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Related Concept Videos

Brain Imaging01:14

Brain Imaging

313
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...
313

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Related Experiment Video

Updated: Sep 11, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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Confound Controlled Multimodal Neuroimaging Data Fusion and Its Application to Developmental Disorders.

Chuang Liang, Rogers F Silva, Tulay Adali

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 14, 2025
    PubMed
    Summary
    This summary is machine-generated.

    We developed CR-mCCAR, a novel method for multimodal brain data fusion that simultaneously optimizes for clinical patterns and removes confounding factors like age and motion. This improves biomarker detection for brain disorders such as ADHD and ASD.

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    Area of Science:

    • Neuroimaging
    • Biostatistics
    • Machine Learning

    Background:

    • Multimodal fusion leverages shared and complementary information from diverse data sources.
    • Supervised fusion is valuable for identifying brain-based patterns linked to clinical measures.
    • Handling confounds in brain data analysis is crucial to avoid spurious findings.

    Purpose of the Study:

    • To introduce CR-mCCAR, a novel method for joint optimization of multimodal fusion and confound removal.
    • To capture reliable multimodal brain patterns associated with clinical domains while accounting for covariates.
    • To enhance the detection of phenotype-linked multimodal biomarkers for neurological and psychiatric disorders.

    Main Methods:

    • CR-mCCAR employs a guided fusion model to simultaneously optimize for target components and discount covariate effects.
    • Simulations were used to validate the accurate separation of reference and covariate factors.
    • Functional and structural neuroimaging data from ADHD and ASD cohorts were analyzed.

    Main Results:

    • CR-mCCAR accurately separates target and covariate factors in simulations.
    • The method identified distinct co-varying patterns in ADHD (striato-thalamo-cortical, salience) and ASD (salience, fronto-temporal) linked to core symptoms, independent of age and motion.
    • These findings were replicated in an independent cohort.
    • CR-mCCAR significantly improved classification accuracy between ADHD/ASD and controls compared to separate fusion or regression approaches.

    Conclusions:

    • CR-mCCAR offers a robust framework for jointly optimizing multimodal fusion and confound removal.
    • The approach enhances the discovery of reliable, phenotype-linked multimodal biomarkers for brain disorders.
    • CR-mCCAR demonstrates superior performance in identifying disease-specific neuroimaging patterns and improving diagnostic classification.