Individual‐level brain morphological similarity networks: Current methodologies and applications
M Cai, J Ma, Z Wang, Y Zhao, Y Zhang… - CNS Neuroscience …, 2023 - Wiley Online Library
Aims The human brain is an extremely complex system in which neurons, clusters of
neurons, or regions are connected to form a complex network. With the development of …
neurons, or regions are connected to form a complex network. With the development of …
Toward individualized connectomes of brain morphology
The morphological brain connectome (MBC) delineates the coordinated patterns of local
morphological features (such as cortical thickness) across brain regions. While classically …
morphological features (such as cortical thickness) across brain regions. While classically …
Four distinct subtypes of Alzheimer's disease based on resting-state connectivity biomarkers
Background Alzheimer's disease (AD) is a neurodegenerative disorder with significant
heterogeneity. Different AD phenotypes may be associated with specific brain network …
heterogeneity. Different AD phenotypes may be associated with specific brain network …
Edge-centric effective connection network based on muti-modal MRI for the diagnosis of Alzheimer's disease
Alzheimer's disease (AD) is an irreversible neurodegenerative disease. But if AD is detected
early, it can greatly reduce the severity of the disease. Functional connection networks …
early, it can greatly reduce the severity of the disease. Functional connection networks …
A neuroimaging biomarker for Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN): a cross-sectional study
Background Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that poses
a worldwide public health challenge. A neuroimaging biomarker would significantly improve …
a worldwide public health challenge. A neuroimaging biomarker would significantly improve …
Morphological connectivity differences in Alzheimer's disease correlate with gene transcription and cell‐type
Alzheimer's disease (AD) is one of the most prevalent forms of dementia in older individuals.
Convergent evidence suggests structural connectome abnormalities in specific brain …
Convergent evidence suggests structural connectome abnormalities in specific brain …
A comprehensive characterization of hippocampal feature ensemble serves as individualized brain signature for Alzheimer's disease: deep learning analysis in 3238 …
Objectives Hippocampal characterization is one of the most significant hallmarks of
Alzheimer's disease (AD); rather, the single-level feature is insufficient. A comprehensive …
Alzheimer's disease (AD); rather, the single-level feature is insufficient. A comprehensive …
Identifying Alzheimer's disease and mild cognitive impairment with atlas-based multi-modal metrics
Z Long, J Li, J Fan, B Li, Y Du, S Qiu, J Miao… - Frontiers in Aging …, 2023 - frontiersin.org
Introduction Multi-modal neuroimaging metrics in combination with advanced machine
learning techniques have attracted more and more attention for an effective multi-class …
learning techniques have attracted more and more attention for an effective multi-class …
Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: A review
Neurodegenerative diseases are associated with heterogeneity in genetics, pathology, and
clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical …
clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical …
Altered large‐scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study
Alzheimer's disease (AD) is a common neurodegeneration disease associated with
substantial disruptions in the brain network. However, most studies investigated static …
substantial disruptions in the brain network. However, most studies investigated static …