Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Brain Imaging01:14

Brain Imaging

247
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...
247
Association Areas of the Cortex01:21

Association Areas of the Cortex

5.5K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
5.5K
Organization of the Brain01:30

Organization of the Brain

833
The brain is an integral component of the nervous system and serves as the center for processing sensory inputs, making decisions, and directing bodily actions. This complex organ is organized into three primary sections: the hindbrain, midbrain, and forebrain, each responsible for a range of vital functions.
Hindbrain
The hindbrain, located at the base of the brain, plays a vital role in regulating automatic processes that sustain life. It includes the medulla oblongata, which is essential for...
833

You might also read

Related Articles

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

Sort by
Same author

Group Joint ICA (gjICA): A Method for Multimodal Fusion of Concurrent EEG and fMRI Data.

Human brain mapping·2026
Same author

Aberrant recovery of timescale-aligned amplitude balance links to symptoms and cognition in schizophrenia.

Translational psychiatry·2026
Same author

Multimodal subspace independent vector analysis effectively captures latent relationships between brain structure and function.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

The Regional Vulnerability Index (RVI) as a Neuroimaging-Based Biomarker for Autism: Associations with Likelihood, Cognition, and Longitudinal Social Outcomes.

bioRxiv : the preprint server for biology·2026
Same author

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

bioRxiv : the preprint server for biology·2026
Same author

Structure-function coupling of large-scale cortical networks across the lifespan is spectrally specific.

Communications biology·2026

Related Experiment Video

Updated: Jul 15, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K

Multimodal subspace independent vector analysis effectively captures the latent relationships between brain structure

Xinhui Li1,2, Peter Kochunov3, Tulay Adali4

  • 1Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.

Biorxiv : the Preprint Server for Biology
|September 25, 2023
PubMed
Summary

This study introduces Multimodal Subspace Independent Vector Analysis (MSIVA) to analyze complex brain data from multiple imaging types. MSIVA reveals subject-specific brain patterns linked to age, sex, and schizophrenia, offering new biomarker insights.

Keywords:
agelatent variable modelsmultimodal fusionschizophreniasexstructural and functional MRI

More Related Videos

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K

Related Experiment Videos

Last Updated: Jul 15, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K

Area of Science:

  • Neuroscience
  • Neuroimaging Analysis
  • Biostatistics

Background:

  • Understanding brain structure-function relationships from high-dimensional, multimodal neuroimaging data is a key neuroscience challenge.
  • Conventional methods often oversimplify statistical assumptions, limiting the capture of complex, multi-dimensional relationships within and between brain data modalities.
  • Existing approaches struggle to account for subject-level variability in latent brain sources.

Purpose of the Study:

  • To introduce Multimodal Subspace Independent Vector Analysis (MSIVA), a novel methodology for analyzing high-dimensional, multimodal neuroimaging data.
  • To capture joint and unique vector sources from multiple data modalities by defining flexible, variable-dimension subspaces.
  • To enable the estimation of subject-level variability within independent subspaces, overcoming limitations of traditional methods.

Main Methods:

  • Developed Multimodal Subspace Independent Vector Analysis (MSIVA) to define cross-modal and unimodal subspaces with variable dimensions.
  • Enabled flexible estimation of independent subspaces within modalities and their linkage across modalities.
  • Compared MSIVA against unimodal and multimodal baseline methods using synthetic and real neuroimaging datasets (sMRI, fMRI) with varying subspace structures.

Main Results:

  • MSIVA successfully identified ground-truth subspace structures in synthetic datasets, outperforming a multimodal baseline that failed to detect high-dimensional subspaces.
  • MSIVA demonstrated superior detection of latent subspace structures in large multimodal neuroimaging datasets (sMRI/fMRI) compared to a unimodal baseline.
  • Subspace-specific analyses revealed strong associations between MSIVA-derived sources and phenotype variables (age, sex, schizophrenia, lifestyle, cognition).

Conclusions:

  • MSIVA effectively captures complex, multi-dimensional relationships in multimodal neuroimaging data, including subject-level variability.
  • The method identified modality- and group-specific brain regions associated with key phenotype measures, highlighting potential biomarkers for neurological and psychiatric conditions.
  • Findings suggest MSIVA provides valuable insights into the linked brain structure and function underlying various phenotypic traits and neuropsychiatric disorders.