Neurology

The Impact of a Neuroradiologist on the Report of a Real-World CT Perfusion Imaging Map Derived from Artificial Intelligence/Machine Learning-Driven Software.

BACKGROUND AND PURPOSE: According to the guideline, CT perfusion should be read and analyzed by using computer-aided software. This study evaluates the efficacy of artificial intelligence (AI)/machine learning-driven software in CTP imaging and the effect of neuroradiologists' interpretatios on these automated results. MATERIALS AND METHODS: We conducted a retrospective, single-center cohort study...

Constructing fine-grained subcortical atlases with connectional consensus graph representation learning.

Objective.The complex internal organization of subcortical structures forms the foundation of critical neural circuits that support sensorimotor processing, emotion regulation, and memory. However, their complex internal organization poses a significant challenge to reliable, fine-scale parcellation.Approach.To overcome the trade-off between anatomical specificity and cross-subject consistency, we...

Smart assistive technologies for neurodisorders: A review on AI, IoT, and wearable systems for enhanced patient care.

Neurological disorders of the brain and spinal cord affect millions of individuals worldwide and continue to rise in prevalence. Conditions such as Al...

Distinct clinical clusters of paediatric patients with status epilepticus: Retrospective cohort study.

AIM: To characterize the clinical features, management, and outcomes of paediatric patients with status epilepticus, and to explore whether distinct c...

Osteoporosis prediction using lumbar CT Hounsfield units: comparative performance and clinical implications of seven machine learning models.

PURPOSE: This study aimed to evaluate the utility of L1-L4 average Hounsfield Unit (HU) values from lumbar spine computed tomography (CT) in predictin...

Real-world evaluation of an automated EEG spike detection software in a tertiary centre compared to a clinical reference standard.

BACKGROUND: Interictal epileptiform discharges (IEDs) are transient spikes or waves that occur in electroencephalography (EEG) records and can help su...

Data Augmentation for Subject-Independent SSVEP-BCIs via Simultaneous Spatial-Energy Representation.

OBJECTIVE: Data augmentation is important for enhancing subject-independent classification in deep learning (DL) approaches for steady-state visual ev...

Clinical Validation of Deep Learning-Accelerated versus Wave-CAIPI Postcontrast 3D T1-MPRAGE for Evaluation of Intracranial Enhancing Lesions.

BACKGROUND AND PURPOSE: Deep learning (DL) reconstruction methods have shown promise in accelerating 2D MRI sequences but have yet to be extensively v...

CABaNe, an automated, high throughput ImageJ macro for cell and neurite analysis.

Measuring neurite length is crucial in neurobiology because it provides valuable insights into the growth, development, and function of neurons. In pa...

Health trajectories of patients with amyotrophic lateral sclerosis before and after initiation of non-invasive ventilation: a French nationwide database analysis.

BACKGROUND: Management of amyotrophic lateral sclerosis (ALS) is complicated by heterogeneous presentation and unpredictable disease course. This stud...

Alzheimer's disease diagnosis support for brain perfusion SPECT scans in a real-world clinical cohort.

BackgroundDementia diagnosis is challenging and often delayed. Brain imaging techniques such as single-photon emission computed tomography (SPECT) ima...

Adversarial robust EEG-based brain-computer interfaces using a hierarchical convolutional neural network.

Brain-Computer Interfaces (BCIs) based on electroencephalography (EEG) are widely used in motor rehabilitation, assistive communication, and neurofeed...

Balancing noise reduction and neural signature preservation in EEG biometrics.

EEG-based subject identification is an emerging biometric approach with strong potential for secure authentication, but reliable performance requires ...

Deep learning enhanced ALPS reveals genetic and environmental factors of brain glymphatic function.

BACKGROUND: The glymphatic system plays a critical role in brain waste clearance and health. Diffusion tensor imaging along the perivascular space (DT...

The role of dentists in the recognition of neurodegenerative and systemic conditions with neurological involvement.

Dentists are often the first healthcare providers to observe subtle orofacial and behavioral changes that may reflect underlying neurological diseases...

ESM-AnatTractNet: Advanced deep learning model of true positive eloquent white matter tractography to improve preoperative evaluation of pediatric epilepsy surgery.

Accurate preoperative identification of true positive white matter pathways involved in critical eloquent functions such as motor, language, and visio...

Asymmetric fiber orientation distribution estimation via unsupervised deep learning.

Diffusion magnetic resonance imaging (dMRI) tractography is a key technique for reconstructing brain structural connectivity. A widely recognized limi...

Estimating proximity to muscular failure using surface EMG and deep learning.

Understanding how close an individual is to muscular failure during exercise can be used to personalize resistance training dynamically. We propose a ...

Spatiotemporal abnormalities in brain networks as a signature of neurological damage in Wilson's disease.

OBJECTIVES: Resting-state electroencephalogram (EEG) microstates serve as dynamic markers of intrinsic brain activity, reflecting the transient coordi...

Molecular features of human pathological tau distinguish tauopathy-associated dementias.

In Alzheimer's disease (AD), pathological tau protein shows a progressive accumulation of post-translational modifications (PTMs), reflecting disease ...