AIMC Topic: Brain
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Deep learning based tractography with TractSeg in patients with hemispherotomy: Evaluation and refinement.
NeuroImage. Clinical
Jan 28, 2025
Deep learning-based tractography implicitly learns anatomical prior knowledge that is required to resolve ambiguities inherent in traditional streamline tractography. TractSeg is a particularly widely used example of such an approach. Even though it ...
Data- and Physics-Driven Deep Learning Based Reconstruction for Fast MRI: Fundamentals and Methodologies.
IEEE reviews in biomedical engineering
Jan 28, 2025
Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advan...
Utilizing machine learning techniques for EEG assessment in the diagnosis of epileptic seizures in the brain: A systematic review and meta-analysis.
Seizure
Jan 27, 2025
PURPOSE: Advancements in Machine Learning (ML) techniques have revolutionized diagnosing and monitoring epileptic seizures using Electroencephalogram (EEG) signals. This analysis aims to determine the effectiveness of ML techniques in recognizing pat...
A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy.
Nature methods
Jan 27, 2025
Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that gener...
Significance of gender, brain region and EEG band complexity analysis for Parkinson's disease classification using recurrence plots and machine learning algorithms.
Physical and engineering sciences in medicine
Jan 27, 2025
Parkinson Disease (PD) is a complex neurological disorder attributed by loss of neurons generating dopamine in the SN per compacta. Electroencephalogram (EEG) plays an important role in diagnosing PD as it offers a non-invasive continuous assessment ...
Beyond averaging: A transformer approach to decoding event related brain potentials.
NeuroImage
Jan 27, 2025
The objective of this study is to assess the potential of a transformer-based deep learning approach applied to event-related brain potentials (ERPs) derived from electroencephalographic (EEG) data. Traditional methods involve averaging the EEG signa...
An unsupervised learning approach for clustering joint trajectories of Alzheimer's disease biomarkers: An application to ADNI Data.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Jan 27, 2025
INTRODUCTION: Current models of Alzheimer's disease (AD) progression assume a common pattern and pathology, oversimplifying the heterogeneity of clinical AD.
Contrastive learning in brain imaging.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 26, 2025
Contrastive learning is a type of deep learning technique trying to classify data or examples without requiring data labeling. Instead, it learns about the most representative features that contrast positive and negative pairs of examples. In literat...
Identifying autism spectrum disorder based on machine learning for multi-site fMRI.
Journal of neuroscience methods
Jan 26, 2025
BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by repetitive stereotypical behavior and social impairment. Early diagnosis is essential for developing a treatment plan for autism. Although multi-site data ca...
Research on noise-induced hearing loss based on functional and structural MRI using machine learning methods.
Scientific reports
Jan 26, 2025
Noise-induced hearing loss (NIHL) is a common occupational condition. The aim of this study was to develop a classification model for NIHL on the basis of both functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sM...
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