Neurophysiological variability masks differences in functional neuroanatomical networks and their effectiveness to modulate response inhibition between children and adults

Abstract

Executive functions are well-known to undergo developmental changes from childhood to adulthood. Considerable efforts have been made to elucidate the affected system neurophysiological mechanisms. But while it is well-known that developmental changes affect intra-individual variability, this potential bias has largely been neglected when investigating the neurophysiology underlying developmental differences between children and adults. We hypothesize that due to differences in intra-individual variability of neural processes between children and adults, reliable group differences will only be evident after accounting for intra-individual variability in neurophysiological processes. We, therefore, investigate response-inhibition processes as an important instance of executive control in children (between 10 and 14 years) and adults (between 20 and 29 years) and decompose EEG data on the basis of the latency and temporal variability. This was combined with source localization. Children showed more impulsive behavior than adults. Importantly, a reliable match between the neurophysiological and behavioral data could only be found when accounting for intra-individual variability in the EEG data. These decomposed data showed that children and adults use similar neurophysiological mechanisms at the response selection level to accomplish inhibitory control, but seem to engage different neuroanatomical structures to do so according to source localization results: In adults, these processes were related to the medial frontal cortex. In children, the same processes were reflected in a shift of the scalp topography and related to the superior parietal cortex. These shifts in neural networks were associated with lower effectiveness in exerting inhibitory control. However, these differences in the functional neuroanatomical architecture can only be seen when intra-individual variability is taken into account.

The neurophysiological basis of developmental changes during sequential cognitive flexibility between adolescents and adults

“…Yet, reliable interactions "condition × group" were obtained after accounting for intra-individual variability in the data using RIDE (Ouyang et al, 2011). Already previous results suggested that differences in intra-individual variability bias comparisons between adolescents and adults when it comes to neurophysiological correlates of cognitive control (Bodmer et al, 2018). It is shown that the interaction "condition × group" was obtained for the S-cluster in the time window of the target-P1 and the C-cluster in the search interval of the target-N2.…”

“…However, this is an expected finding since it is likely that intra-individual variability in neurophysiological data may preclude the detection of reliable differences between adolescents and adults (Bodmer et al, 2018). The sLORETA analysis was performed because the choice of time and electrodes to quantify the ERP data may have failed to capture the difference.…”

“…The sLORETA analysis was performed because the choice of time and electrodes to quantify the ERP data may have failed to capture the difference. However, this is an expected finding since it is likely that intra-individual variability in neurophysiological data may preclude the detection of reliable differences between adolescents and adults (Bodmer et al, 2018). To account for this, the RIDE analysis was performed.…”

“…Considering the electrophysiological measures used, it is important that ERPs can only provide reliable insights into neurophysiological mechanisms when there is little intra-individual variability in the EEG data (Ouyang, Herzmann, Zhou, & Sommer, 2011;Ouyang, Sommer, & Zhou, 2015a, b). Yet, intra-individual variability is strongly affected by developmental processes (Bielak, Cherbuin, Bunce, & Anstey, 2014;Garrett, Macdonald, & Craik, 2012;Mella, Fagot, Lecerf, & de Ribaupierre, 2015;Störmer, Eppinger, & Li, 2014;Tamnes, Fjell, Westlye, stby, & Walhovd, 2012) and impedes reliable comparisons between adolescents and adults when it comes to neurophysiological correlates of cognitive flexibility (Bodmer, Mückschel, Roessner, & Beste, 2018). It is, therefore, very likely, that there are no reliable effects in electrophysiological correlates when analyzing above-mentioned standard ERP-components without accounting for intra-individual variability.…”

“…ICA, infomax algorithm), which was used to correct blinks, pulse artifacts, and vertical eye-movements. By means of this transformation, it is possible to identify electrodes which best reflect ERP-component reflecting relevant cognitive neurophysiological subprocesses(Bodmer et al, 2018;Bodmer & Beste, 2017;Kamarajan, Pandey, Chorlian, & Porjesz, 2015;Kayser & Tenke, 2015a). For trials comprising correct responses, cue-locked segments were formed for all conditions separately.…”