Empirical evidence for discrete neurocognitive subgroups in patients with non-psychotic major depressive disorder: clinical implications

Pu, Shenghong; Noda, Takamasa; Setoyama, Shiori; Nakagome, Kazuyuki

doi:10.1017/s003329171800034x

Cited by 37 publications

(38 citation statements)

References 95 publications

(120 reference statements)

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“…Only two studies have been carried out clustering analysis with exclusively MDD patients, and their findings point toward cognitive heterogeneity along the different stages of the disorder. One included patients in partial remission and reported three clusters (Pu et al, 2018) in accordance with the above-mentioned studies, and the other was centered into first-episode patients and showed two clusters, one preserved and one impaired (Vicent-Gil et al, 2018). These previous studies, except for the one with firstepisode patients, endorse our current findings of a subgroup of intact or mostly preserved patients, of a globally impaired subgroup with a general cognitive affectation, and of a subgroup with specific domains impaired.…”

Section: Discussionsupporting

confidence: 89%

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

et al. 2020

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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.

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Section: Discussionsupporting

confidence: 89%

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

et al. 2020

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“…However, contrary to conclusions from systematic reviews in MDD (Rock et al, 2014;Ahern and Semkovska, 2017), the present sample did not display objective attention deficits at baseline. Additionally, previous research has suggested distinct neurocognitive subgroups for MDD, with a majority showing near-normative performance on neuropsychological tests (Pu et al, 2018). Participants in the Return-to-Work program report less overall illness severity and are more likely to hold a job compared with other outpatients (Victor et al, 2016).…”

Section: Discussionmentioning

confidence: 93%

“…Major depressive disorder (MDD) is characterized by deficits in cognitive functions, including attention, memory, and executive functions (EF) (Snyder, 2012;Ahern and Semkovska, 2017). However, the heterogeneity of the cognitive profile in MDD appears to be large, with distinct neurocognitive subgroups (Pu et al, 2018). For those experiencing cognitive difficulties, deficits often persist into remission (Rock et al, 2014) and have a deleterious effect on everyday functioning (Baune et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Predictors of Long-Term Improvement Following Cognitive Remediation in a Sample With Elevated Depressive Symptoms

et al. 2020

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Objective: Cognitive remediation (CR) techniques (interventions to enhance cognitive functioning) have proven moderately effective in improving cognition and daily functioning in major depressive disorder (MDD). However, baseline predictors of treatment response are lacking. The present study aimed to identify factors influencing long-term CR outcomes in a sample with current or previous, mild or moderate MDD and with self-reported cognitive deficits. Methods: Forty-two completers of group-based CR (strategy learning or drill-andpractice), were pooled into one sample. Based on change scores from baseline to 6-month follow-up, participants were categorized as "improvers" or "non-improvers" using reliable change index calculations. Measures included a questionnaire of everyday executive functioning and a neuropsychological test of attention. Finally, improvers and non-improvers were compared in terms of various sociodemographic, psychological, illness-related, and neuropsychological baseline variables. Results: Seventeen participants improved reliably in everyday executive functioning, and fourteen demonstrated a reliable improvement in attention. No statistically significant differences emerged between improvers and non-improvers. Conclusion: No major predictors of CR were identified. Importantly, the current findings are insufficient to guide clinical decision-making. Large-scale studies with a priori hypotheses are needed to make advances in the future.

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“…14 Data-driven studies in schizophrenia and more broadly defined psychotic disorders have typically separated patients into subgroups of global neurocognitive impairment, normal range ability and mixed or intermediate profiles. 14–17 Recently, evidence of similar subgroups have been shown within samples of participants with depressive 18 and bipolar disorders, 19 and notably, across broader samples comprised of people with multiple major diagnostic groups. 20–23 Taken together, these findings of homogeneous subgroups within diagnostic groups suggest that neurocognitive impairment may follow a continuum of severity distributed across mental disorders, with data-driven subgroups potentially representing a more useful level of analysis as regards neurocognition and associated factors.…”

Section: Data-driven Neurocognitive Subgroups In Mental Disordersmentioning

confidence: 97%

Transdiagnostic neurocognitive subgroups and functional course in young people with emerging mental disorders: a cohort study

et al. 2020

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Background Neurocognitive impairments robustly predict functional outcome. However, heterogeneity in neurocognition is common within diagnostic groups, and data-driven analyses reveal homogeneous neurocognitive subgroups cutting across diagnostic boundaries. Aims To determine whether data-driven neurocognitive subgroups of young people with emerging mental disorders are associated with 3-year functional course. Method Model-based cluster analysis was applied to neurocognitive test scores across nine domains from 629 young people accessing mental health clinics. Cluster groups were compared on demographic, clinical and substance-use measures. Mixed-effects models explored associations between cluster-group membership and socio-occupational functioning (using the Social and Occupational Functioning Assessment Scale) over 3 years, adjusted for gender, premorbid IQ, level of education, depressive, positive, negative and manic symptoms, and diagnosis of a primary psychotic disorder. Results Cluster analysis of neurocognitive test scores derived three subgroups described as ‘normal range’ (n = 243, 38.6%), ‘intermediate impairment’ (n = 252, 40.1%), and ‘global impairment’ (n = 134, 21.3%). The major mental disorder categories (depressive, anxiety, bipolar, psychotic and other) were represented in each neurocognitive subgroup. The global impairment subgroup had lower functioning for 3 years of follow-up; however, neither the global impairment (B = 0.26, 95% CI −0.67 to 1.20; P = 0.581) or intermediate impairment (B = 0.46, 95% CI −0.26 to 1.19; P = 0.211) subgroups differed from the normal range subgroup in their rate of change in functioning over time. Conclusions Neurocognitive impairment may follow a continuum of severity across the major syndrome-based mental disorders, with data-driven neurocognitive subgroups predictive of functional course. Of note, the global impairment subgroup had longstanding functional impairment despite continuing engagement with clinical services.

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Empirical evidence for discrete neurocognitive subgroups in patients with non-psychotic major depressive disorder: clinical implications

Cited by 37 publications

References 95 publications

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

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

Predictors of Long-Term Improvement Following Cognitive Remediation in a Sample With Elevated Depressive Symptoms

Transdiagnostic neurocognitive subgroups and functional course in young people with emerging mental disorders: a cohort study

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