstrated increased BOLD responses in the midbrain (Stice

et al., 2012). A multilocus composite score of dopaminergic genotypes —

including ANKK1 and four others — did not predict decreased striatal re-

sponses for the consumption of food reward, but only for the receipt of

monetary reward (Stice et al., 2012).

Thus, genetic fMRI studies suggest that individual differences in do-

paminergic genes play a role in brain responses to food reward, but their

effects are not always replicated and seem to depend on the anticipation

or the consumption of food reward.

2.2.3. Future directions for dopaminergic imaging

Together, SPECT, PET, and genetic fMRI studies suggest that brain DA

is involved in obesity. However, these neuroimaging ﬁndings are not

easily interpreted as a simple hypo- or hyper-activation of the DA sys-

tem in obesity. Moreover, there is an abundance of non-replications

and null ﬁndings, possibly due to small sample sizes. In order to use do-

paminergic imaging as a phenotyping method indicating vulnerability

for obesity or for prediction of treatment efﬁcacy, reliability should be

increased. Genetic pathway analyses (e.g. Bralten et al., 2013) or ge-

nome wide association studies (e.g. El-Sayed Moustafa and Frogue l,

2013; Stergiakouli et al., 2014) might be more sensitive and speciﬁcin

revealing DA3s role in obesity. In the context of personalized medicine,

DA genetic fMRI studies could be combined with phar macology (see

Kirsch et al., 2006; Cohen et al., 2007; Aarts et al., 2015) to reveal the

mechanisms of anti-obesity drugs as well as individual differences in

treatment response.

Another reason for the observed inconsistencies might be that obe-

sity (i.e. BMI) is too complex and unspeciﬁc as a phenotype (see also

Ziauddeen et al., 2012), which is also evident from the fact that studies

using polygenic risk scores have only obtained small associations with

obesity phenotypes (e.g. Domingue et al., 2014). Neuroimaging studies

might more clearly reveal dopaminergic effects when using cognitive

paradigms that manipulate fo od motivation (i.e. effort provision) or

the learning of cue-reward associations, as st riatal DA is well known

for its role in these processes (Robbins and Everitt, 1992; Sch ultz et al.,

1997; Berridge and Robinson, 1998). Assessing task-related responses,

however, is a challenge during PET and SPECT due to their low temporal

resolution. Nevertheless, PET/SPECT measures could be related to off-

line task behavior (see, e.g. Wallace et al., 2014). Moreover, combina-

tions of imaging modalities such as PET and fMRI holds a strong poten-

tial for future studies (see, e.g. Sand er et al., 2013 in non-h uman

primates), making optimal use of the speciﬁcity of PET and the temporal

and spatial resolution of fMRI.

3. The contribution of functional near-infrared spectroscopy (fNIRS)

Unlike the other neuroimaging techniques, such as PET and fMRI,

fNIRS does not require subjects to be in a supine position and does not

strictly restrict head movements, thus allowing to adopt a wide range

of experimental tasks suitable for properly investigating eating disor-

ders and food intake/stimuli. In addition, fNIRS us es a relatively low

cost instrumentation (with a sampling time in the order of the ms and

a spatial resolution of up to about 1 cm). On the other hand, although

EEG is a useful electrophysiological technique, its very low spatial reso-

lution makes it difﬁcult to precisely identify the activated areas of the

brain, limiting its application to speciﬁc research questions related to

eating disorders (Jauregui-Lobera, 2012). R ecently, to deal with this

problem EEG has been combined successfully with fMRI to overcome

the spatial limitations of EEG and the temporal limitations of fMRI,

using their complementary features (Jorge et al., 2014). The parallel or

sequential use of EEG and fMRI in food related studies may provide ad-

ditional insights into neural processing cascades. However, combined

EEG–fMRI food related studies have not been reported yet. In conclu-

sion, all the above mentioned advantages of using fNIRS and EEG offer

the great promise to explore tast e-related higher cognitive brain

functions, which require tasks involving even the ingestion of food/

beverages under more natural situations.

2.3.1. Brief overview of the principles, advantages and limitations of fNIRS

The principles, advantages, and limitations of fNIRS or optical topogra-

phy or near-infrared (NIR) imaging have been summarized in recent

reviews (Hoshi, 2011; Cutini et al., 2012; Ferrari and Quaresima, 2012;

Scholkmann et al., 2014). fNIRS is a non-invasive vascular-based

neuroimaging technology that measures concentration changes of

oxygenated-hemoglobin (O

Hb) and deoxygenated-hemoglobin (HHb)

in cortical microcirculation blood vessels. fNIRS relies on neurovascular

coupling to infer changes in neural activity that is mirrored by changes

in blood oxygenation in the region of the activated cortical area (i.e. the

increase in O

Hb and the decrease in HHb). Unlike the BOLD signal of

fMRI, which is gathered from the paramagnetic properties of HHb, the

fNIRS signal is based on the changes in the intrinsic optical absorption

of both HHb and O

Hb (Steinbrink et al., 2006). fNIRS systems vary in

7D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

Table 2

fNIRS cognitive processing studies in patients with eating disorders, as well as healthy subj ects /patients upon food intake or food stimuli.

Food stimulus or food intake Task (s) Subjects, status Age

(years; mean ± SD)

Range (years) Device Ch Cortical

area

Main ﬁnding References

Frontal cortex reactivity in patients with eating disorders

n.u. VFT; RPST 14, HC; 10, AN;

14, BN

24.1 ± 3.0; 26.1 ± 7.1 n.a. D8 2 PFC Higher dlPFC activation in BN Sutoh et al. (2013)

n.u. VFT; control: FOT 12, HC; 16, AN 14.3 ± 1.3; 14.2 ± 1.3 n.a. D4 24 PFC VFT: AN poor PFC activation; FOT: similar

PFC activation in AN and HC

Nagamitsu et al. (2011)

n.u. VFT 27, HC; 27, ED 22.4 ± 2.0; 23.5 ± 5.2 n.a. D4 52 FT ED: bilateral OFC and right FT smaller

activation

Suda et al. (2010)

n.u. VFT 11, HC; 11, ED 26.9 ± 2.2; 21.2 ± 6.0 18–32; 14–38 D3 24 PFC Lower PFC activation in ED Uehara et al. (2007)

Effects of food taste

Sweet taste: sucrose (10%); sour taste:

citric acid (10%)

Pleasant/unpleasant

tasting task

16, HC 26.3 ± 5.5 n.a. D10 16 PFC Bilateral FP and dlPFC deactivation to

both tastes; higher right PFC activation

with citric acid

Hu et al. (2014)

Sweet snacks Taste stimulation 6, HC 21.5 ± 1.3 19–27 D5 44 PFC Bilateral primary taste area, inferior

frontal gyrus, and dlPFC activation

Ono (2012)

Different liquid taste-stimuli Encoding and retrieval of

taste memory

28, HC 32 ± 7 21–49 D12 23 PFC Bilateral FP and right DLPFC larger

activation in retrieval

Okamoto et al. (2011)

Bitter: 6-n-propylthiouracil Tasting task 48, HC n.a. 24–40 D3 24 dlPFC,

vlPFC

dlPFC and vlPFC activation Bembich et al. (2010)

Different sugar based taste-stimuli;

control: VFT, TTT

Taste stimulation 19, HC 32.1 ± 6.9 23–44 D12 17 PFC vlPFC is involved in the act of tasting. Okamoto et al. (2009)

7 green tea samples Sensory evaluation 12, HC n.a. 23–42 D12 14 lPFC Left lPFC and right inferior frontal gyrus

activation

Okamoto et al. (2006a)

Different liquid taste-stimuli; control:

TTT

Taste encoding task 18, HC n.a. 25–44 D12 17 PFC vlPFC activation Okamoto et al. (2006b)

Effects of food ﬂavor

Sweet taste/sweet taste-lemon odor/no

taste-odor gums

Chewing test 25, HC 27.8 ± 2.8 n.a. D8 2 PFC Combination of taste/odor increases PFC

activation

Hasegawa et al. (2013)

Ethylmaltol-ﬂavored 4% sucrose solution Sensory evaluation tasks 7, HC 31.4 ± 4.5 n.a. D4 52 PFC Ethylmaltol enhances the TC activation

when combined with a sweet taste

Saito-Iizumi et al.

(2013)

Flavored and odorless broth stimuli Sensory evaluation task 10, HC 30.5 ± 4.6 n.a. D4 52 FP, FT Bilateral TC activation upon ﬂavored

broth taste

Matsumoto et al.

(2012)

Effects of odor food components

Irritating and hedonic odors Olfactory stimulation test 11, HC; 12, MCS n.a. n.a. D13 42 PFC PFC activation in MCS and controls Azuma et al. (2013)

Isovaleric acid (sweet smell) Olfactory stimulation test 19, HC; 36, D 42.5; 60.9 22–67; 37–81 D3 22 PFC Activation of the lower part of the PFC in

HC; no activation in D subjects

Kobayashi et al. (2012)

2-Phenyl ethanol and citral Olfactory stimulation test 14, HC 19.6 18–23 D1 2 OFC Left OFC activation; right OFC activation

upon

odor recognition

Kokan et al. (2011)

Linalool (mixed olfactory stimulant) Olfactory stimulation test 22, HC; 27, ADHD 12.4 ± 1.6; 12.7 ± 1.4 n.a. D4 48 PFC Higher TC activation in ADHD without

methylphenidate therapy

Schecklmann et al.

(2011a)

2-Phenyl ethanol; linalool (mixed

olfactory stimulant)

Olfactory stimulation test 29, HC; 29, ADHD 27.8 ± 4.1; 28.2 ± 4.5 n.a. D4 44 PFC Methylphenidate normalizes the ADHD

TC activation

Schecklmann et al.

(2011b)

8 D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

Isovaleric acid (sweet smell) Olfactory stimulation test 8, HC; 5, D 28.9; 46.9 22–39; 17–69 D3 22 PFC Activation of the lower part of the PFC in

HC; no activation in D subjects

Kobayashi et al. (2009)

Isovaleric acid (sweet smell) Olfactory stimulation test 8, HC 28.9 22–39 D3 22 PFC Activation of the lower part of the PFC Kobayashi et al. (2007)

Pleasant: vanilla essence, strawberry

essence; unpleasant: scatol

Olfactory stimulation test 13, HC 23–31 D9 2 PFC PFC activation related to odor strength Harada et al. (2006)

Pleasant: vanilla substance (1%) Olfactory stimulation test 8, HC; 13, MA 66; 66 56–79; 56–72 D9 2 TC Bilateral TC activation only in HC Fladby et al. (2004)

2-Phenyl ethanol, isovaleric acid Olfactory stimulation test 12, HC 32.6 ± 14.9 n.a. D15 2 TC Bilateral TC activation (right TC higher

activation)

Ishimaru et al. (2004)

Effects of nutrition/food components

7-day essence of chicken/placebo

supplementation

Working memory and

reaction tasks

12, HC 62.3 ± 2.5 60–68 D4 24 PFC dlPFC activation only with chicken

essence upon working memory task

Konagai et al. (2013a)

12-week krill/sardine oil

supplementation

Working memory and

calculation tasks

45, HC 67.1 ± 3.4 n.a. D4 24 PFC Greater dlPFC activation with krill oil Konagai et al. (2013b)

Glucose drink (50 mg) Divided attention task 20, HC 69.4 n.a. D2 36 PFC Glucose ingestion enhances the lateral

and ventral PFC activation of the right

hemisphere to the two concurrent tasks

Gagnon et al. (2012)

12-week docosahexaenoic acid-rich ﬁsh

oil supplementation

Battery of cognitive tasks 65, HC 20.6 18–29 D14 2 PFC Dose response PFC activation Jackson et al. (2012)

Single dose green tea polyphenol

epigallocatechin gallate (135 mg)

Battery of cognitive tasks 27, HC 22 18–33 D14 12 PFC FC CBF decrease Wightman et al. (2012)

Single dose soybean peptide Battery of cognitive tasks 10, HC n.a. 20–25 D4 52 PFC FP, dlPFC activation (frequency band

amplitude increase)

Yimit et al. (2012)

Single dose caffeine (75 mg) Battery of cognitive tasks 20, HC 21.4 19–28 D14 12 PFC FC CBF decrease only in non-habitual

consumers

Kennedy and Haskell

(2011)

Single dose trans-resveratrol

(250/500 mg)

Battery of cognitive tasks 22, HC 20.2 18–25 D14 12 PFC Dose-dependent FC CBF increase Kennedy et al. (2010)

Casein hydrolysate drink ingestion;

carbohydrate drink

n.u. 11, HC 22.5 ± 2.3 21–28 D16 10 PFC Casein hydrolysate drink does not

change [tHb]; carbohydrate drink

increases [tHb]

Nakamura et al. (2010)

Single dose caffeine (180 mg) UKP calculation tests

before/after caffeine intake

14, HC n.a. 21–50 D11 2 PFC The same PFC activation before and after

caffeine intake

Higashi et al. (2004)

5-day creatine supplementation UKP calculation tests

before/after

24, HC 24.3 ± 9.1 n.a. D6 1 PFC Reduced left FC activation Watanabe et al. (2002)

Effects of food images

Visual stimulation: food photos Like/dislike test 5, HC 23.4 ± 3.4 n.a. D4 52 FP, FT FP activation Hosseini et al. (2011)

Visual: images of body types/high-calorie

food/attachment

Symptom-provocative

views

task

13, HC; 12, AN 14.3 ± 1.3; 14.4 ± 1.3 n.a. D4 24 PFC No difference in PFC activation between

HC and AN viewing body types/food; AN

higher PFC activation viewing

mother–child attachment

Nagamitsu et al. (2010)

Visual stimulation: drinks photos Preference evaluation task 9, HC 24.0 ± 4.4 n.a. D7 14 PFC Medial PFC activation Luu and Chau (2009)

Visual stimulation: food photos Preference evaluation task 8, HC 23 18–30 D13 32 PFC vmPFC activation Shimokawa et al.

(2008)

[tHb]: total hemoglobin concentration; ADHD: attention- deﬁcit/hyperactivity diso rder; AN: anorexia nervosa; BN: bulimia nervosa; CBF: cerebral blood ﬂow; CH: channels; D: dysosmia; dlPFC: dorsolateral prefrontal cortex; D1: BOM-L1W (Omega

Wave, Japan); D2:CW-6 (Techen, USA); D3: ETG-100 (Hitachi, Japan); D4: ETG-4000 (Hitachi, Japan); D5: ETG-7100 (Hitachi, Japan); D6: HEO-200 (Omron, Japan); D7: Imagent (ISS, USA); D8:NIRO-200 (Hamamatsu Photonics, Japan); D9:NIRO-300

(Hamamatsu Photonics, Japan); D10: OEG-16 (Spectratech, Japan); D11: OM-200 (Shimadzu, Japan); D12: OMM-2000 (Shimadzu, Japan); D13: OMM-3000 (Shimadzu, Ja pan); D14: OXYMON MkIII (Artini s, The Netherlands); D15:PSA-500 (Bio-

medical Sciences, Japan); D16: TRS-10 (Hamamatsu Photonics, Japan); ED: eating disorders; FOT: ﬁnger opposition task; FP: fr ontopolar; FT: frontotempo ral; HC: healthy controls; lPFC: lateral prefrontal cortex; MA: mild Alzheimer; MCS: multiple

chemical sensitivity; n.a.: not available; n.u.: not utilize d; OFC: orbitofrontal cortex; PFC: prefrontal cortex; RPST: rock-paper-scissors intentionallosstask;TC:temporalcortex;TTT:tonguetappingtask;UKP:Uchida–Kraepelin psychodiagnostic test;

VFT: verbal ﬂuency task; vmPFC: ventromedial prefrontal cortex; vlPFC: ventrolateral prefrontal cortex.

9D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

complexity from dual channels to ‘whole-head’ arrays of several dozen

channels. Data processing/analysis methods permit topographical assess-

ment of real-time regional cortical hemodynamic changes. However, the

relatively low spatial resolution of fNIRS makes it difﬁcult to precisely

identify the activated cortical regions. Moreover, the fNIRS measure-

ments, being limited to the cortical surface, cannot examine the primary

and secondary taste areas, which are located deep inside the brain

(Okamoto and Dan, 2007). Therefore, deeper brain areas, such as ventral

striatum and hypothalamus, which would be key for investigating eating

behavior, can be explored only by fMRI and/or PET.

2.3.2. Application of fNIRS for mapping human cortical responses in the

context of food stimuli/intake and eating disorders

The use of fNIRS in the context of food stimuli/intake and eating

disorders studies represents a relatively novel application, as witnessed

by the limited number of publications: 39 over the last 10 years. Table 2

summarizes these studies. The related fNIRS results mainly include: 1) a

lower frontal cortical activatio n upon different cognitive conditions/

stimuli in patients with ED, an d 2) the different activation patterns

over the frontal and temporal cortices upon different conditions/stimuli

(i.e. food taste, food ﬂavor, odor food components, nutrition/food com-

ponents ingestion, and food images) in healthy subjects. So far, few

forms of ED have been investigated by fNIRS. Only one study has report-

ed PFC responses to visual stimuli in AN patients (Nagamitsu et al.,

2010). The other 4 ED-related studies reported in Table 2, and the ex-

tensive fMRI literature (see García-García et al., 2013 review summariz-

ing 86 studies) suggest the existence of neural differences between

normal and abnormal eating behavior in response to the sight of food.

Recently, Bartholdy et al. (2013) have reviewed the studies in which

neurofeedback was combined with neuroimaging techniques, suggest-

ing the potential use of fNIRS for evaluating ED treatments. However,

the interpretation of the fNIRS ﬁndings might be complicated by the

longer scalp-to-cortex distance in some patients with severe AN as a

consequence of their brain alteration following gray matter volume re-

duction and/or cerebrospinal ﬂuid volume increase (Bartholdy et al.,

2013; Ehlis et al., 2014). Therefore, an assessment of the degr ee to

which cortical atrophy and scalp perfusion could affect the sensitivity

of fNIRS is essential for evaluating the usefulness of this technique ﬁrst

as a research tool in patients with severe AN.

Thirty-four out of the 39 studies have been carried out only in healthy

subjects (Table 2). Twenty studies of them have demonstrated how fNIRS

can provide a useful contribution to map taste processing mainly localized

in the lateral prefrontal cortex (lPFC). Eleven studies are related to the ap-

plication of fNIRS in nutritional intervention studies in both acute and

chronic intervention paradigms (Jackson and Kennedy, 2013; Sizonenko

et al., 2013 for reviews). These studies have suggested that fNIRS is capa-

ble to detect the effect of nutrients and food components on PFC

activation.

Unfortunately, most of the studies reported in Table 2 have been

performed in small sample size, and the comparison between patients

and controls was ofte n insufﬁcient . In addition, only a s ingle fNIRS

study, carried out using a high-cost fNIRS instrument based on time-

resolved spectroscopy, has reported absolute concentration values of

Hb and HHb.

In most of the reported studies, fNIRS probes covered only frontal

brain regions. Therefore, the involvement of other cortical areas includ-

ing parietal, fronto-temporal, and occipital regions, which might be as-

sociated with visuospatial processing, attention, and other perceptive

networks, were not investigated. In addition, most of the studies have

reported only ch anges in O

Hb making a comparison with fMRI ﬁndings

difﬁcult.

These preliminary studies indicate that, when used in well-designed

studies, fNIRS neuroimaging may be a useful tool in helping to elucidate

the effects of dietary intake/supplementation. In addition, fNIRS could

be easily adopted for: 1) evaluating the efﬁcacy of ED treatment pro-

grams and behavioral train ing pro grams, and 2) investigating the

inhibitory control of the dlPFC to visual food cues in healthy subjects

as well as in ED patients.

3. Non-invasive neuromodulation approaches: recent developments

and curren t ch allenges

3.1. Real-time fMRI neurofeedback and cognitive therapy

3.1.1. Introduction to neurofeedback in cognitive reappraisal

Cognitive reappraisal is an explicit emotion regulation strategy in-

volving the modiﬁcation of cognitive processes in order to alter the di-

rection and/or magnitude of an emotional response (Ochsner et al.,

2012). The brain systems that generate and apply reappraisal strategies

include the prefrontal, dorsal anterior cingulate (dACC), and inferior pa-

rietal cortices (Ochsner et al., 2012). These regions function to modulate

emotional responses in the amygda la, vent ral striat um (VS), ins ula, an d

ventromedial prefrontal cortex (vmPFC) (Ochsner et al., 2012; Fig. 1).

Finally, the use of cognitive reappraisal strategies has been shown to

regulate appetitive responses to highly palatable foods via these same

neural systems (Kober et al., 2010; Hollmann et al., 2012; Siep et al.,

2012; Yokum and Stice, 2013).

Neurofeedback using functional magnetic resonance imaging (fMRI)

data is a non-invasive training method used to alter neural plasticity

and learned behavior by providing individuals with real time informa-

tion about their brain activity to support learned self-regulation of this

neural activity (Sulzer et al., 2013; Stoeckel et al., 2014; Fig. 2). Combin-

ing real time fMRI (rtfMRI) neurofeedback with cognitive reappraisal

strategies is a cutting-edge strategy for translating the latest advances

in neuroscience, clinical psychology, and technology into a therapeutic

tool that may enhance learning (Birbaumer et al., 2013), neuroplasticity

(Sagi et al., 2012), and clinical outcomes (deCharms et al., 2005). This

approach complements other existing neurotherapeutic technologies,

including deep brain and transcranial stimulation, by offering a non-

invasive alternative for brain disorders and it may add value above psy-

chotherapy alone, including cognitive behavioral therapy, by providing

information about how and where changes in cognitions are causing

changes in brain function (Adcock et al., 2005).

There appear to be abnormalities in the use of cognitive reappraisal

strategies and the brain systems that implement them that contribute to

disorders of ingestive behavior, including AN, BN, BED, obesity, and ad-

diction (Kelley et al., 2005b; Aldao and Nolen-Hoeksema, 2010; Kaye

et al., 2013). Across these disorders, there is often dysfunction in two

major brain systems that also have key roles in cognitive reappraisal:

one involving hypersensitivity to rewarding cues (e.g. VS, amygdala, an-

terior insula, vmPFC, including orbitofrontal cortex) and the other in-

volving deﬁcient cognitive control over food or other subst ance use

(e.g. anterior cingulate, lateral prefrontal cortex — lPFC, including dorso-

lateral prefrontal cortex — dlPFC). Novel interventions designed to di-

rectly target dysfunctional emotion regulation strategies and patterns

neural activity may p rovide a new direction and hope for these

difﬁcult-to-treat disorders.

3.1.2. Cognitive reappraisal, obesity, and eating disorders

Obesity is one ca ndidate di sorder tha t will be used to illustra te how

this novel, neuroscience-driven intervention approach may be imple-

mented. Different studies suggest that obese vers us lean individuals

show elevated reward region responsivity to images of high-fat/high-

sugar foods, which increases risk for weight gain (cf. Sect ion 2.1). Fortu-

nately, cognitive reappraisals, such as thinking of the long-term health

consequences of eating unhealthy food when viewing images of such

foods, increases inhibitory region (dlPFC, vlPFC, vmPFC, lateral OFC,

superior and inferior frontal gyrus) activation and decreases reward re-

gion (ventral striatum, amygdala, aCC, VTA, posterior insula) and atten-

tion region (precuneus, posterior cin gulate cortex — PCC) activation

relative to contrast conditions (Kober et al., 2010; Hollmann et al.,

2012; Siep et al., 2012; Yokum and Stice, 2013). These data suggest

10 D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

that cognitive reappraisals may reduce hyper-responsivity of reward re-

gions to food cues and in crease inhibitory control re gion activation,

which is crucial because our environment is replete with food images

and cues (e.g. ads on TV) that contribute to overeati ng. Ac cordingly,

Stice et al. (2015) developed an obesity prevention program that trained

participants to use cognitive reappraisals when confronted with un-

healthy foods, reasoning that if participants learn to automatically apply

these reappraisals, they will show reduced reward and attention region

responsivity and increased inhibitory region responsivity to food images

and cues for high-fat/high-sugar food, which should reduce caloric intake.

Young adults at risk for weight gain by virtue of weight concerns (N =

148) were randomized to this new Minding Health preven tion program,

a prevention program promoting gradual reductions in caloric intake

and increases in exercise (the Healthy Weight intervention), or an obesity

education video control condition (Stice et al., 2015). A subset of Minding

Health and control participants completed an fMRI scan pre and post in-

tervention to assess neural responses to images of high-fat/sugar foods.

Minding Health participants showed signiﬁcantly greater reductions in

body fat than controls and percentage of caloric intake from fat and

sugar than Healthy Weight participants, though these effects attenuated

by 6-month follow-up. Further, Minding Health participants showed

greater activation of an inhibitory control region (inferior frontal gyrus)

and reduced activation of an attention/expectation region (mid cingulate

gyrus) in response to palatable food images relative to pretest and con-

trols. Although the Minding Health intervention produced some of the

hypothesized effects, it only affected some outcomes and the effects

often showed limited persistence.

It is possible that the addition of rtfMRI neurofeedback training to

the Minding Health intervention may lead to more persistent effects

and improved treatment outcomes. Given the emphasis on the use of

cognitive reappraisal in the Minding Health intervention, fMRI-based

neurofeedback was preferred compared to other, complementary tech-

nologies su ch as electroencephalography (EEG) due to th e superior

spati al resolution of fMRI, including the ability to target su bcortical

brain structures critical to the re gulation of food intake behavior for

neurofeedback. The ﬁrst study demonstrating the therapeutic potential

of rtfMRI neurofeedback was published in 2005 (deCharms et al.,

2005). There have been several studies now demonstrating rtfMRI

neurofeedback-induced changes in brain function in multiple structures

of relevance to disorders of ingestive behavior, including the amygdala

(Zotev et al., 2011; Zotev et al., 2013; Bruhl et al., 2014), insula (Caria

et al., 2007; Caria et al., 2010; Frank et al., 2012), aCC (deCharms et al.,

2005; Chapin et al., 2012; Li et al., 2013), and PFC (Rota et al., 2009;

Sitaram et al., 2011). Several groups have also reported successful appli-

cation of rtfMRI to modify cognitive and behavioral processes relevant

for the treatment of clinical disorders (for review of these studies see

deCharms, 2007

; W

eiskopf et al., 2007; deCharms, 2008; Bi rbaumer

et al. , 2009; Caria et al. , 2012; Chapin et al., 2012; Weiskopf, 2012;

Sulzer et a l., 2013), including an application in the area of obesity

(Frank et al., 2012). For a revi ew of potential applications of rtfMRI

Fig. 1. A model of the cognitive control of emotion (MCCE). (A) Diagram of the processing steps involved in generating an emotion and the ways in which cognitive control processes (blue

box) might be used to regulate them. As described in the text, the effects of different emotion regulation strategies (the red arrows descending from the cognitive control processes box)

can be understood in terms of the stages of the emotion generation sequence that they inﬂuence. The pink box seen at the appraisal stage is meant to indicate that neural systems involved

in generating emotion support this process. (B) Neural systems involved in using cognitive strategies, such as reappraisal, to regulate emotion (left, blue boxes), systems involved in gen-

erating those responses (left, pink boxes), and systems with an undeﬁned or intermediary role in reappraisal (left, yellow boxes; adapted from Ochsner et al., 2012 with permission). Brain

schematic representations were provided by Servier Medical Art (http://www.servier.fr).

11D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

neurofeedback for disorders of ingestive behavior, see Bartholdy et al.

(2013).

3.1.3. Proof-of-concept for the use of rtfMRI neurofeedback with cognitive

reappraisal for the regulation of food intake behavior

As a proof-of-concep t, Stoec kel et al. (2013a) completed a study

combining the use of cognitive reappraisal strategies (described

above) and rtfMRI neurofeedback in 16 healthy-weight participants

(BMI b 25) without a history of disordered eating who we re acutely

fasted. In a pilot study, an independent sample of 5 participants were

able to improve control of inhibition-related (lateral inferior frontal cor-

tex), but not reward-related (ventral striatum), brain activation using

rtfMRI neurofeedback (Stoeckel et al., 2011). Therefore, lateral inferior

frontal cortex was selected as the target bra in region of interest for

neurofeedback. Participants completed two neurofeedback visits,

1 week apart. At each visit, participants initially performed a functional

localizer task, the stop signal task, which is a well-known test of

inhibitory control (Logan et al., 1984) that activates lateral inferior fron-

tal co rtex (Xue et al., 2008). Participants then attempted to self-regulate

brain activity within this region of interest using cognitive regulation

strategies while viewing highly palatable food images. While viewing

the food images, participants were asked to either mentalize their

urge to eat the food (crave or 'upregulation’) or consider the long-

term future consequences of over-consuming the food (cognitive reap-

praisal or ‘downregulation’). At the end of each neurofeedback training

trial, participants received feedback from the brain region identiﬁed by

the localizer scan using custom in-house software developed at the

Massachusetts Institute of Technology (for technical details, see Hinds

et al., 2011). Participants also recorded their subjective cravings in re-

sponse to the food images throughout the session. Compared to upreg-

ulation trials, participants had less reward circuit activity (ventral

tegmental area (VTA), VS, amygdala, hypothalamus, and vmPFC) and

decreased craving when using reappraisal strategies (ps b 0.01). In addi-

tion, the difference in activity in the VTA and hypothalamus during up-

regulation vs. reappraisal was correlated with craving (rs = 0.59 and

0.62, ps b 0.05). Neurofeedback training led to improved control of lat-

eral inferior frontal cortex; however, this was not related to mesolimbic

reward circuit activation or craving. rtfMRI neurofeedback training led

to increased control of brain activity in healthy-weight participants ;

however, neurofeedback did not enhance the effect of cognitive regula-

tion strategies on mesolimbic reward circuit activity or craving after two

sessions (Stoeckel et al., 2013a).

3.1.4. Consideration for rtfMRI neurofeedback ex periments targeting

disorders of ingestive behavior

Before testing this protocol in individuals with disorders of ingestive

behavior, including obesity, it will be important to consider which brain

region(s) are good targets for rtfMRI neurofeedback training and how

best to represent neuropsychological functions at the neural systems

level. For example, the hypothalamus has a central role in the regulation

of ingestive behavior; however, it is a relatively small structure with sev-

eral subnuclei with heterogeneous functional properties that contribute

to the regulation of hunger, satiety, and metabolism, but also less closely

related functions such as sleep. Given the resolution of rtfMRI, it is possi-

ble that a neurofeedback signal from the hypothalamus would include in-

formation from a combination of these subnuclei, which may impact the

effective ness of efforts to improve voluntary regulation of a speciﬁcfunc-

tion (e.g. hunger). It is also important to consider the likelihood that the

targeted function is amenable to training. For example, it is possible that

targeting the homeostatic control of feeding represented in the hypothal-

amus and brainstem may lead to compensatory behaviors to defend the

set point of body weight given that these are central, highly conserved

neural circuits that control normal en

ergy homeostasis. However, it may

be possible to target hedonic, cognitive control, or other “non-homeostat-

ic” mechanisms (and their supporting neural circuits) that may help indi-

viduals more effectively to adapt to their environment while minimizing

compensatory behaviors that may lead to persistent obesity. It is also un-

clear whether better outcomes would be expected from ne urofeedback

from an anatomically-restricted brain region or set of brain regions or

whether a network approach using connectivity-based feedback or

multi-voxel pattern classiﬁcation (MVPA) may be preferable given the

regulation of ingestive behavior involves both homeostatic and non-

homeostatic mechanisms represented in a distributed neural circuitry in

the brain (Kelley et al., 2005a). An ROI-based approach could be used to

Fig. 2. Schematic of real-time functional magnetic resonance imaging (rtfMRI) control loop. Typically, echo planar imaging (EPI) images are extracted from the magnetic resonance (MR)

scanner online, analyzed by third-party software, and then presented back to the subject for the purposes of neural self-regulation (adapted from Weiskopf et al., 2004) mEPI: multi-echo

EPI; EMG: electromyography.

12 D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

target a speciﬁc brain region (e.g., vmPFC for the regulation of subjective

reward value of highly palatable food cues). Another option is to

normalize disrupted functional connections between a set of brain

regions instantiating a well-characterized function (e.g., the entire

mesocorticolimbic reward system consisting of VTA-amygdala-VS-

vmPFC). MVPA may be preferable if there is a distributed set of multiple

brain networks that underlie a complex neuropsychological construct

such as cue-induce food craving. It may also be necessary to augment

rtfMRI neurofeedback training by incl uding a psychological or cognitive

training intervention, such as Minding Health, prior to neurofeedback. Fi-

nally, it may be necessary to augment psychological or cognitive training

with adjunctive pharmacotherapy or device-based neuromodulation

such as TMS to enhance the efﬁcacy of neurofeedback training. For a

more detailed discussion of these and other issues of relevance to the de-

sign of rtfMRI neurofeedback studies of disorders of ingestive behavior,

see Stoeckel et al. (2014).

3.2. Transcranial magnetic stimulation (TMS) an d transcranial dir ect-

current stimulation (tDCS)

3.2.1. Introduction to TMS and tDCS

Non-invasive neuromodulation techniques allow the external ma-

nipulation of the human brain in a safe manner, without the require-

ment of a neurosurgical procedure. Over the past two decades there

has been growing interest in the use of non-invasive neuromodulation

in neuro logy and p sychiatry, mo tivated by the shortage of effective

treatments. The most commonly used techniques are transcranial mag-

netic stimulation (TMS) and transcranial direct current simulation

(tDCS). TMS is based on the application of rapidly changing magnetic

ﬁelds that are delivered with a coil encased in plastic that is placed

over the scalp of the subject (Fig. 3A). These varying magnetic ﬁelds

cause an induction of se condary currents in the adjacent c ortex that

can be strong enough to trigger neuronal action potentials (Barker,

1991; Pascual-Leone et al., 2002; Hallett, 2007; Ridding and Rothwell,

2007). TMS can be administered in single or multiple pulses, also called

repetitive TMS (rTMS). In the case of tDCS, mild DC currents (typically in

the order of 1–2 mA) are applied directly over the head through a pair of

saline-soaked electrode pads connected to a battery-like device

(Fig. 3B). Approximately 50% of the current delivered by tDCS pene-

trates the scalp and can raise or decrease the resting membrane poten-

tial of neurons in underlying areas (anodal or cathodal tDCS stimulation,

respectively), causing changes in spontaneous ﬁring (Nitsche et al.,

2008). rTMS and tDCS can induce transient/lasting changes that are be-

lieved to be mediated by changes in synaptic strength. A comprehensive

overview of these techniques and their mechanisms of action are be-

yond the scope of this section and can be found elsewhere (Pascual-

Leone et al., 2002; Wassermann et al., 2008; Stagg and Nitsche, 2011).

Table 3 presents a summary of key differences between TMS and

tDCS. While TMS and tDCS have been and still remain the dominant

techniques in the ﬁeld, other novel or modiﬁed forms of non-invasive

neuromodulation have been developed in recent years and are actively

under investigation, such as deep TMS (dTMS) (Zangen et al. , 2005),

high-deﬁnition tDCS (HD-tDCS) (Datta et al., 2009), transcranial

alternate current simulation (tACS) (Kanai et al., 2008), or transcran ial

random noise stimulation (tRNS) (Terney et al., 2008

). Additional tech-

ques for neuromodulation are those that are invasive (cf. Section 4),

such as deep brain stimulation (DBS), or those that target peripheral

nerves, such as vagus nerve stimulation (VNS).

Over the past two decades there has been remarkable progress in

our understanding of the neurocognitive basis of human eating behav-

ior, obesity and eating disorders. A number of neuroimaging and neuro-

psychology studies have identiﬁed the crosstalk between reward and

cognition as a central component in the regulation of eating behavior

and body weight in humans (Alonso-Alonso and Pascual-Leone, 2007;

Wang et al., 2009a; Kober et al., 2010; Hollmann et al., 2012; Siep

et al., 2012; Vainik et al., 2013; Yokum an d Stice, 2013). As research con-

tinues in this ﬁeld, the available knowledge makes it possible to begin

exploring interventions that shift from behavior to neurocognition as

the primary targ et. Overall, neuromodulatory techniques can bring

valuable insights and open novel therapeutic avenues in this new

Fig. 3. Pictures of (A) butterﬂy coils for transcranial magnetic stimulation (TMS) and (B) electrodes and battery for transcranial direct current stimulation (tDCS).

Table 3

Comparative between TMS and tDCS.

Characteristics Transcranial magnetic stimulation (TMS) Transcranial direct current stimulation (tDCS)

Spatial resolution Very good (approximately 1 cm

) Poor (conventional tDCS) to good (HD-tDCS)

Temporal resolution Excellent (ms) Poor (s)

Tolerability Very good to fair, depending on protocols Excellent to very good

Safety Good (can rarely cause seizures) Excellent

Cost High range (typically $30,000–$100,000) Low to middle range ($250 –$10,000)

Portability Fair Excellent

Regulatory status Cleared for some speciﬁc devices and applications (depression, cortical mapping, migraine) Not cleared. Only off label application

Consumer versions No Yes

13D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

Table 4

Summary of studies with TMS and tDCS in the ﬁeld of human eating behavior.

Study characteristics Subjects, status Stimulation protocol Main outcome measures Main ﬁndings References

TMS studies

Acute effects (single session);

parallel design, randomized,

double-blind, sham-controlled

n = 37 subjects (mean age: 30;

86.8% of women) with

bulimic-type eating disorders

Target: DLPFC; two groups: active (left DLPFC, 5 cm

anterior to hand motor area) and control (sham rTMS);

parameters: 1000 pulses, 10 Hz rTMS, 20 min,

intensity 110% motor threshold

Food craving (VAS) while exposed

to real food and a movie of food;

frequency of bingeing in a 24-hour

follow-up period

Decrease in food craving; reduction in bingeing

in 24 h post rTMS

Van den Eynde

et al. (2013)

Acute effects (single session);

crossover design, randomized,

single-blind, sham-controlled;

improved sham condition

matched for perceived

painfulness of the stimulation

n = 10 women (mean age: 28.3)

with frequent food cravings

(≥3 times/week during the past

month); 3-hour fasting

Target: DLPFC; two conditions: active (left DLPFC) and

control (sham rTMS); Parameters: 3000 pulses, 10 Hz

rTMS, 15 min, intensity 100% motor threshold

Food craving (VAS) while exposed

to food images

No differences between conditions Barth et al. (2011)

3-week intervention; parallel

design, randomized,

double-blind, sham-controlled;

preceded by 1-week of sham

rTMS in all participants

n = 14 women (mean age: 27.4)

with bulimia nervosa

Target: DLPFC; 1 week with sham rTMS before

randomization to avoid high placebo responders; two

groups: active (left DLPFC) and control (sham rTMS);

parameters: 3 weeks, 15 sessions, 2000 pulses per

session. 20 Hz rTMS, intensity 120% motor threshold

Change in binges and purges;

mood and compulsive symptoms

No differences between groups Walpoth et al.

(2008)

Acute effects (single session);

parallel design, randomized,

double-blind, sham-controlled

n = 28 women (mean age: 25.8)

with frequent food cravings

(≥3 times/week); 3–4 h fasting

Target: DLPFC; two groups: active (left DLPFC) and

control (sham rTMS); parameters: 1000 pulses, 10 Hz

rTMS, 20 min, intensity 110% motor threshold

Food craving (VAS); consumption

of snack foods

Decrease in food craving; no effect on snack

consumption

Uher et al. (2005)

tDCS studies

Acute effects (single session);

crossover design, randomized,

double-blind, sham-controlled

n = 9 women (mean age: 23.4);

all lean with frequent food

cravings (≥3 times/day); 3-hour

fasting

Target: DLPFC; two conditions: active (anode over

F4/cathode over F3) and control (sham tDCS);

parameters: 2 mA, 20 min, 35 cm

sponge electrodes

EEG event-related potentials

during an Go/No-Go task; food

craving (VAS) while exposed to

real food and a movie of food;

snack intake; attentional bias for

food (eye tracking)

Reduction of the frontal N2 component and

enhancement of the P3a component of No-Go

responses; reduction in caloric intake

Lapenta et al.

(2014)

8-day intervention; crossover

design, randomized,

single-blind, sham-controlled

n = 14 men (mean age: 24.8), all

lean, with low scores in

three-factor eating

questionnaire; 6-hour fasting

Target: DLPFC; two conditions: active (anode over an

area 5 cm anterior to the right motor cortex/cathode

over the left forehead) and control (sham tDCS);

parameters: 1 mA, 20 min, 35 cm

sponge electrodes

Subjective appetite (ratings and

VAS); free eating from a

standardized multi-choice test

buffet

14% decrease in total calorie consumption, at

the expense of carbohydrates; decrease in

appetite: nonspeciﬁc and speciﬁc (sweets and

savory food)

Jauch-Chara et al.

(2014)

Acute effects (single session);

crossover design, randomized,

double-blind, sham-controlled

n = 17 women (mean age: 26.4;

29.4% of overweight) with

frequent food cravings (≥1/day)

Target: DLPFC; two conditions: active (anode over

F4/cathode over F3) and control (sham tDCS);

parameters: 2 mA, 20 min, 4 cm

sponge electrodes

Food craving ratings while

viewing movies of food; temporal

discounting task; free eating test

Decrease in craving for sweets; no effect on

temporal discounting no change in free eating;

moderating effect of temporal discounting:

participants with more reﬂective choice

behavior showed more susceptibility to

anticraving effects of tDCS

Kekic et al. (2014)

Acute effects (single session), in

combination with an exercise

bout of about 200 calories;

crossover design, randomized,

single-blind, sham-controlled

n = 9 subjects (mean age: 24;

55% of men; all overweight or

obese), 2- to 3-hour fasting

Target: DLPFC; two conditions: active (anode over

F3/cathode over Fp2) and control (sham tDCS);

parameters: 2 mA, 20 min, 35 cm

pads

Subjective appetite (VAS) Decrease in desire to eat with tDCS; greater

appetite suppression with the combination of

tDCS and exercise

Montenegro et al.

(2012)

Acute effects (single session);

crossover design, randomized,

single-blind, sham-controlled

n = 19 subjects (mean age: 32.5;

68.4% of women; about 58% of

overweight or obese) with

frequent food cravings

(≥3 times/week during the past

month); 4-hour fasting

Target: DLPFC; two conditions: active (anode over

F4/cathode over F3) and control (sham tDCS);

parameters: 2 mA, 20 min, standard sponge electrodes

Food craving and ability to resist

tasting (VAS) while viewing food

images; free consumption of

previously presented foods

Decrease in food craving, particularly for sweets

and carbohydrates; no change in food

consumption

Goldman et al.

(2011)

Acute effects (single session);

crossover design, randomized,

double-blind, sham-controlled

n = 23 subjects (mean age: 23.7;

91% of women) with frequent

food cravings (≥3 times/day);

3-hour fasting

Target: DLPFC; three conditions: active 1 (anode over

F3/cathode over F4), active 2 (anode over F4/cathode

over F3), control (sham tDCS); parameters: 2 mA,

20 min, 35 cm

sponge electrodes

Food craving (VAS) while exposed

to real food and a movie of food;

snack intake; attentional bias for

food (eye tracking)

Decrease in food craving only in condition

active 1; decrease in snack intake in conditions

active 1 and 2; decrease in attentional bias for

food only in condition active 1

Fregni et al.

(2008)

rTMS: repetitive transcranial magnetic stimulation; tDCS: transcranial di r ect current stimulation; DLPFC: dorsolateral prefrontal co r tex; VAS: visual analogue scale; Electrode montage for tDCS: F3 (left DLPFC), F4 (right DLPFC), Fp2 (right supra-

orbital); EEG: electroencephalography; N2, P3a: speciﬁc EEG electrophysiological measures.

14 D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

scenario that places neurocognition as a central component of human

eating behavior.

3.2.2. Summary of clinical studies to mo dify eating behavior and eating

disorders

Eating behavior is a recent application in the ﬁeld of non-invasive

neuromodulation, with the earliest study dating back to 2005 (Uher

et al., 2 005). TMS and tDCS are the only techniques that have been

used in this context. Table 4 provides a summary of randomized, con-

trolled, proof-of-concept studies. To date, these studies have tested

acute, single-session effects only, with two exceptions: one study with

rTMS in bulimic patients (3 weeks), and a recent study with tDCS in

healthy men (8 days). The targeted area, dorsolateral prefrontal cortex

(dlPFC), is a complex brain region related to executive functions that

supports cogn itive control of food intake. Overall, the underlying

hypothesis is that enhancin g dlPFC activ ity may alte r the reward –

cognition balance towards facilitation of cognitive control and possibly

suppression of reward-related mechanisms tha t drive food craving and

overeating. The speciﬁc dlPFC-dependent cognitive processes being

affected by rTMS or tDCS and mediating the observed behavioral effects

remain largely unknown. Possibilities include changes in reward valua-

tion mechanisms (Camus et al., 2009), attentional biases (Fregni et al.,

2008), or inhibitory control (Lapenta et al., 2014). rTMS studies have

targeted the left dlPFC only, via excitatory protocols (10 and 20 Hz).

tDCS studies have targeted both the right and left dlPFC, with slightly dif-

ferent approaches/montages. The majority of studies — allwithtDCSand

one with rTMS — have evaluated effects on food craving, subjective appe-

tite and food intake. Altogether, they have consistently found an acute

suppression in the scores of self-reported food craving and appetite mea-

sured by ratings or visual analogue scales (VAS). There is some indication

that the effect with tDCS may be more speciﬁc for craving of sweets.

Changes in food intake have been rather inconsistent with a single session

of rTMS or tDCS. In the longest study to date with tDCS (8 days), the au-

thors found a 14% decrease in calorie consumption (Jauch-Chara et al.,

2014). An important bias in some studies is the use of a sham procedure

without any current ﬂow as control, instead of sham stimulation in areas

that are irrelevant to food intake for example. Since the stimulation is

sometimes perceptible by the patient, we cannot exclude a placebo effect

in some cases.

Studies with eating disorder pati ents so far have used only rTMS.

Several case reports (Ka molz et al., 2008; McClelland et al., 2013b)

and an open-label study (Van den Eynde et al., 2013) (not included in

the table) suggest potential for rTMS in anorexia nervosa, but ﬁndings

should be replicated in placebo-control led trials. For the case of B N, an

early case report suggested potential beneﬁts with rTMS (Hausmann

et al., 2004), but this was not conﬁrmed in a subsequent clinical trial

that used this technique over 3 weeks (Walpoth et al., 2008). A recent

case study reported beneﬁcial effects using 10 Hz rTMS applied over a dif-

ferent target, the dorsomedial prefrontal cortex, in a refractory patient

with BN (20 sessions, 4 weeks) (Downar et al., 2012). This brain region

represents a promising target given its general role in cognitive control,

speciﬁcally performance monitoring and action selection (

Bush et al.,

00; Krug and Carter, 2012), and its link with th e clinical course of AN

and BN (McCormick et al., 2008; Goddard et al., 2013; Lee et al., 2014).

3.2.3. Future needs: from empirically-driven studies to rational and mecha-

nistic approaches

Results from these initial studies provide a good proof of concept for

the translation of non-invasive neuromodulation into the ﬁeld of eating

behavior. Potential applications can be the enhancement of cognitive

control and underlying brain regions to support successful weight loss

maintenance in obesity (DelParigi et al., 2007; McCaffery et al., 2009;

Hassenstab et al., 2012), or rebalancing ventral and dorsal brain systems

in AN and B N (Kaye et al., 2010). While the overall rationale is quite

clear, the speciﬁcs of using noninvasive neuromodulation in the treat-

ment of obesity and eating disorders are currently under investigation

and the best approaches and protocols remain to be deﬁned. Noninva-

sive neuromodulation could be used alone or in combination with

other strategies such as behavioral therapy, cognitive training, physical

ﬁtness and nutrition, to create synergistic effects. Aside from therapeu-

tic applications, neuromodulation techniques can be used to inform

disease mechanisms, e.g. examining the causal involvement of a speciﬁc

region in a gi ven cognitive process or behavioral manifestation

(Robertson et al., 2003). Recent st udies hav e examined the potential

of TMS to quantify reward responses (Robertson et al., 2003) and results

from this line of work could eventually lead to the development of ob-

jective biomarkers that can help study eating phenotypes.

While there is a high potential for future uses of neuromodulation in

the ﬁeld of eating behavior, there are still many limitations and open

questions. Blinding is a key issue, called into question by one rTMS

study in food craving and a tDCS study where subjects were able to

guess the condition they had received with 79% accuracy (Barth et al.,

2011; Goldman et al., 2011). Future studies should consider parallel de-

signs to overcome this problem, or at least rule out the possibility of in-

complete blinding when crossover designs are used. Another need to

address in future studies is the addition of more clinically meaningful

outcomes. rTMS and tDCS have caused changes in measures that are

sensitive and valid in an experimental setting, e.g. visual analogue

scales, but their clinical relevance remains uncertain.

All studies to date have targeted the DLPFC, as in other applications

of tDCS and rTMS in neuropsychiatry. There is need to explore addition-

al targets; dorsomedial prefrontal cortex/dorsal anterior cingulate cor-

tex (daCC), parietal regions and anterior insular cortex are particularly

promising. Both rTMS and tDCS are currently optimized to target brain

regions located on the surface. Reaching deeper brain structures may

be more feasible with HD-tDCS, or with dTMS for the case of mid-

depth areas such as insular cortex (Zangen et al., 2005). A recently de-

scribed method for rTMS consists of guiding stimulation on the basis

of intrinsic functional connectivity determined by resting-state fMRI

(Fox et al., 2012a; Fox et al., 2012b). Aside from targeting brain regions

alone, non-invasive neuromodulation can be administered with simul-

taneous cognitive training. This approach may lead to more functional

effects (Martin et al., 2013; Martin et al., 2014) and is articularly suited

for eating disorders and obesity, where there are impairments in specif-

ic neurocognitive domains, such as executive functions, even though the

picture is complex (Alonso-Alonso, 2013; Balodis et al., 2013

). The use

cognitive performance and/or ways of measuring brain activity can

also facilitate target monitoring and overall contribute to optimize the

delivery of neuromodulation. A recent tDCS study points in that direc-

tion, with a combination of EEG event-related potentials and behavioral

measures of food craving and food intake (Lapenta et al., 2014).

More work is needed to understand potential sources of variability

in the response to neuromodulation. The majority of participants in

these rTMS/tDCS studies have been young women, with variable BMI.

Gender effects remain unaddressed, with no direct comparisons so far

between women and men, but differences are likely based on the effect

of gender on brain correlates of appetite (Del Parigi et al., 2002; Wang

et al., 2009a). When studying food-related processes and mechanisms,

it is also important to consider the underlying variability in brai n activ-

ity related to metabolic state. As mentioned in Table 4, subjects have

been stimulated typically in an intermediate state, i.e. about 2–4h

after a meal. It is unknown whether different conditions can cause bet-

ter results. Another potential confounder that remains unaddressed is

the role of dieting. Patients with eating disorders and obesity usually

follow diets that can be quite restrictive and, more importantly, could

have substa ntial eff ects on brain excitability and also in the se nsitivity/

response to neuromodulation (Alonso-Alonso, 2013). An additional fac-

tor is whether a person receives TMS or tDCS in a weight-reduced state

or in a weight-stable state, which would also have consequences in the

resting brain state and neuromodulatory response (Alonso-Alonso,

2013). Lastly, at a more technical level, individual head anatomy can

alter electric or electromagnet ic tra nsmission. This issue has been

15D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

extensively addressed using computational models of tDCS ( Bikson

et al., 2013). A particular concern in this regard is whether head fat, a

relatively resistive tissue, could affect current density distribution

(Nitsche et al., 2008; Truong et al., 2013).

Regarding side effects, both TMS and tDCS are non-invasive, safe and

rather painless techniques that are very well tolerated in the vast major-

ity of cases (Nitsche et al., 2008; Rossi et al., 2009). The most common

adverse effects with rTMS is headache, which occurs approximately in

25–35% of patients during dlPFC stimulation, followed by neck pain

(12.4%) (Machii et al., 2006). With tDCS, a substantial proportion of peo-

ple (N50%) report transient sensations under the electrode that can be

deﬁned as tingling, itching, burning or pain, and are usually mild or

moderate (Bruno ni et al., 2011). When designing a study it is important

to exclude participants with contraindications to receive either TMS or

tDCS, and collect adverse events in a systematic manner. There are stan-

dardized questionnaires available for that purpose (Rossi et al., 2009;

Brunoni et al., 2011). The most worrisome adverse effect of non-

invasive neuromodulation is the induction of seizure, which has been

reported onl y a few times with rTMS (Rossi et al., 2009).

The ﬁeld of neuromodulation is expanding very quickly and it has

started to cross boundaries beyond the medical and research community

to curious individual consumers and recreational users. It is important

that we, the community of scientists working in neuromodulation,

remain committed to guarantee research integrity and maintain high eth-

ical standards in the use of these methods. The possibility of manipulating

the human brain can be as fascinating and tempting as trying a new diet

to curb appetite, but it is important to remind that the current state of sci-

ence in this ﬁeld is far from being conclusive. And, as importantly, trans-

cranial devices are not playthings (Bikson et al., 2013).

4. Invasive neuromodulation strategies: recent developments and

current challenges

4.1. Overview of the peripheral neuromodulation strategies in the context of

food intake and weight control

4.1.1. Changes in vagal signaling during obesity

The homeostatic control of food intake involves a complex, bidirec-

tional communication system between the periphery and the central

nervous system that h as been extensively reviewed (Williams and

Elmquist, 2012). The vagus nerve, because it contains mainly afferents

neurons that arise from the gut, the pancreas and the liver, plays a key

role in this communication. In non-obese individuals, chemosensory

(acid-sensing ion channels) and mechanosensory vagal receptors signal

immediate availability of food (Page et al., 2012). Further, several

hormones including ghrelin, cholecystokinin (CCK) and peptide tyro-

sine tyrosine (PYY) have the capability to activate vagal afferents

(Blackshaw et al., 2007).

Aside from an excessive accumulation of fat, a substantial body of ev-

idence suggests that obesity and/or high fat diet is associated with alter-

ation of peripheral responses to nutrients. Studies in rodents subjected

to a high-fat diet (HFD), or in diet-induced obesity consistently show re-

duced suppressive effects of intestinal nutrients on food intake com-

pared to control animals (Covasa and Ritter, 2000; Little, 2010). This is

associated with a reduced sensitivity of jejunal afferents (primarily

vagal) to low-level distension and reduced excitability of identiﬁed jeju-

nal vagal afferents within the nodose ganglion to CCK and 5-HT expo-

sure (

Daly et al., 2011).

Corresponding reductions in vagal afferent

expression of receptors for CCK, 5-HT and other anorexic GI peptides

have been reported in the nodose ganglion (Donovan and Bohland,

2009). Additionally, HFD reduced the responses of gastric vagal tension

receptors to distension and augmented the inhibitory effect of ghrelin

on vagal afferents. Alternatively, while leptin potentiated vagal mucosal

afferent responses, potentiation of mucosal afferents by leptin was lost

after HFD (Kentish et al., 2012). The loss of vagal afferent signaling to-

gether with the altered processing of vagal signals within the dorsal

vagal complex suggest that resetting these sensitivities by chronic

vagal stimulation (VNS) might reduce overeating.

4.1.2. Effects of vagal stimulation

Unilateral left cervical vagal stimulation is approved for treatment-

resistant depression and intractable epilepsy in the European Union,

the United States and Canada. Epileptic patients reported frequently

changes in eating behavior with alteration in diet preferences (Abubakr

and Wambacq, 2008). These reports generated further investigations, ini-

tially through pure serendipity, which subsequently used animal models

to evaluate the effects of VNS on food intake and related weight control

(for synthetic tables on VNS studies, please see Val-Laillet et al., 2010;

McClelland et al., 2013a). The original studies in 2001 of Roslin and

Kurian (2001) in dogs and the other from Krolczyk et al. (2001) in rat s

suggested a decrease in weight gain or a weight loss during chronic

vagal stimulation. Surprisingl y, despite different surgical approache s, the

results demonstrated by these authors were identical. Indeed, Roslin

and Kurian (2001) used a bilateral cuff placement within the thorax

(hence stimulating both dorsal and ventral vagal trunks) while Krolczyk

et al. (2001) used a cervical placement on the sole left vagus to be similar

with the clinical setup for intractable epilepsy. Since these pioneering

studies, several research groups, including us, have published positive re-

sults using various electrodes locations, electrodes set-up and stimulation

parameters. The ﬁrst attempt to evaluate the adequate location of the

electrodes for food intake control was performed by Laskiewicz et al.

(2003). They demonstrated that bilateral VNS is more effective than uni-

lateral stimulation. Using a large animal pre-clinical model, we used

juxta-abdominal bilateral vagal stimulation on the longest longitudinal

study performed to date. We show that chronic vagus nerve stimulation

decreased weight gain, food consumption and sweet craving in adult

obese minipigs (Val-Laillet et al., 2010). Further, unlike others studies per-

formed in smaller animal models, efﬁcacy improves over time in a man-

ner comparable that already exempliﬁed in intractable epilepsy patients

(Arle and Shils, 2011).

Unfortunately, the positive results observed in almost all animal pre-

clinical studies have not been conﬁrmed in humans. Because of regula-

tory restraints, all human studies have been performed using left cervi-

cal vagal cuff only with stimulation settings similar or closely identical

to those used for depression or epilepsy. Despite using long-term stim-

ulation, weight loss was found in about half of the subjects (Burneo

et al., 2002; Pardo et al., 2007; Verdam et al., 2012). At present, no

clear explanation for these non-responsive subjects can be offered. A re-

cent study by Bodenlos et al. (2014) suggests that large BMI individuals

are less responsive to VNS than lean people. Indeed, in their study, VNS

suppressed food intake in lean patients only.

Several authors have investigated the physiological basis of VNS

with speciﬁc reference to the left cervical placement of the electrode.

Vijgen et al. (2013) have demonstrated in an elegant study combining

PET imaging of the brown adipose tissue (BAT) and a cohort of VNS ep-

ileptic patients that VNS signiﬁc

antly increases energy expenditure.

Moreover, the change in energy expenditure was related to the change

in BAT activity suggesting a role for BAT in the VNS increase in energy

expenditure. VN S has been demons trated to change brain activity

throughout the entire cerebrum (Conway et al., 2012 ) and modulate

the monoaminergic systems (Manta et al., 2013). In humans, left VNS

induced rCBF (regional cerebral brain ﬂow) decreases in the left an d

right lateral OFC and left inferior temporal lobe. Signiﬁcant increases

were found also in the right dorsal anterior cingulate, left posterior

limb of the internal capsule/medial putamen, the right superior tempo-

ral gyrus. Despite the critical importance of these areas towards control

of food intake and depression, no correlation was found between brain

activation and the outcome of depression score after 12 months of VNS

therapy. Therefore, it remains to be demonstrated that the observed

brain activity changes are causative factors to explain VNS effects. The

demonstration in rats that VNS modulates visceral pain-related affective

memory (Zhang et al., 2013) might represent an alternative pathway

16 D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

that could explain the beneﬁcial effects observed on about half of the

patients. Our early studies on brain activation after juxta-abdominal bi-

lateral VNS performed in growing pigs (Biraben et al., 2008) using single

photon gamma scintigraphy was the ﬁrst to evaluate VNS effects on the

non-pathological brain. We showed the activation of two networks. The

ﬁrst one is associated with the olfactory bulb and primary olfactory

projections areas. The second one involves areas that are essential to in-

tegrate gastro-duodenal mechanosensory information (hippocampus,

pallidum) so to give a hedonic value to these. Similar results have

been reported in rats either using PET (Dedeurwaerdere et al., 2005)

or MRI (Reyt et al., 2010). Unlike behaviora l effects that take several

weeks to be identiﬁed, alterations in brain metabolism identiﬁed by

PET imaging were present 1 week only after the onset of VNS therapy.

In our porcine model of juxta-abdominal VNS, the cingulate cortex, pu-

tamen, caudate nucleus and substantia nigra/tegmental ventral area, i.e.

the main reward meso-limbic dopaminergic network, presented chang-

es in brain metabolism (Malbert, 201 3; Divoux et al., 2014)(Fig. 4). The

massive activation of the reward network at an early stage of the chron-

ic stimulation suggests that brain imaging might be used as a tool to op-

timize the vagal stimulation parameters.

As with several others therapies, the relatively poor success of VNS

in obese humans could be explained by an insufﬁcient understanding

of the action of VNS on the brain networks controlling food intake.

Translation of animal models into clinical practice was (too) quick with-

out experimental clues towards a normalized procedure for stimulation.

For instance, as mentioned above, early human studies were performed

with unilateral cervical vagal st imulation whereas all anima l studies

suggested that bilateral juxta-abdominal location for the stimulating

cuffs was more appropriate. Furthermore, we are still in need for early

clues to reﬁne stimulation parameters without having to wait for chang-

es in body weight. It can be speculated that brain-imaging methods to-

gether with computational model of VNS (Helmers et al., 2012)might

be of signiﬁcant help towards this clinical requirement.

4.1.3. Effects of vagal blockade

Several patients after vagotomy performed as a cure for ulcer disease

report short-term loss of appetite; less commonly, prolonged loss of ap-

petite and further weight loss or failure to regain weight have been

noted (Gortz et al. , 1990). Bilateral truncal vagotomy has been used

historically as a treatment for obesity refractory to other therapies,

and has been associated with satiety and weight loss (Kral et al.,

2009). Based upon this observation and although that it has been re-

ported that the effects on body weight are lost over time (Camilleri

et al., 2008) and that truncal vagotomy was virtually ineffective to re-

duce solid food intake (Gortz et al., 1990), vagal blockade therapy was

tested in humanswith the primary objective to reduce weight of morbid

obese individuals. Vagal blockade was performed bilaterally at the ab-

dominal level using high frequency (5 kHz) current pulses. The large

scale, long lasting study called EMPOWER (Sa rr et al., 2012)demon-

strated that weight loss was not greater in treated compared to control.

Despite this therapeutic failure, Vbloc therapy in type 2 diabetic patients

(DM2) reduces the level of HbA

and hypertension shortly after activa-

tion of the device (Shikora et al., 2013). This beneﬁt and the stability of

the improvement over time suggest that the mechanisms of action may

be, at least in part, independent from weight loss. Since these parame-

ters are entirely related to fat deposition and truncal vagotomy led to

signiﬁcant reductions in diet-induced visceral abdominal fat deposition

(Stearns et al., 2012), it is quite possible that the efferent neurons

blocked by the therapy might be responsible for the improvements ob-

served in DM2 patients.

4.2. State of the art of deep brain stimulation (DBS) and its potential for

tackling obesity and eating disorders

4.2.1. Overview on the state of the art in DBS

4.2.1.1. Current therapeutic applications of DBS. Deep brain stimulation

(DBS) is a technique based on implanted elect rodes for treating

neuromotor disorders such as Parkinson3sdisease(PD),aswellasepilep-

sy, while showing promise for psychological disorders like treatment-

resistant depression (TRD) and obsessive–compulsive disorders (OCD)

(Perlmutter and Mink, 2006).

The subthalamic nucleus (STN) is commonly targeted for PD, while

the anterior nucleus of the thalamus (ANT), subgenual cingulate

(Cg25), and nucleus accumbens (Nac) are respectively targeted for

epilepsy, TRD and OCD (Fig. 5). The penetration of DBS, roughly

10,000 patients per year worldwide, is minuscule compared to the prev-

alence of treatment-resis tant PD, ep ilepsy, and psychiatric disorders

(see allcountries.org; TRD: Fava, 2003; PD: Tanner et al., 2008; OCD:

Denys et al., 2010). This section is aimed at identifying these technolog-

ical developments and their potential to combat obesity and eating

disorders.

4.2.1.2. Traditional surgery planning in DBS. In the traditional deep-brain

therapy (DBT) framework, preoperative brain MRI is acquired, a stereo-

tactic frame is af ﬁxed to the patient, who then undergoes a CT scan, and

the insertion trajectory is set based on the registered modalities and a

deep brain atlas in printed form (Sierens et al., 2008). This framework

places restrictions on the choice of approach, and surgical planning in-

volves considerable mental computation by the surgeon. Modern DBS

practice relies on intra-operative microelectrode recordings (MER) for

conﬁrmation comes at the cost of extended operating times and greater

potential for complications (Lyons et al., 2004). Whi le MER use is com-

mon in PD, feedback on targeting success is not possible for many non-

motor disorders.

4.2.1.3. Potential complications of DBS. In traditional and image-guided

approaches, targeting does not account for brain shift, and this neglect

leads to a heightened risk of complications. While brain shift may be

negligible under some conditions (Petersen et al., 2010), other studies

suggest that shifts up to 4 mm can occur (Miyagi et al., 2007; Khan

et al., 2008). The worst case is a cerebrovascular complication, especially

when multiple trajectories are used during exploration (Hariz, 2002).

Moreover, the risk of penetration of a ventricular wall is an important

Fig. 4. Changes in glucose metabolism observed via positron emission tomography (PET)

imaging after injection of

FDG (ﬂuorodeoxyglucose), between vagal stimulated vs. sham

animals. N = 8 Yucatán minipigs in both groups. VNS (vagus nerve stimulation) therapy

was applied during 8 days on ventral and dorsal vagal trunks at the level of the abdomen.

The cuff electrodes were placed surgically using a coelioscopic approach. p b 0.0001 with

FDR (false discovery rate correction) (see text for details).

17D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

consideration (Gologorsky et al., 2011), which correlates strongly with

neurological sequelae. Despite the foregoing, DBS still has a relatively

low complication rate compared to bariatric surgery (Gorgulho et al.,

2014) and recent DBS innovations will considerably improve the safety

and accuracy of this surgery.

4.2.2. Recent DBS innovations and emerging DBS therapies

A number of innovative techniques have been proposed in image-

guided DBS, improving the functionally descriptive aspects of surgery

planning. Most groups emphasize only a small number of these tech-

niques at once, which include 1) a digital deep-brain atlas depicting

deep-brain structures in hu mans (D3Haese et al., 2005; Chak ravarty

et al., 2006) and animal models such as the pig (Saikali et al., 2010);

2) a surface model, featuring shape statistics, for registering an atlas to

patient data (Patenaude et al., 2011); 3) an electrophysiological data-

base with successful target coordinates (Guo et al., 2006); 4) a model

of venous and arterial structures, identiﬁed from the combination of

Susceptibility Weighted Imaging and Time-Of-Flight angiographic mag-

netic resonance imaging (Bériault et al., 2011); 5) multi-contrast MRI

that directly delineates the basal ganglia structures through

coregistered images weigh ted on T1, R2* (1/T2*), and susceptibility

phase/magnitude (Xiao et al., 2012); 6) validation of deep brain therapy

through animal trials, mostly conﬁned to rodents (Bove and Perier,

2012) but also applied to (mini)pigs (Sauleau et al., 2009a; Knight

et al., 2013); 7) computer simulation of DBS (McNeal, 1976;

Miocinovic et al., 2006), using a ﬁnite element model of voltage distri-

bution of the stimulating electrode as well as an anatomical model of

the stimulated neural tissue; and 8) connectomic surgery planning for

DBS (Henderson, 2012; Lambert et al., 2012), where patient-speciﬁc

white matter tracts identiﬁed from diffusion tensor/spectrum imaging

(DTI/DSI) are exploited for effective targeting.

The above technologies relate to preoperative planning; Meanwhile,

very little effort has been devoted to intraoperative accuracy. The main

exception is intraoperative MRI (ioMRI)-guided DBS, which was pro-

posed in Starr et al. (2010), using an MRI-compatible frame. Another

recent intraoperative development is closed-loop deep-brain therapy de-

livery, based on electrical or neurochemical feedback (Rosin et al., 2011;

Chang et al., 2013).

Last, highly selective therapies have been proposed for the treat-

ment of epilepsy, which target mutated genes that modulate ion chan-

nels (Pat han et al., 201 0).

Therapies that address molecular pathways speciﬁctoPD(LeWitt

et al., 2011), and TRD (Alexander et al., 2010) are also being developed.

In this kind of deep-brain therapy, the electrical stimulation is replaced

by the infusion of substances that modulate the neurotransmission

loc

ally.

4.2.3. Applicability of DBS in the context of obesity and eating disorders

4.2.3.1. The effects of DBS on eating behavior and body weight. In a compre-

hensive review, McClelland et al. (2013a) presented evidence from

human and animal studies on the effects of ne uromodulation on eating

behavior and body weight. Four studies observed clinical improvements

and weight gain in patients with anorexia nervosa (AN) treated with

DBS (in the Cg25, Nac, or ventral capsule/striatum – VC/VS) (Israel et al.,

2010; Lipsman et al., 2013; McLaughlin et al., 2013; Wu et al., 2013); a sin-

gle case report showed a signiﬁcant weight loss in a DBS-trea ted patient

suffering from obsessive–compulsive disorders (M antione et al., 2010);

and eleven studies reported either over-eating and/or increases in

Fig. 5. DBT targets: (A) subthalamic nucleus (coronal view, yellow, labeled “STN”); (B) anterior nucleus of thalamus (3D rendering, dark blue, labeled “anterior”); (C) subgenual anterior

cingulate (medial view, region high-lighted in red); (D) nucleus accumbens (medial view, blue circle) (Wiki).

18 D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

cravings, weight gain and BMI following DBS of the STN and/or globus

pallidus — GP (Macia et al., 2004; Tuite et al., 2005; Montaurier et al.,

2007; Novakova et al., 2007; Bannier et al., 2009; Sauleau et al., 2009b;

Walker et al., 2009; Strowd et al., 2010; Locke et al., 2011; Novakova

et al., 2011; Zahodne et al., 2011). In patients treated for PD, we can as-

sume that the decrease in motor activity, and thus in energy expenditure ,

might explain part of the increased weight gain, even though Amami et al.

(2014) recently suggested that compulsive eating may be speciﬁcally re -

lated to STN stimulation.

Amongst the 18 animal studies (mainly rats) assessing food intake

and weight further DBS (McClelland et al., 2013a), only two stimulated

the Nac or dorsal striatum, while the others focused on the lateral (LHA)

or ventromedial (vmH) hypothalamus. Halpern et al. (2013) showed

that DBS of Nac can reduce binge eating, while van der Plasse et al.

(2012) interestingly revealed different effects on sugar motivation and

food intake according to the sub-area of Nac stimulated (core, lateral

or med ial shell). LHA stimulation mos tly induced food intake and

weight gain (Delgado and Anand, 1953; Mogenson, 1971; Stephan

et al., 1971; Schallert, 1977; Halperin et al ., 1983), even though Sani

et al. (2007) showed a decreased weight gain in rats. vmH stimulation

decreased food intake and/or weight gain in most cases (Brown et al.,

1984; Stenger et al., 1991; Bielajew et al., 1994; Rufﬁn and Nicolaidis,

1999; Lehmkuhle et al., 2010), but two studies showed increased food

intake (Lacan et al., 2008; Torres et al., 2011).

Tomycz et al. (2012) published the theoretical foundations and de-

sign of the ﬁrst human pilot study aimed at using DBS to combat obesity

speciﬁcally. Preliminary results from this study (Whiting et al., 2013)in-

dicate that DBS of the LHA may be applied safely to humans with intrac-

table obesity, and induce some weight loss under metabolically optimized

settings. Two clinical trials on DBS for AN are also in progress according to

Gorgulho et al. (2014), which demonstrate that DBS is a hot topic and

promising alternative strategy to combat obesity and eating disorders.

4.2.3.2. What the future has to offer. Mos

t of the DBS studies aimed at

modifying eating behavior or body weight in animal models were per-

formed one to several decades ago, and almost exclusively focused on

the hypothalamus, which plays a pivotal rol e in homeostatic regula-

tions. The explosion of functional brain imaging studies and the descrip-

tion of brain anomalies in the reward and dopaminergic circuits of

subjects suffering from obesity or eating disorders show that hedonic

regulations ar e of the utmost imp ortance for food intake control.

The most effective treatment against obesity remains bariatric sur-

gery, and especially the gastric bypass surgery. We have a lot to learn

from the effectiveness of this treatment in terms of brain mechanisms

and potential targets for DBS, and recent studies managed to describe

the surgery-induced remodeling of bra in responses to food reward,

hunger or satiety (Geliebter, 2013; Frank et al., 2014; Scholtz et al.,

2014). The Nac and PF C are part of the brain areas impa cted. Knight

et al. (2013) showed in pigs that DBS of the Nac can modulate the activ-

ity of psychiatrically important brain areas, such as the PFC, for which

anomalies were described in obese humans (Le et al., 2006; Volkow

et al ., 2008) and minipigs (Val-La illet et al., 201 1). All the DBS improve-

ments described beforehand will help targeting the best structures and

coping with brain shift, and large animal models such as the minipig are

an asset in perfecting surgical strategies.

Basal nuclei have a complex ‘somatotopy’ (Choi et al., 2012), and DA

spatial and temporal release involves distinct neural microcircuits with-

in subregions of these nu clei (Besson et al., 2010; Bassareo et al., 2011;

Saddoris et al ., 2013), which means that small errors in terms of

targeting can have dramatic consequences in terms of neural networks

and neurotransmission processes impacted. Once this challenge will be

achie ved, highly innovative deep-brain therapies could target some

functions of the dopaminergic system for example, which is altered in

patients suffering from obes ity (Wang et al., 2002; Volkow et al.,

2008) and animal mod els of addictive-like cravings or bingeing

(Avena et al., 2006; Avena et al., 2008), with the aim of normalizing

the functional processes of the DA system (as in Parkinson3s for the

motor disorders). Even though ﬁndings relating obesity and DA abnor-

malities appear sometimes inconsistent, it is probably because incorrect

interpretations or comparisons have been done. Most of the discrepan-

cies in the DA literature arose because different pathological stages (dif-

ferent degrees of obesity with different comorbidities, reward deﬁcit vs.

surfeit phenotypes), brain processes (basal activity vs. response to food

stimuli), or cognitive processes (liking vs. wanting, occasional vs.

habitual consumption) were compared. Before proposing a DBS strate-

gy, there is a need for phenotyping patients in terms of neural circuits/

functions impacted. For example, the individual reward sensitivity phe-

notyp e may determine the treatment target in terms of goal brain

change (i.e. increased/decreased DA regions responsivity for deﬁcit vs.

surfeit phenotypes, respectively). In other patients for whom there is

no alteration of the reward circuit but rather neural abnormalities in

metabolic centers (such as the hypothalamus), the DBS strategy might

be completely different (e.g. modulate the LHA or vMH activity in AN

or obese patients to stimulate or decrease food intake, respectively).

Real-time fMRI neurofeedback combined with cognitive therapy (cf.

Section 3.1) might also be used for closed-loop DBS therapy. Even

though it has never been tested in our knowledge, the efﬁcacy of

targeting speciﬁc nuclei for DBS might be validated through its ability

to improve real-time brain and cognitive processes related to self-

control over highly palatable food stimuli (Ma

ntione et al., 2014). This

approach might be used to ﬁnely tune the DBS parameters and location

to maximize its impact on speciﬁc cognitive tasks or processes (e.g. self-

control over palatable foods).

Overall, these data offer a large ﬁeld of research and developments

to improve DBS surgery and make it, one day, a safer, ﬂexible and re-

versible alternative to classical bariatric surgery.

5. General discussion and conclusions: the brain at the core of

research, prevention and therapy in the context of obesity and

eating disorders

As described in this review, neuroimaging and neuromodulation ap-

proaches are emergent and promising tools to explore the neural vul-

nerability factors and obesity-related brain anomalies, and eventually

to provide innovative therapeutic strategies to combat obesity and ED.

The different sections of this review article can raise several questions

in terms of implementation of these tools in fundamental research, pre-

vention programs and therapeutic plans. How can these new technolo-

gies and exploratory approaches ﬁnd a place within the current medical

workﬂow, from prevention to treatment? What are the requisites for

their implementation, for which added value in comparison to existing

solutions, and where could they slot into the current therapeutic plan?

To answer these questions, we propose to initiate three debates that will

inevitably need further work and reﬂection. First, we will discuss the

possibility to identify new biologic al markers of key brain functions.

Second, we will h ighlight the potential role of neuroimaging and

neuromodulation in individualized medicine to improve the clinical path-

ways and strategies. Third, we will introduce the ethical questions that are

unavoidably concomitant to the emergence of new neuromodulation

therapies in humans.

5.1. Towards new biological markers?

“It is far more important to know what person the disease has than

what disease the person has.” This quote from Hippocrates bears the

quintessence of preventive medicine. Indeed, reliable prediction and ef-

ﬁcient prevention are the ultimate objective in public health. Similarly,

accurate diagnosis, prognosis and treatment are mandatory for a good

medical practice. But all of these cannot be reached without a good

knowledge of the healthy and ill (or at risk) indi vidual phenotypes,

19D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

which can be achieved through the description and validation of consis-

tent biological markers.

Psychiatric studies ext ensively des cribed the symptomology as well

as the environmental and behavioral risk factors underlying ED, while

obesity has been described through the lenses of multiple disciplines

as a multifactorial disease with a complex etiology. Despite all of this

knowledge, accurate biomarkers or clinical criteria are still lacking and

obsolete indices (such as BMI) are still used all over the world to deﬁne

and categorize patients. Yet, as reminded by Denis and H amilton

(2013), many persons classiﬁed as obese (BMI N 30) are healthy and

should not be treated and categorized as diseased. On the contrary, sub-

jects that are not considered at risk with classical clinical criteria might

show a real vulnerability with more accurate markers, as described for

the TOFI sub-phenotype (i.e. thin-on-the-outside, fat-on-the-inside),

characterizing individuals at increased metabolic risk with normal

body mass, BMI and waist circumference, but with abdominal adiposity

and ectopic fat that MRI and MRS phenotyping can help to diagnose

(Thomas et al., 2012). In the context of neuroimaging, neural vulnerabil-

ity factors could help predicting a risk for further weight gain or suscep-

tibility to contract a contentious relationship with food, as described in

Burger and Stice (2014). For obvious practical and economical reasons,

this approach could not be used for a systematic screening, but might

be proposed to subjects that are particularly at risk, because of an unfa-

vorable genetic or en vironmental ground. Since plasmatic gut-brain

obesity-associated biomarkers were fo und to be associated with

neurocognitive sk ills (Miller et al., 2015), their detection could advocate

the collection of further functional bio markers at the brain level and

contribute to a step-by-step diagnosis. Identifying neural risk factors

in people at risk, preferably in the young age, might guide further inter-

ventions (e.g. cognitive therapy) for pre-symptomatic treatment of

obesity or eating disorders. For example, reward sensitivity phenotype

may dictate the treatment target in terms of goal brain change (i.e.

increased/decreased reward regions responsivity for deﬁcit vs. surfeit

phenotypes, respectively). Another example is the case of patients pre-

senting symptoms that are common to different diseases and for which

speciﬁc explorations are required. Some gastrointestinal diseases com-

monly mimic the presentation of eating disorders, which incites the cli-

nician to consider a broad dif ferential diagnosis when evaluating a

patient for an eating disorder (Bern and O3Brien, 2013). New neuropsy-

chiatric markers would con sequently help diagnosis and should be

added to the battery of decision criteria available.

Omics approaches, referring to innovative technology platforms such

as genetics, genomics, proteomics, and metabolomics, can provide exten-

sive data of which the computation might lead to the formulation of new

biomarkers for prediction and diagnosis (Katsareli and Dedoussis, 2014;

Cox et al., 2015; van Dijk et al., 2015). But the integration between

omics and imaging technologies should potentiate the deﬁnition of

these biomarkers, through the identiﬁcation of organ-speciﬁc(notably

brain-speciﬁc) metabolis ms an d culprits associated with diseases

(Hannukainen et al., 2014). As described in the ﬁrst section of this re-

view, neural vulnerability factors could appear before the onset of ED

or weight problems, highlighting the possible existence of subliminal

predictors that brain imaging only might reveal.

Radiomics is a new discipline referring to the extraction and analysis

of large amounts of advanced quantitative imaging features with high

throughput from medical images obtained with computed tomography,

PET, or structural and functional MRI (Kumar et al., 2012; Lambin et al.,

2012). Radiomics has been initially developed to decode tumor pheno-

types (Aerts et al., 2014

), including brain tumors (Co

query et al., 2014),

but could be applied to other medical ﬁelds than oncology, such as eat-

ing disorders and obesity. As reminded in Section 2.2, the combination

of imaging modalities holds potential for future studies to decipher

the neuropathological mechanisms of a disease or disorder. Radiomics

(or neuromics when applied to brain ima ging) could merge in the

same individual some information about brain activity and cognitive

processes (via fMRI, fNIRS, PET or SPECT) (see Section 2.1), availability

of neurotransmitters, transporters or receptors (via PET or SPECT) (see

Section 2.2), focal differences in brain anatomy (via voxel-based mor-

phometry — VBM) or connectivity (via diffusor tensor imaging – DTI)

(Karlsson et al., 2013; Shott et al., 2015), brain inﬂa mmatory status

(via PET or MRI) (Cazettes et al., 2011; Amhaoul et al., 2014), etc. On

the basis of these multimodal information, neuromics could further

generate synthetic brain mapping to provide an integrative/holistic in-

sight on brain anomalies associated with loss of food intake control or

ED. Moreover, this combination of neurological information might

help clarifying some discrepancies between studies, or apparent incon-

sistent ﬁndings such as those highlighted in the literature relating BMI

and DA signaling for example. Indeed, these discrepancies might de-

pend on the interpretation of studies that have looked at different as-

pects of dopamine sign aling, or that compared processes (associated

to cognitive functions) that were not comparable.

These biomarkers could be used to phenotype patients with a diag-

nosis of obesity and/or ED, as well as establish prognosis further speciﬁc

interventions. They could also be used in prevention programs to iden-

tify subjects with neural vulnerability fac tors and provide some recom-

mendations to prevent the onset of behavioral and health problems. In

terms of therapy, radiomics/neuromics might also be used before

selecting brain target(s) for neuromodulation, because the information

gathered through this method might help predicting the consequences

of neurostimulation on the activation of neural networks or the modu-

lation of neurotransmission.

5.2. Neuroimaging and neuromodulation in the scope of personalized

medicine

Personalized (or individualized) medicine is a medical model that

proposes the customization of healthcare using all clinical, genetic and

environmental information available, with medical decisions, practices,

and/or products being tailored to the individual patient. As reminded by

Cortese (2007), individualized medicine is in a pivotal position in the

evolu tion of national and global health care in the 21st century, and

this assertion is particularly true for nutritional disorders and diseases,

given the societal and economical burden that obesity represents in

the world for example, as well as the complexity and diversity of

obese phenotypes (Blundell and Cooling, 2000; Pajunen et al., 2011).

Advances in computational power and medical imaging are paving the

way for personalized medical treatments that consider a patient3s

genetic, anatomical, and physiological chara cteristics. In addition to

these criteria, cognitive measurements related to eating behavior (see

Gibbons et al., 2014 for a review) should be used in conjunction with

brain imaging because linking imaging data with cognitive processes

(or biological measures) can potentiate the analysis and discrimination

power.

Once the patient and the disease are well portrayed, the question of

thebestsuitabletherapyarises.Ofcourse, individual history (and notably,

previously unsuccessful therapeutic attempts) is particularly important.

There is a graduation in both the severity of the disease and the degree

of invasiveness of treatments available (Fig. 6A).Obviously,basicrequire-

ments for a healthy lifestyle (i.e. balanced diet, minimal physical activity,

good sleep and social life, etc.) are sometimes difﬁcult to achieve for many

people, and never sufﬁcie

nt for those who went beyond a particular

threshold in the disease progression. The classical therapeutic treatment

plan then includes psychological and nutritional interventions, pharma-

cological treatments and, in pharmacorefractory patients, the logical

next step is bariatric sur gery (for morbid obesity) or hospitalization (for

severe eating disorders). All the neuroimaging and neuromodulation

strategies presented in this review can slot into the possible therapeutic

plan at different levels, therefore at different stages of a disease, from

identiﬁcati on of neural vulnerability traits to treatment of severe forms

of the disease (Fig. 6A). Moreover, as illustrated in Fig. 6B, all the

neuromodulation approaches presented do not target the same brain

20 D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

structures or networks. The PFC, which is the primary target for transcra-

nial neuromodulation strategies (e.g. TMS and tDCS), sends inhibitory

projections to the orexigenic network but also has a major role in mood,

food stimuli valuation, decision-making processes, etc. While rtfMRI

neurofeedback could target virtually any moderate-sized brain region,

existing studies mainly focused on the PFC, the ventral striatum, but

also the cingulate cortex, which is very important for attentional process-

es. Lastly, in the context of nutritional disorders, DBS itself can target very

different deep-brain structures, such as reward or homeostatic regions

(Fig. 6B). As a consequence, the choice of a neuromodulation strategy

cannot rest on a single criterion (e.g. balance between the severity of dis-

ease — e.g. high BMI with comorbidities — and the invasiveness of thera-

py), but on multiple assessment criteria, of which some of these are

directly related to the patient3s phenotype and some others to the interac-

tion between patient and therapeutic option (Fig. 6C). For some obese pa-

tients, stimulating the hypothalamus via DBS for example might be

ineffective or counterproductive if their condition takes its roots in

anomalies of the brain reward circuit. There is consequently a great

danger (t he least being wasting time and money, the worst being

worsening t he patient3s condition) in testing neuromodulation in

patients before knowing which regulation process to target — and

if the patient indeed develops iatrogenic neurobehavioral anoma-

lies relate d to this proc ess.

In the future, computational brain network models should play a

major role in integrating, reconstructing, computing, simulating an d

predicting structural and functional brain data from various imaging

modalities, from individual subjects to entire clinical populations. Such

models could integrate functionalities for the reconstruction of structur-

al connectivity from tractographic data, the simulation of neural mass

models connected by realistic parameters, the computation of individu-

alized mea surements used in human brain imaging and their web-

based 3D scientiﬁc visualization (e.g. The Virtual B rain, Jirsa et al.,

2010), leading eventually to pre-operative modeling and predictions

in the ﬁeld of therapeutic neuromodulation.

Fig. 6. Schematic representation showing how potential neurotherapeutic strategies could be included in the therapeutic treatment plan for patients suffering from obesity and/or eating

disorders. (A) Simpliﬁed therapeutic treatment plan categorizing the different options according to the degree of severity of the patient3s condition (BMI, comorbidities, etc.) and/or the

degree of invasiveness of the interventions (in green: prevention programs and basic behavioral requirements for a healthy lifestyle; in blue: minimally invasive interventions; in red:

invasive interventions requiring surgery/anesthesia). In the dotted box are indicated the therapeutic options discussed in the review. (B) Potential neurotherapeutic strategies against obe-

sity and/or eating disorders, which target speciﬁc brain areas or complete neural networks regulating food intake, reward, attention, and homeostasis. (C) Examples of criteria analysis for

the assessment of therapeutic options for an individual patient. Acceptability (pre- or post-intervention) of the therapy is patient-dependent. Some criteria are therapy-dependent, such as

the invasiveness, technical nature, reversibility, and cost. The efﬁcacy and adaptability of the therapy depend on the interaction between patient and therapy, and can be estimated upon

data from a characterized clinical population. Adequation between therapy and patient is conditioned by all the aforementioned criteria, but also by external factors such as the social en-

vironment, the geographical/temporal availability of therapy, and the healthcare system the patient depends on. On the schematic three hypothetical intervention strategies to treat obe-

sity in a lambda patient, e.g. a diet (in green), a minimally invasive therapy (in blue), and an invasive therapy (red) are represented. (D) In the context of individualized medicine, the

absolute requirement is a good phenotyping of the clinical populations and individual patients, but also a good knowledge of the population/individual health trajectories. According

to the type of disease/disorder, the individual history, and the degree of severity of the patient3s condition, different therapeutic options can be considered. But within a given clinical pop-

ulation (e.g. morbidly obese), different phenotypes can exist and condition the choice of the treatment. Neuroimaging can help identifying neural vulnerability factors and markers,

selecting the best treatment option, and shaping therapeutic strategies (e.g. rtfMRI neurofeedback or brain target identiﬁcation for neuromodulation protocols).

21D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

5.3. Ethics related to novel diagnostic and therapeutic tools

As described in this paper, the battle against obesity and eating dis-

orders has given rise to many new interdisciplinary developments.

Novel less invasive treatments (in comparison to classical bariatric sur-

gery for example) are within scrutiny in research and clinics. However, a

sound critical attitude towards these novel techniques should be main-

tained especially before their clinical a pplication. As reminded in

Section 3.2, even minimally invasive neuromodulation techniques are

not playthings (Bikson et al., 2013), and can have neuropsychological

consequences that are not anodyne. Due to our current inability to un-

derstand the intricacies of brain modulations and their consequences

on cognitive processes, eating behavior and body functions, it is crucial

to remember another Hippocrates3 aphorism: “ﬁrst do no harm”. Fur-

ther preclinical studies in relevant animal models (e.g. p ig mo dels,

Sauleau et al., 2009a; Clouard et al., 2012; Ochoa et al., 2015) are thus

mandatory, along with extensive brain imaging programs to reveal

the individual phenotypes and histories (Fig. 6D) that could shape pre-

vention programs and possibly justify the use of neuromodulation

therapy.

To be implemented in the therapeutic treatment plan against obesi-

ty and eating disorders, neuromodulation strategies must have higher

assessment scores than classical options, and this assessment must inte-

grate various criteria such as acceptability, invasiveness, technical na-

ture (i.e. technologies and skills required), reversibility, cost, efﬁcacy,

adaptability and ﬁnally, adequation with the patient (Fig. 6C). The

main advantages of neuromodulation approaches in comparison to

classical bariatric surgery are: minimal invasiveness (e.g. DBS does not

systematically require general anesthesia and leads to less comorbidi-

ties than a gastric by-pass), high reversibility (neuromodulation can

be stopped immediately if problematic — even though insertion of

deep-brain electrodes can induce residual lesions throughout the de-

scent), adaptability/ﬂexibility (brain target and/or stimulation parame-

ters can be easily and quickly modiﬁed). But these advantages are not

sufﬁcient. The cost/advantage balance of each approach must be studied

accurately, and the efﬁciency (cross between efﬁcacy and level of

investment, i.e. time, money, energy) of the alternative technique in im-

proving life expectancy must compete with that of classical techniques.

Minimally invasive and less costly neuroimaging and neuromodulation

methods must receive a particular interest because they will permit a

more important and widespread penetration in healthcare systems

and populations. We gave the example of fNIRS and tDCS as non-

invasive, relatively cheap and portable technologies, in comparison to

other imaging and neuromodulation modalities that are costly, depen-

dent on high-tech infrastructures, and consequently not readily avail-

able. Also, it is important to remind that, in the case of bariatric

surgery, the aim is not to lose the most weight possible but to limit mor-

tality and comorbidities a ssociated with obesity. Some therapeutic

options might be less effective than class ical bariatric surgery to lose

weight quickly but could be as efﬁcient (or even better) to improve

health on the long term, which means that the success criteria of (pre)-

clinical trials should sometimes be revised or augmented with criteria

related to the improvement of neurocognitive processes and control be-

havior, rather than mere weight loss (which is very often the case).

Once again, a lot of obese people are satisﬁed with their own lives/

conditions (sometimes wrongfully) and some obese are indeed

completely healthy. As a matter of fact, recent sociological phenomena,

especia lly in Nor th America, led for example to the emergence of fat

acceptance movements (Kirkland, 2008). Such a phenomenon is far

from being anecdotic or minor in terms of sociological impact on politics

and healthcare systems, because it focuses on civil rights consciousness,

freewill and disc rimination, i.e. que stions tha t affect direct ly a lot of peo-

le (in the USA, two thirds of the population is overweight, one third is

obese). First, some people might perceive neuroimaging-based preven-

tion and diagnosis as stigmatizing tools, which necessitates to focus sci-

entiﬁc communication on the main objectives of this approach, i.e.

improving vulnerability detection and healthcare solutions. Second,

whatever the method employed, artiﬁcially modifying brain activity is

not trivial, because the intervention can modify conscious and uncon-

scious functions, self-control, and decision-making processes, which is

very different than aiming at corre cting motor functions such as for

DBS and Parkinson3s disease. Soda taxes and other dissuasive measures

to ﬁght obesity are usually unpopular and reproved, because it is some-

times perceived as paternalism and an affront against freewill (Parmet,

2014). But let3s think about neuromodulation: Instead of increasing the

monetary value of palatable foods, the aim of neuromodulation is to de-

crease the hedonic value people attribute to these foods, within their

brain. We must foresee that a technology that could change or correct

mental processes will inexorably hatch a serious debate on bioethics,

similarly to cloning, stem cells, genetically modiﬁed organisms, and

gene therapy. Scientists, sociologists and bioethicists must be ready to

address these questions because new exploratory tools and therapies

cannot ﬁnd their place without being accepted at every level of the so-

ciety, i.e. individual patient, medical authorities, politics, and public

opinion. Even if the decision to be subjected to a particular therapy be-

longs to the patient, individual decisions are always inﬂuenced by ideas

that are conveyed at all levels of society, and medical authorities must

approve all therapies. In a recent paper, Petersen (2013) stated that

the rapid development of the life sciences and related technologies (in-

cluding neuroimaging) has underlined the limitations of bioethics3 per-

spectives and reasoning for addressing emergent normative questions.

The author pleads in favor of a normative sociology of bio-knowledge

that could beneﬁt from the principles of justice, beneﬁcence and

nonmaleﬁcence, as well as on the concept of human rights (Petersen,

2013). Even if some approaches are not biologically invasive, they can

be psy chologically and philosoph ically inv asive.

5.4. Conclusion

The technologies and ideas presented in this paper rejoin the state-

ment and conclusions of Schmidt and Campbell (2013), i.e. treatment

of eating disorders and obesity cannot remain ‘brainless’. A biomarker

approach combining genetic, neuroimaging, cognitive and other biolog-

ical measures will facilitate development of early effective precision

treatments (Insel, 2009; Insel et al., 2013), and serve individuali zed

prevention and medicine. Even tho ugh recent scientiﬁcdiscoveries

and innovative technology breakthrough pave the way to new medical

applications, our knowle dge of the neuropsychological mechanisms

governing eating behavior and favoring the emergence of a disease is

still embryonic. Fundamental research in animal models and rigorous

bioethics approach are consequently mandatory for a good translational

science in this ﬁeld.

Acknowledgments

This review topic was proposed by the NovaBrain International Con-

sortium that was created in 2012 with the aim to promote innovative

search to explore the relationships between brain functions and

eating behaviors (Coordinator: David Val-Laillet, INRA, France). The

founding members of the NovaBrain Consortium were: Institut National

de la Recherche Agronomique (INRA, France), INR A Transfert S.A.

(France), Wageningen University (The Netherlands), Institute of Agri-

culture and Food Research and Technology (IRTA, Spain), University

Hospital Bonn (Germany), Institut Européen d3 Administration des Af-

faires (INSEAD, France), University of Surrey (UK), Radboud University

Nijmegen, The Netherlands, Noldus Information Technology BV (The

Netherlands), University of Queensland (Australia), Oregon Research

Institute (USA), Pennington Biomedical Research Centre (USA), Centre

National de La Recherche Scientiﬁque (CNRS, France), Old Dominion

University (USA), Stichting Dienst Landbouwkundig Onderzoek —

Food & Biobased Research, The Netherlands, Aix-Marseille University

22 D. Val-Laillet et al. / NeuroImage: Clinical 8 (2015) 1–31

(France), i3B Innovations BV (The Netherlands), Jožef Stefan Institute

(Slovenia), University of Bologna (Italy). The preparation a nd initial

meetings of the NovaBrain Consortium were co-funded by INRA and

the Britta ny Region (France) in the context of the FP7 European

Program. Dr. Alons o-Alonso is a reci pient of grants fro m the Boston Nu-

trition and Obesity Research Center (BNORC), 5P30 DK046200, and the

Nutrition Obesity Research Center at Harvard (NORCH), P30 DK040561.

Dr. Eric Stice beneﬁted from the following grants for the research men-

tioned herein: Roadmap Supplement R1MH64560A; R01 DK080760;

and R01 DK092468. Bernd Webe r was supported by a Heisenberg

Grant of the German Research Council (DFG; We 4427/3-1). Dr. Esther

Aarts was supported by a VENI grant of The Netherlands Organization

for Scientiﬁc Research (NWO) (016.135.023) and an AXA Research

Fund fellowship (Ref: 2011). Luke Stoeckel received a ﬁnancial support

from the National Institutes of Health (K23DA032612; R21DA030523),

the Norman E. Zinberg Fellowship in Addiction Psychiatry at Harvard

Medic al School, the Charles A. King Trust, the McGovern Institute

Neurotechnology Program, and private funds to the Massachusetts Gen-

eral Hospital Department of Psychiatry. Some research presented in this

paper was carried out in part at the Athinoula A. Martinos Center for

Biomedical Imaging at the McGovern Institute for Brain Research at

the Massachusetts Institute of Technology. All the authors state that

they have no conﬂict of int erests related to this manuscript.

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