AIMC Topic: Brain
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Subject-independent auditory spatial attention detection based on brain topology modeling and feature distribution alignment.
Hearing research
Aug 14, 2024
Auditory spatial attention detection (ASAD) seeks to determine which speaker in a surround sound field a listener is focusing on based on the one's brain biosignals. Although existing studies have achieved ASAD from a single-trial electroencephalogra...
Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data.
Journal of neuroscience methods
Aug 14, 2024
BACKGROUND: Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wa...
The influence of mental calculations on brain regions and heart rates.
Scientific reports
Aug 14, 2024
Performing mathematical calculations is a cognitive activity that can affect biological signals. This study aims to examine the changes in electroencephalogram (EEG) and electrocardiogram (ECG) signals of 36 healthy subjects during the performance of...
Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review.
Tomography (Ann Arbor, Mich.)
Aug 12, 2024
The concept of 'brain age', derived from neuroimaging data, serves as a crucial biomarker reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine learning (ML) and deep learning (DL) integration has transformed t...
DeepEMC-T mapping: Deep learning-enabled T mapping based on echo modulation curve modeling.
Magnetic resonance in medicine
Aug 11, 2024
PURPOSE: Echo modulation curve (EMC) modeling enables accurate quantification of T relaxation times in multi-echo spin-echo (MESE) imaging. The standard EMC-T mapping framework, however, requires sufficient echoes and cumbersome pixel-wise dictionary...
Prognostic enrichment for early-stage Huntington's disease: An explainable machine learning approach for clinical trial.
NeuroImage. Clinical
Aug 10, 2024
BACKGROUND: In Huntington's disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on in vivo brain imaging markers, which reflect neuropathology well ...
Accurate neuron segmentation method for one-photon calcium imaging videos combining convolutional neural networks and clustering.
Communications biology
Aug 9, 2024
One-photon fluorescent calcium imaging helps understand brain functions by recording large-scale neural activities in freely moving animals. Automatic, fast, and accurate active neuron segmentation algorithms are essential to extract and interpret in...
Contrastive fine-grained domain adaptation network for EEG-based vigilance estimation.
Neural networks : the official journal of the International Neural Network Society
Aug 8, 2024
Vigilance state is crucial for the effective performance of users in brain-computer interface (BCI) systems. Most vigilance estimation methods rely on a large amount of labeled data to train a satisfactory model for the specific subject, which limits...
TS-AI: A deep learning pipeline for multimodal subject-specific parcellation with task contrasts synthesis.
Medical image analysis
Aug 8, 2024
Accurate mapping of brain functional subregions at an individual level is crucial. Task-based functional MRI (tfMRI) captures subject-specific activation patterns during various functions and behaviors, facilitating the individual localization of fun...
BrainSegFounder: Towards 3D foundation models for neuroimage segmentation.
Medical image analysis
Aug 8, 2024
The burgeoning field of brain health research increasingly leverages artificial intelligence (AI) to analyze and interpret neuroimaging data. Medical foundation models have shown promise of superior performance with better sample efficiency. This wor...
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