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Research Article| Volume 446, 120586, March 15, 2023

Moving intra-individual variability (IIV) towards clinical utility: IIV measured using a commercial testing platform

Published:February 12, 2023DOI:https://doi.org/10.1016/j.jns.2023.120586

      Highlights

      • Intra-individual variability (IIV) in reaction times from computerized psychomotor tasks may mark early cognitive decline
      • The commercial testing platform Cogstate offers standardized psychomotor task administration and provides measures of IIV
      • We found that Cogstate IIV value is consistent with alternative and experimental approaches to calculating IIV
      • The Cogstate platform provides an IIV value for clinical research and normative comparison for clinical use

      Abstract

      Objectives

      Intra-individual variability (IIV), measured across repeated response times (RT) during continuous psychomotor tasks, is an early marker of cognitive change in the context of neurodegeneration. To advance IIV towards broader application in clinical research, we evaluated IIV from a commercial cognitive testing platform and compared it to the calculation approaches used in experimental cognitive studies.

      Methods

      Cognitive assessment was administered in participants with multiple sclerosis (MS) during the baseline of an unrelated study. Cogstate was used for computer-based measures providing three timed-trial tasks measuring simple (Detection; DET) and choice (Identification; IDN) RT and working memory (One-Back; ONB). IIV for each task was automatically output by the program (calculated as a log10-transformed standard deviation or “LSD”). We calculated IIV from the raw RTs using coefficient of variation (CoV), regression-based, and ex-Gaussian methods. The IIV from each calculation was then compared by rank across participants.

      Results

      A total of n = 120 participants with MS aged 20–72 (Mean ± SD, 48.99 ± 12.09) completed the baseline cognitive measures. For each task, the interclass correlation coefficient was generated. Each ICC showed that LSD, CoV, ex-Gaussian, and regression methods clustered strongly (Average ICC for DET: 0.95 with 95% CI [0.93, 0.96]; Average ICC for IDN: 0.92 with 95% CI [0.88 to 0.93]; Average ICC for ONB: 0.93 with 95% CI [0.90 to 0.94]). Correlational analyses indicated the strongest correlation between LSD and CoV for all tasks (rs ≥ 0.94).

      Conclusion

      The LSD was consistent with research-based methods for IIV calculations. These findings support the use of LSD for the future measurement of IIV for clinical studies.

      Keywords

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      References

        • Lindenberger U.
        • von Oertzen T.
        Variability in cognitive aging: From taxonomy to theory.
        in: Lifespan Cognition: Mechanisms of Change. Oxford University Press, New York, NY, US2006: 297-314
        • Lövdén M.
        • Li S.C.
        • Shing Y.L.
        • Lindenberger U.
        Within-person trial-to-trial variability precedes and predicts cognitive decline in old and very old age: longitudinal data from the Berlin aging study.
        Neuropsychologia. 2007 Sep 20; 45: 2827-2838
        • Eilam-Stock T.
        • Shaw M.T.
        • Krupp L.B.
        • Charvet L.E.
        Early neuropsychological markers of cognitive involvement in multiple sclerosis.
        J. Neurol. Sci. 2021 Apr 15; 423117349
        • Maruff P.
        • Lim Y.Y.
        • Darby D.
        • Ellis K.A.
        • Pietrzak R.H.
        • Snyder P.J.
        • et al.
        Clinical utility of the cogstate brief battery in identifying cognitive impairment in mild cognitive impairment and Alzheimer’s disease.
        BMC Psychol. 2013; 1: 30
        • Stuss D.T.
        • Stethem L.L.
        • Hugenholtz H.
        • Picton T.
        • Pivik J.
        • Richard M.T.
        Reaction time after head injury: fatigue, divided and focused attention, and consistency of performance.
        J. Neurol. Neurosurg. Psychiatry. 1989 Jun 1; 52: 742-748
        • Stuss D.T.
        • Murphy K.J.
        • Binns M.A.
        • Alexander M.P.
        Staying on the job: the frontal lobes control individual performance variability.
        Brain J. Neurol. 2003 Nov; 126: 2363-2380
        • Stuss D.T.
        Characterization of stability of performance in patients with traumatic brain injury: variability and consistency on reaction time tests.
        Neuropsychology. 1994; 8 (1001): 316
        • Yao C.
        • Rich J.B.
        • Tirona K.
        • Bernstein L.J.
        Intraindividual variability in reaction time before and after neoadjuvant chemotherapy in women diagnosed with breast cancer.
        Psychooncology. 2017 Dec; 26: 2261-2268
        • Anderson A.E.
        • Jones J.D.
        • Thaler N.S.
        • Kuhn T.P.
        • Singer E.J.
        • Hinkin C.H.
        Intraindividual variability in neuropsychological performance predicts cognitive decline and death in HIV.
        Neuropsychology. 2018 Nov; 32: 966-972
        • Jones J.D.
        • Kuhn T.
        • Mahmood Z.
        • Singer E.J.
        • Hinkin C.H.
        • Thames A.D.
        Longitudinal intra-individual variability in neuropsychological performance relates to white matter changes in HIV.
        Neuropsychology. 2018 Feb; 32: 206-212
        • Thaler N.S.
        • Sayegh P.
        • Arentoft A.
        • Thames A.D.
        • Castellon S.A.
        • Hinkin C.H.
        Increased neurocognitive intra-individual variability is associated with declines in medication adherence in HIV-infected adults.
        Neuropsychology. 2015 Nov; 29: 919-925
        • Haynes B.I.
        • Bauermeister S.
        • Bunce D.
        A systematic review of longitudinal associations between reaction time intraindividual variability and age-related cognitive decline or impairment, dementia, and mortality.
        J. Int. Neuropsychol. Soc. 2017 May; 23: 431-445
        • Chow R.
        • Rabi R.
        • Paracha S.
        • Vasquez B.P.
        • Hasher L.
        • Alain C.
        • et al.
        Reaction time intraindividual variability reveals inhibitory deficits in single- and multiple-domain amnestic mild cognitive impairment.
        J. Gerontol. B Psychol. Sci. Soc. Sci. 2022 Jan 12; 77: 71-83
        • Christ B.U.
        • Combrinck M.I.
        • Thomas K.G.F.
        Both reaction time and accuracy measures of Intraindividual variability predict cognitive performance in Alzheimer’s disease.
        Front. Hum. Neurosci. 2018; 12: 124
        • Jones J.D.
        • Burroughs M.
        • Apodaca M.
        • Bunch J.
        Greater intraindividual variability in neuropsychological performance predicts cognitive impairment in de novo Parkinson’s disease.
        Neuropsychology. 2020 Jan; 34: 24-30
        • Jones J.D.
        • Valenzuela Y.G.
        • Uribe C.
        • Bunch J.
        • Kuhn T.P.
        Intraindividual variability in neuropsychological performance predicts longitudinal cortical volume loss in early Parkinson’s disease.
        Neuropsychology. 2022 Sep; 36: 513-519
        • Roalf D.R.
        • Rupert P.
        • Mechanic-Hamilton D.
        • Brennan L.
        • Duda J.E.
        • Weintraub D.
        • et al.
        Quantitative assessment of finger tapping characteristics in mild cognitive impairment, Alzheimer’s disease, and Parkinson’s disease.
        J. Neurol. 2018 Jun; 265: 1365-1375
        • Musso M.
        • Westervelt H.J.
        • Long J.D.
        • Morgan E.
        • Woods S.P.
        • Smith M.M.
        • et al.
        Intra-individual variability in prodromal Huntington disease and its relationship to genetic burden.
        J. Int. Neuropsychol. Soc. 2015 Jan; 21: 8-21
        • Wojtowicz M.
        • Berrigan L.I.
        • Fisk J.D.
        Intra-individual variability as a measure of information processing difficulties in multiple sclerosis.
        Int. J. MS Care. 2012; 14: 77-83
        • Holtzer R.
        • Foley F.
        • D’Orio V.
        • Spat J.
        • Shuman M.
        • Wang C.
        Learning and cognitive fatigue trajectories in multiple sclerosis defined using a burst measurement design.
        Mult. Scler. Houndmills Basingstoke Engl. 2013 Oct; 19: 1518-1525
        • Mazerolle E.L.
        • Wojtowicz M.A.
        • Omisade A.
        • Fisk J.D.
        Intra-individual variability in information processing speed reflects white matter microstructure in multiple sclerosis.
        NeuroImage Clin. 2013 Jun 28; 2: 894-902
        • Wojtowicz M.
        • Mazerolle E.L.
        • Bhan V.
        • Fisk J.D.
        Altered functional connectivity and performance variability in relapsing-remitting multiple sclerosis.
        Mult. Scler. Houndmills Basingstoke Engl. 2014 Oct; 20: 1453-1463
        • Riegler K.E.
        • Cadden M.
        • Guty E.T.
        • Bruce J.M.
        • Arnett P.A.
        Perceived fatigue impact and cognitive variability in multiple sclerosis.
        J. Int. Neuropsychol. Soc. 2022 Mar; 28: 281-291
        • Kochan N.A.
        • Bunce D.
        • Pont S.
        • Crawford J.D.
        • Brodaty H.
        • Sachdev P.S.
        Is intraindividual reaction time variability an independent cognitive predictor of mortality in old age? Findings from the Sydney memory and ageing study.
        PLoS One. 2017 Aug 9; 12e0181719
        • Charvet L.E.
        • Shaw M.
        • Frontario A.
        • Langdon D.
        • Krupp L.B.
        Cognitive impairment in pediatric-onset multiple sclerosis is detected by the brief international cognitive assessment for multiple sclerosis and computerized cognitive testing.
        Mult. Scler. Houndmills Basingstoke Engl. 2018 Apr; 24: 512-519
        • De Meijer L.
        • Merlo D.
        • Skibina O.
        • Grobbee E.
        • Gale J.
        • Haartsen J.
        • et al.
        Monitoring cognitive change in multiple sclerosis using a computerized cognitive battery.
        Mult. Scler. J. Exp. Transl. Clin. 2018 Dec 10; 4 (2055217318815513)
        • Darby D.G.
        • Pietrzak R.H.
        • Fredrickson J.
        • Woodward M.
        • Moore L.
        • Fredrickson A.
        • et al.
        Intraindividual cognitive decline using a brief computerized cognitive screening test.
        Alzheimers Dement. J. Alzheimers Assoc. 2012; 8: 95-104
        • Hultsch D.F.
        • MacDonald S.W.
        • Hunter M.A.
        • Levy-Bencheton J.
        • Strauss E.
        Intraindividual variability in cognitive performance in older adults: comparison of adults with mild dementia, adults with arthritis, and healthy adults.
        Neuropsychology. 2000 Oct; 14: 588-598
        • Silvia P.J.
        • Eddington K.M.
        • Harper K.L.
        • Burgin C.J.
        • Kwapil T.R.
        Reward-seeking deficits in major depression: unpacking appetitive task performance with ex-Gaussian response time variability analysis.
        Motiv Sci. 2020 Jun; 7: 219-224
        • Wojtowicz M.
        • Omisade A.
        • Fisk J.D.
        Indices of cognitive dysfunction in relapsing-remitting multiple sclerosis: intra-individual variability, processing speed, and attention network efficiency.
        J. Int. Neuropsychol. Soc. 2013 May; 19: 551-558
        • Lacouture Y.
        • Cousineau D.
        How to use MATLAB to fit ex-Gaussian and other probability functions to a distribution of response times.
        Tutor. Quant. Methods Psychol. 2008 Mar 1; : 4
        • Banh T.
        • Jin C.
        • Neuhaus J.
        • Mackin R.S.
        • Maruff P.
        • Stricker N.
        • et al.
        Unsupervised performance of the CogState brief battery in the brain health registry: implications for detecting cognitive decline.
        J. Prev. Alzheimers Dis. 2022; 9: 262-268
        • Krupp L.B.
        • Alvarez L.A.
        • LaRocca N.G.
        • Scheinberg L.C.
        Fatigue in multiple sclerosis.
        Arch. Neurol. 1988 Apr; 45: 435-437
        • Krupp L.B.
        • Serafin D.J.
        • Christodoulou C.
        Multiple sclerosis-associated fatigue.
        Expert. Rev. Neurother. 2010 Sep; 10: 1437-1447
        • Gallagher P.
        • Nilsson J.
        • Finkelmeyer A.
        • Goshawk M.
        • Macritchie K.A.
        • Lloyd A.J.
        • et al.
        Neurocognitive intra-individual variability in mood disorders: effects on attentional response time distributions.
        Psychol. Med. 2015 Oct; 45: 2985-2997
        • van Belle J.
        • van Raalten T.
        • Bos D.J.
        • Zandbelt B.B.
        • Oranje B.
        • Durston S.
        Capturing the dynamics of response variability in the brain in ADHD.
        NeuroImage Clin. 2014 Dec 1; 7: 132-141
        • Kälin A.M.
        • Pflüger M.
        • Gietl A.F.
        • Riese F.
        • Jäncke L.
        • Nitsch R.M.
        • et al.
        Intraindividual variability across cognitive tasks as a potential marker for prodromal Alzheimer’s disease.
        Front. Aging Neurosci. 2014; 6: 147
        • Bangen K.J.
        • Weigand A.J.
        • Thomas K.R.
        • Delano-Wood L.
        • Clark L.R.
        • Eppig J.
        • et al.
        Cognitive dispersion is a sensitive marker for early neurodegenerative changes and functional decline in nondemented older adults.
        Neuropsychology. 2019 Jul; 33: 599-608