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Alzheimer’s disease (AD) diagnosis hinges on detecting amyloid beta (A β ) 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 A β 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 A β and τ , offering a practical alternative to direct PET imaging. A flexible AI framework integrates multimodal neurology work-up data to estimate amyloid and tau burden, supporting scalable biomarker stratification for Alzheimer’s disease research and trial screening.

Advancing the understanding and management of Alzheimer's disease and related dementias requires integrating and analyzing diverse data modalities. Traditional diagnostic tools, like neuroimaging, provide valuable insights but are limited by accessibility and infrastructure demands. Meanwhile, emerging modalities, including wearable sensors and speech analysis, enable less invasive and more continuous data collection but introduce challenges related to standardization and privacy. The coexistence of these heterogeneous data streams complicates multimodal integration across cohorts, populations, and clinical settings. Current analytical approaches typically require modality‐specific preprocessing pipelines and harmonization methods that were not designed to accommodate modern AI‐based capabilities, such as multimodal fusion. In this perspective, we propose an “AI‐first” strategy for multimodal data integration that aligns data structuring, harmonization, and modeling within a unified set of guiding principles to optimize modern AI development, while remaining flexible enough to support classical analytical approaches. Highlights Understanding and managing ADRD requires integrating biological, cognitive, and behavioral data across multiple modalities. Incorporating multiple modalities requires new standards for harmonization and interoperability. Current data platforms are not necessarily built to support multimodal fusion or generalizable AI models across diverse ADRD populations. Modern AI models are capable of learning from messy, multimodal, and incomplete data but require infrastructure designed for this purpose. We propose rethinking ADRD data systems to prioritize AI compatibility, enabling scalable tools for early diagnosis and longitudinal care.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care. Drawing on 51,269 participants across 9 independent, geographically diverse datasets, an AI model identifies the etiologies contributing to dementia in individuals, harnessing a broad array of data, including demographics, medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging.

The proliferation of scientific podcasts has generated an extensive repository of educational content, rich in specialized terminology, diverse topics, and expert dialogues. Here, we introduce a computational framework designed to enhance large language models by leveraging this informational content from publicly accessible audio podcasts across science, technology, engineering, mathematics, and medicine (STEMM). This dataset, comprising over 3700 hours of audio content, was transcribed to generate over 42 million text tokens. Our model, PodGPT, integrates this wealth of complex dialogue found in audio podcasts to improve understanding of natural language nuances, cultural contexts, as well as scientific and medical knowledge. PodGPT also employs retrieval augmented generation (RAG) on a vector database, providing real-time access to emerging scientific literature. Evaluated on multiple benchmarks, PodGPT demonstrated an average improvement of 1.82 percentage points over standard open-source benchmarks and 2.43 percentage points when augmented with evidence from the RAG pipeline. Moreover, it showcased an average improvement of 1.18 percentage points in its zero-shot multilingual transfer ability, effectively generalizing to different linguistic contexts. By harnessing the untapped potential of podcast content, PodGPT advances natural language processing and conversational AI, offering enhanced capabilities for STEMM research and education.

Introduction It is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging. Methods Diffusion MRI data from several well‐established longitudinal cohorts of aging (Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], Vanderbilt Memory & Aging Project [VMAP]) were free‐water corrected and harmonized. This dataset included 1723 participants (age at baseline: 72.8 ± 8.87 years, 49.5% male) and 4605 imaging sessions (follow‐up time: 2.97 ± 2.09 years, follow‐up range: 1–13 years, mean number of visits: 4.42 ± 1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed. Results While we found a global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging. Conclusions There is a prevalent role of white matter microstructural decline in aging, and future large‐scale studies in this area may further refine our understanding of the underlying neurodegenerative processes. HIGHLIGHTS Longitudinal data were free‐water corrected and harmonized. Global effects of white matter decline were seen in normal and abnormal aging. The free‐water metric was most vulnerable to abnormal aging. Cingulum free‐water was the most vulnerable to abnormal aging.

Introduction White matter microstructure may be abnormal along the Alzheimer's disease (AD) continuum. Methods Diffusion magnetic resonance imaging (dMRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 627), Baltimore Longitudinal Study of Aging (BLSA, n = 684), and Vanderbilt Memory & Aging Project (VMAP, n = 296) cohorts were free‐water (FW) corrected and conventional, and FW‐corrected microstructural metrics were quantified within 48 white matter tracts. Microstructural values were subsequently harmonized using the Longitudinal ComBat technique and inputted as independent variables to predict diagnosis (cognitively unimpaired [CU], mild cognitive impairment [MCI], AD). Models were adjusted for age, sex, race/ethnicity, education, apolipoprotein E (APOE) ε4 carrier status, and APOE ε2 carrier status. Results Conventional dMRI metrics were associated globally with diagnostic status; following FW correction, the FW metric itself exhibited global associations with diagnostic status, but intracellular metric associations were diminished. Discussion White matter microstructure is altered along the AD continuum. FW correction may provide further understanding of the white matter neurodegenerative process in AD. Highlights Longitudinal ComBat successfully harmonized large‐scale diffusion magnetic resonance imaging (dMRI) metrics. Conventional dMRI metrics were globally sensitive to diagnostic status. Free‐water (FW) correction mitigated intracellular associations with diagnostic status. The FW metric itself was globally sensitive to diagnostic status. Multivariate conventional and FW‐corrected models may provide complementary information.

It is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging. Diffusion MRI data from several well-established longitudinal cohorts of aging [Alzheimer's Neuroimaging Initiative (ADNI), Baltimore Longitudinal Study of Aging (BLSA), Vanderbilt Memory & Aging Project (VMAP)] was free-water corrected and harmonized. This dataset included 1,723 participants (age at baseline: 72.8±8.87 years, 49.5% male) and 4,605 imaging sessions (follow-up time: 2.97±2.09 years, follow-up range: 1-13 years, mean number of visits: 4.42±1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed. While we found global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging. There is a prevalent role of white matter microstructural decline in aging, and future large-scale studies in this area may further refine our understanding of the underlying neurodegenerative processes. Longitudinal data was free-water corrected and harmonizedGlobal effects of white matter decline were seen in normal and abnormal agingThe free-water metric was most vulnerable to abnormal agingCingulum free-water was the most vulnerable to abnormal aging.