Top-Down and Bottom-Up Contributions to Memory Performance in OCD: A Multi-Level Meta-Analysis with Clinical Implications
Creators: name: Harkin, Ben
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Cognitive Psychology
subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology

description: TITLE Top-Down and Bottom-Up Contributions to Memory Performance in OCD: A Multi-Level Meta-Analysis with Clinical Implications. AUTHORS Harkin, Ben*. Department of Psychology, Manchester Metropolitan University. Persson, Sofia. School of Social Sciences, Leeds Beckett University Yates, Alan. Department of Psychology, Manchester Metropolitan University. Kessler, Klaus. Aston Neuroscience Institute, Aston University. Jauregi, Ainara. Aston Neuroscience Institute, Aston University. * Corresponding author: Dr Ben Harkin, Department of Psychology, Manchester Metropolitan University, All Saints Building, Manchester, M15 6BH. E-mail: b.harkin@mmu.ac.uk RATIONALE In a novel multi-level meta-analytic approach, the current author and team (see Persson et al., in press) showed that central executive functioning played a key role in explaining memory performance in those with OCD. Specifically, we tested the predictive validity of Harkin and Kessler’s (2011) Executive Function, Binding Complexity and Memory Load (EBL) Classification System concerning affected versus unaffected memory performance in OCD. We employed a multi-level meta-analytic approach (Viechtbauer, 2010) to accommodate the interdependent nature of the EBL model and interdependency of effect sizes (305 effect sizes from 144 studies, including 4424 OCD patients). Results revealed that the EBL model predicted memory performance; i.e., as EBL demand increases, those with OCD performed progressively worse on memory tasks. Executive function was the driving mechanism behind the EBL’s impact on OCD memory performance, as it negated binding complexity, memory load, and visual or verbal task differences. The present multi-level meta-analysis builds upon our previous research and aims to investigate specific features of central executive functioning; i.e., the top-down and bottom-up contributions to memory performance in those with OCD. The aim will be to identify specific components of top-down and bottom-up processing generally and within specific memory domains and tasks. This data will then be used to inform the content of clinical interventions. OBJECTIVES (1) To quantify the top-down (attentional control/inhibition; planning; maintenance/updating) and bottom-up (perceptual integration; perceptual salience/complexity) in the memory performance of those with OCD. (2) To quantify the top-down and bottom-up contributions to memory performance across a range of memory domains (e.g., complex visual tasks; span spatial; recall of complex verbal information) and sub-tasks (e.g., Rey Complex Visual Tasks; n-back tasks, WLM-story recall, respectively). (3) To identify how specific components of top-down and bottom-up processing potentially contribute to key symptoms of OCD. (4) To identify how specific components of top-down and bottom-up processing can inform the content of clinical interventions. METHODS Included studies had to compare memory performance on at least one task between adults with OCD or OCD-type traits (e.g., checking) and healthy controls. Studies examining visual, verbal, and WM were all included. Participants with subclinical OCD were included; clinical versus sub-clinical samples were coded as a dichotomous variable. Participants with acquired OCD (e.g., following head injury) were excluded, as this group is generally considered clinically distinct from idiopathic OCD groups and illness will in most cases be associated with neurological injury (Coetzer, 2004). The search terms used to access literature was ‘(wash* OR check* OR hoard* OR obsessive-compulsive* OR OCD OR clean*) AND (executive OR bind* OR load* OR visual OR verbal) AND (memory)’. Studies were searched via the following social scientific databases: Web of Science, Psychinfo, Medline, PubMed, OVID, CINAHL, PsychArticles. ProQuest Theses was searched to identify potential grey literature, and prominent authors (most prolific 10% as determined by the initial literature search) in the field were also contacted enquiring about unpublished material. All authors contacted about additional data (see information below for further details), were asked about any unpublished material they might have. Where information to facilitate effect size calculation was missing, authors were contacted requesting additional data. To this end, we contacted 10 authors, with five providing the requested data. Search hits were converted to RIS-files (Clark, 2019, personal communication), and imported into the web application Rayyan (Ouzzani, Hammady, Fedorowicz, &amp; Elmagarmid, 2016), where they were screened for eligibility. DATA CODING The below variables are of main interest for this review. • Effect sizes; i.e., memory performance of those with OCD vs controls • Top-Down Factors: Attentional Control/Inhibition; Planning; Maintenance/Updating • Bottom-Up Factors: Perceptual Salience/Complexity; Perceptual Integration Memory Domains and Sub-Tasks o Reproduction of Complex Geometric Shapes: VR Tasks, RCFT, WMS-VR o Span-Sequence: N-back, symbol, digit o Span-Spatial:TOL, SOST, CBTT o DMTS: Basic Storage, Distractor Inhibition o Recall – Simple Verbal: Threat, Cued, Neutral o Recall – Complex Verbal: Complex Verbal Tasks, WLM-Story Recall, CVLT/RAVLT/AVLT o Recognition: Visuo-spatial, objects, verbal o Declarative memory: Prospective, Source, False Each task was coded in terms of the identified features of the top-down and bottom-up features identified above. Tasks were scored 1 through to 3, depending on how much demand was placed on the individual component: 1 indicated low to little demand, 2 moderate demand, and 3 indicated high demand. All of these features were coded blindly by the author BH and then blindly second coded by AH. Agreement was high, with disagreements resolved via discussion. STATISTICAL ANALYSIS The current analysis will be conducted using the rma.va function in the Metafor package for the statistical software environment R (R Core Team, 2013); R Core Team, 2014), and recommendations of Viechtbauer (2010). A mixed-effects model will likely initially be fitted, and estimation based on the restricted maximum likelihood estimator. The analysis examined the variance distribution over two (collapsed top-down and bottom-up) and five levels, the overall effect (i.e., memory performance of those with OCD compared to controls), and the effects of a number of moderating variables. As recommended by Hox (2010) and Assink and Wibbelink (2016), moderators will be first examined individually, and then combined into one analysis. This allows for initial significance screening, whilst also accounting for the possibility of variables of interest being intercorrelated, producing multicollinearity in analyses. For overall mean effects of the meta-analysis and mean effects of categorical moderators, we report Cohen’s d, and for mean effects of the continuous moderator analyses, we report standardised betas. This is in line with reporting by past research using the same statistical approach (Asskin &amp; Wibbelink, 2016; Fradkin et al., 2018). Code was adapted from Assink and Wibbelink (2016) and Harrer, Cuijpers and Ebert (2019). Visuals were created using ggplot2 (Wickham, 2016). Assessment of methodological quality will occur by entering it as a moderator of effect sizes. Publication bias will be assessed using the funnel plot function (funnel) in R, as recommended by Harrer et al. (2019), and Egger’s regression coefficients (Egger, Smith, Schneider, &amp; Minder, 1997). There will likely also be a qualitative synthesis of the findings, pertaining to the variables of interest. A number of moderators of effect sizes will potentially be explored as part of the secondary outcomes of the review.