A robust approach for analyzing and mapping hierarchical brain connectome towards laminar-specific neural networks.

Area of Science:

• Neuroscience
• Brain Imaging
• Connectomics

Background:

• Conventional resting-state fMRI (rs-fMRI) lacks the spatial resolution for laminar-specific analysis in rodent brains.
• Understanding local and remote neural circuits requires detailed mapping of cortical layers and sub-cortical nuclei.
• Existing processing pipelines are insufficient for analyzing small laminar structures in rodent models.

Purpose of the Study:

• To develop and apply a high-resolution rs-fMRI preprocessing pipeline for mouse brains.
• To achieve laminar-specific functional connectomes and map neural circuits at mesoscopic and macroscopic levels.
• To investigate layer-specific neuronal activity and functional connectivity in the resting mouse brain.

Main Methods:

• Conducted ultra-high field rs-fMRI on mouse brains.
• Developed a novel fMRI preprocessing pipeline incorporating RMT-based PCA, non-rigid registration, and voxel shift correction.
• Applied atlas-based connectivity analysis to high-resolution rs-fMRI data.

Main Results:

• Achieved high-quality hierarchical connectomes from cortical layers to large brain regions.
• Demonstrated distinct laminar-specific networks in somatosensory areas, hippocampus, and lateral forebrain.
• Observed previously undetected functional networks and extended connections, consistent with viral tracer projection patterns.

Conclusions:

• The developed pipeline enables high-fidelity mapping of laminar-specific functional connectomes in mouse brains.
• Spontaneous neuronal activity is non-uniform across cortical layers, revealing layer-specific functional networks.
• This approach offers new opportunities to study neural circuits and brain function at multiple scales, though higher resolution may be needed for greater specificity.