Simultaneous EEG and fMRI reveals stronger sensitivity to orthographic strings in the left occipito-temporal cortex of typical versus poor beginning readers.
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ABSTRACT:
SUBMITTER: Pleisch G
PROVIDER: S-EPMC6974919 | biostudies-literature | 2019 Dec
REPOSITORIES: biostudies-literature
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Simultaneous EEG and fMRI reveals stronger sensitivity to orthographic strings in the left occipito-temporal cortex of typical versus poor beginning readers.
Pleisch Georgette G Karipidis Iliana I II Brem Alexandra A Röthlisberger Martina M Roth Alexander A Brandeis Daniel D Walitza Susanne S Brem Silvia S
Developmental cognitive neuroscience 20191004
The level of reading skills in children and adults is reflected in the strength of preferential neural activation to print. Such preferential activation appears in the N1 event-related potential (ERP) over the occipitotemporal scalp after around 150-250 ms and the corresponding blood oxygen level dependent (BOLD) signal in the ventral occipitotemporal (vOT) cortex. Here, orthography-sensitive (print vs. false font) processing was examined using simultaneous EEG-fMRI in 38 first grade children wi ...[more]
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Project description:ObjectiveThe concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution. However, the ballistocardiogram (BCG), a large-amplitude artifact caused by cardiac induced movement contaminates the EEG during EEG-fMRI recordings. Removal of BCG in software has generally made use of linear decompositions of the corrupted EEG. This is not ideal as the BCG signal propagates in a manner which is non-linearly dependent on the electrocardiogram (ECG). In this paper, we present a novel method for BCG artifact suppression using recurrent neural networks (RNNs).MethodsEEG signals were recovered by training RNNs on the nonlinear mappings between ECG and the BCG corrupted EEG. We evaluated our model's performance against the commonly used Optimal Basis Set (OBS) method at the level of individual subjects, and investigated generalization across subjects.ResultsWe show that our algorithm can generate larger average power reduction of the BCG at critical frequencies, while simultaneously improving task relevant EEG based classification.ConclusionThe presented deep learning architecture can be used to reduce BCG related artifacts in EEG-fMRI recordings.SignificanceWe present a deep learning approach that can be used to suppress the BCG artifact in EEG-fMRI without the use of additional hardware. This method may have scope to be combined with current hardware methods, operate in real-time and be used for direct modeling of the BCG.
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