AIMC Topic: Brain Mapping
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Artificial neural network modelling of the neural population code underlying mathematical operations.
NeuroImage
Feb 26, 2023
Mathematical operations have long been regarded as a sparse, symbolic process in neuroimaging studies. In contrast, advances in artificial neural networks (ANN) have enabled extracting distributed representations of mathematical operations. Recent ne...
Direct machine learning reconstruction of respiratory variation waveforms from resting state fMRI data in a pediatric population.
NeuroImage
Jan 26, 2023
In many functional magnetic resonance imaging (fMRI) studies, respiratory signals are unavailable or do not have acceptable quality due to issues with subject compliance, equipment failure or signal error. In large databases, such as the Human Connec...
Extracting a Novel Emotional EEG Topographic Map Based on a Stacked Autoencoder Network.
Journal of healthcare engineering
Jan 19, 2023
Emotion recognition based on brain signals has increasingly become attractive to evaluate human's internal emotional states. Conventional emotion recognition studies focus on developing machine learning and classifiers. However, most of these methods...
Reliability of active robotic neuro-navigated transcranial magnetic stimulation motor maps.
Experimental brain research
Dec 16, 2022
Transcranial magnetic stimulation (TMS) motor mapping is a safe, non-invasive method used to study corticomotor organization and intervention-induced plasticity. Reliability of resting maps is well established, but understudied for active maps and un...
Goals, usefulness and abstraction in value-based choice.
Trends in cognitive sciences
Nov 26, 2022
Colombian drug lord Pablo Escobar, while on the run, purportedly burned two million dollars in banknotes to keep his daughter warm. A stark reminder that, in life, circumstances and goals can quickly change, forcing us to reassess and modify our valu...
Deep learning-regularized, single-step quantitative susceptibility mapping quantification.
NMR in biomedicine
Nov 23, 2022
The purpose of the current study was to develop deep learning-regularized, single-step quantitative susceptibility mapping (QSM) quantification, directly generating QSM from the total phase map. A deep learning-regularized, single-step QSM quantifica...
NeXtQSM-A complete deep learning pipeline for data-consistent Quantitative Susceptibility Mapping trained with hybrid data.
Medical image analysis
Nov 23, 2022
Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great potential in recent years, obtaining similar results to established non-learning approaches. Many current deep learning approaches are not data consistent, require in vivo ...
Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study.
Human brain mapping
Nov 8, 2022
Previous studies have explored resting-state functional connectivity (rs-FC) of the amygdala in patients with autism spectrum disorder (ASD). However, it remains unclear whether there are frequency-specific FC alterations of the amygdala in ASD and w...
Explaining neural activity in human listeners with deep learning via natural language processing of narrative text.
Scientific reports
Oct 25, 2022
Deep learning (DL) approaches may also inform the analysis of human brain activity. Here, a state-of-art DL tool for natural language processing, the Generative Pre-trained Transformer version 2 (GPT-2), is shown to generate meaningful neural encodin...
Inferring Effective Connectivity Networks From fMRI Time Series With a Temporal Entropy-Score.
IEEE transactions on neural networks and learning systems
Oct 5, 2022
Inferring brain-effective connectivity networks from neuroimaging data has become a very hot topic in neuroinformatics and bioinformatics. In recent years, the search methods based on Bayesian network score have been greatly developed and become an e...
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