Grey matter abnormalities within cortico-limbic-striatal circuits in acute and weight-restored anorexia nervosa patients
ABSTRACT Functional disturbances within cortico-striatal control systems have been implicated in the psychobiology (i.e. impaired cognitive-behavioral flexibility, perfectionist personality) of anorexia nervosa. The aim of the present study was to investigate the morphometry of brain regions within cortico-striatal networks in acute anorexia nervosa (AN) as well as long-term weight-restored anorexia nervosa (AN-WR) patients. A total of 39 participants: 12 AN, 13 AN-WR patients, and 14 healthy controls (HC) underwent high-resolution, T1-weighted magnetic resonance imaging (MRI), a cognitive-behavioral flexibility task, and a psychometric assessment. Group differences in local grey matter volume (GMV) were analyzed using whole brain voxel-based morphometry (VBM) and brain-atlas based automatic volumetry computation (IBASPM). Individual differences in total GMV were considered as a covariate in all analyses. In the regional brain morphometry, AN patients, as compared to HC, showed decreased GMVs (VBM and volumetry) in the anterior cingulate cortex (ACC), the supplementary motor area (SMA), and in subcortical regions (amygdala, putamen: VBM only). AN-WR compared to HC showed decreased GMV (VBM and volumetry) in the ACC and SMA, whereas GMV of the subcortical region showed no differences. The findings of the study suggest that structural abnormalities of the ACC and SMA were independent of the disease stage, whereas subcortical limbic-striatal changes were state dependent.
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ABSTRACT: There are currently no neuroanatomical biomarkers of anorexia nervosa (AN) available to make clinical inferences at an individual subject level. We present results of a multivariate machine learning (ML) approach utilizing structural neuroanatomical scan data to differentiate AN patients from matched healthy controls at an individual subject level. Structural neuroimaging scans were acquired from 15 female patients with AN (age = 20, s.d. = 4 years) and 15 demographically matched female controls (age = 22, s.d. = 3 years). Neuroanatomical volumes were extracted using the FreeSurfer software and input into the Least Absolute Shrinkage and Selection Operator (LASSO) multivariate ML algorithm. LASSO was 'trained' to identify 'novel' individual subjects as either AN patients or healthy controls. Furthermore, the model estimated the probability that an individual subject belonged to the AN group based on an individual scan. The model correctly predicted 25 out of 30 subjects, translating into 83.3% accuracy (sensitivity 86.7%, specificity 80.0%) (p < 0.001; χ 2 test). Six neuroanatomical regions (cerebellum white matter, choroid plexus, putamen, accumbens, the diencephalon and the third ventricle) were found to be relevant in distinguishing individual AN patients from healthy controls. The predicted probabilities showed a linear relationship with drive for thinness clinical scores (r = 0.52, p < 0.005) and with body mass index (BMI) (r = -0.45, p = 0.01). The model achieved a good predictive accuracy and drive for thinness showed a strong neuroanatomical signature. These results indicate that neuroimaging scans coupled with ML techniques have the potential to provide information at an individual subject level that might be relevant to clinical outcomes.Psychological Medicine 05/2015; DOI:10.1017/S0033291715000768 · 5.43 Impact Factor
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ABSTRACT: The eating disorders (EDs) anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED) are severe psychiatric disorders with high mortality. There are many symptoms, such as food restriction, episodic binge eating, purging, or excessive exercise that are either overlapping or lie on opposite ends of a scale or spectrum across those disorders. Identifying how specific ED behaviors are linked to particular neurobiological mechanisms could help better categorize ED subgroups and develop specific treatments. This review provides support from recent brain imaging research that brain structure and function measures can be linked to disorder-specific biological or behavioral variables, which may help distinguish ED subgroups, or find commonalities between them. Brain structure and function may therefore be suitable research targets to further study the relationship between dimensions of behavior and brain function relevant to EDs and beyond the categorical AN, BN, and BED distinctions.Current Psychiatry Reports 04/2015; 17(4):559. DOI:10.1007/s11920-015-0559-z · 3.05 Impact Factor
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ABSTRACT: This focussed narrative review examines neurobiological and psychophysiological evidence supporting a role for altered reward processes in the development and maintenance of anorexia nervosa (AN). In AN, there does not appear to be a generalised inability to experience reward. Rather, data suggest that a reluctance to gain weight leads to an aversive appraisal of food- and taste-related stimuli. As a result, cues compatible with this aberrant mode of thinking become rewarding for the individual. Evidence also suggests that attribution of motivational salience to such cues promotes anorectic behaviours. These findings are consistent with models in which interactions between cognition and reward are important in eliciting the anorectic "habit". A model is proposed which is consistent with elements of other theoretical frameworks, but differs in that its emphasis is towards neural overlaps between AN and addiction. It is consistent with AN being a reward-based learned behaviour in which aberrant cognitions related to eating and shape alter functioning of central reward systems. It proposes that the primary neural problem responsible for the development, maintenance, and treatment resistance is centred in the striatal reward system. This helps shift the emphasis of aetiological models towards reward processing, particularly in the context of illness-compatible cues. Furthermore, it suggests that continuing to explore the utility and valued nature of AN in the patient's life would be a useful inclusion in treatment and prevention models. Copyright © 2015. Published by Elsevier Ltd.Neuroscience & Biobehavioral Reviews 02/2015; 52. DOI:10.1016/j.neubiorev.2015.02.012 · 10.28 Impact Factor