Connectopic mapping with resting-state fMRI

TL;DR

This paper introduces a data-driven method using spectral embedding and spatial statistics to map and analyze topographic connectivity patterns ('connectopies') in the brain with resting-state fMRI, revealing biologically plausible maps.

Contribution

It presents a novel, fully data-driven framework combining spectral embedding and spatial inference for mapping connectopies in the brain, enabling rigorous hypothesis testing.

Findings

Successfully mapped connectopies in motor and visual cortex

Revealed biologically plausible, overlapping connectopies

Enabled statistical testing of connectivity profiles

Abstract

Brain regions are often topographically connected: nearby locations within one brain area connect with nearby locations in another area. Mapping these connection topographies, or 'connectopies' in short, is crucial for understanding how information is processed in the brain. Here, we propose principled, fully data-driven methods for mapping connectopies using functional magnetic resonance imaging (fMRI) data acquired at rest by combining spectral embedding of voxel-wise connectivity 'fingerprints' with a novel approach to spatial statistical inference. We applied the approach in human primary motor and visual cortex, and show that it can trace biologically plausible, overlapping connectopies in individual subjects that follow these regions' somatotopic and retinotopic maps. As a generic mechanism to perform inference over connectopies, the new spatial statistics approach enables rigorous statistical testing of hypotheses regarding the fine-grained spatial profile of functional connectivity and whether that profile is different between subjects or between experimental conditions. The combined framework offers a fundamental alternative to existing approaches to investigating functional connectivity in the brain, from voxel- or seed-pair wise characterizations of functional association, towards a full, multivariate characterization of spatial topography.