Dealing with heterogeneity of cognitive dysfunction in acute depression: a clustering approach

Abstract

Background Heterogeneity in cognitive functioning among major depressive disorder (MDD) patients could have been the reason for the small-to-moderate differences reported so far when it is compared to other psychiatric conditions or to healthy controls. Additionally, most of these studies did not take into account clinical and sociodemographic characteristics that could have played a relevant role in cognitive variability. This study aims to identify empirical clusters based on cognitive, clinical and sociodemographic variables in a sample of acute MDD patients. Methods In a sample of 174 patients with an acute depressive episode, a two-step clustering analysis was applied considering potentially relevant cognitive, clinical and sociodemographic variables as indicators for grouping. Results Treatment resistance was the most important factor for clustering, closely followed by cognitive performance. Three empirical subgroups were obtained: cluster 1 was characterized by a sample of non-resistant patients with preserved cognitive functioning (n = 68, 39%); cluster 2 was formed by treatment-resistant patients with selective cognitive deficits (n = 66, 38%) and cluster 3 consisted of resistant (n = 23, 58%) and non-resistant (n = 17, 42%) acute patients with significant deficits in all neurocognitive domains (n = 40, 23%). Conclusions The findings provide evidence upon the existence of cognitive heterogeneity across patients in an acute depressive episode. Therefore, assessing cognition becomes an evident necessity for all patients diagnosed with MDD, and although treatment resistant is associated with greater cognitive dysfunction, non-resistant patients can also show significant cognitive deficits. By targeting not only mood but also cognition, patients are more likely to achieve full recovery and prevent new relapses.

Occupational Depression, Cognitive Performance, and Task Appreciation: A Study Based on Raven’s Advanced Progressive Matrices

“…Between-profile differences were large in terms of both occupational depression and cognitive performance, as indexed by Cohen's ds ≥ 1.263. Such effect sizes are consistent with those found in clusteranalytic research pertaining to neuropsychological functioning in clinical depression (Vicent-Gil et al, 2020). Our findings underline the utility of combining variable-and person-centered approaches in depression research.…”

“…The second cluster (Cluster 2; n = 145) involved individuals with relatively high ODI scores ( M = 2.172, SD = 0.407) and relatively low cognitive performance ( M = 0.226, SD = 0.209). The model showed a satisfactory fit, with a Silhouette coefficient of 0.4 ( Vicent-Gil et al, 2020 ). Welch’s ANOVAs revealed that the two clusters differed substantially from one another in terms of both ODI scores, a F (1, 218.360) = 1083.277, p < 0.001, Cohen’s d = 2.492, and cognitive performance, a F (1, 213.427) = 269.956, p < 0.001, Cohen’s d = 1.263.…”

“…We first examined the link between occupational depression and cognitive performance using correlational analysis, hierarchical linear regression analysis, and two-step cluster analysis. Two-step cluster analysis allows the investigator to identify individual profiles based on characteristics of interest, called classifiers, and is thus anchored in a person-centered approach (as opposed to a variable-centered approach; Howard and Hoffman, 2017 ; for specific examples, see Ahola et al, 2014 ; Bianchi et al, 2015 ; Vicent-Gil et al, 2020 ). We employed occupational depression and cognitive performance as classifiers.…”

Individual differences associated with treatment adherence and transfer effects following gamified web-based cognitive control training for repetitive negative thinking

“…Similarly, in a healthy student sample Vervaeke et al (2020) observed emotional transfer for self-reported anxiety levels, in absence of effects of CCT on RNT or depressive symptomatology. Such inconsistencies may at least partially be due to sample heterogeneity (e.g., community samples, MDD/RMD patients), where strong individual differences have been observed in terms of baseline features (e.g., cognitive impairments; Pu et al, 2018 ; Vicent-Gil et al, 2020 ) and treatment response (e.g., Hoorelbeke et al, 2021 ). Individual differences may be distinct from other factors contributing to variation across studies such as differences in administration modality (lab vs. internet), number of required sessions (varies from 1 to 10 in the literature), software versions (e.g., lab versions typically use multiple training tasks whereas internet protocols have used a single task), and demand characteristics (e.g., high variability in the level of reimbursement for participation across studies).…”

Relationship of insight to neurocognitive function and risk of recurrence in depression: A naturalistic follow-up study

“…Cognitive recovery can improve overall outcomes ( 24 ). However, heterogeneity regarding cognitive impairment has been mentioned in patients with MDD, in particular executive functions ( 25 , 26 ). One study revealed that some medication-free depressed individuals showed significant reductions in executive function ( 27 ), whereas others showed subtle or no executive function deficits ( 26 , 28 ).…”