Abstract
Resting-state fMRI is important in the classification of brain disorders, but highly multimodal and exhibits strong multisite heterogeneity. Existing methods fuse images, BOLD-based functional connectivity, and phenotypic data modalities. Unlike other medical imaging datasets, rs-fMRI datasets rarely include a text modality, so they are generated from phenotypic data or BOLD activations. These text generation methods rely on fixed assumptions for subjects, sites, devices, and protocols, leading to poor generalization across datasets. We propose fMRI-TAMCL, a text-anchored multimodal contrastive learning framework that integrates fMRI images, sparse FC, and generated subject-specific text. Its Subject-Adaptive Threshold Derivation module generates BOLD activation text, while Feature-Value Serialization module generates phenotypic text. All three modalities are encoded as clustered graphs, projected onto a shared unit hypersphere space, aligned using pairwise, text-anchored supervised contrastive learning, and fused with attention. fMRI-TAMCL proves its generalization capability across five datasets outperforming 29 baselines with 78.6%-86.4% accuracy in downstream classification.
Keywords
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Danso, J. M., Gyamfi, E. O., & Farias, M. C. Q. (2026). fMRI-TAMCL: Text-Anchored Supervised Multimodal Contrastive Learning for fMRI-Based Brain Disorder Classification. https://omanscience.com/en/articles/fmri-tamcl-text-anchored-supervised-multimodal-contrastive-learning-for-fmri-based-brain-disorder-classification
MLA 9
Danso, Juliana Mantebea, et al. "fMRI-TAMCL: Text-Anchored Supervised Multimodal Contrastive Learning for fMRI-Based Brain Disorder Classification." https://omanscience.com/en/articles/fmri-tamcl-text-anchored-supervised-multimodal-contrastive-learning-for-fmri-based-brain-disorder-classification.
Chicago (author–date)
Danso, Juliana Mantebea, Enoch Opanin Gyamfi, and Mylene C. Q. Farias. 2026. "fMRI-TAMCL: Text-Anchored Supervised Multimodal Contrastive Learning for fMRI-Based Brain Disorder Classification." https://omanscience.com/en/articles/fmri-tamcl-text-anchored-supervised-multimodal-contrastive-learning-for-fmri-based-brain-disorder-classification.
Harvard
Danso, J. M., Gyamfi, E. O. and Farias, M. C. Q. (2026) 'fMRI-TAMCL: Text-Anchored Supervised Multimodal Contrastive Learning for fMRI-Based Brain Disorder Classification', Available at: https://omanscience.com/en/articles/fmri-tamcl-text-anchored-supervised-multimodal-contrastive-learning-for-fmri-based-brain-disorder-classification.
Vancouver
Danso JM, Gyamfi EO, Farias MCQ. fMRI-TAMCL: Text-Anchored Supervised Multimodal Contrastive Learning for fMRI-Based Brain Disorder Classification. https://omanscience.com/en/articles/fmri-tamcl-text-anchored-supervised-multimodal-contrastive-learning-for-fmri-based-brain-disorder-classification
IEEE
J. M. Danso, E. O. Gyamfi, and M. C. Q. Farias, "fMRI-TAMCL: Text-Anchored Supervised Multimodal Contrastive Learning for fMRI-Based Brain Disorder Classification," https://omanscience.com/en/articles/fmri-tamcl-text-anchored-supervised-multimodal-contrastive-learning-for-fmri-based-brain-disorder-classification.