AI-driven fusion of multimodal data for Alzheimer’s disease biomarker assessment

Varuna H. Jasodanand, Sahana S. Kowshik, Shreyas Puducheri, Michael F. Romano, Lingyi Xu, Rhoda Au and Vijaya B. Kolachalama ()
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Varuna H. Jasodanand: Boston University Chobanian & Avedisian School of Medicine
Sahana S. Kowshik: Boston University Chobanian & Avedisian School of Medicine
Shreyas Puducheri: Boston University Chobanian & Avedisian School of Medicine
Michael F. Romano: University of California San Francisco
Lingyi Xu: Boston University Chobanian & Avedisian School of Medicine
Rhoda Au: Boston University Chobanian & Avedisian School of Medicine
Vijaya B. Kolachalama: Boston University Chobanian & Avedisian School of Medicine

Nature Communications, 2025, vol. 16, issue 1, 1-19

Abstract: Abstract Alzheimer’s disease (AD) diagnosis hinges on detecting amyloid beta (Aβ) plaques and neurofibrillary tau (τ) tangles, typically assessed using PET imaging. While accurate, these modalities are expensive and not widely accessible, limiting their utility in routine clinical practice. Here, we present a multimodal computational framework that integrates data from seven distinct cohorts comprising 12, 185 participants to estimate individual PET profiles using more readily available neurological assessments. Our approach achieved an AUROC of 0.79 and 0.84 in classifying Aβ and τ status, respectively. Predicted PET status was consistent with various biomarker profiles and postmortem pathology, and model-identified regional brain volumes aligned with known spatial patterns of tau deposition. This approach can support scalable pre-screening of candidates for anti-amyloid therapies and clinical trials targeting Aβ and τ, offering a practical alternative to direct PET imaging.

Date: 2025
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DOI: 10.1038/s41467-025-62590-4

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