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

## TL;DR

This paper introduces a high-resolution fMRI method to map brain connectivity at the level of individual cortical layers in mice, revealing new insights into resting-state neural networks.

## Contribution

A novel fMRI preprocessing pipeline enables laminar-specific connectome mapping in mice, revealing hierarchical brain networks previously undetected.

## Key findings

- High-resolution rs-fMRI with a new pipeline successfully maps hierarchical connectomes from large brain regions to cortical layers in mice.
- Distinct laminar-specific resting-state networks were identified, showing non-uniform spontaneous neuronal activity across cortical layers.
- Functional connections were observed in areas with sparse viral tracer projections, suggesting broader network interactions.

## Abstract

Probing neuronal activity and functional connectivity at cortical layer and sub-cortical nucleus level provides opportunities for mapping local and remote neural circuits and resting-state networks (RSN) critical for understanding cognition and behaviors. However, conventional resting-state fMRI (rs-fMRI) has been applied predominantly at relatively low spatial resolution and macroscopic level, unable to obtain laminar-specific information and neural circuits across the cortex at mesoscopic level. In addition, it is lack of sophisticated processing pipeline to deal with small laminar structures in rodent brains. To fill this gap, we conducted a high-resolution rs-fMRI study of mouse brain at ultra-high field and developed an fMRI preprocessing pipeline that features in random matrix theory-based principal component analysis to remove thermal noise, non-rigid image registration strategy to improve head motion estimation, one-time image voxel shift correction to minimize multi-interpolation-induced spatial blur, and improve subject-level alignment to facilitate group analysis. By applying this pipeline to the high-resolution mouse rs-fMRI with atlas-based connectivity analysis, we achieved high-quality hierarchical connectomes covering from large brain regions to cortical layers, and between white matter bundle fibers and cortices in mice. We demonstrate the hierarchical connectomes connecting to three representative brain regions: somatosensory areas, hippocampal regions, and lateral forebrain white matter bundles, showing previously undetected networks. The distinct laminar-specific networks evidence that the spontaneous neuronal activity is not uniform across the cortical layers in the resting brain, consistent with the layer-specific neuronal projection patterns that were observed in AAV viral tracer projections. Additionally, we also observed extended functional connections in areas with sparse viral tracer projections. The feasibility of achieving laminar-specific connectomes with distinct RSNs provides opportunities to study neural circuits and brain functions at multiple scales, though achieving high fidelity and specificity in mapping laminar-specific connectomes may require even higher spatial resolution.