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, D Val-Laillet, E Aarts, B Weber, M Ferrari, V Quaresima, L E Stoeckel, M Alonso-Alonso, M Audette, C H Malbert, E Stice

Abstract

Functional, molecular and genetic neuroimaging has highlighted the existence of brain anomalies and neural vulnerability 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 palatable 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 motivation 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 the 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 relationships. 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 behavior 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 combination with neuroimaging approaches, these techniques are promising experimental tools to unravel the 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 technical 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 possibility to identify new biological markers of brain functions. Second, we will highlight the potential of neuroimaging and neuromodulation in individualized medicine. Third, we will introduce the ethical questions that are concomitant to the emergence of new neuromodulation therapies.

Keywords: 5-HT, serotonin; ADHD, attention deficit hyperactivity disorder; AN, anorexia nervosa; ANT, anterior nucleus of the thalamus; B N, bulimia nervosa; BAT, brown adipose tissue; BED, binge eating disorder; BMI, body mass index; BOLD, blood oxygenation level dependent; BS, bariatric surgery; Brain; CBF, cerebral blood flow; CCK, cholecystokinin; Cg25, subgenual cingulate cortex; DA, dopamine; DAT, dopamine transporter; DBS, deep brain stimulation; DBT, deep brain therapy; DTI, diffusion tensor imaging; ED, eating disorders; EEG, electroencephalography; Eating disorders; GP, globus pallidus; HD-tDCS, high-definition transcranial direct current stimulation; HFD, high-fat diet; HHb, deoxygenated-hemoglobin; Human; LHA, lateral hypothalamus; MER, microelectrode recording; MRS, magnetic resonance spectroscopy; Nac, nucleus accumbens; Neuroimaging; Neuromodulation; O2Hb, oxygenated-hemoglobin; OCD, obsessive–compulsive disorder; OFC, orbitofrontal cortex; Obesity; PD, Parkinson's disease; PET, positron emission tomography; PFC, prefrontal cortex; PYY, peptide tyrosine tyrosine; SPECT, single photon emission computed tomography; STN, subthalamic nucleus; TMS, transcranial magnetic stimulation; TRD, treatment-resistant depression; VBM, voxel-based morphometry; VN, vagus nerve; VNS, vagus nerve stimulation; VS, ventral striatum; VTA, ventral tegmental area; aCC, anterior cingulate cortex; dTMS, deep transcranial magnetic stimulation; daCC, dorsal anterior cingulate cortex; dlPFC, dorsolateral prefrontal cortex; fMRI, functional magnetic resonance imaging; fNIRS, functional near-infrared spectroscopy; lPFC, lateral prefrontal cortex; pCC, posterior cingulate cortex; rCBF, regional cerebral blood flow; rTMS, repetitive transcranial magnetic stimulation; rtfMRI, real-time functional magnetic resonance imaging; tACS, transcranial alternate current stimulation; tDCS, transcranial direct current stimulation; tRNS, transcranial random noise stimulation; vlPFC, ventrolateral prefrontal cortex; vmH, ventromedial hypothalamus; vmPFC, ventromedial prefrontal cortex.

Figures

Fig. 1
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 generating 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).
Fig. 2
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.
Fig. 3
Fig. 3
Pictures of (A) butterfly coils for transcranial magnetic stimulation (TMS) and (B) electrodes and battery for transcranial direct current stimulation (tDCS).
Fig. 4
Fig. 4
Changes in glucose metabolism observed via positron emission tomography (PET) imaging after injection of 18FDG (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 < 0.0001 with FDR (false discovery rate correction) (see text for details).
Fig. 5
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).
Fig. 6
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 patient's 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 obesity 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 environment, the geographical/temporal availability of therapy, and the healthcare system the patient depends on. On the schematic three hypothetical intervention strategies to treat obesity 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 patient's condition, different therapeutic options can be considered. But within a given clinical population (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).

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