EEG data quality: determinants and impact in a multicenter study of children, adolescents, and adults with Attention-Deficit/Hyperactivity Disorder (ADHD)
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 o...
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| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
10 February 2021
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| In: |
Brain Sciences
Year: 2021, Jahrgang: 11, Heft: 2, Pages: 1-36 |
| ISSN: | 2076-3425 |
| DOI: | 10.3390/brainsci11020214 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.3390/brainsci11020214 Verlag, lizenzpflichtig, Volltext: https://www.mdpi.com/2076-3425/11/2/214 
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| Verfasserangaben: | Anna Kaiser, Pascal-M. Aggensteiner, Martin Holtmann, Andreas Fallgatter, Marcel Romanos, Karina Abenova, Barbara Alm, Katja Becker, Manfred Döpfner, Thomas Ethofer, Christine M. Freitag, Julia Geissler, Johannes Hebebrand, Michael Huss, Thomas Jans, Lea Teresa Jendreizik, Johanna Ketter, Tanja Legenbauer, Alexandra Philipsen, Luise Poustka, Tobias Renner, Wolfgang Retz, Michael Rösler, Johannes Thome, Henrik Uebel-von Sandersleben, Elena von Wirth, Toivo Zinnow, Sarah Hohmann, Sabina Millenet, Nathalie E. Holz, Tobias Banaschewski, Daniel Brandeis, and on behalf of the ESCAlife-Consortium |
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| 245 | 1 | 0 | |a EEG data quality |b determinants and impact in a multicenter study of children, adolescents, and adults with Attention-Deficit/Hyperactivity Disorder (ADHD) |c Anna Kaiser, Pascal-M. Aggensteiner, Martin Holtmann, Andreas Fallgatter, Marcel Romanos, Karina Abenova, Barbara Alm, Katja Becker, Manfred Döpfner, Thomas Ethofer, Christine M. Freitag, Julia Geissler, Johannes Hebebrand, Michael Huss, Thomas Jans, Lea Teresa Jendreizik, Johanna Ketter, Tanja Legenbauer, Alexandra Philipsen, Luise Poustka, Tobias Renner, Wolfgang Retz, Michael Rösler, Johannes Thome, Henrik Uebel-von Sandersleben, Elena von Wirth, Toivo Zinnow, Sarah Hohmann, Sabina Millenet, Nathalie E. Holz, Tobias Banaschewski, Daniel Brandeis, and on behalf of the ESCAlife-Consortium |
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| 520 | |a 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. | ||
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