Neurology

Force Learning in Balanced Cortical E-I Networks.

Force learning is a learning method for generating various types of complex dynamics in recurrent neural networks (RNNs), which is related to the reservoir computing (RC). RC uses an RNN called reservoir whose synaptic weights are randomly generated and fixed during learning. Force learning trains these synaptic weights inside the reservoir networks. Although force learning can be used as an effec...

Automated detection of new cerebral infarctions and prognostic implications using deep learning on serial MRI.

We developed and externally validated a deep learning model to automatically detect new ischemic lesions on serial FLAIR MRI scans in patients with stroke. Manual interpretation of follow-up imaging is labor-intensive and variable, and silent brain infarctions (SBIs) are frequently missed despite their prognostic importance. Using 25,451 paired slices from 1055 patients across two hospitals, we tr...

Human-in-the-loop validation of an artificial intelligence-driven real-time prostatic capsule recognition model for nerve-sparing robot-assisted radical prostatectomy.

BACKGROUND: Nerve-sparing robot-assisted radical prostatectomy (NS-RARP) requires precise prostatic capsule identification to balance oncological cont...

Dynamic coactivation patterns during repetitive negative thinking: A cross-sectional fMRI study.

BACKGROUND: Repetitive negative thinking (RNT) and neuroticism are risk factors for internalizing psychopathology. However, their interaction has only...

High MAP3K6 Expression in Spinal Cord Injury Tissues Enhances Neuronal Apoptosis: Insights from Multi-Omics Data Integrating WGCNA, Machine Learning, and Experimental Validation.

Spinal cord injury (SCI) is a major global health issue with severe complications, yet effective biomarkers remain elusive. We analyzed the GSE226238 ...

Time-frequency-spatial channel attention network for semantic decoding: an exploratory EEG study.

Semantic decoding is a crucial approach for investigating the neural mechanisms underlying language processing and representation. Informed by brain-c...

Mechanism-guided engineering of phosphatase-like nanozymes for sensing and remediation of organophosphate esters.

Organophosphate esters (OPEs), widely used as flame retardants, plasticizers, pesticides, and nerve-agent simulants, are emerging contaminants of glob...

Carbon quantum dots exacerbate Parkinson's disease-like pathology through microglia-mediated neuroinflammatory responses with toxicological implications.

The global burden of Parkinson's disease (PD) is projected to double by 2050, with early-onset cases demonstrating accelerated progression and limited...

Genotype and Age at Onset Drive Vermis Atrophy in CACNA1A- and GAA-FGF14-related Ataxias.

UNLABELLED: CACNA1A- and GAA-FGF14-related channelopathies are among the most frequent genetic etiologies of cerebellar ataxia. They display overlappi...

Risk factors for postoperative recurrence of sinonasal inverted papilloma: a retrospective analysis of 171 cases.

BACKGROUND: Sinonasal inverted papilloma(SNIP) is a benign tumor with a potential of malignant transformation but has a certain recurrence. OBJECTIVES...

Cognitive reinforcement: capturing tacit knowledge and enhancing expertise with a biofeedback interface for visual attention.

Objective.Tacit or implicit knowledge refers to know-how that experts possess but often cannot articulate, codify, or explicitly transfer to others. T...

Machine Learning-Enhanced TCAB Score for Predicting Postoperative Ischemic Stroke After CABG.

BACKGROUND: Postoperative acute ischemic stroke remains a critical complication of coronary artery bypass grafting. This study aimed to develop a nove...

MRI In Vivo Detection of Amyloid-β Protein Deposition in Different Brain Regions of Patients with AD and MCI.

To investigate a non-invasive magnetic resonance imaging (MRI)-based method for detecting amyloid-β (Aβ) protein deposition in different brain regions...

STAND-Net: A Spiking Temporal Attention autoeNcoDer Network for Efficient EEG Artifact Removal.

Electroencephalography (EEG)-based brain computer interface (BCI) systems hold significant promise across diverse applications; however, their perform...

Adaptive Segmentation of EEG for Machine Learning Applications.

OBJECTIVE: Electroencephalography (EEG) data is derived by sampling continuous neurological time series signals. In order to prepare EEG signals for m...

Cell-free DNA fragmentomes for noninvasive detection of liver cirrhosis and other diseases.

Accessible liquid biopsies, including analyses of genome-wide cell-free DNA (cfDNA) fragmentation, are emerging for early detection of cancer but rema...

Interpretable EEG biomarkers for neurological disease models in mice using bag-of-waves classifiers.

OBJECTIVE: Electroencephalograms (EEGs) are time-series records of the electrical potential from collective neural activity in the brain. EEG waveform...

CTP-Free Method for Automated Lesion Water Uptake in Acute Ischemic Stroke.

BACKGROUND AND PURPOSE: Net water uptake (NWU) in the infarct core of patients with ischemic stroke has been correlated with clinical outcome and lesi...

Multimodal CT-based imaging biomarkers for mechanical reperfusion in acute ischemic stroke.

Mechanical thrombectomy (MT) has emerged as the primary treatment for restoring blood flow in acute ischemic stroke (AIS) patients with large vessel o...

Bone Mineral Density of Spine Patients with CT Hounsfield Units and AI Segmentation: A Contemporary Review.

It is widely accepted that bone mineral density affects outcomes for spinal arthrodesis surgeries. Traditional techniques such as dual-energy X-ray ab...