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An FMRI-Compatible Symbol Search Task

  • Spencer W. Liebel (a1), Uraina S. Clark (a2), Xiaomeng Xu (a3), Hannah H. Riskin-Jones (a4), Brittany E. Hawkshead (a1), Nicolette F. Schwarz (a1), Donald Labbe (a4), Beth A. Jerskey (a4) and Lawrence H. Sweet (a1) (a4)...


Our objective was to determine whether a Symbol Search paradigm developed for functional magnetic resonance imaging (FMRI) is a reliable and valid measure of cognitive processing speed (CPS) in healthy older adults. As all older adults are expected to experience cognitive declines due to aging, and CPS is one of the domains most affected by age, establishing a reliable and valid measure of CPS that can be administered inside an MR scanner may prove invaluable in future clinical and research settings. We evaluated the reliability and construct validity of a newly developed FMRI Symbol Search task by comparing participants’ performance in and outside of the scanner and to the widely used and standardized Symbol Search subtest of the Wechsler Adult Intelligence Scale (WAIS). A brief battery of neuropsychological measures was also administered to assess the convergent and discriminant validity of the FMRI Symbol Search task. The FMRI Symbol Search task demonstrated high test–retest reliability when compared to performance on the same task administered out of the scanner (r=.791; p<.001). The criterion validity of the new task was supported, as it exhibited a strong positive correlation with the WAIS Symbol Search (r=.717; p<.001). Predicted convergent and discriminant validity patterns of the FMRI Symbol Search task were also observed. The FMRI Symbol Search task is a reliable and valid measure of CPS in healthy older adults and exhibits expected sensitivity to the effects of age on CPS performance. (JINS, 2015, 22, 1–8)


Corresponding author

Correspondence and reprint requests to: Spencer W. Liebel, 139 Psychology Building, University of Georgia, Athens, GA 30606. E-mail:


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Bloom, D.E. (2011). 7 billion and counting. Science, 333(6042), 562569. doi:10.1126/science.1209290
Centers for Disease Control and Prevention. (2013). The state of aging and health in America. Atlanta, GA: Centers for Disease Control and Prevention, US Department of Health and Human Services.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155159. doi:10.1037/0033-2909.112.1.155
Cohen, J.E. (2003). Human population: The next half century. Science, 302(5648), 11721175. doi:10.1123/science.1088665
Cohen, R.A., & Sweet, L.H. (2011). Brain imaging in behavioral medicine and clinical neuroscience: Synthesis. In R.A. Cohen & L.H. Sweet (Eds.), Brain imaging in behavioral medicine and clinical neuroscience (pp. 383394). New York: Springer. doi:10.1007/978-1-4419-6373-4
Cox, R.W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29, 162173.
Cronbach, L.J., & Meehl, P.E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281302. doi:10.1037/h0040957
Debette, S., & Markus, H.S. (2010). The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: Systematic review and meta-analysis. British Medical Journal, 341, c3666. doi:10.1136/bmj.c3666
DeCarli, C., Massaro, J., Harvey, D., Hald, J., Tullberg, M., Au, R., … Wolf, P.A. (2005). Measures of brain morphology and infarction in the Framingham heart study: Establishing what is normal. Neurobiology of Aging, 26(4), 491510. doi:10.1016/j.neurobiolaging.2004.05.004
Delis, D.C., Kaplan, E., & Kramer, J.H. (2001). Delis Kaplan Executive Function System. San Antonio, TX: The Psychological Corporation.
Duering, M., Gesierich, B., Seiler, S., Pirpamer, L., Gonik, M., Hofer, E., … Dichgans, M. (2014). Strategic white matter tracts for processing speed deficits in age-related small vessel disease. Neurology, 84, 19461950. doi:10.1212/WNL.0000000000000475
Eckert, M.A. (2011). Slowing down: Age-related neurobiological predictors of processing speed. Frontiers in Neuroscience, 5, 25. doi:10.3389/fnins.2011.00025
Finkel, S.I., Mintzer, J.E., Dysken, M., Krishnan, K.R.R., Burt, T., & McRae, T. (2004). A randomized, placebo-controlled study of the efficacy and safety of sertraline in the treatment of the behavioral manifestations of Alzheimer’s disease in outpatients treated with donepezil. International Journal of Geriatric Psychiatry, 19, 918. doi:10.1002/gps.998
Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Hasselgrove, C., … Dale, A.M. (2002). Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), 341355.
Folstein, M.F., Folstein, S.E., & McHugh, P.R. (1975). “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189198. doi:10.1016/0022-3956(75)90026-6
Gunning-Dixon, F.M., Brickman, A.M., Cheng, J.C., & Alexopoulos, G.S. (2009). Aging of cerebral white matter: A review of MRI findings. International Journal of Geriatric Psychiatry, 24(2), 109117. doi:10.1002/gps.2087
Gunning-Dixon, F.M., & Raz, N. (2000). The cognitive correlates of white matter abnormalities in normal aging: A quantitative review. Neuropsychology, 14, 224232. doi:10.1037//0894-4105.14.2.224
Hemphill, J.F. (2003). Interpreting the magnitudes of correlation coefficients. American Psychologist, 58(1), 7879. doi:10.1037/0003-066X.58.1.78
Langenecker, S.A., Nielson, K.A., & Rao, S.M. (2004). fMRI of healthy older adults during Stroop interference. Neuroimage, 21, 192200. doi:10.1016/j.neuroimage.2003.08.027
Leavitt, V.M., Wylie, G., Genova, H.M., Chiaravalloti, N.D., & Deluca, J. (2012). Altered effective connectivity during performance of an information processing speed task in multiple sclerosis. Multiple Sclerosis, 18(4), 409417. doi:10.1177/1352458511423651
Lezak, M.D., Howieson, D.B., Bigler, E.D., & Tranel, D. (2012). Neuropsychological assessment (5th ed.). New York: Oxford University Press.
Matthews, P.M., Honey, G.D., & Bullmore, E.T. (2006). Applications of fMRI in translational medicine and clinical practice. Nature Reviews, Neuroscience, 7(9), 732744. doi:10.1038/nrn1929
Papp, K.V., Kaplan, R.F., Springate, B., Moscufo, N., Wakefield, D.B., Guttmann, C.R., & Wolfson, L. (2014). Processing speed in normal aging: Effects of white matter hyperintensities and hippocampal volume loss. Aging, Neuropsychology, and Cognition, 21(2), 197213. doi:10.1080/13825585.2013.795513
Park, D.C., & Reuter-Lorenz, P. (2009). The adaptive brain: Aging and neurocognitive scaffolding. Annual Review of Psychology, 60, 173196. doi:10.1146/annurev.psych.59.103006.093656
Phelps, E.A., Hyder, F., Blamire, A.M., & Shulman, R.G. (1997). FMRI of the prefrontal cortex during overt verbal fluency. Neuroreport, 8, 561565.
Rabbitt, P. (2002). Aging and cognition. In: H. Pashler (Ed.), Steven’s handbook of experimental psychology (3rd ed.). New York: John Wiley & Sons.
Rabbitt, P., Scott, M., Lunn, M., Thacker, N., Lowe, C., Pendelton, N., … Jackson, A. (2007). White matter lesions account for all age-related declines in speed but not in intelligence. Neuropsychology, 21(3), 363370. doi:10.1037/0894-4105.21.3.363
Randolph, C., Tierney, M.C., Mohr, E., & Chase, T.N. (1998). The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): Preliminary clinical validity. Journal of Clinical and Experimental Neuropsychology, 20(3), 310319.
Riskin-Jones, H.H., Xu, X., Clark, U.S., Labbe, D.R., & Sweet, L.H. (2014, April). New tool for quantification of white matter hyperintensities. Poster presented at the meeting of the Cognitive Aging Conference, Atlanta, Georgia.
Rypma, B., & Prabhakaran, V. (2009). When less is more and when more is more: The mediating roles of capacity and speed in brain-behavior efficiency. Intelligence, 37(2), 207222. doi:10.1016/j.intell.2008.12.004
Saad, Z.S. (2004). SUMA: An interface for surface-based intra- and inter-subject analysis with AFNI. Biomedical Imaging: Nano to Macro, 2, 15101513. doi:10.1109/ISBI.2004.1398837
Salthouse, T.A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103(3), 403428. doi:10.1037/0033-295X.103.3.403
Salthouse, T.A. (2000). Aging and measures of processing speed. Biological Psychology, 54, 3554. doi:10.1016/S0301-0511(00)00052-1
Salthouse, T.A., & Coon, V.E. (1993). Influence of task-specific processing speed on age differences in memory. Journal of Gerontology, 48, 245255.
Salthouse, T.A., & Ferrer-Caja, E. (2003). What needs to be explained to account for age-related effects on multiple cognitive variables? Psychology and Aging, 18(1), 91110. doi:10.1037/0882-7974.18.1.91
Schmidt, R., Scheltens, P., Erkinjuntti, T., Pantoni, L., Markus, H.S., Wallin, A., … Fazekas, F. (2004). White matter lesion progression: A surrogate endpoint for trials in cerebral small-vessel disease. Neurology, 63(1), 139144.
Shulman, K.I., Herrmann, N., Brodaty, H., Chiu, H., Lawlar, B., Ritchie, K., & Scanlan, J.M. (2006). IPA survey of brief cognitive screening instruments. International Psychogeriatrics, 18(2), 281294. doi:10.1017/S1041610205002693
Söderlund, H., Nilsson, L.G., Berger, K., Breteler, M.M., Dufouil, C., Fuhrer, R., … Launer, L.J. (2006). Cerebral changes on MRI and cognitive function: The CASCADE study. Neurobiology of Aging, 27(1), 1623. doi:10.1016/j.neurobiolaging.2004.12.008
Staffen, W., Mair, A., Zauner, H., Unterrainer, J., Neiderhofer, H., Kutzelnigg, A., … Ladurner, G. (2002). Cognitive function and fMRI in patients with multiple sclerosis: Evidence for compensatory cortical activation during an attention task. Brain, 125, 12751282. doi:10.1093/brain/awf125
Sweet, L.H., Paskavitz, J.F., O’Connor, M.J., Browndyke, J.N., Wellen, J.W., & Cohen, R.A. (2005). FMRI correlates of the WAIS-III Symbol Search subtest. Journal of the International Neuropsychological Society, 11, 471476. doi:10.1017/S1355617705050575
van den Heuvel, D.M., ten Dam, V.H., de Craen, A.J., Admiraal-Behloul, F., Olofsen, H., Bollen, E.L., … van Buchem, M.A. (2006). Increase in periventricular white matter hyperintensities parallels decline in mental processing speed in a non-demented elderly population. Journal of Neurology, Neurosurgery, and Psychiatry, 77(2), 149153. doi:10.1136/jnnp.2005.070193
Wechsler, D. (1997). Wechsler Adult Intelligence Scale, Third Edition. New York: Pearson.
Wechsler, D. (2001). Wechsler Test of Adult Reading. New York: Pearson.
Wechsler, D. (2008). Wechsler Adult Intelligence Scale, Fourth Edition. New York: Pearson.
Wen, W., & Sachdev, P. (2004). The topography of white matter hyperintensities on brain MRI in healthy 60- to 64-year-old individuals. Neuroimage, 22, 144154. doi:10.1016/j.neuroimage.2003.12.027
Whiting, W.L. IV, & Smith, A.D. (1997). Differential age-related processing limitation in recall and recognition tasks. Psychology and Aging, 12(2), 216224. doi:10.1037/0882-7974.12.2.216


An FMRI-Compatible Symbol Search Task

  • Spencer W. Liebel (a1), Uraina S. Clark (a2), Xiaomeng Xu (a3), Hannah H. Riskin-Jones (a4), Brittany E. Hawkshead (a1), Nicolette F. Schwarz (a1), Donald Labbe (a4), Beth A. Jerskey (a4) and Lawrence H. Sweet (a1) (a4)...


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