Topological cluster statistic (TCS): Toward structural connectivity–guided fMRI cluster enhancement

TL;DR

This paper introduces TCS, a new method that improves fMRI analysis by incorporating brain connectivity data, leading to better detection of brain activity patterns.

Contribution

TCS is a novel clustering framework that integrates anatomical connectivity data to enhance fMRI cluster inference.

Findings

TCS improves statistical power by 10%–50% for detecting brain activity, especially for medium-sized effects.

TCS enables the identification of anatomical networks linking distant active regions in the brain.

TCS is implemented in the PALM software, making it accessible for future neuroimaging research.

Abstract

Functional magnetic resonance imaging (fMRI) studies most commonly use cluster-based inference to detect local changes in brain activity. Insufficient statistical power and disproportionate false-positive rates reportedly hinder optimal inference. We propose a structural connectivity–guided clustering framework, called topological cluster statistic (TCS), that enhances sensitivity by leveraging white matter anatomical connectivity information. TCS harnesses multimodal information from diffusion tractography and functional imaging to improve task fMRI activation inference. Compared to conventional approaches, TCS consistently improves power over a wide range of effects. This improvement results in a 10%–50% increase in local sensitivity with the greatest gains for medium-sized effects. TCS additionally enables inspection of underlying anatomical networks and thus uncovers knowledge regarding the anatomical underpinnings of brain activation. This novel approach is made available in the PALM software to facilitate usability. Given the increasing recognition that activation reflects widespread, coordinated processes, TCS provides a way to integrate the known structure underlying widespread activations into neuroimaging analyses moving forward.

Neuroimaging studies often encounter challenges in reliable inference of statistical maps due to limited statistical power. This article introduces TCS, a novel method that integrates anatomical connectivity data from diffusion tractography into cluster-based inference techniques. Our findings demonstrate that TCS enhances statistical power, improves the detection of spatially disjoint localized activations, and identifies the underlying network linking distant inferred active regions. By elucidating the coordinated network supporting inferred effects, TCS enables data-driven interpretation of inference results. The availability of TCS as a publicly accessible tool offers a promising avenue for future neuroimaging research to leverage anatomical connectivity for enhanced inference and interpretation.

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