One size does not fit all: notable individual variation in brain activity correlates of antidepressant treatment response.

Introduction: To date, no robust electroencephalography (EEG) markers of antidepressant treatment response have been identified. Variable findings may arise from the use of group analyses, which neglect individual variation. Using a combination of group and single-participant analyses, we explored individual variability in EEG characteristics of treatment response. Methods: Resting-state EEG data and Montgomery-Åsberg Depression Rating Scale (MADRS) symptom scores were collected from 43 patients with depression before, at 1 and 12 weeks of pharmacotherapy. Partial least squares (PLS) was used to: 1) identify group differences in EEG connectivity (weighted phase lag index) and complexity (multiscale entropy) between eventual medication responders and non-responders, and 2) determine whether group patterns could be identified in individual patients. Results: Responders showed decreased alpha and increased beta connectivity, and early, widespread decreases in complexity over treatment. Non-responders showed an opposite connectivity pattern, and later, spatially confined decreases in complexity. Thus, as in previous studies, our group analyses identified significant differences between groups of patients with different treatment outcomes. These group-level EEG characteristics were only identified in ~40-60% of individual patients, as assessed quantitatively by correlating the spatiotemporal brain patterns between groups and individual results, and by independent raters through visualization. Discussion: Our single-participant analyses suggest that substantial individual variation exists, and needs to be considered when investigating characteristics of antidepressant treatment response for potential clinical applicability. Clinical trial registration: https://clinicaltrials.gov, identifier NCT00519428.

EEG complexity during mind wandering: A multiscale entropy investigation.

Our attention often drifts away from the ongoing task to task-unrelated thoughts, a phenomenon commonly referred to as mind wandering. Ample studies dedicated to delineating its electrophysiological correlates have revealed distinct event-related potentials (ERP) and spectral patterns associated with mind wandering. It remains less clear whether the complexity of the electroencephalography (EEG) changes when our minds wander, a metric that captures the predictability of the time series at varying timescales. Accordingly, this study investigated whether mind wandering impacts EEG signal complexity. We further explored whether such effects differ across timescales, and change in a context-dependent manner as indexed by global and local levels of processing. To address this, we recorded participants' EEG while they completed Navon's global and local processing task and occasionally reported whether they were on-task or mind wandering throughout the task. We found that brain signal complexity as indexed by multiscale entropy decreased at medium timescales in centro-parietal regions and increased at coarse timescales in anterior and posterior regions during mind wandering, as compared to the on-task state, for global processing. Moreover, global processing showed increased complexity at fine to medium timescales compared to local processing. Finally, behavioral performance revealed a context-dependent effect in accuracy measures, with mind wandering showing lower accuracy compared to the on-task state only during the local condition. Taken together, these results indicate that changes in brain signal complexity across timescales may be an important feature of mind wandering.

Increased Neural Efficiency in Visual Word Recognition: Evidence from Alterations in Event-Related Potentials and Multiscale Entropy.

Visual word recognition is a relatively effortless process, but recent research suggests the system involved is malleable, with evidence of increases in behavioural efficiency after prolonged lexical decision task (LDT) performance. However, the extent of neural changes has yet to be characterized in this context. The neural changes that occur could be related to a shift from initially effortful performance that is supported by control-related processing, to efficient task performance that is supported by domain-specific processing. To investigate this, we replicated the British Lexicon Project, and had participants complete 16 h of LDT over several days. We recorded electroencephalography (EEG) at three intervals to track neural change during LDT performance and assessed event-related potentials and brain signal complexity. We found that response times decreased during LDT performance, and there was evidence of neural change through N170, P200, N400, and late positive component (LPC) amplitudes across the EEG sessions, which suggested a shift from control-related to domain-specific processing. We also found widespread complexity decreases alongside localized increases, suggesting that processing became more efficient with specific increases in processing flexibility. Together, these findings suggest that neural processing becomes more efficient and optimized to support prolonged LDT performance.

Conflict of interest explains the size of student evaluation of teaching and learning correlations in multisection studies: a meta-analysis.

We examined the associations between the size of student evaluation of teaching and learning (SET/learning) correlations and presence of several conflicts of interest (COIs) including corporate, administrative, evaluation unit, SET author, and funder interests. Our meta-analyses of SET/learning correlations reported by multisection studies show that researchers with a vested interest in finding large positive SET/learning correlations found, on average, large positive SET/learning correlations. In contrast, researchers with no identifiable COIs found that SET/learning correlations were zero or nearly zero. The largest SET/learning correlations were reported by authors with ties to SET selling corporations. Smaller but still substantial SET/learning correlations were reported by researchers with administrative assignments and by researchers in evaluation units/departments responsible for the administration of SET. Moreover, authors with the most significant COIs were publishing their studies primarily prior to 1981 whereas authors with no or less significant COIs were publishing their studies in 1981 or afterwards. Studies published prior to 1981 reported small but significant (r = .31) SET/learning correlations whereas studies published in 1981 and after reported near zero, non-significant SET/learning correlations (r = .06). The presence of COIs was associated with earlier publication date but also with smaller samples. Finally, whereas corporate, administrative, and evaluation unit authors nearly ceased publishing multisection studies on SET/learning correlations, authors from business and economics departments are now responsible for the substantial portion of newer, larger, and higher quality studies published in 1981 and after.

Prospective memory, retrospective memory, and individual differences in cognitive abilities, personality, and psychopathology.

Although individual differences in processing speed, working memory, intelligence, and other cognitive functions were found to explain individual differences in retrospective memory (RetM), much less is known about their relationship with prospective memory (ProM). Moreover, the studies that investigated the relationship between ProM and cognitive functions arrived to contradictory conclusions. The relationship between ProM, personality, and psychopathology is similarly unsettled. Meta-analytic reviews of the relationships of ProM with aging and personality suggest that the contradictory findings may be due to widespread methodological problems plaguing ProM research including the prevalent use of inefficient, unreliable binary measures; widespread ceiling effects; failure to distinguish between various ProM subdomains (e.g., episodic ProM versus vigilance/monitoring); various confounds; and, importantly, small sample sizes, resulting in insufficient statistical power. Accordingly, in a large scale study with nearly 1,200 participants, we investigated the relationship between episodic event-cued ProM, episodic RetM, and fundamental cognitive functions including intelligence, personality, and psychopathology, using reliable continuous measures of episodic event-cued ProM. Our findings show that (a) continuous measures of episodic event-cued ProM were much more reliable than binary measures, (b) episodic event-cued ProM was associated with measures of processing speed, working memory, crystallized and fluid intelligence, as well as RetM, and that such associations were similar for ProM and RetM, (c) personality factors did not improve prediction of neither ProM nor RetM beyond the variance predicted by cognitive ability, (d) symptoms of psychopathology did not improve the prediction of ProM although they slightly improved the prediction of RetM, and (e) participants' sex was not associated with ProM but showed small correlations with RetM. In addition to advancing our theoretical understanding of ProM, our findings highlight the need to avoid common pitfalls plaguing ProM research.