Learning Active Multimodal Subspaces in the Brain
Batta, I.; Abrol, A.; Fu, Z.; Calhoun, V.
Show abstract
Here we introduce a multimodal framework to identify subspaces in the human brain that are defined by collective changes in structural and functional measures and are actively linked to demographic, biological and cognitive indicators in a population. We determine the multimodal subspaces using principles of active subspace learning (ASL) and demonstrate its application on a sample learning task (biological ageing) on a Schizophrenia dataset. The proposed multimodal ASL method successfully identifies latent brain representations as subsets of brain regions and connections forming co-varying subspaces in association with biological age. We show that Schizophrenia is characterized by different subspace patterns compared to those in a cognitively normal brain. The multimodal features generated by projecting structural and functional MRI components onto these active subspaces perform better than several PCA-based transformations and equally well when compared to non-transformed features on the studied learning task. In essence, the proposed method successfully learns active brain subspaces associated with a specific brain condition but inferred from the brain imaging data along with the biological/cognitive traits of interest.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A method for estimating dynamic functional network connectivity gradients (dFNG) from ICA captures smooth inter-network modulation. 96%
- A Multimodal Vision Transformer for InterpretableFusion of Functional and Structural NeuroimagingData 96%
- Time-varying Spatial Propagation of Brain Networks in fMRI data 96%
Similar papers in this journal
- Providing context: Extracting non-linear and dynamic temporal motifs from brain activity 96%
- Multiclass Classification of Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, and Typically Developed Individuals Using fMRI Functional Connectivity Analysis 94%
- Eigenvector alignment: assessing functional network changes in amnestic mild cognitive impairment and Alzheimer's disease 94%
Similar papers in this journal
- Building Models of Functional Interactions Among Brain Domains that Encode Varying Information Complexity: A Schizophrenia Case Study 97%
- Unraveling Integration-Segregation Imbalances in Schizophrenia Through Topological High-Order Functional Connectivity 96%
- Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging 96%
Similar papers in this journal
- Bridging Structural MRI with Cognitive Function for Individual Level Classification of Early Psychosis via Deep Learning 95%
- Explainable Fuzzy Clustering Framework Reveals Divergent Default Mode Network Connectivity Dynamics in Schizophrenia 95%
- Causality analysis in major depressive disorder for early prediction of treatment outcomes with pharmacological and neuromodulation therapies 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.