Transcriptomic and Macroscopic Architectures of Multimodal Covariance Network Reveal Molecular-Structural-Functional...

Area of Science:

• Neuroscience
• Genetics
• Computational Biology

Background:

• Human cognition relies on complex interactions between brain structure and function.
• Quantifying the interplay between structural and functional neural circuits and their genetic underpinnings remains challenging.
• Understanding these relationships is crucial for advancing knowledge of cognition and neurological diseases.

Purpose of the Study:

• To propose a novel multimodal covariance network (MCN) construction approach.
• To capture individual-specific covarying of structural and functional brain activities.
• To explore the association between gene expression patterns and structural-functional covarying in cognitive tasks and major depression disorder (MDD).

Main Methods:

• Developed a multimodal covariance network (MCN) approach to analyze individual brain structure-function relationships.
• Integrated multimodal data from a human brain transcriptomic atlas and two independent cohorts.
• Correlated MCN differences with brain-wide gene expression patterns in healthy individuals performing a gambling task and in MDD patients.

Main Results:

• Identified a replicable cortical structural-functional fine map in healthy individuals using MCN.
• Found spatial correlations between cognition- and disease-related gene expression and MCN differences.
• Excitatory and inhibitory neuron transcriptomic changes explained task-evoked MCN variations; MDD-related MCN changes were linked to synapse function and neuroinflammation.

Conclusions:

• The MCN approach successfully captures genetically validated structural-functional differences at the individual level.
• Gene expression patterns are significantly correlated with MCN variations, offering insights into cellular-level mechanisms of cognition and MDD.
• Findings highlight potential therapeutic targets for MDD by identifying specific cellular processes and pathways involved in disease-related brain network alterations.