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Explainable Deep Learning Reveals Distributed Neurodegeneration Signatures of Neuropsychiatric Symptoms Across the Alzheimer's Continuum
Yaghooti, B.; Ishrat, S.; Le, H. N.; Sapkota, R. P.; Murad, T.; Thakuri, D. S.; Wong, D. F.; Aschenbrenner et al.. medRxiv (preprint), 2026-08-27
Topic: Amyloid, tau and blood-test diagnosis
Neuropsychiatric symptoms (NPS) are increasingly recognized as critical components of the disease progression in Alzheimers disease (AD), yet their relationship with neurodegeneration remain poorly characterized. We investigated the multivariate relationships between structural MRI (sMRI)-based regional neurodegenerative biomarkers and NPS using the Alzheimers Disease Neuroimaging Initiative (ADNI) and Knight Alzheimer Disease Research Center (Knight-ADRC) cohorts (N = 1,756). The machine learning regression models were compared for NPS prediction, and the best-performing deep neural network (named NPSNet) was integrated with three feature-importance methods: SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Layer-wise Relevance Propagation (LRP). To establish a known ground truth, we introduced predefined regional perturbations into semi-simulated data and tested whether NPSNetSHAP, NPSNetLIME, and NPSNetLRP could recover them. The NPSNet strongly predicted NPS scores (pooled Spearman{rho} = 0.927, p < 2.2 x 10-3; fold-wise{rho} = 0.912 - 0.963) and NPSNetSHAP recovered all 100% perturbed regions, compared with 90% for NPSNetLIME and 40% for NPSNetLRP. In the experimental data (N = 1,756), the NPSNet produced the highest held-out correlation ({rho} = 0.387, p = 1.7 x 10-{superscript 1}), exceeding gradient boosting ({rho} = 0.301), support vector regression ({rho} = 0.266), and others ({rho} < 0.266). NPSNetSHAP identified individual-level regional contribution patterns relevant to NPS predictions. Comparing NPSNetSHAP attributions between cognitively normal (CN) and mild cognitive impairment (MCI)/AD groups revealed distributed multivariate neurodegenerative signatures of NPS, with the largest differences between CN and AD participants. This study introduces an explainable deep learning framework for identifying distributed, individualized neurodegeneration signatures of NPS burden across the AD continuum.
Key findings
- The NPSNet strongly predicted NPS scores (pooled Spearman{rho} = 0.927, p < 2.2 x 10-3; fold-wise{rho} = 0.912 - 0.963) and NPSNetSHAP recovered all 100% perturbed regions, compared with 90% for NPSNetLIME and 40% for NPSNetLRP.
- Comparing NPSNetSHAP attributions between cognitively normal (CN) and mild cognitive impairment (MCI)/AD groups revealed distributed multivariate neurodegenerative signatures of NPS, with the largest differences between CN and AD participants.
Why it matters
This deep learning study found that structural MRI-based neurodegeneration patterns predicted neuropsychiatric symptom scores and revealed distributed signatures differing most between cognitively normal and Alzheimer's participants. It is a modelling approach to understanding symptoms, not a treatment or cure.
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Study details
- Participants1,756
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 28 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on impact for its field and year, so that was left out: it earned 26 of the 94 points we could judge it on, which is 28 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv (preprint)) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| 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 | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 3 / 4 | 1,756 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 | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| 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 Ganesh B. Chand: h-index 17 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Washington University in St. Louis: institution h-index 1,962 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 2 countries |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Points | 26 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 26 of the 94 points we could judge it on is 28 out of 100. |
| Total | 28 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Yaghooti, B., Ishrat, S., Le, H. N., Sapkota, R. P., Murad, T., Thakuri, D. S., et al. (2026). Explainable Deep Learning Reveals Distributed Neurodegeneration Signatures of Neuropsychiatric Symptoms Across the Alzheimer's Continuum. medRxiv (preprint). https://doi.org/10.64898/2026.08.24.26361256
Vancouver
Yaghooti B, Ishrat S, Le HN, Sapkota RP, Murad T, Thakuri DS, et al. Explainable Deep Learning Reveals Distributed Neurodegeneration Signatures of Neuropsychiatric Symptoms Across the Alzheimer's Continuum. medRxiv (preprint). 2026. doi:10.64898/2026.08.24.26361256.
BibTeX
@article{yaghooti2026-300ee1e9,
author = {Yaghooti, B. and Ishrat, S. and Le, H. N. and Sapkota, R. P. and Murad, T. and Thakuri, D. S. and Wong, D. F. and Aschenbrenner and others},
title = {{Explainable Deep Learning Reveals Distributed Neurodegeneration Signatures of Neuropsychiatric Symptoms Across the Alzheimer's Continuum}},
journal = {medRxiv (preprint)},
year = {2026},
doi = {10.64898/2026.08.24.26361256},
url = {https://doi.org/10.64898/2026.08.24.26361256}
}
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