Neurology

Prediction of survival in patients with spinal metastatic disease.

The spine is among the most frequent sites of metastatic disease, with an increasing prevalence, leading to increasing rates of surgical interventions. The choice of surgical technique (ranging from minimally invasive techniques, to radical resection), is usually based on expected survival, treatment-related morbidity, and outcome after treatment. A number of scoring systems have been developed to...

Opportunities and challenges related to participant stratification and cohort enrichment in ALS clinical trials.

Amyotrophic lateral sclerosis (ALS) is marked by substantial clinical heterogeneity. This heterogeneity has impacted clinical trials by obscuring treatment effects and causing inefficiency. In this review, we summarize potential approaches for addressing heterogeneity in ALS via patient stratification and cohort enrichment methods and highlight potential challenges and limitations. These categorie...

Dementia screening with the Rivermead Behavioural Memory Test: A machine learning analysis.

BackgroundWith the advent of anti-amyloid-β monoclonal antibody therapies and the growing societal burden of dementia, early identification of Alzheim...

Brain PET in the era of anti-amyloid-β antibody therapy for Alzheimer disease.

Here the current and emerging roles of brain positron emission tomography (PET) in Alzheimer's disease (AD) in the era of anti-amyloid-β antibody ther...

Comment on "Exploration of a Multimodal Machine Learning Model Integrating Ultrasound and Clinical Indicators for the Diagnosis of Diabetic Peripheral Neuropathy".

Adaptive neighborhood aggregation graph neural network for early diagnosis of Alzheimer's disease.

Early diagnosis of Alzheimer's disease (AD) is crucial for timely intervention but remains challenging due to subtle and heterogeneous brain alteratio...

A frontotemporal dementia-like phenotype in schizophrenia: links to striatal dopamine and iron accumulation.

Some schizophrenia patients share characteristics with behavioral variant frontotemporal dementia (bvFTD) including gray matter volume (GMV) similarit...

Coupled cross-sectional and longitudinal non-negative matrix factorization reveals dominant brain aging trajectories in 48,949 individuals.

Machine learning can unravel heterogeneous patterns of brain aging and neurodegeneration, but existing methods offer limited insights into disease pro...

Longitudinal assessment of chorea in Huntington's disease using digital passive monitoring.

Chorea is a hallmark of Huntington's disease (HD), yet its clinical assessment is of limited reliability, impairing our ability to sensitively evaluat...

MMP9 as a shared immune-related gene in Alzheimer's and Huntington's diseases: a cross-tissue transcriptomic analysis.

Alzheimer's disease (AD) and Huntington's disease (HD) share neuroinflammatory mechanisms, yet their specific immune microenvironments remain poorly u...

Causal ordinal connections based characterization of weighted effective brain network for schizophrenia detection.

The human brain is responsible for a wide range of a person's behavioral and cognitive capabilities. The functionality of the brain is affected by var...

Alzheimer's disease with progression analysis using a novel dilated convolutional attention based long short term memory model.

Alzheimer's disease (AD) is an irreversible neurodegenerative syndrome that affects memory, cognitive abilities and behaviour. Detecting AD in the ear...

High-resolution 3D flow reconstruction of cerebrospinal fluid microcirculation using physics-informed neural network: Conceptualization and application to a large animal model.

In this study, we present a novel Physics-Informed Neural Network (PINN) framework that reconstructs 3D flows using planar velocity projections from a...

Systematic review of machine learning and deep learning models for EEG-based detection of depression.

OBJECTIVE: Depression is a leading cause of global disability, motivating the development of objective and scalable diagnostic approaches. Quantitativ...

Baseline brain volumes predict cognitive decline more robustly than atrophy rates: Evidence for brain reserve.

The concept of brain reserve offers a framework for understanding the mechanisms of cognitive resilience in later life. This study investigated whethe...

Mapping knowledge structure and emerging trends in non-invasive brain-computer interface for stroke rehabilitation.

OBJECTIVE: To explore the current research landscape and emerging frontiers in the application of non‑invasive brain-computer interface (BCI) technolo...

Association of MRI-Visible Perivascular Spaces With Longitudinal Cognitive Decline Over a Decade.

BACKGROUND AND OBJECTIVES: Cerebral small vessel disease (SVD) is the most common vascular contributor to dementia. SVD markers often coexist, contrib...

EEG-based schizophrenia detection using handcrafted biomarkers and a TOA-optimized hybrid multi-branch CNN-Transformer framework.

Schizophrenia is a chronic psychiatric disorder for which electroencephalography (EEG) offers a low-cost, non-invasive window into abnormal neural dyn...

Machine learning-based prediction of Familial Hemiplegic Migraine risk from genetic variants.

BACKGROUND: Familial Hemiplegic migraine (FHM) is a rare migraine subtype characterised by transient unilateral motor weakness. Although familial form...

Generalizable spinal cord multiple sclerosis lesion segmentation across MRI contrasts, protocols, and centers.

BACKGROUND/OBJECTIVES: Characterizing spinal cord multiple sclerosis (MS) lesions in MRI is critical for diagnosis, monitoring, and treatment evaluati...