Leveraging sinusoidal representation networks to predict fMRI signals from EEG [PDF]
arXiv, 2023 In modern neuroscience, functional magnetic resonance imaging (fMRI) has been a crucial and irreplaceable tool that provides a non-invasive window into the dynamics of whole-brain activity. Nevertheless, fMRI is limited by hemodynamic blurring as well as high cost, immobility, and incompatibility with metal implants.
arxiv
EEG-assisted retrospective motion correction for fMRI: E-REMCOR [PDF]
NeuroImage 63 (2012) 698-712, 2012 We propose a method for retrospective motion correction of fMRI data in simultaneous EEG-fMRI that employs the EEG array as a sensitive motion detector. EEG motion artifacts are used to generate motion regressors describing rotational head movements with millisecond temporal resolution. These regressors are utilized for slice-specific motion correction
arxiv +1 more source
Comparison of Semantic and Episodic Memory BOLD fMRI Activation in Predicting Cognitive Decline in Older Adults [PDF]
, 2012 Previous studies suggest that task-activated functional magnetic resonance imaging (fMRI) can predict future cognitive decline among healthy older adults.
Butts, Alissa +11 more
core +2 more sources
Automatic artifact removal of resting-state fMRI with Deep Neural Networks [PDF]
Conference: 2021 29th European Signal Processing Conference
(EUSIPCO), 2020 Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for studying brain activity. During an fMRI session, the subject executes a set of tasks (task-related fMRI study) or no tasks (resting-state fMRI), and a sequence of 3-D brain images is obtained for further analysis. In the course of fMRI, some sources of activation are caused by
arxiv +1 more source
F1000Research, 2014 Sharing drafts of scientific manuscripts on preprint hosting services for early exposure and pre-publication feedback is a well-accepted practice in fields such as physics, astronomy, or mathematics.
Chaogan Yan, Qingyang Li, Lei Gao
doaj +1 more source
Within-Subject Joint Independent Component Analysis of Simultaneous fMRI/ERP in an Auditory Oddball Paradigm [PDF]
, 2012 The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. This research aimed to determine the sensitivity and limitations
Beardsley, Scott A. +2 more
core +2 more sources
Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis [PDF]
Frontiers in Neuroscience, 2020 Graph neural networks (GNN) rely on graph operations that include neural network training for various graph related tasks. Recently, several attempts have been made to apply the GNNs to functional magnetic resonance image (fMRI) data.
Byung-Hoon Kim, J. C. Ye
semanticscholar +1 more source
Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data
, 2019 Aggregating multi-subject functional magnetic resonance imaging (fMRI) data is indispensable for generating valid and general inferences from patterns distributed across human brains.
Chen, Fang +3 more
core +1 more source
The spatio-temporal mapping of epileptic networks: Combination of EEG–fMRI and EEG source imaging [PDF]
, 2009 Simultaneous EEG–fMRI acquisitions in patients with epilepsy often reveal distributed patterns of Blood Oxygen Level Dependant (BOLD) change correlated with epileptiform discharges.
A W Mcevoy +12 more
core +2 more sources
The Neural Correlates of Consciousness and Attention: Two Sister Processes of the Brain
Frontiers in Neuroscience, 2019 During the last three decades our understanding of the brain processes underlying consciousness and attention has significantly improved, mainly because of the advances in functional neuroimaging techniques.
Andrea Nani +13 more
doaj +1 more source