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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Related Experiment Video

Updated: Jun 23, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Comparing and scaling fMRI features for brain-behavior prediction.

Mikkel Schöttner Sieler¹, Thomas A W Bolton¹, Jagruti Patel¹

¹Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.

Imaging Neuroscience (Cambridge, Mass.)

|September 17, 2025

Summary

This summary is machine-generated.

Functional connectivity (FC) best predicts cognition, age, and sex from resting-state fMRI data. Graph signal processing and variability features also show promise for neuroimaging biomarker development.

Keywords:

behavior prediction functional connectivity graph signal processing machine learning magnetic resonance imaging neuroimaging biomarkers

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Area of Science:

Neuroimaging and Computational Neuroscience
Brain Activity Analysis
Biomarker Discovery

Background:

Predicting behavioral variables from neuroimaging, like magnetic resonance imaging (MRI), is key for developing biomarkers for neurological and mental health disorders.
Feature extraction from neuroimaging data is a critical step, with different features varying in predictive power and scalability with sample size and scan time.

Purpose of the Study:

To compare nine feature subtypes from resting-state functional MRI (fMRI) for their ability to predict behavioral variables.
To investigate the scaling properties of these features concerning sample size and scan time.
To identify optimal features for developing neuroimaging biomarkers.

Main Methods:

Analysis of resting-state fMRI data from 979 subjects in the Human Connectome Project Young Adult dataset.
Extraction and comparison of nine feature subtypes, including functional connectivity (FC) and graph signal processing (GSP) metrics.
Prediction of behavioral scores (mental health, cognition, processing speed, substance use) and demographic variables (age, sex).

Main Results:

Functional connectivity (FC) emerged as the top feature for predicting cognition, age, and sex.
Graph power spectral density showed the second-best performance for cognition and age prediction; variability-based features showed potential for sex prediction.
Low-pass graph-filtered coupled FC slightly outperformed simple FC for sex prediction; other targets were not significantly predicted.

Conclusions:

FC is a robust feature for behavior prediction, with GSP and variability-based measures also demonstrating potential.
Performance scaling indicates reserves for better-performing features and highlights the importance of balancing sample size and scan time.
Findings inform future prediction studies regarding data acquisition strategies and sample composition for robust neuroimaging biomarker development.