Anorexia nervosa (AN) is a debilitating illness and existing interventions are only modestly effective. This study aimed to determine whether AN pathophysiology is associated with altered connections within fronto-accumbal circuitry subserving reward processing. Diffusion and resting-state functional MRI scans were collected in female inpatients with AN (n = 22) and healthy controls (HC; n = 18) between the ages of 16 and 25 years. Individuals with AN were scanned during the acute, underweight phase of the illness and again following inpatient weight restoration. HC were scanned twice over the same timeframe. Based on univariate and multivariate analyses of fronto-accumbal circuitry, underweight individuals with AN were found to have increased structural connectivity (diffusion probabilistic tractography), increased white matter anisotropy (tract-based spatial statistics), increased functional connectivity (seed-based correlation in resting-state fMRI), and altered effective connectivity (spectral dynamic causal modeling). Following weight restoration, fronto-accumbal structural connectivity continued to be abnormally increased bilaterally with large (partial η2 = 0.387; right NAcc-OFC) and moderate (partial η2 = 0.197; left NAcc-OFC) effect sizes. Increased structural connectivity within fronto-accumbal circuitry in the underweight state correlated with severity of eating disorder symptoms. Taken together, the findings from this longitudinal, multimodal neuroimaging study offer converging evidence of atypical fronto-accumbal circuitry in AN. Hum Brain Mapp 37:3835-3846, 2016. © 2016 Wiley Periodicals, Inc.
Pubmed ID: 27273474 RIS Download
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Probabilistic atlases covering 48 cortical and 21 subcortical structural areas, derived from structural data and segmentations kindly provided by the Harvard Center for Morphometric Analysis. T1-weighted images of 21 healthy male and 16 healthy female subjects (ages 18-50) were individually segmented by the CMA using semi-automated tools developed in-house. The T1-weighted images were affine-registered to MNI152 space using FLIRT (FSL), and the transforms then applied to the individual labels. Finally, these were combined across subjects to form population probability maps for each label. Segmentations used to create these atlases were provided by: David Kennedy and Christian Haselgrove, Centre for Morphometric Analysis, Harvard; Bruce Fischl, the Martinos Center for Biomedical Imaging, MGH; Janis Breeze and Jean Frazier from the Child and Adolescent Neuropsychiatric Research Program, Cambridge Health Alliance; Larry Seidman and Jill Goldstein from the Department of Psychiatry of Harvard Medical School.
View all literature mentionsOpen source software suite for processing and analyzing human brain MRI images. Used for reconstruction of brain cortical surface from structural MRI data, and overlay of functional MRI data onto reconstructed surface. Contains automatic structural imaging stream for processing cross sectional and longitudinal data. Provides anatomical analysis tools, including: representation of cortical surface between white and gray matter, representation of the pial surface, segmentation of white matter from rest of brain, skull stripping, B1 bias field correction, nonlinear registration of cortical surface of individual with stereotaxic atlas, labeling of regions of cortical surface, statistical analysis of group morphometry differences, and labeling of subcortical brain structures.Operating System: Linux, macOS.
View all literature mentions