@article{KaiserAggensteinerHoltmannetal.2021,
  author    = {Kaiser, Anna and Aggensteiner, Pascal-M. and Holtmann, Martin and Fallgatter, Andreas and Romanos, Marcel and Abenova, Karina and Alm, Barbara and Becker, Katja and D{\"o}pfner, Manfred and Ethofer, Thomas and Freitag, Christine M. and Geissler, Julia and Hebebrand, Johannes and Huss, Michael and Jans, Thomas and Jendreizik, Lea Teresa and Ketter, Johanna and Legenbauer, Tanja and Philipsen, Alexandra and Poustka, Luise and Renner, Tobias and Retz, Wolfgang and R{\"o}sler, Michael and Thome, Johannes and Uebel-von Sandersleben, Henrik and von Wirth, Elena and Zinnow, Toivo and Hohmann, Sarah and Millenet, Sabina and Holz, Nathalie E. and Banaschewski, Tobias and Brandeis, Daniel},
  title     = {EEG data quality: determinants and impact in a multicenter study of children, adolescents, and adults with attention-deficit/hyperactivity disorder (ADHD)},
  series = {Brain Sciences},
  volume    = {11},
  journal   = {Brain Sciences},
  number    = {2},
  issn      = {2076-3425},
  doi       = {10.3390/brainsci11020214},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-228788},
  year      = {2021},
  abstract  = {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 (n\(_{total}\) = 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.},
  language  = {en}
}
@article{ZieglerKaiserIgeletal.2021,
  author    = {Ziegler, Mirjam and Kaiser, Anna and Igel, Christine and Geissler, Julia and Mechler, Konstantin and Holz, Nathalie E. and Becker, Katja and D{\"o}pfner, Manfred and Romanos, Marcel and Brandeis, Daniel and Hohmann, Sarah and Millenet, Sabina and Banaschewski, Tobias},
  title     = {Actigraphy-derived sleep profiles of children with and without attention-deficit/hyperactivity disorder (ADHD) over two weeks — comparison, precursor symptoms, and the chronotype},
  series = {Brain Sciences},
  volume    = {11},
  journal   = {Brain Sciences},
  number    = {12},
  issn      = {2076-3425},
  doi       = {10.3390/brainsci11121564},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-250084},
  year      = {2021},
  abstract  = {Although sleep problems are common in children with ADHD, their extent, preceding risk factors, and the association between neurocognitive performance and neurobiological processes in sleep and ADHD, are still largely unknown. We examined sleep variables in school-aged children with ADHD, addressing their intra-individual variability (IIV) and considering potential precursor symptoms as well as the chronotype. Additionally, in a subgroup of our sample, we investigated associations with neurobehavioral functioning (n = 44). A total of 57 children (6-12 years) with (n = 24) and without ADHD (n = 33) were recruited in one center of the large ESCAlife study to wear actigraphs for two weeks. Actigraphy-derived dependent variables, including IIV, were analyzed using linear mixed models in order to find differences between the groups. A stepwise regression model was used to investigate neuropsychological function. Overall, children with ADHD showed longer sleep onset latency (SOL), higher IIV in SOL, more movements during sleep, lower sleep efficiency, and a slightly larger sleep deficit on school days compared with free days. No group differences were observed for chronotype or sleep onset time. Sleep problems in infancy predicted later SOL and the total number of movements during sleep in children with and without ADHD. No additional effect of sleep problems, beyond ADHD symptom severity, on neuropsychological functioning was found. This study highlights the importance of screening children with ADHD for current and early childhood sleep disturbances in order to prevent long-term sleep problems and offer individualized treatments. Future studies with larger sample sizes should examine possible biological markers to improve our understanding of the underlying mechanisms.},
  language  = {en}
}