Detecting neural activity and connectivity by perfusion-based fMRI

TL;DR

This paper introduces a method combining perfusion and BOLD signals from ASL MRI to localize brain functions and connectivity, revealing detailed network interactions and the relationship between signals and neural activity.

Contribution

It presents a novel approach using Structural Equation Modeling to analyze simultaneous CBF and BOLD signals for functional localization and connectivity mapping.

Findings

Identified visual cortex localization via perfusion data.

Mapped connectivity in sensory-motor and default-mode networks.

BOLD signals are more directly linked to neural activity than CBF signals.

Abstract

This study proposes an approach to estimate the functional localization and connectivity from CBF and BOLD signals simultaneously measured by ASL (arterial spin labeling) MRI, especially using exploratory Structural Equation Modeling analysis. In a visual task experiment, the primary visual cortices were located by analyzing the perfusion data. In the resting state experiment, two structural equation models were estimated at each voxel regarding to the sensory-motor network and default-mode network. The resulting connectivity maps indicate that supplementary motor area has significant connections to left/right primary motor areas, and inferior parietal lobules link significantly with posterior cingulate cortex and medial prefrontal cortex. The model fitting results imply that BOLD signal is more directly linked to the underlying cause of functional connectivity than CBF signal.