AI-driven fusion of multimodal data for Alzheimer’s disease biomarker assessment
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-62590-4 |
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| author | Varuna H. Jasodanand Sahana S. Kowshik Shreyas Puducheri Michael F. Romano Lingyi Xu Rhoda Au Vijaya B. Kolachalama |
| author_facet | Varuna H. Jasodanand Sahana S. Kowshik Shreyas Puducheri Michael F. Romano Lingyi Xu Rhoda Au Vijaya B. Kolachalama |
| author_sort | Varuna H. Jasodanand |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-edbe0b4f76a7426d87d2dfa4502830bc |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-edbe0b4f76a7426d87d2dfa4502830bc2025-08-20T03:05:10ZengNature PortfolioNature Communications2041-17232025-08-0116111910.1038/s41467-025-62590-4AI-driven fusion of multimodal data for Alzheimer’s disease biomarker assessmentVaruna H. Jasodanand0Sahana S. Kowshik1Shreyas Puducheri2Michael F. Romano3Lingyi Xu4Rhoda Au5Vijaya B. Kolachalama6Department of Medicine, Boston University Chobanian & Avedisian School of MedicineDepartment of Medicine, Boston University Chobanian & Avedisian School of MedicineDepartment of Medicine, Boston University Chobanian & Avedisian School of MedicineDepartment of Radiology & Biomedical Imaging, University of California San FranciscoDepartment of Medicine, Boston University Chobanian & Avedisian School of MedicineDepartment of Medicine, Boston University Chobanian & Avedisian School of MedicineDepartment of Medicine, Boston University Chobanian & Avedisian School of MedicineAbstract 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.https://doi.org/10.1038/s41467-025-62590-4 |
| spellingShingle | Varuna H. Jasodanand Sahana S. Kowshik Shreyas Puducheri Michael F. Romano Lingyi Xu Rhoda Au Vijaya B. Kolachalama AI-driven fusion of multimodal data for Alzheimer’s disease biomarker assessment Nature Communications |
| title | AI-driven fusion of multimodal data for Alzheimer’s disease biomarker assessment |
| title_full | AI-driven fusion of multimodal data for Alzheimer’s disease biomarker assessment |
| title_fullStr | AI-driven fusion of multimodal data for Alzheimer’s disease biomarker assessment |
| title_full_unstemmed | AI-driven fusion of multimodal data for Alzheimer’s disease biomarker assessment |
| title_short | AI-driven fusion of multimodal data for Alzheimer’s disease biomarker assessment |
| title_sort | ai driven fusion of multimodal data for alzheimer s disease biomarker assessment |
| url | https://doi.org/10.1038/s41467-025-62590-4 |
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