A Novel Directed Seed-Based Connectivity Analysis Toolbox Applied to Human and Marmoset Resting-State FMRI

Area of Science:

• Neuroscience
• Brain Imaging
• Computational Neuroscience

Background:

• Estimating directed functional connectivity (FC) is crucial for understanding complex brain function.
• Voxel-level directed FC estimation and performance evaluation in fMRI data remain underexplored.

Purpose of the Study:

• To develop and validate a novel directed seed-based connectivity analysis (SCA) method for voxel-level fMRI data.
• To assess the performance of the directed SCA method against ground truth cellular connectivity data.
• To investigate the functional organization of the human default mode network (DMN) using the developed method.

Main Methods:

• Developed a directed SCA method based on normalized pairwise Granger causality.
• Validated the method against 145 cortical retrograde tracer injections in marmosets (ground truth).
• Calculated receiver operating characteristic (ROC) curves to evaluate performance (area under the curve).
• Applied directed SCA to analyze the human default mode network (DMN).

Main Results:

• Directed SCA achieved high accuracy, with an area under the ROC curve of 0.942 against cellular connectivity.
• The method reliably estimates strong cellular connections between voxels in fMRI data.
• Analysis of the human DMN revealed distinct roles for the dorsolateral prefrontal cortex (dlPFC) and temporal lobe within the language network.
• The cerebellum (Crus I-II) was identified as a target, not a core component, of the DMN and language network.

Conclusions:

• The novel directed SCA method offers greater detail and accuracy than traditional ROI-based approaches.
• This method provides a reliable way to estimate voxel-level functional connectivity from fMRI data.
• Directed SCA, combined with functional flat mapping, introduces a new paradigm for whole-brain functional analysis.