Morphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application.

Area of Science:

• Neuroimaging
• Neuroscience
• Brain Network Analysis

Background:

• White matter (WM) constitutes a significant portion of the adult brain, yet its intricate wiring diagram remains largely uncharacterized.
• Understanding WM architecture is crucial for comprehending brain function and neurological disorders.

Purpose of the Study:

• To develop and validate a novel approach for constructing white matter networks based on morphological similarity.
• To investigate the biological significance, reliability, and clinical utility of these morphological white matter networks.

Main Methods:

• Utilized structural magnetic resonance imaging (MRI) to estimate interregional morphological similarity for WM network construction.
• Integrated multimodal and multiscale data, including functional coherence, metabolic synchronization, gene expression, and chemoarchitecture.
• Applied the approach to study white matter alterations in multiple sclerosis and neuromyelitis optica spectrum disorders.

Main Results:

• Morphological WM networks demonstrated nontrivial topology, high test-retest reliability, and association with cognitive differences and genetic factors.
• These networks successfully predicted patterns of hemodynamic coherence, metabolic synchronization, gene co-expression, and chemoarchitectonic covariance.
• Disease-specific morphological dysconnectivity was identified in multiple sclerosis and neuromyelitis optica spectrum disorders, correlating with clinical variables and enabling diagnosis.

Conclusions:

• Morphological WM networks provide a reliable and biologically meaningful framework for exploring brain white matter architecture in both healthy individuals and patients with neurological diseases.
• This approach holds potential for advancing our understanding of brain connectivity and developing diagnostic/prognostic tools for neurological conditions.