Binge-eating (BE) subjects have shown altered brain activity at frontal regions during food presentation. The aim of this study was to examine the frontal brain electrical activity in obese BE women (n = 12) and in obese women without BE (non-BE, n = 13). Brain electrical activity was measured using a quantitative electroencephalography during a resting state (eyes-closed) and when the subjects focused (eyes-open) their attention on a picture of a landscape (control experiment) or on a meal (food experiment). The BE showed greater frontal beta activity (14-20 Hz) than the non-BE in both the eyes-closed (on average 52%) and the eyes-open situations and independently of the stimulus (control experiment: 57% and food experiment: 71%). No significant differences between the groups were found in alpha, delta or theta amplitudes. Increased beta activity correlated positively with the disinhibition factor of the Three-Factor Eating Questionnaire. Thus, our results suggest that elevated frontal beta activity may be a marker of dysfunctional disinhibition-inhibition mechanism, which could make the obese BE women more vulnerable or sensitive to food and the environmental cues.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/j.1475-097X.2009.00916.x | DOI Listing |
Netw Neurosci
December 2024
Retired Professor, The University of Melbourne, Victoria, Australia.
Several recent studies have optimized deep neural networks to learn high-dimensional relationships linking structural and functional connectivity across the human connectome. However, the extent to which these models recapitulate individual-specific characteristics of resting-state functional brain networks remains unclear. A core concern relates to whether current individual predictions outperform simple benchmarks such as group averages and null conditions.
View Article and Find Full Text PDFNetw Neurosci
December 2024
Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea.
The study of large-scale brain connectivity is increasingly adopting unsupervised approaches that derive low-dimensional spatial representations from high-dimensional connectomes, referred to as gradient analysis. When translating this approach to study interindividual variations in connectivity, one technical issue pertains to the selection of an appropriate group-level template to which individual gradients are aligned. Here, we compared different group-level template construction strategies using functional and structural connectome data from neurotypical controls and individuals with autism spectrum disorder (ASD) to identify between-group differences.
View Article and Find Full Text PDFNetw Neurosci
December 2024
Neuroradiology Group, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain.
Multiple sclerosis (MS) is a neurodegenerative disease that affects the central nervous system. Structures affected in MS include the corpus callosum, connecting the hemispheres. Studies have shown that in mammalian brains, structural connectivity is organized according to a conservation principle, an inverse relationship between intra- and interhemispheric connectivity.
View Article and Find Full Text PDFNetw Neurosci
December 2024
Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, VIC, Australia.
Connectome generative models, otherwise known as generative network models, provide insight into the wiring principles underpinning brain network organization. While these models can approximate numerous statistical properties of empirical networks, they typically fail to explicitly characterize an important contributor to brain organization-axonal growth. Emulating the chemoaffinity-guided axonal growth, we provide a novel generative model in which axons dynamically steer the direction of propagation based on distance-dependent chemoattractive forces acting on their growth cones.
View Article and Find Full Text PDFNetw Neurosci
December 2024
Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA.
We generated asynchronous functional networks (aFNs) using a novel method called optimal causation entropy and compared aFN topology with the correlation-based synchronous functional networks (sFNs), which are commonly used in network neuroscience studies. Functional magnetic resonance imaging (fMRI) time series from 212 participants of the National Consortium on Alcohol and Neurodevelopment in Adolescence study were used to generate aFNs and sFNs. As a demonstration of how aFNs and sFNs can be used in tandem, we used multivariate mixed effects models to determine whether age interacted with node efficiency to influence connection probabilities in the two networks.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!