Organization Of Large-scale Brain Networks Research Articles

Aberrant topological patterns of brain structural network in temporal lobe epilepsy.

Although altered large-scale brain network organization in patients with temporal lobe epilepsy (TLE) has been shown using morphologic measurements such as cortical thickness, these studies, have not included critical subcortical structures (such as hippocampus and amygdala) and have had relatively small sample sizes. Here, we investigated differences in topological organization of the brain volumetric networks between patients with right TLE (RTLE) and left TLE (LTLE) with unilateral hippocampal atrophy. We performed a cross-sectional analysis of 86 LTLE patients, 70 RTLE patients, and 116 controls. RTLE and LTLE groups were balanced for gender (p = 0.64), seizure frequency (Mann-Whitney U test, p = 0.94), age (p = 0.39), age of seizure onset (p = 0.21), and duration of disease (p = 0.69). Brain networks were constructed by thresholding correlation matrices of volumes from 80 cortical/subcortical regions (parcellated with Freesurfer v5.3 https://surfer.nmr.mgh.harvard.edu/) that were then analyzed using graph theoretical approaches. We identified reduced cortical/subcortical connectivity including bilateral hippocampus in both TLE groups, with the most significant interregional correlation increases occurring within the limbic system in LTLE and contralateral hemisphere in RTLE. Both TLE groups demonstrated less optimal topological organization, with decreased global efficiency and increased local efficiency and clustering coefficient. LTLE also displayed a more pronounced network disruption. Contrary to controls, hub nodes in both TLE groups were not distributed across whole brain, but rather found primarily in the paralimbic/limbic and temporal association cortices. Regions with increased centrality were concentrated in occipital lobes for LTLE and contralateral limbic/temporal areas for RTLE. These findings provide first evidence of altered topological organization of the whole brain volumetric network in TLE, with disruption of the coordinated patterns of cortical/subcortical morphology.

Relationship of white matter network topology and cognitive outcome in adolescents with d-transposition of the great arteries.

Patients with congenital heart disease (CHD) are at risk for neurocognitive impairments. Little is known about the impact of CHD on the organization of large-scale brain networks. We applied graph analysis techniques to diffusion tensor imaging (DTI) data obtained from 49 adolescents with dextro-transposition of the great arteries (d-TGA) repaired with the arterial switch operation in early infancy and 29 healthy referent adolescents. We examined whether differences in neurocognitive functioning were related to white matter network topology. We developed mediation models revealing the respective contributions of peri-operative variables and network topology on cognitive outcome. Adolescents with d-TGA had reduced global efficiency at a trend level (p = 0.061), increased modularity (p = 0.012), and increased small-worldness (p = 0.026) as compared to controls. Moreover, these network properties mediated neurocognitive differences between the d-TGA and referent adolescents across every domain assessed. Finally, structural network topology mediated the neuroprotective effect of longer duration of core cooling during reparative neonatal cardiac surgery, as well as the detrimental effects of prolonged hospitalization. Taken together, worse neurocognitive function in adolescents with d-TGA is mediated by global differences in white matter network topology, suggesting that disruption of this configuration of large-scale networks drives neurocognitive dysfunction. These data provide new insights into the interplay between perioperative factors, brain organization, and cognition in patients with complex CHD.

Robust Long-Range Coordination of Spontaneous Neural Activity in Waking, Sleep and Anesthesia.

Although the emerging field of functional connectomics relies increasingly on the analysis of spontaneous fMRI signal covariation to infer the spatial fingerprint of the brain's large-scale functional networks, the nature of the underlying neuro-electrical activity remains incompletely understood. In part, this lack in understanding owes to the invasiveness of electrophysiological acquisition, the difficulty in their simultaneous recording over large cortical areas, and the absence of fully established methods for unbiased extraction of network information from these data. Here, we demonstrate a novel, data-driven approach to analyze spontaneous signal variations in electrocorticographic (ECoG) recordings from nearly entire hemispheres of macaque monkeys. Based on both broadband analysis and analysis of specific frequency bands, the ECoG signals were found to co-vary in patterns that resembled the fMRI networks reported in previous studies. The extracted patterns were robust against changes in consciousness associated with sleep and anesthesia, despite profound changes in intrinsic characteristics of the raw signals, including their spectral signatures. These results suggest that the spatial organization of large-scale brain networks results from neural activity with a broadband spectral feature and is a core aspect of the brain's physiology that does not depend on the state of consciousness.