Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Lag-adjusted functional network connectivity reveals sensorimotor and higher cognitive network alterations in depression.

Research square·2026

Same author

Structural co-modulation: An individualized measure of inter-component interactions in source-based morphometry.

NeuroImage·2026

Same author

Measuring the Impacts of Urbanicity and Different Exposome Factors on Human Brain through Exposure Network Mapping.

Neuroscience bulletin·2026

Same author

Large-scale brain dynamics are organized by a directional coordination hierarchy.

bioRxiv : the preprint server for biology·2026

Same author

Brain regions with gestational age differences mediate cognition in adolescents born very premature.

Communications biology·2026

Same author

NeuroFLAME: A Scalable, Privacy-Preserving Federated Framework for Secure, Reproducible, and Multi-Site Neuroimaging Analysis.

bioRxiv : the preprint server for biology·2026

Search research articles

Related Experiment Video

Updated: Mar 11, 2026

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Published on: September 8, 2021

A robust sparse-modeling framework for estimating schizophrenia biomarkers from fMRI.

Keith Dillon¹, Vince Calhoun², Yu-Ping Wang¹

¹Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA; Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA.

Journal of Neuroscience Methods

|November 22, 2016

Summary

This study introduces unambiguous components, a novel method for identifying brain regions linked to mental illness using neuroimaging. The approach improves robustness and classification accuracy, offering a more reliable way to find critical imaging biomarkers.

Keywords:

Functional MRI Optimization PCA Schizophrenia Sparsity

More Related Videos

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Published on: December 2, 2015

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

Related Experiment Videos

Last Updated: Mar 11, 2026

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Published on: September 8, 2021

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Published on: December 2, 2015

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

Area of Science:

Neuroimaging
Machine Learning
Psychiatric Disorders

Background:

Identifying brain regions associated with mental illness is crucial.
Current machine learning methods face repeatability challenges with large, diverse populations in neuroimaging studies.

Purpose of the Study:

To develop a robust method for identifying brain regions relevant to mental illness using neuroimaging data.
To improve the reliability and accuracy of neuroimaging biomarkers for psychiatric disorders.

Main Methods:

Revisiting dimensionality reduction and sparse modeling within a unified optimization framework.
Introducing "unambiguous components" to combine benefits of both approaches.
Estimating image components with constrained variability correlated with disease mechanisms.

Main Results:

Applied the method to estimate neuroimaging biomarkers for schizophrenia using task fMRI data.
Achieved improved robustness and classification accuracy compared to existing methods.
Unambiguous components identified key brain regions, with some overlap and some unique findings compared to LASSO and elastic net.

Conclusions:

Unambiguous components offer a robust and effective approach for estimating significant regions in neuroimaging data.
This method enhances the identification of neuroimaging biomarkers for mental illness.
The approach shows superior classification accuracy in distinguishing patient groups.