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

1.0K
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...
1.0K

You might also read

Related Articles

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

Sort by
Same author

Evolving landscape of glioblastoma research : A 75-year bibliometric study on survival, treatment and gender in authorship.

Wiener klinische Wochenschrift·2026
Same author

Contralateral language network integration predicts and protects against naming decline after temporal lobe resection.

Epilepsia·2026
Same author

Accurate and efficient data-driven psychiatric assessment using machine learning.

BMC medical informatics and decision making·2026
Same author

Developmental variations in recurrent spatiotemporal brain propagations from childhood to adulthood.

Nature communications·2026
Same author

Toward a science of prospective learning.

Neuron·2025
Same author

Is Pearson's correlation coefficient enough for functional connectivity in fMRI?

Imaging neuroscience (Cambridge, Mass.)·2025

Related Experiment Video

Updated: Apr 18, 2026

Profiling Maternal Behavior Responses During Whole-Brain Imaging
07:12

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

1.6K

Statistically valid explainable black-box machine learning: applications in sex classification across species using

Tingshan Liu1, Jayanta Dey1, Beiya Xu1

  • 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.

Plos One
|April 16, 2026
PubMed
Summary

We developed a new framework using Oblique Random Forests (ORFs) and NEOFIT to accurately classify sex from brain scans. This method reveals key neuroanatomical differences, improving personalized diagnostics and understanding sex-based brain variations.

More Related Videos

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.7K
Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

907

Related Experiment Videos

Last Updated: Apr 18, 2026

Profiling Maternal Behavior Responses During Whole-Brain Imaging
07:12

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

1.6K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.7K
Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

907

Area of Science:

  • Neuroimaging
  • Machine Learning
  • Comparative Anatomy

Background:

  • Sex classification from neuroimaging data offers potential for personalized diagnostics by identifying sex-specific brain differences linked to disease risks.
  • Traditional machine learning methods struggle with high-dimensional neuroimaging data, lacking accuracy and interpretable feature importance.
  • Existing techniques like random forests, LIME, and SHAP face challenges with complex feature interactions and noise in large datasets.

Purpose of the Study:

  • To develop an integrated framework combining Oblique Random Forests (ORFs) and a novel permutation-based feature importance testing algorithm (NEOFIT).
  • To enhance classification accuracy and provide statistically validated, interpretable feature importance for sex classification using neuroimaging data.
  • To enable cross-species comparison of sex-based brain structure differences in humans and macaques.

Main Methods:

  • Developed an integrated framework using Oblique Random Forests (ORFs) with oblique decision boundaries for complex feature interactions.
  • Introduced NEOFIT, a permutation-based feature importance testing algorithm for rigorous statistical validation and corrected p-values.
  • Validated the framework on simulated datasets and applied it to voxel-wise structural MRI and cortical thickness data in humans and macaques.

Main Results:

  • ORFs achieved high classification accuracy, with AUC > 0.80 in humans and > 0.70 in macaques.
  • NEOFIT identified statistically significant neuroanatomical features aligned with sex-dimorphic patterns.
  • The framework demonstrated robustness, scalability, and provided interpretable insights into sex-distinguishing brain features.

Conclusions:

  • The proposed framework significantly enhances sex classification performance from neuroimaging data.
  • It provides clear, interpretable insights into neuroanatomical features that differentiate sexes, aiding in understanding sex-specific disease risks.
  • These advancements contribute to improved diagnostic tools and a deeper understanding of the evolutionary basis of sex differences in brain structure.