Abstract
The efficient organization and communication of brain networks underlie cognitive processing and their disruption can lead to pathological behaviours. Few studies have focused on whole-brain networks in obesity and binge eating disorder (BED). Here we used multi-echo resting-state functional magnetic resonance imaging (rsfMRI) along with a data-driven graph theory approach to assess brain network characteristics in obesity and BED. Multi-echo rsfMRI scans were collected from 40 obese subjects (including 20 BED patients) and 40 healthy controls and denoised using multi-echo independent component analysis (ME-ICA). We constructed a whole-brain functional connectivity matrix with normalized correlation coefficients between regional mean blood oxygenation level-dependent (BOLD) signals from 90 brain regions in the Automated Anatomical Labeling atlas. We computed global and regional network properties in the binarized connectivity matrices with an edge density of 5%-25%. We also verified our findings using a separate parcellation, the Harvard-Oxford atlas parcellated into 470 regions. Obese subjects exhibited significantly reduced global and local network efficiency as well as decreased modularity compared with healthy controls, showing disruption in small-world and modular network structures. In regional metrics, the putamen, pallidum and thalamus exhibited significantly decreased nodal degree and efficiency in obese subjects. Obese subjects also showed decreased connectivity of cortico-striatal/cortico-thalamic networks associated with putaminal and cortical motor regions. These findings were significant with ME-ICA with limited group differences observed with conventional denoising or single-echo analysis. Using this data-driven analysis of multi-echo rsfMRI data, we found disruption in global network properties and motor cortico-striatal networks in obesity consistent with habit formation theories. Our findings highlight the role of network properties in pathological food misuse as possible biomarkers and therapeutic targets.
Highlights
The resting-state brain network shows functional topological features such as small-world and modular organization, which enables efficient information processing and communication through the network (Achard et al 2006; Achard & Bullmore, 2007; Bullmore & Sporns, 2009, 2012)
While the application of graph-theoretic analysis to the brain networks is still relatively new, brain network properties in resting-state functional magnetic resonance imaging (rsfMRI) measurements have been found to be disrupted in various neuropsychiatric disorders such as Alzheimer’s disease (Supekar et al 2008; Yao et al 2010), schizophrenia (Liu et al 2008; van den Heuvel et al 2010), major depression (Zhang et al 2011) and attention-deficit/ hyperactivity disorder (Wang et al 2009)
We examine global and regional network properties of the resting-state brain network in 40 obese subjects in comparison with 40 matched healthy controls in a data-driven approach using graph theory analysis and network-based statistics (NBS) (Zalesky et al 2010)
Summary
The resting-state brain network shows functional topological features such as small-world and modular organization, which enables efficient information processing and communication through the network (Achard et al 2006; Achard & Bullmore, 2007; Bullmore & Sporns, 2009, 2012). Graph-theoretical analysis of resting-state functional magnetic resonance imaging (rsfMRI) data reveals the topological properties of whole-brain functional networks in a data-driven manner. BED is a compulsive eating behaviour characterized by rapid food intake that has been hypothesized in preclinical models to have overlaps with disorders of addiction We used multi-echo resting-state functional magnetic resonance imaging (rsfMRI) along with a data-driven graph theory approach to assess brain network characteristics in obesity and BED
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
