In order to promote survival through flexible cognition and goal-directed behaviour, the brain has to optimize segregation and integration of information into coherent, distributed dynamical states. Certain organizational features of the brain have been proposed to be essential to facilitate cognitive flexibility, especially hub regions in the so-called rich club which show dense interconnectivity. These structural hubs have been suggested to be vital for integration and segregation of information. Yet, this has not been evaluated in terms of resulting functional temporal dynamics. A complementary measure covering the temporal aspects of functional connectivity could thus bring new insights into a more complete picture of the integrative nature of brain networks. Here, we use causal whole-brain computational modelling to determine the functional dynamical significance of the rich club and compare this to a new measure of the most functionally relevant brain regions for binding information over time ("dynamical workspace of binding nodes"). We found that removal of the iteratively generated workspace of binding nodes impacts significantly more on measures of integration and encoding of information capability than the removal of the rich club regions. While the rich club procedure produced almost half of the binding nodes, the remaining nodes have low degree yet still play a significant role in the workspace essential for binding information over time and as such goes beyond a description of the structural backbone.
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http://dx.doi.org/10.1016/j.neuroimage.2016.10.047 | DOI Listing |
Unlabelled: The rat offers a uniquely valuable animal model in neuroscience, but we currently lack an individual-level understanding of the in vivo rat brain network. Here, leveraging longitudinal measures of cortical magnetization transfer ratio (MTR) from in vivo neuroimaging between postnatal days 20 (weanling) and 290 (mid-adulthood), we design and implement a computational pipeline that captures the network of structural similarity (MIND, morphometric inverse divergence) between each of 53 distinct cortical areas. We first characterized the normative development of the network in a cohort of rats undergoing typical development (N=47), and then contrasted these findings with a cohort exposed to early life stress (ELS, N=40).
View Article and Find Full Text PDFHum Brain Mapp
January 2025
Center for MR Research, University Children's Hospital Zurich, Zurich, Switzerland.
The human brain connectome is characterized by the duality of highly modular structure and efficient integration, supporting information processing. Newborns with congenital heart disease (CHD), prematurity, or spina bifida aperta (SBA) constitute a population at risk for altered brain development and developmental delay (DD). We hypothesize that, independent of etiology, alterations of connectomic organization reflect neural circuitry impairments in cognitive DD.
View Article and Find Full Text PDFNat Commun
January 2025
MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
Structural brain organization in infancy is associated with later cognitive, behavioral, and educational outcomes. Due to practical limitations, such as technological advancements and data availability of fetal MRI, there is still much we do not know about the early emergence of topological organization. We combine the developing Human Connectome Project's large infant dataset with generative network modeling to simulate the emergence of network organization over early development.
View Article and Find Full Text PDFThe connectome describes the complete set of synaptic contacts through which neurons communicate. While the architecture of the $\textit{C. elegans}$ connectome has been extensively characterized, much less is known about the organization of causal signaling networks arising from functional interactions between neurons.
View Article and Find Full Text PDFAnn Intern Med
January 2025
Washington University School of Medicine, St. Louis, Missouri, USA (M.W.R.).
GIM/FP/GP: [Formula: see text] Cardiology: [Formula: see text] Geriatrics: [Formula: see text].
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