Application of Cluster Analysis to Investigate Neuropsychological Heterogeneity in Psychiatric and Neurological Patients

Goldstein, Gérald

doi:10.1007/978-1-4614-6744-1_3

Cited by 3 publications

(4 citation statements)

References 59 publications

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“…Conversely, obtaining very discrepant solutions could indicate that there is no meaningful underlying structure or subgroups within the dataset (Everitt et al, 2011). As Goldstein (2013) suggests, ultimately, researchers should choose the most interpretable, robust and parsimonious solutions that can be derived from their dataset. Third, the cross-sectional nature of our study does not allow for any claims regarding the course of patients' cognitive performance.…”

Section: Limitationsmentioning

confidence: 99%

Comparing cognitive clusters across first- and multiple-episode of psychosis

Sauvé

Malla

Joober

et al. 2018

Psychiatry Research

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Cognitive impairments in psychotic disorders (PD) present heterogeneously across patients. Between 2 and 5 clusters have been identified in previous studies with first-episode (FEP) and multiple-episodes of psychosis (MEP) patients suggesting different profiles of impairment. Past findings suggest there are differences between FEP and MEP patients regarding severity and number of affected cognitive domains. Heterogeneity of cognitive deficits in PD has perhaps hindered our understanding of their course. The present study compared non-affective FEP and MEP patients to assess whether illness chronicity could influence cognitive impairment profiles. We analyzed cognitive data, collected with the Cogstate Schizophrenia Battery, of FEP and MEP patients using cluster analysis. We compared clustering methods to obtain a more robust solution. For FEP patients, data were collected at their entry to a specialized clinic; the MEP group consisted of in-and outpatients. Results suggested cognitive heterogeneity was similar in FEP and MEP samples, although in different proportions. Three clusters were identified as the most stable solution and comprised groups of patients with either 1-no cognitive impairment (over-representation of FEP), 2-generalized deficits (over-representation of MEP), or 3intermediate impairments. These findings encourage early interventions adapted to the profile of impairment.

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Section: Limitationsmentioning

confidence: 99%

Comparing cognitive clusters across first- and multiple-episode of psychosis

Sauvé

Malla

Joober

et al. 2018

Psychiatry Research

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“…29 Specifically, clustering algorithms can be applied to a battery of neuropsychological variables to identify clusters of cognitive performance. 30 In this line, Jessen et al identified three clusters among 2389 unimpaired subjects, which corresponded to subjects without memory complaints, with general memory complaints, and with both general memory complaints and complaints regarding tasks of daily living. 31 However, the value of these clusters to predict cognitive decline or to identify people with early AD is unknown.…”

Section: Introductionmentioning

confidence: 98%

“…Unsupervised machine learning methods may help unravel these kinds of questions 29 . Specifically, clustering algorithms can be applied to a battery of neuropsychological variables to identify clusters of cognitive performance 30 . In this line, Jessen et al.…”

Section: Introductionmentioning

confidence: 99%

Clusters of cognitive performance predict long‐term cognitive impairment in elderly patients with subjective memory complaints and healthy controls

Jiménez‐Huete,

Villino‐Rodríguez,

Ríos‐Rivera

et al. 2024

Alzheimer's & Dementia

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INTRODUCTIONPatients with subjective memory complaints (SMC) may include subgroups with different neuropsychological profiles and risks of cognitive impairment.METHODSCluster analysis was performed on two datasets (n: 630 and 734) comprising demographic and neuropsychological data from SMC and healthy controls (HC). Survival analyses were conducted on clusters. Bayesian model averaging assessed the predictive utility of clusters and other biomarkers.RESULTSTwo clusters with higher and lower than average cognitive performance were detected in SMC and HC. Assignment to the lower performance cluster increased the risk of cognitive impairment in both datasets (hazard ratios: 1.78 and 2.96; Plog‐rank: 0.04 and <0.001) and was associated with lower hippocampal volumes and higher tau/amyloid beta 42 ratios in cerebrospinal fluid. The effect of SMC was small and confounded by mood.DISCUSSIONThis study provides evidence of the presence of cognitive clusters that hold biological significance and predictive value for cognitive decline in SMC and HC.Highlights Patients with subjective memory complaints include two cognitive clusters. Assignment to the lower performance cluster increases risk of cognitive impairment. This cluster shows a pattern of biomarkers consistent with incipient Alzheimer's disease pathology. The same cognitive cluster structure is found in healthy controls. The effect of memory complaints on risk of cognitive decline is small and confounded.

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“…Identifying subgroups of patients who have distinguishable cognitive profiles that, in turn can assist in treatment planning and patient care, is crucial. Cluster analysis (CA) allows for the identification of homogeneous subgroups where cognitive heterogeneity is present, based on similarities in performance on baseline neuropsychological tests ( 10 , 11 ).…”

Section: Introductionmentioning

confidence: 99%

Neuropsychological Assessments of Patients With Acquired Brain Injury: A Cluster Analysis Approach to Address Heterogeneity in Web-Based Cognitive Rehabilitation

et al. 2021

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We aimed to (1) apply cluster analysis techniques to mixed-type data (numerical and categorical) from baseline neuropsychological standard and widely used assessments of patients with acquired brain injury (ABI) (2) apply state-of-the-art cluster validity indexes (CVI) to assess their internal validity (3) study their external validity considering relevant aspects of ABI rehabilitation such as functional independence measure (FIM) in activities of daily life assessment (4) characterize the identified profiles by using demographic and clinically relevant variables and (5) extend the external validation of the obtained clusters to all cognitive rehabilitation tasks executed by the participants in a web-based cognitive rehabilitation platform (GNPT). We analyzed 1,107 patients with ABI, 58.1% traumatic brain injury (TBI), 21.8% stroke and 20.1% other ABIs (e.g., brain tumors, anoxia, infections) that have undergone inpatient GNPT cognitive rehabilitation from September 2008 to January 2021. We applied the k-prototypes algorithm from the clustMixType R package. We optimized seven CVIs and applied bootstrap resampling to assess clusters stability (fpc R package). Clusters' post hoc comparisons were performed using the Wilcoxon ranked test, paired t-test or Chi-square test when appropriate. We identified a three-clusters optimal solution, with strong stability (>0.85) and structure (e.g., Silhouette > 0.60, Gamma > 0.83), characterized by distinctive level of performance in all neuropsychological tests, demographics, FIM, response to GNPT tasks and tests normative data (e.g., the 3 min cut-off in Trail Making Test-B). Cluster 1 was characterized by severe cognitive impairment (N = 254, 22.9%) the mean age was 47 years, 68.5% patients with TBI and 22% with stroke. Cluster 2 was characterized by mild cognitive impairment (N = 376, 33.9%) mean age 54 years, 53.5% patients with stroke and 27% other ABI. Cluster 3, moderate cognitive impairment (N = 477, 43.2%) mean age 33 years, 83% patients with TBI and 14% other ABI. Post hoc analysis on cognitive FIM supported a significant higher performance of Cluster 2 vs. Cluster 3 (p < 0.001), Cluster 2 vs. Cluster 1 (p < 0.001) and Cluster 3 vs. Cluster 1 (p < 0.001). All patients executed 286,798 GNPT tasks, with performance significantly higher in Cluster 2 and 3 vs. Cluster 1 (p < 0.001).

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Application of Cluster Analysis to Investigate Neuropsychological Heterogeneity in Psychiatric and Neurological Patients

Cited by 3 publications

References 59 publications

Comparing cognitive clusters across first- and multiple-episode of psychosis

Comparing cognitive clusters across first- and multiple-episode of psychosis

Clusters of cognitive performance predict long‐term cognitive impairment in elderly patients with subjective memory complaints and healthy controls

Neuropsychological Assessments of Patients With Acquired Brain Injury: A Cluster Analysis Approach to Address Heterogeneity in Web-Based Cognitive Rehabilitation

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