Home › Research › Cognitive tests and assessment
Heterogeneity in Multimodal AI Feature Engineering Utility for Explaining Change in Dementia
Espero MJ.. PsyArXiv, 2026-08-01
Topic: Brain imaging, connectivity and assessment
The present work builds upon progress in foundation artificial intelligence (AI) models for thepurpose of supporting the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Open-weight largelanguage models (LLMs) are employed (collaborative inference from eight labs) for AI magneticresonance imaging (MRI) insight extraction and compared in terms of relative feature importance(between-model, between-feature) at the AI-engineered parameter-level (along with other engineeredmeta-features) for relative explanatory utility with regard to participant change in an assessment of mildcognitive impairment (MCI) and dementia. A gradient boosting machine (GBM) suggests severalneuroimage-inferred dimensions extracted by (2 of 8) LLMs outperform all others in explanatory utility(except for baseline clinical dementia rating) including inferred representations of Alzheimer's (AD)probability, small vessel disease burden, cortical atrophy, and cognitive impairment severity. Ak-means-derived cluster feature corroborates among the high importance neuroimage features,suggesting multimodal superiority in the context of the present work. Moreover, given the estimatedvariation in feature importance (beyond the vision superiority evidence) by LLM, input features, andextracted dimensions, the secondary finding suggests heterogeneity with regard to explanatory utilityacross a class of clinically relevant composite dimensions extracted and evaluated between LLM andbetween inputs in the context of a predictive model. Furthermore, unambiguous explanatory importanceis suggested with regard to AD probability and (neuroimage) novelty/atypicality given the estimatedvalue for compression of these AI-inferred dimensions - regardless of source of inference.
Key findings
- Open-weight largelanguage models (LLMs) are employed (collaborative inference from eight labs) for AI magneticresonance imaging (MRI) insight extraction and compared in terms of relative feature importance(between-model, between-feature) at the AI-engineered parameter-level (along with other engineeredmeta-features) for relative explanatory utility with regard to participant change in an assessment of mildcognitive impairment (MCI) and dementia.
- A gradient boosting machine (GBM) suggests severalneuroimage-inferred dimensions extracted by (2 of 8) LLMs outperform all others in explanatory utility(except for baseline clinical dementia rating) including inferred representations of Alzheimer's (AD)probability, small vessel disease burden, cortical atrophy, and cognitive impairment severity.
- Ak-means-derived cluster feature corroborates among the high importance neuroimage features,suggesting multimodal superiority in the context of the present work.
- Moreover, given the estimatedvariation in feature importance (beyond the vision superiority evidence) by LLM, input features, andextracted dimensions, the secondary finding suggests heterogeneity with regard to explanatory utilityacross a class of clinically relevant composite dimensions extracted and evaluated between LLM andbetween inputs in the context of a predictive model.
- Furthermore, unambiguous explanatory importanceis suggested with regard to AD probability and (neuroimage) novelty/atypicality given the estimatedvalue for compression of these AI-inferred dimensions - regardless of source of inference.
Study details
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 21 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 13 of the 62 points we could judge it on, which is 21 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (PsyArXiv) |
| 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 | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| 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 | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 13 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 13 of the 62 points we could judge it on is 21 out of 100. |
| Total | 21 / 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
Free copy unknown: no free-copy check has an answer for this record yet. That does not mean it is paywalled.
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. Check the citation against the original before you use it.
APA 7th
Espero, M. J. (2026). Heterogeneity in Multimodal AI Feature Engineering Utility for Explaining Change in Dementia. PsyArXiv. https://doi.org/10.31234/osf.io/s58bj_v1
Vancouver
Espero MJ. Heterogeneity in Multimodal AI Feature Engineering Utility for Explaining Change in Dementia. PsyArXiv. 2026. doi:10.31234/osf.io/s58bj_v1.
BibTeX
@article{espero2026-ef7df3b7,
author = {Espero, M. J.},
title = {{Heterogeneity in Multimodal AI Feature Engineering Utility for Explaining Change in Dementia}},
journal = {PsyArXiv},
year = {2026},
doi = {10.31234/osf.io/s58bj_v1},
url = {https://doi.org/10.31234/osf.io/s58bj_v1}
}
Related studies in Cognitive tests and assessment
Also tagged “convolutional”
205 studies in the library carry the topic tag “convolutional”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 205 tagged studies.