Neurology

Reconstruction of complete cerebral arterial anatomy from non-contrast CT using deep learning for pre-thrombectomy guidance.

BACKGROUND AND PURPOSE: Traditional imaging methods, like CT angiography (CTA), are unable to visualize the occluded vessels in acute ischemic stroke (AIS). We aimed to develop a deep learning based segmentation model for reconstructing the complete cerebral vasculature from non-contrast CT (NCCT) in LVO-AIS patients for endovascular thrombectomy planning. METHODS: A nnU-Net model was trained and ...

Recent advances in the detection and functional analysis of circRNAs with short-read RNA sequencing-based methods.

Circular RNAs (circRNAs) were first identified approximately 50 years ago in pathogenic viroids as single-stranded, covalently closed RNA molecules. Initially considered by-products of splicing, circRNAs are now recognised as an important class of regulatory RNAs involved in microRNA sponging, RNA-protein interactions, and cellular pathways. Their closed-loop structure, generated through backsplic...

Sedation in Acute Ischemic Stroke: Pathophysiological Basis and Evidence for Commonly Used Agents.

Sedation is frequently required in the acute management of ischemic stroke (AIS), yet its potential to influence secondary brain injury and systemic c...

Machine Learning-Based Predictive Model for Functional Independence in Spinal Cord Injury: Protocol for a Predictive Rule Development and Validation Study.

BACKGROUND: Spinal cord injury (SCI) causes substantial disability by disrupting spinal pathways, making functional independence a central rehabilitat...

Exploring Informal Caregivers' Perception of the Olera Digital Caregiving Assistance Platform for Dementia Care: Mixed Methods Evaluation Study.

BACKGROUND: Informal caregivers of people living with dementia often experience high rates of caregiver burnout while providing care. Although there a...

Real-Time Smartphone Monitoring Assessments as a Cognitive Biomarker of Alzheimer Disease: Protocol for a Development Study.

BACKGROUND: The diagnosis and monitoring of Alzheimer disease (AD) currently rely on clinician-administered, in-person, and cross-sectional pen-and-pa...

Development of a human-artificial intelligence collaboration-based storybook series for understanding epilepsy and supporting self-management.

Epilepsy is a chronic condition that requires ongoing self-management, including medication adherence, trigger control, lifestyle regulation, and psyc...

Neuroimaging of Heterogeneity in Neuropsychiatric Disorders: Toward Disease Progression Modeling.

Neuropsychiatric disorders are characterized by substantial biological heterogeneity, with patients sharing the same diagnosis often exhibiting distin...

Neuroimaging-derived biotypes of self-reported insomnia symptoms are differentially associated with affective symptoms.

BACKGROUND: Sleep problems are among the most prevalent health complaints during adolescence and show a reciprocal relationship with the development o...

Segmentation of the parasagittal dura mater on multi-center 3D-FLAIR MRI.

The parasagittal dura (PSD), located bilaterally alongside the superior sagittal sinus (SSS), has been increasingly implicated in cerebrospinal fluid-...

Structure aware graph community cluster pruning for efficient neural network compression in Parkinson's disease diagnosis.

Deep neural networks in medical and edge environments often face computational and memory constraints, which necessitate effective model compression. ...

The landscape of artificial intelligence in neurodegenerative diseases: a systematic review.

BACKGROUND: The rising global burden of neurodegenerative diseases underscores an urgent need for advanced research in diagnosis, prognosis, and treat...

Refined AI-ASPECTS with modified atlas and lesion-load thresholds: advancing acute ischemic stroke imaging and prognostic prediction.

BACKGROUND: The artificial intelligence-assisted ASPECTS (AI-ASPECTS) system has become an increasingly common tool in clinical practice for assessing...

Semiquantitative [¹²³I]FP-CIT SPECT metrics combined with machine learning improve clinical differentiation of Parkinson's disease and atypical parkinsonian syndrome.

PURPOSE: To evaluate whether semiquantitative striatal [¹²³I]FP-CIT SPECT-derived metrics improve clinical differentiation of degenerative parkinsonis...

Accelerated brain aging in Veterans with posttraumatic stress symptoms and mild traumatic brain injury.

Exposure to trauma and mild traumatic brain injury (mTBI), which often co-occur and can both trigger acute and chronic pathophysiological processes, h...

Generalizable Protein Folding Pathway Exploration with DA2-GRASP: Extending Beyond Miniproteins.

Elucidating protein dynamics is crucial for deciphering fundamental biological processes, from enzyme catalysis to cellular signaling, as its dysregul...

Developing and validating an interpretable machine learning model for predicting postoperative nausea and vomiting in pulmonary resection patients: a retrospective observational study.

BACKGROUND: Postoperative nausea and vomiting (PONV) remains a prevalent complication hindering Enhanced Recovery After Surgery (ERAS) protocols, part...

Structural-functional network decoupling in early stage amyotrophic lateral sclerosis reveals cell-type specific transcriptional signatures.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) involves widespread brain network dysfunction, yet the molecular mechanisms linked to these alteration...

Low-Field Neuroimaging: Opportunities and Limitations.

Magnetic resonance imaging (MRI) at low magnetic field strengths (under 1 T) has seen renewed interest, driven by technological advances that enhance ...

Retinal imaging for cerebrovascular risk stratification: evidence, limitations, and practical thresholds for the ophthalmologist.

BACKGROUND: The retina shares developmental origin, microvascular anatomy, and barrier physiology with the brain, making non-invasive retinal imaging ...