The recent development of neuroimaging technology and network theory allows us to visualize and characterize the whole-brain functional connectivity in vivo. The importance of conventional structural image atlas widely used in population-based neuroimaging studies has been well verified. Similarly, a "common" brain connectivity map (also called connectome atlas) across individuals can open a new pathway to interpreting disorder-related brain cognition and behaviors. However, the main obstacle of applying the classic image atlas construction approaches to the connectome data is that a regular data structure (such as a grid) in such methods breaks down the intrinsic geometry of the network connectivity derived from the irregular data domain (in the setting of a graph). To tackle this hurdle, we first embed the brain network into a set of graph signals in the Euclidean space via the diffusion mapping technique. Furthermore, we cast the problem of connectome atlas construction into a novel learning-based graph inference model. It can be constructed by iterating the following processes: (1) align all individual brain networks to a common space spanned by the graph spectrum bases of the latent common network, and (2) learn graph Laplacian of the common network that is in consensus with all aligned brain networks. We have evaluated our novel method for connectome atlas construction in comparison with non-learning-based counterparts. Based on experiments using network connectivity data from populations with neurodegenerative and neuropediatric disorders, our approach has demonstrated statistically meaningful improvement over existing methods.
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http://dx.doi.org/10.1007/s12021-020-09482-8 | DOI Listing |
Hum 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 PDFHum Brain Mapp
January 2025
State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
Working memory (WM) plays a crucial role in human cognition. Previous candidate and genome-wide association studies have reported many genetic variations associated with WM. However, little research has examined genetic basis of WM by using transcriptome, even though it reflects gene function more directly than does the genome.
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 PDFSchizophr Bull
January 2025
Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China.
Background And Hypothesis: Population-based morphological covariance networks are widely reported to be altered in schizophrenia. Individualized morphological brain network approaches have emerged recently. We hypothesize that individualized morphological brain networks are disrupted in schizophrenia.
View Article and Find Full Text PDFCureus
November 2024
Department of Neurosurgery, Fukushima Medical University, Fukushima, JPN.
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