scholarly journals Safety and data quality of simultaneous EEG-fMRI using multi-band fMRI imaging

2020 ◽  
Author(s):  
Maximillian K. Egan ◽  
Ryan Larsen ◽  
Jonathan Wirsich ◽  
Brad P. Sutton ◽  
Sepideh Sadaghiani
Keyword(s):  
Data Quality ◽  
Resting State ◽  
Human Subjects ◽  
Rf Power ◽  
Power Deposition ◽  
Eyes Closed ◽  
Eeg Data ◽  
Magnetic Field Magnitude ◽  
Rf Power Deposition ◽  
Multi Band

AbstractPurposeSimultaneously recorded electroencephalography and functional magnetic resonance imaging (EEG-fMRI) is highly informative yet technically challenging. Until recently, there has been little information about the data quality and safety when used with newer multi-band (MB) fMRI sequences. Here, we assessed heating-related safety of a MB protocol on a phantom, then evaluated EEG quality recorded concurrently with the MB protocol on humans.Materials and MethodsWe compared radiofrequency (RF)-related heating and magnetic field magnitude () of a fast MB fMRI sequence with whole-brain coverage (TR=440ms, MB factor=4) against a previously recommended, safe single-band (SB) sequence using a phantom outfitted with a 64-channel EEG cap. Temperatures were recorded at the ECG and TP7 electrodes using a fluoroptic thermometer. Next, 6 human subjects underwent eyes-closed resting state EEG-fMRI with the MB sequence. EEG data quality was assessed by the ability to remove gradient and cardioballistic artifacts and a clean spectrogram.ResultsRF induced heating was lower at both electrodes in the MB sequence compared to the SB sequence at ratios of 0.7 and 0.8, respectively. These ratios are slightly greater than the ratio of RF power deposition of the sequences, which is 0.64. However, our results are consistent with the use of RF power deposition, characterized by , in predicting less heating in the MB sequence than the SB sequence. In the resting state EEG data, gradient and cardioballistic artifacts were successfully removed using traditional template subtraction. All subjects showed an individual alpha peak in the spectrogram with a posterior topography characteristic of eyes-closed EEG.ConclusionsOur study shows that is a useful indication of the relative heating of fMRI protocols. This observation indicates that simultaneous EEG-fMRI recordings using this MB sequence can be safe in terms of RF-related heating, and that EEG data recorded using this sequence is of acceptable quality.

scholarly journals EEG Data Quality: Determinants and Impact in a Multicenter Study of Children, Adolescents, and Adults with Attention-Deficit/Hyperactivity Disorder (ADHD)

2021 ◽  
Vol 11 (2) ◽  
pp. 214
Author(s):  
Anna Kaiser ◽  
Pascal-M. Aggensteiner ◽  
Martin Holtmann ◽  
Andreas Fallgatter ◽  
Marcel Romanos ◽  
...  
Keyword(s):  
Data Quality ◽  
Large Scale ◽  
Linear Models ◽  
Spectral Power ◽  
Control Group ◽  
Continuous Performance Task ◽  
Related Data ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open

Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (ntotal = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.

scholarly journals Classification of Resting-State Status Based on Sample Entropy and Power Spectrum of Electroencephalography (EEG)

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ahmed M. A. Mohamed ◽  
Osman N. Uçan ◽  
Oğuz Bayat ◽  
Adil Deniz Duru
Keyword(s):  
Resting State ◽  
Sample Entropy ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Frequency Bands ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open ◽  
The Brain

An electroencephalogram (EEG) is a significant source of diagnosing brain issues. It is also a mediator between the external world and the brain, especially in the case of any mental illness; however, it has been widely used to monitor the dynamics of the brain in healthy subjects. This paper discusses the resting state of the brain with eyes open (EO) and eyes closed (EC) by using sixteen channels by the use of conventional frequency bands and entropy of the EEG signal. The Fast Fourier Transform (FFT) and sample entropy (SE) of each sensor are computed as methods of feature extraction. Six classifiers, including logistic regression (LR), K-Nearest Neighbors (KNN), linear discriminant (LD), decision tree (DT), support vector machine (SVM), and Gaussian Naive Bayes (GNB) are used to discriminate the resting states of the brain based on the extracted features. EEG data were epoched with one-second-length windows, and they were used to compute the features to classify EO and EC conditions. Results showed that the LR and SVM classifiers had the highest average classification accuracy (97%). Accuracies of LD, KNN, and DT were 95%, 93%, and 92%, respectively. GNB gained the least accuracy (86%) when conventional frequency bands were used. On the other hand, when SE was used, the average accuracies of SVM, LD, LR, GNB, KNN, and DT algorithms were 92% 90%, 89%, 89%, 86%, and 86%, respectively.

scholarly journals Atypical Electrophysiological Indices of Eyes-Open and Eyes-Closed Resting-State in Children and Adolescents with ADHD and Autism

2020 ◽  
Vol 10 (5) ◽  
pp. 272
Author(s):  
Alessio Bellato ◽  
Iti Arora ◽  
Puja Kochhar ◽  
Chris Hollis ◽  
Madeleine J. Groom
Keyword(s):  
Resting State ◽  
Spectral Power ◽  
Children With Autism ◽  
Alpha Power ◽  
Brain Functioning ◽  
Hyperactivity Disorder ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open ◽  
The Difference

Investigating electrophysiological measures during resting-state might be useful to investigate brain functioning and responsivity in individuals under diagnostic assessment for attention deficit hyperactivity disorder (ADHD) and autism. EEG was recorded in 43 children with or without ADHD and autism, during a 4-min-long resting-state session which included an eyes-closed and an eyes-open condition. We calculated and analyzed occipital absolute and relative spectral power in the alpha frequency band (8–12 Hz), and alpha reactivity, conceptualized as the difference in alpha power between eyes-closed and eyes-open conditions. Alpha power was increased during eyes-closed compared to eyes-open resting-state. While absolute alpha power was reduced in children with autism, relative alpha power was reduced in children with ADHD, especially during the eyes-closed condition. Reduced relative alpha reactivity was mainly associated with lower IQ and not with ADHD or autism. Atypical brain functioning during resting-state seems differently associated with ADHD and autism, however further studies replicating these results are needed; we therefore suggest involving research groups worldwide by creating a shared and publicly available repository of resting-state EEG data collected in people with different psychological, psychiatric, or neurodevelopmental conditions, including ADHD and autism.

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