intraindividual cognitive variability
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2020 ◽  
pp. 1-11
Author(s):  
Lauren A. Rutter ◽  
Ipsit V. Vahia ◽  
Eliza Passell ◽  
Brent P. Forester ◽  
Laura Germine

Abstract Objectives: Cognitive impairments are directly related to severity of symptoms and are a primary cause for functional impairment. Intraindividual cognitive variability likely plays a role in both risk and resiliency from symptoms. In fact, such cognitive variability may be an earlier marker of cognitive decline and emergent psychiatric symptoms than traditional psychiatric or behavioral symptoms. Here, our objectives were to survey the literature linking intraindividual cognitive variability, trauma, and dementia and to suggest a potential research agenda. Design: A wide body of literature suggests that exposure to major stressors is associated with poorer cognitive performance, with intraindividual cognitive variability in particular linked to the development of posttraumatic stress disorder (PTSD) in the aftermath of severe trauma. Measurements: In this narrative review, we survey the empirical studies to date that evaluate the connection between intraindividual cognitive variability, PTSD, and pathological aging including dementia. Results: The literature suggests that reaction time (RT) variability within an individual may predict future cognitive impairment, including premature cognitive aging, and is significantly associated with PTSD symptoms. Conclusions: Based on our findings, we argue that intraindividual RT variability may serve as a common pathological indicator for trauma-related dementia risk and should be investigated in future studies.


2017 ◽  
Vol 24 (2) ◽  
pp. 156-162 ◽  
Author(s):  
Wesley R. Cole ◽  
Emma Gregory ◽  
Jacques P. Arrieux ◽  
F. Jay Haran

AbstractObjectives:The Automated Neuropsychological Assessment Metrics 4 TBI-MIL (ANAM4) is a computerized cognitive test often used in post-concussion assessments with U.S. service members (SMs). However, existing evidence remains mixed regarding ANAM4’s ability to identify cognitive issues following mild traumatic brain injury (mTBI). Studies typically examine ANAM4 using standardized scores and/ or comparisons to a baseline. A more fine-grained approach involves examining inconsistency within an individual’s performance (i.e., intraindividual variability).Methods:Data from 237 healthy control SMs and 105 SMs within seven days of mTBI who took the ANAM4 were included in analyses. Using each individual’s raw scores on a simple reaction time (RT) subtest (SRT1) that is repeated at the end of the battery (SRT2), we calculated mean raw RT and the intraindividual standard deviation (ISD) of trial-by-trial RT. Analyses investigated differences between groups in mean RT, RT variability (i.e., ISD), and change in ISD from SRT1 and SRT2.Results:Using regression residuals to control for demographic variables, analysis of variance, and pairwise comparisons revealed the control group had faster mean RT and smaller ISD compared to the mTBI group. Furthermore, the mTBI group had a significant increase in ISD from SRT1 to SRT2, with effect sizes exceeding the minimum practical effect for comparisons of ISD in SRT2 and change in ISD from SRT1 to SRT2.Conclusions:While inconsistencies in performance are often viewed as test error, the results suggest intraindividual cognitive variability may be more sensitive than traditional metrics in detecting changes in cognitive function after mTBI. Additionally, the findings highlight the utility of the ANAM4’s repeating a RT subtest at two points in the same session for exploring within-subject differences in performance variability. (JINS, 2018,24, 156–162)


2016 ◽  
Vol 22 (10) ◽  
pp. 1016-1025 ◽  
Author(s):  
Rebecca L. Koscik ◽  
Sara E. Berman ◽  
Lindsay R. Clark ◽  
Kimberly D. Mueller ◽  
Ozioma C. Okonkwo ◽  
...  

AbstractObjectives: Intraindividual cognitive variability (IICV) has been shown to differentiate between groups with normal cognition, mild cognitive impairment (MCI), and dementia. This study examined whether baseline IICV predicted subsequent mild to moderate cognitive impairment in a cognitively normal baseline sample. Methods: Participants with 4 waves of cognitive assessment were drawn from the Wisconsin Registry for Alzheimer’s Prevention (WRAP; n=684; 53.6(6.6) baseline age; 9.1(1.0) years follow-up; 70% female; 74.6% parental history of Alzheimer’s disease). The primary outcome was Wave 4 cognitive status (“cognitively normal” vs. “impaired”) determined by consensus conference; “impaired” included early MCI (n=109), clinical MCI (n=11), or dementia (n=1). Primary predictors included two IICV variables, each based on the standard deviation of a set of scores: “6 Factor IICV” and “4 Test IICV”. Each IICV variable was tested in a series of logistic regression models to determine whether IICV predicted cognitive status. In exploratory analyses, distribution-based cutoffs incorporating memory, executive function, and IICV patterns were used to create and test an MCI risk variable. Results: Results were similar for the IICV variables: higher IICV was associated with greater risk of subsequent impairment after covariate adjustment. After adjusting for memory and executive functioning scores contributing to IICV, IICV was not significant. The MCI risk variable also predicted risk of impairment. Conclusions: While IICV in middle-age predicts subsequent impairment, it is a weaker risk indicator than the memory and executive function scores contributing to its calculation. Exploratory analyses suggest potential to incorporate IICV patterns into risk assessment in clinical settings. (JINS, 2016, 22, 1016–1025)


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