Dementia

Leveraging a Foundation Model for the EEG-Based Diagnosis of Alzheimer's Disease

Biological heterogeneity in Alzheimer's Disease (AD) poses a critical diagnostic challenge, particularly for traditional linear methods that fail to capture non-linear neural dynamics. To address this, we propose a diagnostic framework utilizing the Large Brain Model (LaBraM), pretrained on over 2,500 hours of EEG data. By integrating these high-dimensional latent embeddings with a non-linear Rand...

Explainable Deep Learning Reveals Distributed Neurodegeneration Signatures of Neuropsychiatric Symptoms Across the Alzheimer's Continuum

Neuropsychiatric symptoms (NPS) are increasingly recognized as critical components of the disease progression in Alzheimer's disease (AD), yet their relationship with neurodegeneration remain poorly characterized. We investigated the multivariate relationships between structural MRI (sMRI)-based regional neurodegenerative biomarkers and NPS using the Alzheimer's Disease Neuroimaging Initiative (AD...

Integrating cognitive, linguistic and acoustic features to identify individuals with cognitive impairment: a proof-of-concept study

Early identification of cognitive impairment remains challenging in settings where comprehensive cognitive and clinical assessments are not available....

Behind the [MASK]: Disentangling Representation and Faithfulness in DAPF-Based Dementia Detection

Spoken-language analysis via prompt-based domain-adaptive models is a promising direction for low-resource, non-invasive dementia screening, but such ...

Cell type-resolved chromatin accessibility clocks for brain aging

Aging is a progressive decline in biological function that is proposed to be driven by the accumulation of epigenetic noise and the loss of epigenetic...

Spoken Recall Reveals Lexical and Mnemonic Function Differences in Temporal Lobe Epilepsy Patients

Background and Objectives: Word-finding difficulty is common in healthy aging and in neurologic disorders, including temporal lobe epilepsy (TLE) and ...

Glucagon-like peptide-1 receptor agonist initiation and risk of clinically recorded Alzheimer's disease-type dementia in older adults with type 2 diabetes: a target trial emulation using causal machine learning

Background Glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose cotransporter-2 (SGLT2) inhibitors are increasingly used for type 2 di...

Mapping Alzheimer's neuropathology signatures to the whole brain transcriptome using machine learning data fusion

In Alzheimer's disease (AD), misfolded proteins emerge across the entire brain in structured, yet not rigid, spatiotemporal patterns. Yet, a systemati...

Reliability and disease sensitivity are dissociable properties of EEG foundation-model representations

Abstract EEG foundation models (EEG-FMs) are evaluated almost entirely on disease-discrimination accuracy. A clinical biomarker additionally requires ...

Predicting Subjective Cognitive Decline on Future BRFSS Survey Years: An Open Multi-Language Machine Learning Benchmark

Background and Objectives: Subjective cognitive decline (SCD), self-reported worsening confusion or memory over the past year, is a common early marke...

Biomarker Fidelity Score - A Quantitative Framework for Individual-Level Validation of Explainability Methods in 3D Alzheimer's Disease MRI Classification

Explainability methods applied to deep learning models for Alzheimer's disease neuroimaging produce attribution maps that vary substantially across me...

Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum

Alzheimer's disease (AD) progresses as a continuous biological process, whereas most existing neuroimaging-based artificial intelligence methods remai...

Delta2Gamma: Band-Wise Adaptive Contrastive Learning of EEG for Alzheimer's Disease Detection

Low-cost, scalable screening for dementia remains an open problem. Imaging-based diagnosis is costly and hard to deploy widely. Electroencephalography...

Low-dimensional factorized neural computations underlie risk-adaptive choices

Real-world decision-making rarely occurs with perfect information. Instead, individuals must constantly weigh potential rewards against the probabilit...

A Comprehensive Benchmark of EEG-Based BCI Deep Learning Models for MCI and Dementia Classification

Electroencephalography (EEG) is a promising tool for automated detection of mild cognitive impairment (MCI) and dementia, but comparisons across studi...

Data-driven techniques for translational neuroscience and personalized neuro-health

Neurodegenexrative diseases such as Alzheimer's disease and Parkinson's disease are diagnosed most reliably only after substantial, often irreversible...

A comparison of CNN architectures for Alzheimer's disease detection in single-view MRI scans

Alzheimer's disease is a leading cause of death with no cure. Therefore, early detection is critical to slow progression and preserve quality of life....

Parcel2Progression: An Anatomy-aware Longitudinal Framework for Alzheimer's Disease Diagnosis

Alzheimer's disease (AD) progression is a longitudinal process with subtle pathological cues in the early stages. Yet, computational constraints have ...

Decoding Phenotypes: A Framework for Fusing Genomic Language Models and Neuroimaging

Neuroimaging and genetic testing are two important clinical references for nervous system diseases, offering complementary diagnostic information. How...

Predicting Cognitive Function Using Transformer-Derived Speech Representations and Longitudinal Coherence Features

Dementia affects more than 55 million people worldwide, and its progressive decline is difficult to track using infrequent in-person assessments, whic...