Neurology

Knowledge-Augmented Deep Learning for Segmenting and Detecting Cerebral Aneurysms With CT Angiography: A Multicenter Study.

Background Deep learning (DL) could improve the labor-intensive, challenging processes of diagnosing cerebral aneurysms but requires large multicenter data sets. Purpose To construct a DL model using a multicenter data set for accurate cerebral aneurysm segmentation and detection on CT angiography (CTA) images and to compare its performance with radiology reports. Materials and Methods Consecutive...

Discriminating retinal microvascular and neuronal differences related to migraines: Deep Learning based Crossectional Study

Migraine, a prevalent neurological disorder, has been associated with various ocular manifestations suggestive of neuronal and microvascular deficits. However, there is limited understanding of the extent to which retinal imaging may discriminate between individuals with migraines versus without migraines. In this study, we apply convolutional neural networks to color fundus photography (CFP) an...

Decoding Linguistic Representations of Human Brain

Language, as an information medium created by advanced organisms, has always been a concern of neuroscience regarding how it is represented in the b...

Classification of Alzheimer's Dementia vs. Healthy subjects by studying structural disparities in fMRI Time-Series of DMN

Time series from different regions of interest (ROI) of default mode network (DMN) from Functional Magnetic Resonance Imaging (fMRI) can reveal sign...

Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders.

BACKGROUND AND HYPOTHESIS: The brain-predicted age difference (brain-PAD) may serve as a biomarker for neurodegeneration. We investigated the brain-PA...

Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder worldwide, frequently presents with early-stage speech impairments. R...

EEG-SSM: Leveraging State-Space Model for Dementia Detection

State-space models (SSMs) have garnered attention for effectively processing long data sequences, reducing the need to segment time series into shor...

Preliminary Results of Neuromorphic Controller Design and a Parkinson's Disease Dataset Building for Closed-Loop Deep Brain Stimulation

Parkinson's Disease afflicts millions of individuals globally. Emerging as a promising brain rehabilitation therapy for Parkinson's Disease, Closed-...

Identification of molecular subtypes of dementia by using blood-proteins interaction-aware graph propagational network.

Plasma protein biomarkers have been considered promising tools for diagnosing dementia subtypes due to their low variability, cost-effectiveness, and ...

2D and 3D Deep Learning Models for MRI-based Parkinson's Disease Classification: A Comparative Analysis of Convolutional Kolmogorov-Arnold Networks, Convolutional Neural Networks, and Graph Convolutional Networks

Parkinson's Disease (PD) diagnosis remains challenging. This study applies Convolutional Kolmogorov-Arnold Networks (ConvKANs), integrating learnabl...

Hyperbolic embedding of brain networks detects regions disrupted by neurodegeneration in Alzheimer's disease

Graph theoretical methods have proven valuable for investigating alterations in both anatomical and functional brain connectivity networks during Al...

PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets

Acute stroke demands prompt diagnosis and treatment to achieve optimal patient outcomes. However, the intricate and irregular nature of clinical dat...

Early Recognition of Parkinson's Disease Through Acoustic Analysis and Machine Learning

Parkinson's Disease (PD) is a progressive neurodegenerative disorder that significantly impacts both motor and non-motor functions, including speech...

A discontinuous Galerkin method for the three-dimensional heterodimer model with application to prion-like proteins' dynamics

Neurocognitive disorders, such as Alzheimer's and Parkinson's, have a wide social impact. These proteinopathies involve misfolded proteins accumulat...

From data to discovery: AI-guided analysis of disease-relevant molecules in spinal muscular atrophy (SMA).

Spinal Muscular Atrophy is caused by partial loss of survival of motoneuron (SMN) protein expression. The numerous interaction partners and mechanisms...

NeuroBind: Towards Unified Multimodal Representations for Neural Signals

Understanding neural activity and information representation is crucial for advancing knowledge of brain function and cognition. Neural activity, me...

A review of handcrafted and deep radiomics in neurological diseases: transitioning from oncology to clinical neuroimaging

Medical imaging technologies have undergone extensive development, enabling non-invasive visualization of clinical information. The traditional revi...

CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation

Multi-modal neuroimaging analysis is crucial for a comprehensive understanding of brain function and pathology, as it allows for the integration of ...

MARVEL: MR Fingerprinting with Additional micRoVascular Estimates using bidirectional LSTMs

The Magnetic Resonance Fingerprinting (MRF) approach aims to estimate multiple MR or physiological parameters simultaneously with a single fast acqu...

Quantitative Evaluation of the Saliency Map for Alzheimer's Disease Classifier with Anatomical Segmentation

Saliency maps have been widely used to interpret deep learning classifiers for Alzheimer's disease (AD). However, since AD is heterogeneous and has ...