To date, most neuropsychological research has focused on comparing mean performance between groups on traditional outcome measures of neurocognition (i.e., test scores), as opposed to the variability of performance within the individuals who comprise the groups. Little attention has been given to intra-individual variability (IIV) on performance measures. This is a serious omission given that if IIV in neurocognitive performance is large, than relying on the mean performance can provide skewed estimates of group performances and thus lead to erroneous inferences 1,2. Neuropsychological IIV is generally defined as either variability in performance within a single task (i.e., across different trials in a reaction time task) or as dispersion of scores across various tasks administered in a single occasion (i.e., a neuropsychological battery3). An individual standard deviation is calculated across scores (i.e., RT trials, NP scores) to represent IIV.
Research suggests that IIV captures unique variance in cognitive abilities that is not reflected from measuring absolute or mean level of performance,4 and that it is a sensitive behavioral indicator of compromised CNS functioning, particularly in aging5-7, neurodegenerative disorders (e.g., Alzheimer’s, Parkinson’s, and Multiple Sclerosis)8,9, and infectious diseases (e.g., HIV)10-12. Overall, greater IIV is associated with worse overall neurocognitive functioning3,5,6,13,14 and can place individuals at risk for incident neurocognitive decline5,6,15. In fact, some studies indicate that IIV is a more sensitive marker of incident neurocognitive decline than typical measures of mean neurocognitive performance, whereas others indicate that when considered in conjunction with mean performance, IIV measures may be useful to better predict future declines5,16,17. In a prospective study of patients with Parkinson’s disease with and without incident dementia, increase in reaction time IIV significantly differentiated between those who developed dementia and those who did not17. Similarly, greater neurocognitive dispersion predicted worse neurocognitive functioning at a 4-year follow-up in a sample of community dwelling elders5, and predicted the development of dementia within a prospective community cohort of older adults16. In sum, greater IIV, as measured as either by reaction time measures or neurocognitive dispersion, at baseline is a strong predictor of incident neurocognitive decline several years later15.
In terms of neuroanatomical correlates, frontostriatal dysfunction has been implicated as a potential source for increased IIV3,9,18-23. Various studies implicate structural abnormalities, white-matter hyperintensities, and/or reduced neuromodulation of dopamine as potential sources for increases in IIV. Specifically, smaller prefrontal white-matter volumes20 and frontal white-matter hyperintensities18,19 are associated with greater IIV. In addition to compromised white-matter integrity within the frontal lobes, direct injury to the frontal lobes is linked with greater IIV. Focal injury to the frontal lobes in TBI patients, specifically in the dorsolateral prefrontal cortices, demonstrate greater IIV compared to individuals with lesions in other brain regions such as parietal, temporal, and occipital23. Furthermore, age-related losses of dopamine neurons in the anterior cingulate cortex, dorsolateral prefrontal cortex, and parietal cortex were directly associated with increases in IIV21.
Operationalizing neurocognitive IIV can be variable across the literature. The general procedure is to create an individual standard deviation (ISD) across the scores of a neurocognitive battery or across RT trials, however, certain studies have differed on whether selecting core tests or developing it across the entire battery. Additionally, a coefficient of variation (CoV) is used when one chooses to adjust for mean or overall performance24,25. For instance, within the context of HIV, all studies have developed a composite neurocognitive dispersion although different neurocognitive batteries were used between the studies and varied between the use of an ISD or a CoV10-12,24,26. Within the context of aging and dementia, researchers have derived a neurocognitive dispersion score from only three measures, as opposed to using scores from a full neuropsychological battery16. Currently, no study to date has evaluated whether neurocognitive dispersion scores created from different batteries or from different specific subtests yield differing neurocognitive dispersion scores, as such, it is unclear how generalizable the findings can be across these studies.
The Neuropsychological Assessment of Cognitive Deficits Considering Measures of Performance Variability (2015)
Neuropsychologists often face interpretational difficulties when assessing cognitive deficits, particularly in cases of unclear cerebral etiology. How can we be sure whether a single test score below the population average is indicative of a pathological brain condition or normal? In the past
Few years, the topic of intra-individual performance variability has gained great interest. On the basis of a large normative sample, two measures of performance variability and their importance for neuropsychological interpretation will be presented in this paper: the number of low scores and the level of dispersion. We conclude that low scores are common in healthy individuals. On the other hand, the level of dispersion is relatively small. Here, base rate information about abnormally low scores and abnormally high dispersion across cognitive abilities are provided to improve the awareness of normal variability and to serve clinicians as additional interpretive measures in the diagnostic process.
Citation: Tanner-Eggen, C., Balzer, C., Perrig, W. J., & Gutbrod, K. (2015). The neuropsychological assessment of cognitive deficits considering measures of performance variability. Archives of Clinical Neuropsychology, 30(3), 217-227.
1. Hultsch DF, MacDonald S. Intraindividual variability in performance as a theoretical window onto cognitive aging. New frontiers in cognitive aging. 2004:65-88.
2. Nesselroade JR. Elaborating the differential in differential psychology. Multivariate Behavioral Research. 2002;37(4):543-561.
3. MacDonald SW, Nyberg L, Bäckman L. Intra-individual variability in behavior: links to brain structure, neurotransmission and neuronal activity. Trends in neurosciences. 2006;29(8):474-480.
4. Ram N, Rabbitt P, Stollery B, Nesselroade JR. Cognitive performance inconsistency: intraindividual change and variability. Psychology and Aging.2005;20(4):623.
5. Christensen H, Mackinnon A, Korten A, Jorm A, Henderson A, Jacomb P. Dispersion in cognitive ability as a function of age: A longitudinal study of an elderly community sample. Aging, Neuropsychology, and Cognition. 1999;6(3):214-228.
6. Bielak A, Hultsch D, Strauss E, MacDonald S, Hunter M. Intraindividual variability is related to cognitive change in older adults: evidence for within-person coupling. Psychology and aging. 2010;25(3):575.
7. Bielak AA, Cherbuin N, Bunce D, Anstey KJ. Intraindividual variability is a fundamental phenomenon of aging: Evidence from an 8-year longitudinal study across young, middle, and older adulthood. Developmental psychology. 2014;50(1):143.
8. Burton CL, Strauss E, Hultsch DF, Moll A, Hunter MA. Intraindividual variability as a marker of neurological dysfunction: a comparison of Alzheimer's disease and Parkinson's disease. Journal of Clinical and Experimental Neuropsychology. 2006;28(1):67-83.
9. Murtha S, Cismaru R, Waechter R, Chertkow H. Increased variability accompanies frontal lobe damage in dementia. Journal of the International Neuropsychological Society. 2002;8(03):360-372.
10. Morgan EE, Woods SP, Delano-Wood L, Bondi MW, Grant I. Intraindividual variability in HIV infection: Evidence for greater neurocognitive dispersion in older HIV seropositive adults. Neuropsychology. 2011;25(5):645.
11. Morgan EE, Woods SP, Grant I. Intra-individual Neurocognitive Variability Confers Risk of Dependence in Activities of Daily Living among HIV-Seropositive Individuals without HIV-Associated Neurocognitive Disorders. Archives of Clinical Neuropsychology. 2012;27(3):293-303.
12. Morgan EE, Woods SP, Rooney A, et al. Intra-individual variability across neurocognitive domains in chronic hepatitis C infection: elevated dispersion is associated with serostatus and unemployment risk. The Clinical Neuropsychologist. 2012;26(4):654-674.
13. Reckess GZ, Varvaris M, Gordon B, Schretlen DJ. Within-person distributions of neuropsychological test scores as a function of dementia severity.Neuropsychology. 2014;28(2):254.
14. Hilborn JV, Strauss E, Hultsch DF, Hunter MA. Intraindividual variability across cognitive domains: investigation of dispersion levels and performance profiles in older adults. Journal of Clinical and Experimental Neuropsychology. 2009;31(4):412-424.
15. Bielak AA, Hultsch DF, Strauss E, MacDonald SW, Hunter MA. Intraindividual variability in reaction time predicts cognitive outcomes 5 years later.Neuropsychology. 2010;24(6):731.
16. Holtzer R, Verghese J, Wang C, Hall CB, Lipton RB. Within-person across-neuropsychological test variability and incident dementia. Jama. 2008;300(7):823-830.
17. de Frias CM, Dixon RA, Camicioli R. Neurocognitive Speed and Inconsistency in Parkinson's Disease with and without Incipient Dementia: An 18-Month Prospective Cohort Study. Journal of the International Neuropsychological Society. 2012;18(04):764-772.
18. Bunce D, Anstey KJ, Cherbuin N, et al. Cognitive deficits are associated with frontal and temporal lobe white matter lesions in middle-aged adults living in the community. PLoS One. 2010;5(10):e13567.
19. Bunce D, Anstey KJ, Christensen H, Dear K, Wen W, Sachdev P. White matter hyperintensities and within-person variability in community-dwelling adults aged 60–64 years. Neuropsychologia. 2007;45(9):2009-2015.
20. Lövdén M, Schmiedek F, Kennedy KM, Rodrigue KM, Lindenberger U, Raz N. Does variability in cognitive performance correlate with frontal brain volume?NeuroImage. 2013;64:209-215.
21. MacDonald SW, Karlsson S, Rieckmann A, Nyberg L, Bäckman L. Aging-related increases in behavioral variability: relations to losses of dopamine D1 receptors. The Journal of Neuroscience. 2012;32(24):8186-8191.
22. MacDonald SW, Li S-C, Bäckman L. Neural underpinnings of within-person variability in cognitive functioning. Psychology and aging. 2009;24(4):792.
23. Stuss DT, Murphy KJ, Binns MA, Alexander MP. Staying on the job: the frontal lobes control individual performance variability. Brain. 2003;126(11):2363-2380.
24. Thaler NS, Sayegh P, Arentoft A, Thames AD, Castellon SA, Hinkin CH. Increased Neurocognitive Intra-individual Variability Is Associated With Declines in Medication Adherence in HIV-Infected Adults. 2015.
25. Tractenberg RE, Pietrzak RH. Intra-individual variability in Alzheimer's disease and cognitive aging: definitions, context, and effect sizes. PloS one.2011;6(4):e16973.
26. Morgan EE, Doyle KL, Minassian A, et al. Elevated intraindividual variability in methamphetamine dependence is associated with poorer everyday functioning.Psychiatry research. 2014;220(1):527-534.