AIMC Topic: Brain Mapping
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Domain-specific and domain-general neural network engagement during human-robot interactions.
The European journal of neuroscience
Sep 27, 2022
To what extent do domain-general and domain-specific neural network engagement generalize across interactions with human and artificial agents? In this exploratory study, we analysed a publicly available functional MRI (fMRI) data set (n = 22) to pro...
Understanding transformation tolerant visual object representations in the human brain and convolutional neural networks.
NeuroImage
Sep 15, 2022
Forming transformation-tolerant object representations is critical to high-level primate vision. Despite its significance, many details of tolerance in the human brain remain unknown. Likewise, despite the ability of convolutional neural networks (CN...
BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources.
Zeitschrift fur medizinische Physik
Sep 2, 2022
INTRODUCTION: Background field removal (BFR) is a critical step required for successful quantitative susceptibility mapping (QSM). However, eliminating the background field in brains containing significant susceptibility sources, such as intracranial...
Young children with autism show atypical prefrontal cortical responses to humanoid robots: An fNIRS study.
International journal of psychophysiology : official journal of the International Organization of Psychophysiology
Aug 28, 2022
BACKGROUND: Previous behavioral studies have found that children with autism spectrum disorder (ASD) show greater interest in humanoid robots than in humans. However, the neural mechanism underlying this is not clear. This study compared brain activa...
Predicting social anxiety in young adults with machine learning of resting-state brain functional radiomic features.
Scientific reports
Aug 17, 2022
Social anxiety is a symptom widely prevalent among young adults, and when present in excess, can lead to maladaptive patterns of social behavior. Recent approaches that incorporate brain functional radiomic features and machine learning have shown po...
Uncovering Brain Differences in Preschoolers and Young Adolescents with Autism Spectrum Disorder Using Deep Learning.
International journal of neural systems
Aug 9, 2022
Identifying brain abnormalities in autism spectrum disorder (ASD) is critical for early diagnosis and intervention. To explore brain differences in ASD and typical development (TD) individuals by detecting structural features using T1-weighted magnet...
Towards in vivo ground truth susceptibility for single-orientation deep learning QSM: A multi-orientation gradient-echo MRI dataset.
NeuroImage
Jul 26, 2022
Recently, deep neural networks have shown great potential for solving dipole inversion of quantitative susceptibility mapping (QSM) with improved results. However, these studies utilized their limited dataset for network training and inference, which...
Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics.
Proceedings of the National Academy of Sciences of the United States of America
Jul 26, 2022
Many efforts have been made to image the spatiotemporal electrical activity of the brain with the purpose of mapping its function and dysfunction as well as aiding the management of brain disorders. Here, we propose a non-conventional deep learning-b...
Deep learning-based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi-echo phase training data.
Magnetic resonance in medicine
Jun 17, 2022
PURPOSE: To enable a fast and automatic deep learning-based QSM reconstruction of tissues with diverse chemical shifts, relevant to most regions outside the brain.
Multi-Level Functional Connectivity Fusion Classification Framework for Brain Disease Diagnosis.
IEEE journal of biomedical and health informatics
Jun 3, 2022
Brain disease diagnosis is a new hotspot in the cross research of artificial intelligence and neuroscience. Quantitative analysis of functional magnetic resonance imaging (fMRI) data can provide valuable biomarkers that contributes to clinical diagno...
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