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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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Machine learning in small sample neuroimaging studies: Novel measures for schizophrenia analysis.

Carmen Jimenez-Mesa¹, Javier Ramirez¹, Zhenghui Yi²

¹Department of Signal Theory, Telematics and Communications, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain.

Human Brain Mapping

|March 28, 2024

Summary

This study introduces a novel computer-aided diagnosis (CAD) system using brain sulcal patterns to distinguish schizophrenia patients from healthy individuals. The system demonstrates effective classification even with limited data, highlighting key differentiating brain regions.

Keywords:

cross‐validation deep learning explanaible AI machine learning resubstitution with upper bound correction schizophrenia sulcal morphology

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Area of Science:

Neuroimaging
Artificial Intelligence
Computational Psychiatry

Background:

Computer-aided diagnosis (CAD) systems integrate imaging and AI for clinical support and biological pattern investigation.
Existing CAD systems often lack comprehensive evaluation frameworks, especially for novel feature types and small datasets.

Purpose of the Study:

To develop and evaluate a CAD system for classifying schizophrenia using structural brain imaging-derived sulcal patterns.
To demonstrate a comprehensive evaluation methodology for CAD systems with limited empirical guidance and small sample sizes.

Main Methods:

Extracted sulcal features from the entire cerebral cortex of 58 schizophrenia patients and 56 healthy controls.
Applied sequential statistical, machine learning, and deep learning techniques for classification.
Utilized explainable AI (XAI) methods (LIME, SHAP) to identify feature relevance and dimensionality reduction for optimization.

Main Results:

Successfully differentiated schizophrenia patients from controls based on sulcal patterns.
Identified specific differentiating sulcal patterns in temporal and precentral areas, and the collateral fissure.
Validated the benefits of dimensionality reduction and advanced validation methods for small sample performance optimization.

Conclusions:

Sulcal patterns from the entire cerebral cortex can serve as effective biomarkers for schizophrenia detection using CAD systems.
The proposed comprehensive evaluation framework is suitable for developing CAD systems with novel features and limited data.
Explainable AI techniques are valuable for understanding feature importance in neuroimaging-based CAD systems.