Neurology

The impact of EEG preprocessing parameters on ultra-low-power seizure detection

Closed-loop neurostimulation is a promising treatment for drug-resistant focal epilepsy. A major challenge is fast and reliable seizure detection via electroencephalography (EEG). While many approaches have been published, they often lack statistical power and practical utility. The use of various EEG preprocessing parameters and performance metrics hampers comparability. Additionally, the critica...

Comprehensive Cerebral Aneurysm Rupture Prediction: From Clustering to Deep Learning

Cerebral aneurysm is a silent yet prevalent condition that affects a substantial portion of the global population. Aneurysms can develop due to various factors and present differently, necessitating diverse treatment approaches. Choosing the appropriate treatment upon diagnosis is paramount, as the severity of the disease dictates the course of action. The vulnerability of an aneurysm, particularl...

Epigenetic signatures of regional tau pathology and cognition in the aging and pathological brain

Primary age-related tauopathy (PART) and Alzheimer’s disease (AD) share hippocampal phospho-tau (p-tau) pathology but differ in ß-amyloid burden and d...

Evaluating the Potential of AI-Generated Synthetic Diaries in Parkinson’s Disease Research

The integration of Artificial Intelligence (AI), particularly large language models like GPT-4o, into Parkinson’s Disease (PD) research presents a nov...

AENEAS Project: Machine Vision-Based Real-Time Anatomy Detection. Application to the Pterional Trans-Sylvian Approach

Surgical success hinges on two core factors: technical execution and cognitive planning. While the former can be trained and potentially augmented thr...

Anatomy-guided, modality-agnostic segmentation of neuroimaging abnormalities

Magnetic resonance imaging (MRI) offers multiple sequences that provide complementary views of brain anatomy and pathology. However, real-world datase...

PyHFO 2.0: An Open-Source Platform for Deep Learning–Based Clinical High-Frequency Oscillations Analysis

Accurate detection and classification of high-frequency oscillations (HFOs) in electroencephalography (EEG) recordings have become increasingly import...

A Task-Optimized Approach for High-Accuracy Alzheimer’s Diagnosis from Handwriting Data

Training complex models on Alzheimer’s Disease (AD) datasets is challenging due to the costly process of extracting features from a wide range of pati...

Contextualized Biomedical Language Processing Enhances ICU Survival Prediction

Cerebrospinal fluid (CSF) culture is the diagnostic gold standard for neuroinfectious diseases such as bacterial meningitis, but its sensitivity is li...

Probabilistic Mapping and Automated Segmentation of Human Brainstem White Matter Bundles

Brainstem white matter bundles are essential conduits for neural signaling involved in modulation of vital functions ranging from homeostasis to human...

From Lab to Life: Enhancing Wearable Airbag Fall Detection Performance with Minimal Real-World Data

Falls are the leading cause of accidental injury or death among older adults, particularly those with neurological conditions like stroke or Parkinson...

DISCO: A Delta-NIHSS-Based Machine Learning Model for Predicting Recurrence, Disability, and Mortality Following Acute Ischemic Stroke

An accurate, robust, clinically accessible, and explainable predictive model for post-stroke composite outcomes could identify high-risk patients for ...

A Clinical Neuroimaging Platform for Rapid, Automated Lesion Detection and Personalized Post-Stroke Outcome Prediction

Predicting long-term functional outcomes for individuals with stroke is a significant challenge. Solving this challenge will open new opportunities fo...

Leveraging Large Language Models for Identifying Interpretable Linguistic Markers and Enhancing Alzheimer’s Disease Diagnostics

Alzheimer’s Disease (AD) is a progressive irreversible neurodegenerative disorder. Early AD detection is crucial for timely intervention. This study p...

Loneliness, Social Isolation, and Effects on Cognitive Decline in Patients with Dementia: A Retrospective Cohort Study Using Natural Language Processing

The study aimed to compare cognitive trajectories between patients with reports of social isolation and loneliness and those without. Reports of socia...

Longitudinal Assessment of DNA Repair Signature Trajectory in Prodromal versus Established Parkinson’s Disease

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms. DNA repair dysfunction and integra...

Towards Causal Interpretability in Deep Learning for Parkinson’s Detection from Voice Data

This research introduces a comprehensive framework for Parkinson’s Disease (PD) detection using voice recording data. We implemented and evaluated mul...

From subthalamic local field potentials to the selection of chronic deep brain stimulation contacts in Parkinson’s disease - A systematic review

Programming deep brain stimulation (DBS) of the subthalamic nucleus for optimal symptom control in Parkinson’s Disease (PD) requires time and trained ...

Machine Learning-Enabled EEG Biomarkers Predict Divergent Antidepressant and Placebo Response in a Clinical Trial of Major Depression

Major depressive disorder (MDD) is a heterogeneous neuropsychiatric disorder with highly variable antidepressant outcomes. In randomized controlled tr...

Automated detection of bottom-of-sulcus dysplasia on MRI-PET in patients with drug-resistant focal epilepsy

Bottom-of-sulcus dysplasia (BOSD) is a diagnostically challenging subtype of focal cortical dysplasia, 60% being missed on patients’ first MRI. Automa...