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 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

The effects of lung recruitment on the phase III slope of volumetric capnography in morbidly obese patients

In this study, we analyzed the effect of the alveolar recruitment strategy (ARS) and positive end-expiratory pressure (PEEP) titration on Phase III slope (S III) of volumetric capnography (VC) in morbidly obese patients. METHODS: Eleven anesthetized morbidly obese patients were studied. Lungs were ventilated with tidal volumes of 10 mL · kg−1, respiratory rates of 12–14 bpm, inspiration:expiration ratio of 1:2, and Fio2 of 0.4. ARS was performed by increasing PEEP in steps of five from 0 end-expiratory pressure to 15 cm H2O. During lung recruitment, plateau pressure was limited to 50 cm H2O, whereas tidal volume was increased to the ventilator’s maximum value of 1400 mL, and PEEP was increased to 20 cm H2O for 2 min. Thereafter, PEEP was reduced in steps of 5 cm H2O, from 15 to 0. VC, arterial blood gases, and lung mechanics data were determined for each PEEP step. RESULTS: S III decreased from 0.014 ± 0.006 to 0.005 ± 0.005 mm Hg/mL when 0 end-expiratory pressure was compared against 15 cm H2O of PEEP after ARS (15ARS, P < 0.05). This decrement in S III was accompanied by increases in Pao2 (27%, P < 0.002) and compliance (32%, P < 0.001), whereas Paco2 decreased by 8% (P < 0.038) when comparing values before and after ARS. A good prediction of the lung recruitment effect by S III was derived from the receiver operating characteristic curve analysis (area under the curve of 0.81, sensitivity of 0.75, and specificity of 0.74; P < 0.001). CONCLUSION: The S III in VC was useful to detect the optimal level of PEEP after lung recruitment in anesthetized morbidly obese patients.Fil: Bohm, Stephan H.. University Hospital; AlemaniaFil: Maisch, Stefan. University Hospital; AlemaniaFil: Von Sandersleben, Alexandra. University Hospital; AlemaniaFil: Thamm, Oliver. University of Witten/Herdecke; AlemaniaFil: Martinez Arca, Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaFil: Tusman, Gerardo. Fundación Medica de Mar del Plata. Hospital Privado de Comunidad; Argentin

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 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

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 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