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An Examination of Task-Evoked fMRI Data Processing in Functional Connectivity.

Alice Giubergia^1,2, Sara Mascheretti^3,4, Valentina Lampis^3,4

¹Neuroimaging Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy.

Journal of Neuroscience Research

|June 10, 2026

Summary

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Functional connectomics analysis of task fMRI data can reveal resting-state connectivity. This approach successfully differentiates between typical readers and those with Developmental Dyslexia, even after task regression.

Area of Science:

Neuroscience
Cognitive Science
Medical Imaging

Background:

Functional connectomics typically uses resting-state fMRI.
Task fMRI data can be analyzed using connectomic methods to explore broader brain region involvement.
Investigating task fMRI for resting-state connectivity is crucial for understanding brain function during tasks.

Purpose of the Study:

To assess the feasibility of inferring resting-state connectivity from task fMRI data.
To investigate the impact of connectomic analysis on behavioral trait studies, specifically Developmental Dyslexia.
To explore the influence of preprocessing techniques on task fMRI data for connectomic analysis.

Main Methods:

Subjects underwent two visual fMRI tasks.
Blood-Oxygen-Level-Dependent (BOLD) time-series were processed to create 'task' and 'pseudo-resting' conditions using different task regression setups.

Keywords:

functional connectivity pipelines machine learning pseudo‐resting state task‐fMRI

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Stimulus-classification and Support Vector Machine (SVM) experiments were used to compare connectomes and discriminate between groups.

Main Results:

Stimulus information was inferable from connectomes even after task regression, indicating retained task-related signals.
Support Vector Machine (SVM) effectively discriminated between children with Developmental Dyslexia (DD) and Typical Readers (TR) in both 'task' and 'pseudo-resting' conditions.
Preprocessing methods did not affect the inference of classification rules for DD vs. TR, despite variations in discriminative connections across tasks.

Conclusions:

Task fMRI data can be utilized to infer resting-state connectivity.
Connectomic analysis of task fMRI is a viable approach for behavioral trait studies, including differentiating developmental disorders.
Preprocessing choices in task fMRI do not hinder the identification of key discriminative features for group classification.