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Brain Mapping

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Benchmarking explanation methods for mental state decoding with deep learning models.

NeuroImage
Deep learning (DL) models find increasing application in mental state decoding, where researchers seek to understand the mapping between mental states (e.g., experiencing anger or joy) and brain activity by identifying those spatial and temporal feat...

Inter-individual deep image reconstruction via hierarchical neural code conversion.

NeuroImage
The sensory cortex is characterized by general organizational principles such as topography and hierarchy. However, measured brain activity given identical input exhibits substantially different patterns across individuals. Although anatomical and fu...

Artificial neural network modelling of the neural population code underlying mathematical operations.

NeuroImage
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
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
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
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
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
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
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
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...