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Multimodal Deep Learning for Dynamic and Static Neuroimaging: Integrating MRI and fMRI for Alzheimer Disease Analysis
Anima Kujur, Zahra Monfared. Journal of Machine Learning for Modeling and Computing, 2026-01-01
Topic: Amyloid, tau and blood-test diagnosis
Magnetic Resonance Imaging (MRI) provides detailed structural information, while functional MRI (fMRI) captures temporal brain activity. In this work, we present a multimodal deep learning framework that integrates MRI and fMRI for multi-class classification of Alzheimer's Disease (AD), Mild Cognitive Impairment, and Normal Cognitive State. Structural features are extracted from MRI using 3D convolutional neural networks, while temporal features are learned from fMRI sequences using recurrent architectures. These representations are fused to enable joint spatial-temporal learning. Experiments were conducted on a small paired MRI-fMRI dataset (29 subjects), both with and without data augmentation. Results show that data augmentation substantially improves classification stability and generalization, particularly for the multimodal 3DCNN-LSTM model. In contrast, augmentation was found to be ineffective for a large-scale single-modality MRI dataset. These findings highlight the importance of dataset size and modality when designing augmentation strategies for neuroimaging-based AD classification.
Key findings
- Results show that data augmentation substantially improves classification stability and generalization, particularly for the multimodal 3DCNN-LSTM model.
- These findings highlight the importance of dataset size and modality when designing augmentation strategies for neuroimaging-based AD classification.
Why it matters
A multimodal deep learning framework combined MRI and fMRI for classifying AD, MCI, and normal cognition, and data augmentation improved stability on a small paired dataset of 29 subjects. The small sample size makes this preliminary, and it addresses diagnosis rather than treatment or cure.
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Study details
- Participants29
- Evidence strengthLow
How this study scored 32/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of Machine Learning for Modeling and Computing) |
| Journal's citation record | 0 / 4 | Journal of Machine Learning for Modeling and Computing: journal h-index 18 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 1 / 4 | 29 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.0 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 0 / 6 | Cited 0.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Zahra Monfared: h-index 7 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Heidelberg University: institution h-index 1,344 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Total | 32 / 100 | — |
What each of these 20 components measures, and what this score is not →
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Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Kujur, A., & Monfared, Z. (2026). Multimodal Deep Learning for Dynamic and Static Neuroimaging: Integrating MRI and fMRI for Alzheimer Disease Analysis. Journal of Machine Learning for Modeling and Computing. https://doi.org/10.1615/jmachlearnmodelcomput.2026065081
Vancouver
Kujur A, Monfared Z. Multimodal Deep Learning for Dynamic and Static Neuroimaging: Integrating MRI and fMRI for Alzheimer Disease Analysis. Journal of Machine Learning for Modeling and Computing. 2026. doi:10.1615/jmachlearnmodelcomput.2026065081.
BibTeX
@article{kujur2026-dff82a10,
author = {Kujur, Anima and Monfared, Zahra},
title = {{Multimodal Deep Learning for Dynamic and Static Neuroimaging: Integrating MRI and fMRI for Alzheimer Disease Analysis}},
journal = {Journal of Machine Learning for Modeling and Computing},
year = {2026},
doi = {10.1615/jmachlearnmodelcomput.2026065081},
url = {https://doi.org/10.1615/jmachlearnmodelcomput.2026065081}
}