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A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

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Individual cognitive traits can be predicted from task-based dynamic functional connectivity with a deep

Erick Almeida de Souza¹, Bruno Hebling Vieira^2,3, Carlos Ernesto Garrido Salmon^1,4

¹InBrain Lab, Departamento de Física, FFCLRP, Universidade de São Paulo, Prof. Aymar Batista Prado Street, Vila Monte Alegre, Ribeirão Preto - SP, 14040-900, Brazil.

Cerebral Cortex (New York, N.Y. : 1991)

|October 24, 2024

Summary

Deep learning models predict general intelligence using functional brain connectivity from functional magnetic resonance imaging (fMRI) tasks. Task-based connectivity offers higher predictive power than resting-state, with intelligence distributed homogeneously across the brain.

Keywords:

deep learning dynamic functional connectivity intelligence task-fmri

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Last Updated: May 5, 2026

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

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Published on: May 7, 2014

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

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Published on: June 30, 2018

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Area of Science:

Neuroimaging
Cognitive Neuroscience
Artificial Intelligence

Background:

Understanding the neural basis of intelligence is a growing area of research.
Neuroimaging techniques, particularly functional magnetic resonance imaging (fMRI), offer insights into brain function.
Deep learning models show promise in predicting cognitive measures from complex neuroimaging data.

Purpose of the Study:

To predict general and fluid intelligence scores using deep learning models.
To investigate the predictive power of dynamic functional connectivity during specific cognitive tasks (language and working memory).
To compare task-based functional connectivity with resting-state connectivity for intelligence prediction.

Main Methods:

Utilized neuroimaging and behavioral data from 874 subjects from the Human Connectome Project.
Employed a deep learning model with multiscale convolutional and long short-term memory layers.
Analyzed dynamic functional connectivity derived from language and working memory fMRI task states, controlling for confounding variables.

Main Results:

The model explained 17.1% of general intelligence variance for working memory tasks and 16% for language tasks.
Task-based dynamic functional connectivity demonstrated superior predictive power compared to resting-state connectivity.
Controlling for confounding factors like age and gender significantly reduced prediction performance.

Conclusions:

Dynamic functional connectivity during cognitive tasks is a significant predictor of general intelligence.
Intelligence appears to be a spatially homogeneous construct across cortical networks.
Task-based fMRI analysis provides a more potent approach for predicting intelligence than resting-state analysis.