Neurology

Investigating AD and non-AD [18F]flortaucipir distribution patterns in a memory clinic cohort.

PURPOSE: Amyloid (A) deposition represents a specific pathological hallmark of Alzheimer's disease (AD). Clinical diagnostic protocols frequently rely on the combined use of amyloid (A)- and tau (T)- positron emission tomography (PET) imaging to distinguish AD from non-amyloid-associated neurodegenerative conditions. Many neurodegenerative disorders are characterized by distinct tauopathies, which...

A Multiscale Decoding Approach of Subject-Independent Motor Imagery EEG Signal Combined with Data Alignment Strategy.

PURPOSE: Brain-computer interface (BCI) leverages artificial intelligence (AI) and wearable electroencephalography (EEG) sensors to decode brain signals, significantly enhancing quality of life. EEG-based motor imagery (MI) brain signals are widely used in various BCI applications, including smart healthcare, intelligent vehicle, smart homes, and robotics control. However, the substantial individu...

Diagnosis and Prediction of Alzheimer's Disease via a High-Level Convolutional Block Attention Module-Residual Network.

With the rapid development of artificial intelligence and medical image analysis, MRI-based automated diagnosis has provided an effective approach for...

Acoustic Analysis of Primary Care Patient-Clinician Conversations to Screen for Cognitive Impairment.

IMPORTANCE: Cognitive impairment (CI) is often underdetected in primary care due to time and resource constraints. Passive analysis of clinical dialog...

From Ambient Assistance to Data-Driven Precision in AI-Enabled Alzheimer's Care (2004-2025): A Bibliometric Perspective.

BACKGROUND AND OBJECTIVES: Population aging and the rising prevalence of Alzheimer's Disease and Related Dementias (AD/ADRD) have created an urgent ne...

Variational autoencoder for interpretable seizure onset phases detection.

OBJECTIVE: In this study, we describe a deep learning framework for automated seizure annotation in stereo electroencephalography (SEEG) data of patie...

From prediction to mechanism: Explainable AI uncovers plasma and CSF proteomic signatures of Alzheimer's disease.

Alzheimer's disease (AD) plasma and cerebrospinal fluid (CSF) proteomics can distinguish AD from cognitively normal controls, but the generalizability...

A novel neural network model with SAGPooling graph decodes changes in cognitive trajectories.

Accurately predicting the progression of neurodegenerative diseases and identifying key brain regions influencing the disease course are critical rese...

Artificial intelligence-based retinal imaging for brain health assessment: a scoping review.

Brain health is an emerging and rapidly expanding interdisciplinary field that requires optimisation across the life course of a person. The retina se...

Interpretable Machine Learning Model for Individualized Survival Prediction of Multiple System Atrophy.

BACKGROUND: Multiple system atrophy (MSA) is a fatal neurodegenerative disease with highly variable progression and poor prognosis. This study aimed t...

Machine learning versus deep learning for screening ischemic stroke among asymptomatic population.

Ischemic stroke puts great health burden in public. However, the diagnosis is based on head CT or MRI scanning. We aim to develop classifier models to...

A multimodal multi-label deep learning framework with temporal attention for detection of upper-limb motor activities for prosthetic control.

Accurate detection of simultaneous grip and elbow flexion is critical for advanced rehabilitation monitoring, intuitive prosthetic control and human-m...

Plasma proteomic signatures of cellular aging predict human disease.

Aging is asynchronous across cells and organs. Here we tested whether plasma proteomics can be used to analyze cell type-specific aging. From analyses...

Patient-independent hybrid generative-discriminative modeling for seizure detection in long-term scalp EEG.

Automated seizure detection from long-term scalp electroencephalography (EEG) remains challenging because seizure windows are sparse, channel configur...

Development and temporal validation of an interpretable prediction model for delayed diagnosis of benign paroxysmal positional vertigo: a retrospective study from Beijing, China.

OBJECTIVE: To develop and validate an interpretable prediction model for delayed diagnosis of benign paroxysmal positional vertigo (BPPV). METHODS: Th...

A computational model to describe multi-regional brain architecture during neurodegeneration in Alzheimer's disease.

We previously proposed an MRI-based machine learning model to describe the mesoscopic architecture of the human brain to aid in classifying subjects a...

A Time-Frequency Decoupled Contrastive Learning Framework for Electroencephalography-Based Parkinson's Disease Diagnosis.

Parkinson's disease (PD), a prototypical neurodegenerative disorder, poses significant challenges for early diagnosis. Motivated by recent advances in...

Pyramid Vision Transformer-Enhanced Conformer Network for Epileptic Seizure Recognition Using MultiChannel EEG Signals.

The classification of electroencephalogram (EEG) signals plays an important role in neuroscience research and clinical diagnosis of epileptic seizures...

ActiTect: a generalizable machine learning pipeline for REM sleep behavior disorder screening through standardized actigraphy.

Isolated rapid eye movement sleep behavior disorder (iRBD) is a major prodromal marker of α-synucleinopathies, often preceding the clinical onset of P...

Predicting major adverse cardiovascular and cerebrovascular events in chronic heart failure: a machine learning study.

BACKGROUND: Heart failure (HF) is a clinical syndrome characterized by impaired cardiac diastolic and systolic function due to structural or functiona...