Dimensional clinical phenotyping using post-mortem brain donor medical records: post-mortem RDoC profiling is associated with Alzheimer's disease neuropathology

Transdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to work, as assessments of phenotypic concepts need to rely on existing records. We adapted well-validated methodologie...

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Published in:Alzheimer's & dementia : diagnosis, assessment & disease monitoring assessment & disease monitoring, 2023-07, Vol.15 (3), p.e12464-n/a
Main Authors: Vogelgsang, Jonathan, Dan, Shu, Lally, Anna P, Chatigny, Michael, Vempati, Sangeetha, Abston, Joshua, Durning, Peter T, Oakley, Derek H, McCoy, Thomas H, Klengel, Torsten, Berretta, Sabina
Format: Article
Language:English
Subjects:
Algorithms
Alzheimer's disease
biological specimen banks
Brain research
Clinical outcomes
Cognition & reasoning
Dementia
Electronic health records
Machine learning
medical informatics
Medical records
Natural language processing
Neuropathology
Pathophysiology
Review boards
Short Report
Short Reports
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Summary:Transdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to work, as assessments of phenotypic concepts need to rely on existing records. We adapted well-validated methodologies to compute National Institute of Mental Health Research Domain Criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from brain donors and tested whether cognitive domain scores were associated with Alzheimer's disease neuropathological measures. Our results confirm an association of EHR-derived cognitive scores with neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal (ß = 0.38,  = 0.0004), parietal (ß = 0.35, P = 0.0008), temporal (ß = 0.37, P = 0.0004) and occipital (ß = 0.37, P = 0.0003) lobes. This proof-of-concept study supports the validity of NLP-based methodologies to obtain quantitative measures of RDoC clinical domains from EHR. The associations may accelerate brain research beyond classical case-control designs.
ISSN:2352-8729
2352-8729
DOI:10.1002/dad2.12464