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Neuroscience Informatics
Scopus
ISSN 2772-5286 · Elsevier · 255 artikel di indeks
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Artikel Jurnal
From epilepsy seizure classification to detection: A deep learning-based approach for raw EEG signals
Epilepsy is the most prevalent neurological disorder in the world. Although epilepsy has been recognized for centuries, clinical doctors still lack reliable automated tools to diagnose epileptic seizures in electroencephalograms (EEGs). The research community has made significant efforts to develop…
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CleanEEG: A U-Net based deep learning framework for robust EEG artifact removal
High-frequency oscillations (HFOs) are vital biomarkers for identifying the seizure onset zone (SOZ) in patients with drug-resistant epilepsy (DRE). However, EEG artifacts especially muscle and power-line noise overlapping with the HFO frequency range (80–250 Hz) pose significant challenges for…
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A proof-of-concept study on the use of large language models for assessing research methodology in neuroimaging
Careful evaluation of research methodology is fundamental to scientific progress but represents a significant burden on human experts. The complexity of functional MRI (fMRI) methods makes transparent reporting, as suggested by OHBM COBIDAS guidelines, particularly critical. Large Language Models…
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Circle of Willis centerline graphs: A dataset and baseline algorithm
The Circle of Willis (CoW) is a critical network of brain arteries, often implicated in cerebrovascular pathologies. Voxel-level segmentation is an important first step toward automated CoW assessment, but full quantitative analysis requires centerline representations. However, conventional…
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TRELLIS -enhanced surface features for comprehensive intracranial aneurysm analysis
Intracranial aneurysms pose a significant clinical risk yet are difficult to detect, delineate, and model due to limited annotated 3D data. We propose a cross-domain feature-transfer approach that leverages the latent geometric embeddings learned by TRELLIS, a generative model trained on…
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Integrating cross-sectional imaging data into functional outcome prediction models for acute ischemic stroke of the anterior circulation
In acute ischemic stroke, large vessel occlusions of the anterior circulation are increasingly treated with endovascular therapy (EVT). The efficacy of this therapy depends on adequate treatment selection. Treatment decisions can be based on predictions of functional outcome. Most existing studies…
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A study on the potential relationship between the diagnosis and functional connectivity in the brain in major depressive disorder
Background: Many studies on resting-state functional connectivity (FC) in major depressive disorder (MDD) have investigated FC as a biomarker of disease pathogenesis. However, few studies have examined conditional dependencies among FC, clinical status, and demographic variables. Considering such…
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Evaluating SegResNet for single-modality meningioma segmentation on T1 contrast-enhanced MRI on a New Zealand clinical cohort
Accurate and automated meningioma segmentation remains a biomedical engineering challenge, particularly when relying on single-modality MRI data. We evaluate SegResNet, a U-Net-based deep learning architecture, for meningioma segmentation using 817 T1 contrast-enhanced (T1CE) magnetic resonance…
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From text to code – Leveraging machine learning for neurology outpatient clinical codingBox 1. Prompt engineering template used in calls to Mistral 7B. was replaced with each processed clinic letter body (the complete prompt is available in Appendix 4).Box 2. Example of hallucination using Mistral 7b model
Background: Most neurological care is delivered in outpatient settings without mandated clinical coding. The clinical records remain stored as unstructured text with inconsistent formatting. There is a significant opportunity to increase the value of these data through automated clinical coding…
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Spatiotemporal dynamics of TMS-Evoked responses: A dual damped sine model analysis of cortical site and stimulation condition effects
Background: Transcranial magnetic stimulation combined with EEG (TMS-EEG) provides a non-invasive window into cortical excitability and connectivity. However, interpreting TMS-evoked potentials (TEPs) remains challenging due to pervasive artifacts and the limited physiological interpretability of…
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Exploring the effects of wavelet types and windowing on EMG-based IONM through deep learning architectures
Intraoperative neuromonitoring (IONM) plays a critical role in preserving nerve function during high-risk surgeries through real-time monitoring of electromyographic (EMG) activity. Routine EMG analysis, in real-time, is complex and prone to variability. This work presents an end-to-end deep…
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Age-related changes in brain fiber pathways based on directional decomposition of DTI tractograms
This study investigated age-related changes of brain fiber pathways from diffusion tensor imaging (DTI) tractograms with directional decomposition. Two hundred subjects were stratified into three age groups. Tractograms were generated at two levels: from individual DTI images (subject-level), and…
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Advances in acquisition and post-processing optimization of IVIM MRI for brain imaging: A systematic review
Purpose: Diffusion-weighted MRI is widely used to probe brain microstructure, but its signal reflects both diffusion and perfusion effects. Intravoxel Incoherent Motion (IVIM) MRI enables non-contrast separation of these components, offering potential clinical value in neuroimaging. However…
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Advances in deep learning for multimodal brain imaging: A comprehensive survey
In recent years, the field of medical brain imaging has witnessed remarkable advancements with the integration of artificial intelligence (AI) and deep learning techniques. Traditional unimodal imaging methods, such as MRI and CT, often fall short in providing comprehensive insights into…
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Advancing brain tumor diagnostics and therapy (2023–2025): A global bibliometric perspective on innovation and collaboration
Background: Brain tumors present a daunting clinical challenge, necessitating unwavering innovation in diagnostics, imaging, and therapeutics. Emerging advances in artificial intelligence (AI), molecular biomarkers, and neuroimaging have transformed the research landscape. Objective: The current…
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Evaluating the effect of point-sampling on univariate point and interval forecasting of cerebral physiologic signals using ARIMA modeling in acute traumatic neural injury
High-resolution physiological signals, such as intracranial pressure (ICP) and regional cerebral oxygen saturation (rSO2), are critical for managing traumatic brain injury (TBI) by enabling continuous monitoring of cerebral autoregulation and vascular reactivity. These signals provide essential…
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15 Years of optimizers in medical deep learning: A systematic review
Optimization algorithms are pivotal in training deep learning (DL) models for medical imaging, determining how efficiently models learn, generalize, and perform across modalities. This systematic review analyzed 69 peer-reviewed studies (2010–2025) on optimizer performance in classification…
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NeuroFusion: A forensic enriched ensemble framework for cerebellum disease classification
Accurate and timely classification of cerebellar diseases is crucial for effective diagnostic, yet it remains challenging due to the inherent heterogeneity of these disorders and the subtlety of their neuroimaging manifestations. This study investigated a novel multi-stage ensemble framework…