Search research articles

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

The Prodromal Synucleinopathy Rating Scale: An Assessment in Patients With REM Sleep Behavior Disorder.

Neurology·2026

Same author

DANCE: Deep Learning-Assisted Analysis of ProteiN Sequences Using Chaos Enhanced Kaleidoscopic Images.

Journal of computational biology : a journal of computational molecular cell biology·2026

Same author

Deep learning for estimating right ventricular function from routine coronary angiography.

European heart journal. Digital health·2026

Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

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

Search research articles

Related Experiment Video

Updated: May 22, 2025

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Characterizing multivariate regional hubs for schizophrenia classification, sex differences, and brain age estimation

Yuzheng Nie^1,2, Taslim Murad¹, Hui-Yuan Miao¹

¹Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.

Medrxiv : the Preprint Server for Health Sciences

|March 17, 2025

Summary

This summary is machine-generated.

This study used artificial intelligence (AI) to identify brain patterns in schizophrenia (SZ) and aging. The deep learning model revealed specific regional brain changes in SZ patients, differing by sex, and highlighted aging-related brain regions.

Keywords:

Brain age prediction Deep learning Machine learning Schizophrenia classification Sex difference Shapley additive explanations

More Related Videos

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

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

Related Experiment Videos

Last Updated: May 22, 2025

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

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

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

Area of Science:

Neuroimaging
Artificial Intelligence
Psychiatry

Background:

Schizophrenia (SZ) diagnosis and treatment can be improved by understanding its neurobiological underpinnings.
Sex differences in brain structure and aging processes are not fully understood in SZ.

Purpose of the Study:

To investigate multivariate regional brain patterns for schizophrenia classification, sex differences, and brain age prediction.
To utilize structural MRI, demographics, and explainable AI for enhanced analysis.

Main Methods:

Employed and compared various AI models including Support Vector Classifier (SVC), k-nearest neighbor (KNN), and Deep Learning (DL) for classification and regression.
Integrated the best-performing DL model with Shapley Additive Explanations (SHAP) to identify significant multivariate brain regional patterns.

Main Results:

Deep Learning (DL) models outperformed other AI models in schizophrenia classification, sex difference identification, and brain age prediction.
The integrated DL-SHAP approach identified specific anatomical changes in SZ patients (e.g., left pallidum, insula, hippocampus, putamen) that varied between sexes.
Key brain regions associated with aging were identified in both healthy controls and SZ patients.

Conclusions:

This study successfully employed predictive modeling and explainable AI to reveal complex multivariate brain regions involved in schizophrenia, sex differences, and brain aging.
The findings offer deeper insights into the neurobiological mechanisms of schizophrenia, paving the way for precision medicine and improved diagnosis/treatment.