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Abstract: Neurophysiological measures of preparation and attention are often atypical in ADHD. Still, replicated findings that these measures predict which patients improve after Neurofeedback (NF), reveal neurophysiological specificity, and reflect ADHD-severity are limited. Methods: We analyzed children’s preparatory (CNV) and attentional (Cue-P3) brain activity and behavioral performance during a cued Continuous Performance Task (CPT) before and after slow cortical potential (SCP)-NF or semi-active control treatment (electromyogram biofeedback). Mixed-effects models were performed with 103 participants at baseline and 77 were assessed for pre-post comparisons focusing on clinical outcome prediction, specific neurophysiological effects of NF, and associations with ADHD-severity. Results: Attentional and preparatory brain activity and performance were non-specifically reduced after treatment. Preparatory activity in the SCP-NF group increased with clinical improvement. Several performance and brain activity measures predicted non-specific treatment outcome. Conclusion: Specific neurophysiological effects after SCP-NF were limited to increased neural preparation associated with improvement on ADHD-subscales, but several performance and neurophysiological measures of attention predicted treatment outcome and reflected symptom severity in ADHD. The results may help to optimize treatment.
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.
Research indicates that autism is the extreme end of a continuously distributed trait. The Social Responsiveness Scale (SRS) and the Social and Communication Disorders Checklist (SCDC) aim to assess autistic traits. The objective of this study was to compare their clinical validity. The SRS showed sensitivities of .74 to .80 and specificities of .69 to 1.00 for autism. Sensitivities were .85 to .90 and specificities .28 to.82 for the SCDC. Correlations with the ADI-R, ADOS and SCQ were higher for the SRS than for the SCDC. The SCDC seems superior to the SRS to screen for unspecific social and communicative deficits including autism. The SRS appears more suitable than the SCDC in clinical settings and for specific autism screening.