The ability to exercise appropriate inhibitory control is critical in the regulation of body weight, but the exact mechanisms are not known. In this systematic review, we identified 37 studies that used specific neuropsychological tasks relevant to inhibitory control performance in obese participants with and without binge eating disorder (BED). We performed a meta-analysis of the studies that used the stop signal task (N=8). We further examined studies on the delay discounting task, the go/no-go task and the Stroop task in a narrative review. We found that inhibitory control is significantly impaired in obese adults and children compared to individuals with body weight within a healthy range (Standardized Mean Difference (SMD): 0.30; CI=0.00, 0.59, p=0.007). The presence of BED in obese individuals did not impact on task performance (SMD: 0.05; CI: -0.22, 0.32, p=0.419). Neuroimaging studies in obesity suggest that lower prefrontal cortex activity affects inhibitory control and BMI. In summary, impairment in inhibitory control is a critical feature associated with obesity and a potential target for clinical interventions.

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Cerebellum seems to have a role both in feeding behavior and emotion regulation; therefore, it is a region that warrants further neuroimaging studies in eating disorders, severe conditions that determine a significant impairment in the physical and psychological domain. The aim of this study was to examine the cerebellum intrinsic connectivity during functional magnetic resonance imaging resting state in anorexia nervosa (AN), bulimia nervosa (BN), and healthy controls (CN). Resting state brain activity was decomposed into intrinsic connectivity networks (ICNs) using group spatial independent component analysis on the resting blood oxygenation level dependent time courses of 12 AN, 12 BN, and 10 CN. We extracted the cerebellar ICN and compared it between groups. Intrinsic connectivity within the cerebellar network showed some common alterations in eating disordered compared to healthy subjects (e.g., a greater connectivity with insulae, vermis, and paravermis and a lesser connectivity with parietal lobe); AN and BN patients were characterized by some peculiar alterations in connectivity patterns (e.g., greater connectivity with the insulae in AN compared to BN, greater connectivity with anterior cingulate cortex in BN compared to AN). Our data are consistent with the presence of different alterations in the cerebellar network in AN and BN patients that could be related to psychopathologic dimensions of eating disorders.

Higher cortical thickness in medial orbitofrontal cortex and insula probably contributes to higher gray matter volume in AN in those regions. The machine-learning algorithm identified a mixed pattern of mostly higher orbital and insular, but relatively lower superior frontal cortical thickness in individuals with current AN. These novel results suggest that regional cortical thickness patterns could be state markers for AN.

Background Neuroanatomical abnormalities in Bipolar disorder (BD) have previously been reported. However, the utility of these abnormalities in distinguishing individual BD patients from Healthy controls and stratify patients based on overall illness burden has not been investigated in a large cohort. Methods In this study, we examined whether structural neuroimaging scans coupled with a machine learning algorithm are able to distinguish individual BD patients from Healthy controls in a large cohort of 256 subjects. Additionally, we investigated the relationship between machine learning predicted probability scores and subjects’ clinical characteristics such as illness duration and clinical stages. Neuroimaging scans were acquired from 128 BD patients and 128 Healthy controls. Gray and white matter density maps were obtained and used to ‘train’ a relevance vector machine (RVM) learning algorithm which was used to distinguish individual patients from Healthy controls. Results The RVM algorithm distinguished patients from Healthy controls with 70.3 % accuracy (74.2 % specificity, 66.4 % sensitivity, chi-square p<0.005) using white matter density data and 64.9 % accuracy (71.1 % specificity, 58.6 % sensitivity, chi-square p<0.005) with gray matter density. Multiple brain regions – largely covering the fronto – limbic system were identified as ‘most relevant’ in distinguishing both groups. Patients identified by the algorithm with high certainty (a high probability score) – belonged to a subgroup with more than ten total lifetime manic episodes including hospitalizations (late stage). Conclusions These results indicate the presence of widespread structural brain abnormalities in BD which are associated with higher illness burden – which points to neuroprogression.