# Excluding spontaneous thought periods enhances functional connectivity test–retest reliability and machine learning performance in fMRI

## TL;DR

This paper shows that removing low-quality time points from resting-state fMRI data improves the reliability of brain connectivity measurements and machine learning outcomes.

## Contribution

The novel time-enhanced functional connectivity method improves reliability by removing poor-quality time points without extending scan duration.

## Key findings

- Time-enhanced functional connectivity significantly improves machine learning performance in tasks like sex classification.
- The method increases test–retest reliability and reveals stronger group differences compared to conventional approaches.
- Removing low-quality time points enhances the sensitivity and reliability of functional connectivity measurements.

## Abstract

Resting-state functional magnetic resonance imaging (rs-fMRI) is a widely used non-invasive technique for investigating brain function and identifying potential disease biomarkers. Compared with task-based fMRI, rs-fMRI is easier to acquire because it does not require explicit task paradigms. However, functional connectivity measures derived from rs-fMRI often exhibit poor reliability, which substantially limits their clinical applicability.

To address this limitation, we propose a novel method termed time-enhanced functional connectivity, which improves reliability by identifying and removing poor-quality time points from rs-fMRI time series. This approach aims to enhance the quality of functional connectivity estimation without extending scan duration or relying on dataset-specific constraints.

Experimental results demonstrate that the proposed method significantly improves performance in downstream machine learning tasks, such as sex classification. In addition, time-enhanced functional connectivity yields higher test–retest reliability and reveals more pronounced statistical differences between groups compared with conventional functional connectivity measures.

These findings suggest that selectively removing low-quality time points provides a practical and effective strategy for improving the reliability and sensitivity of functional connectivity measurements in rs-fMRI, thereby enhancing their potential utility in both neuroscience research and clinical applications.