Dementia

Machine learning prediction algorithms for 2- , 5- and 10-year risk of Alzheimer’s, Parkinson’s and dementia at age 65: a study using medical records from France and the UK General Practitioners

Leveraging machine learning on electronic health records offers a promising method for early identification of individuals at risk for dementia and neurodegenerative diseases. Current risk algorithms heavily rely on age, highlighting the need for alternative models with strong predictive power, especially at age 65, a crucial time for early screening and prevention. This prospective study analyzed...

Social Determinants of Health and Functional Brain Connectivity Predict Long-Term Physical Activity in Older Adults with a New Cardiovascular Diagnosis

Physical activity is essential for preventing cognitive decline, stroke and dementia in older adults. A new cardiovascular diagnosis offers a critical window for positive lifestyle changes. However, sustaining physical activity behavior change remains challenging and the underlying mechanisms are poorly understood. To identify the neural, behavioral and contextual predictors of successful longer-t...

SLaM Image Bank – a real-world diverse London cohort linking brain MRI to electronic mental health and dementia records for the development of clinical decision support tools using artificial intelligence

Rapid developments are occurring in artificial intelligence (AI) and machine learning (ML) applied to neuroimaging. To date, advances in this space ha...

Predicting Dementia in People with Parkinson’s Disease

Parkinson’s disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experiencing mild cognitive impairment and 45% developing ...

The genetics of TDP43-Type-C neurodegeneration: a whole genome sequencing study

Frontotemporal lobar degeneration-TDP Type C (TDP-C) is a unique neurodegenerative disease that starts by attacking the anterior temporal lobe leading...

VR-based Gamma Sensory Stimulation: A feasibility study

Alzheimer’s disease (AD) presents a critical global health challenge, with current therapies offering limited efficacy and safety in halting disease p...

Generalizable Prediction of Alzheimer Disease Pathologies with a Scalable Annotation Tool and an High-Accuracy Model

Characterizing the cardinal neuropathologies in Alzheimer disease (AD) can be laborious, time consuming, and susceptible to intra- and inter-observer ...

Explainable artificial intelligence for neuroimaging-based dementia diagnosis and prognosis

INTRODUCTION: Artificial intelligence and neuroimaging enable accurate dementia prediction, but ‘black box’ models can be difficult to trust. Explaina...

Short-Term Mortality After Opioid Initiation Among Opioid-Naïve and Non-Naïve Patients with Dementia: A Retrospective Cohort Study

Despite the ongoing opioid epidemic, the mortality risk of opioid initiation in patients with dementia or mild cognitive impairment (MCI) remains unde...

Transcriptomic analyses of human brains with Alzheimer’s disease identified dysregulated epilepsy-causing genes

Alzheimer’s Disease (AD) patients at multiple stages of disease progression have a high prevalence of seizures. However, whether AD and epilepsy share...

DUNE: a versatile neuroimaging encoder captures brain complexity across three major diseases: cancer, dementia and schizophrenia

Magnetic resonance images (MRI) of the brain exhibit high dimensionality that pose significant challenges for computational analysis. While models pro...

Investigating the causal network of dementia by employing a causal discovery approach combined with natural language processing models

Comprehensively studying modifiable risk factors altogether to explore how they contribute to dementia mechanism is imperative for effective intervent...

Leveraging functional annotations to map rare variants associated with Alzheimer’s disease with gruyere

The increasing availability of whole-genome sequencing (WGS) has begun to elucidate the contribution of rare variants (RVs), both coding and non-codin...

AgeNet-SHAP: An explainable AI approach for optimally mapping multivariate regional brain age and clinical severity patterns in Alzheimer’s disease

Age is a significant risk factor for mild cognitive impairment (MCI) and Alzheimer’s disease (AD) and identifying brain age patterns is critical for c...

Interpretable multivariate survival models: Improving predictions for conversion from mild cognitive impairment to Alzheimer’s disease (AD) via data fusion and machine learning

Accurately predicting which individuals with mild cognitive impairment (MCI) will progress to Alzheimer’s disease (AD) can improve patient care. This ...

Integration of Deep Learning Annotations with Functional Genomics Improves Identification of Causal Alzheimer’s Disease Variants

Genetic variants associated with Alzheimer’s disease (AD) through genome-wide association studies (GWAS) are challenging to interpret because most lie...

Machine Learning Classification of Mild Cognitive Impairment using Advanced Multi-Shell Diffusion MRI and CSF Biomarkers

Machine learning applied to neuroimaging can help with medical diagnosis and early detection by identifying biomarkers of subtle changes in brain stru...

Modeling the Spread of Misfolded Proteins in Alzheimer’s Disease using Higher-Order Simplicial Complex Contagion

Neurodegenerative diseases are characterized by complex proteins misfolded that propagate within the brain. For instance, current findings highlight t...

AutoRADP: An Interpretable Deep Learning Framework to Predict Rapid Progression for Alzheimer’s Disease and Related Dementias Using Electronic Health Records

Alzheimer’s disease (AD) and AD-related dementias (ADRD) exhibit heterogeneous progression rates, with rapid progression (RP) posing significant chall...

MRI2PET: Realistic PET Image Synthesis from MRI for Automated Inference of Brain Atrophy and Alzheimer’s

Positron Emission Tomography (PET) scans are a crucial tool in the diagnosing and monitoring of a number of complex conditions, including cancer, hear...