[HTML][HTML] A comprehensive survey of complex brain network representation
Recent years have shown great merits in utilizing neuroimaging data to understand brain
structural and functional changes, as well as its relationship to different neurodegenerative …
structural and functional changes, as well as its relationship to different neurodegenerative …
SD-CNN: A static-dynamic convolutional neural network for functional brain networks
Static functional connections (sFCs) and dynamic functional connections (dFCs) have been
widely used in the resting-state functional MRI (rs-fMRI) analysis. sFCs, calculated based on …
widely used in the resting-state functional MRI (rs-fMRI) analysis. sFCs, calculated based on …
[HTML][HTML] TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI …
Neuroimaging-based prediction of neurocognitive measures is valuable for studying how
the brain's structure relates to cognitive function. However, the accuracy of prediction using …
the brain's structure relates to cognitive function. However, the accuracy of prediction using …
Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics
The advances in technologies for acquiring brain imaging and high-throughput genetic data
allow the researcher to access a large amount of multi-modal data. Although the sparse …
allow the researcher to access a large amount of multi-modal data. Although the sparse …
Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome
An emerging trend is to use regression‐based machine learning approaches to predict
cognitive functions at the individual level from neuroimaging data. However, individual …
cognitive functions at the individual level from neuroimaging data. However, individual …
White matter tracts are point clouds: neuropsychological score prediction and critical region localization via geometric deep learning
White matter tract microstructure has been shown to influence neuropsychological scores of
cognitive performance. However, prediction of these scores from white matter tract data has …
cognitive performance. However, prediction of these scores from white matter tract data has …
Interpretable temporal graph neural network for prognostic prediction of Alzheimer's disease using longitudinal neuroimaging data
Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder characterized
by memory loss and cognitive decline. Early detection and accurate prognosis of AD is an …
by memory loss and cognitive decline. Early detection and accurate prognosis of AD is an …
TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance
We propose a geometric deep-learning-based framework, TractGeoNet, for performing
regression using diffusion magnetic resonance imaging (dMRI) tractography and associated …
regression using diffusion magnetic resonance imaging (dMRI) tractography and associated …
Altered higher-order coupling between brain structure and function with embedded vector representations of connectomes in schizophrenia
B Wang, M Guo, T Pan, Z Li, Y Li, J Xiang, X Cui… - Cerebral …, 2023 - academic.oup.com
It has been shown that the functional dependency of the brain exists in both direct and
indirect regional relationships. Therefore, it is necessary to map higher-order coupling in …
indirect regional relationships. Therefore, it is necessary to map higher-order coupling in …
Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis
Motivation With the rapid development of modern technologies, massive data are available
for the systematic study of Alzheimer's disease (AD). Though many existing AD studies …
for the systematic study of Alzheimer's disease (AD). Though many existing AD studies …