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An FMRI-Compatible Symbol Search Task

Published online by Cambridge University Press:  20 March 2015

Spencer W. Liebel Open the ORCID record for Spencer W. Liebel [Opens in a new window] ,
Uraina S. Clark ,
Xiaomeng Xu Open the ORCID record for Xiaomeng Xu [Opens in a new window] ,
Hannah H. Riskin-Jones ,
Brittany E. Hawkshead ,
Nicolette F. Schwarz ,
Donald Labbe ,
Beth A. Jerskey  and
Lawrence H. Sweet
Spencer W. Liebel*
Affiliation:
University of Georgia, Department of Psychology, Athens, Georgia
Uraina S. Clark
Affiliation:
Icahn School of Medicine at Mount Sinai, Department of Neurology, New York, New York
Xiaomeng Xu
Affiliation:
Idaho State University, Department of Psychology, Pocatello, Idaho
Hannah H. Riskin-Jones
Affiliation:
Warren Alpert Medical School of Brown University, Department of Psychiatry, Providence, Rhode Island
Brittany E. Hawkshead
Affiliation:
University of Georgia, Department of Psychology, Athens, Georgia
Nicolette F. Schwarz
Affiliation:
University of Georgia, Department of Psychology, Athens, Georgia
Donald Labbe
Affiliation:
Warren Alpert Medical School of Brown University, Department of Psychiatry, Providence, Rhode Island
Beth A. Jerskey
Affiliation:
Warren Alpert Medical School of Brown University, Department of Psychiatry, Providence, Rhode Island
Lawrence H. Sweet
Affiliation:
University of Georgia, Department of Psychology, Athens, Georgia Warren Alpert Medical School of Brown University, Department of Psychiatry, Providence, Rhode Island
*
Correspondence and reprint requests to: Spencer W. Liebel, 139 Psychology Building, University of Georgia, Athens, GA 30606. E-mail: [email protected]
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Abstract

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)

Keywords

Cognitive processing speedAgingNeuropsychological assessmentCognitive declineConstruct validityReliability
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 21 , Issue 3 , March 2015 , pp. 231 - 238
Copyright
Copyright © The International Neuropsychological Society 2015 

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References

Bloom, D.E. (2011). 7 billion and counting. Science, 333(6042), 562–569. doi:10.1126/science.1209290 CrossRefGoogle ScholarPubMed
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.Google Scholar
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.Google Scholar
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. doi:10.1037/0033-2909.112.1.155 CrossRefGoogle ScholarPubMed
Cohen, J.E. (2003). Human population: The next half century. Science, 302(5648), 1172–1175. doi:10.1123/science.1088665 CrossRefGoogle ScholarPubMed
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. 383–394). New York: Springer. doi:10.1007/978-1-4419-6373-4 CrossRefGoogle Scholar
Cox, R.W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29, 162–173.CrossRefGoogle ScholarPubMed
Cronbach, L.J., & Meehl, P.E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302. doi:10.1037/h0040957 CrossRefGoogle ScholarPubMed
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 CrossRefGoogle ScholarPubMed
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), 491–510. doi:10.1016/j.neurobiolaging.2004.05.004 CrossRefGoogle Scholar
Delis, D.C., Kaplan, E., & Kramer, J.H. (2001). Delis Kaplan Executive Function System. San Antonio, TX: The Psychological Corporation.Google Scholar
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, 1946–1950. doi:10.1212/WNL.0000000000000475 CrossRefGoogle Scholar
Eckert, M.A. (2011). Slowing down: Age-related neurobiological predictors of processing speed. Frontiers in Neuroscience, 5, 25. doi:10.3389/fnins.2011.00025 CrossRefGoogle ScholarPubMed
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, 9–18. doi:10.1002/gps.998 CrossRefGoogle ScholarPubMed
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), 341–355.CrossRefGoogle ScholarPubMed
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), 189–198. doi:10.1016/0022-3956(75)90026-6 CrossRefGoogle ScholarPubMed
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), 109–117. doi:10.1002/gps.2087 CrossRefGoogle ScholarPubMed
Gunning-Dixon, F.M., & Raz, N. (2000). The cognitive correlates of white matter abnormalities in normal aging: A quantitative review. Neuropsychology, 14, 224–232. doi:10.1037//0894-4105.14.2.224 CrossRefGoogle ScholarPubMed
Hemphill, J.F. (2003). Interpreting the magnitudes of correlation coefficients. American Psychologist, 58(1), 78–79. doi:10.1037/0003-066X.58.1.78 CrossRefGoogle ScholarPubMed
Langenecker, S.A., Nielson, K.A., & Rao, S.M. (2004). fMRI of healthy older adults during Stroop interference. Neuroimage, 21, 192–200. doi:10.1016/j.neuroimage.2003.08.027 CrossRefGoogle ScholarPubMed
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), 409–417. doi:10.1177/1352458511423651 CrossRefGoogle ScholarPubMed
Lezak, M.D., Howieson, D.B., Bigler, E.D., & Tranel, D. (2012). Neuropsychological assessment (5th ed.). New York: Oxford University Press.Google Scholar
Matthews, P.M., Honey, G.D., & Bullmore, E.T. (2006). Applications of fMRI in translational medicine and clinical practice. Nature Reviews, Neuroscience, 7(9), 732–744. doi:10.1038/nrn1929 CrossRefGoogle ScholarPubMed
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), 197–213. doi:10.1080/13825585.2013.795513 CrossRefGoogle ScholarPubMed
Park, D.C., & Reuter-Lorenz, P. (2009). The adaptive brain: Aging and neurocognitive scaffolding. Annual Review of Psychology, 60, 173–196. doi:10.1146/annurev.psych.59.103006.093656 CrossRefGoogle ScholarPubMed
Phelps, E.A., Hyder, F., Blamire, A.M., & Shulman, R.G. (1997). FMRI of the prefrontal cortex during overt verbal fluency. Neuroreport, 8, 561–565.CrossRefGoogle ScholarPubMed
Rabbitt, P. (2002). Aging and cognition. In: H. Pashler (Ed.), Steven’s handbook of experimental psychology (3rd ed.). New York: John Wiley & Sons.Google Scholar
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), 363–370. doi:10.1037/0894-4105.21.3.363 CrossRefGoogle ScholarPubMed
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), 310–319.CrossRefGoogle ScholarPubMed
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.Google Scholar
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), 207–222. doi:10.1016/j.intell.2008.12.004 CrossRefGoogle ScholarPubMed
Saad, Z.S. (2004). SUMA: An interface for surface-based intra- and inter-subject analysis with AFNI. Biomedical Imaging: Nano to Macro, 2, 1510–1513. doi:10.1109/ISBI.2004.1398837 Google Scholar
Salthouse, T.A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103(3), 403–428. doi:10.1037/0033-295X.103.3.403 CrossRefGoogle ScholarPubMed
Salthouse, T.A. (2000). Aging and measures of processing speed. Biological Psychology, 54, 35–54. doi:10.1016/S0301-0511(00)00052-1 CrossRefGoogle ScholarPubMed
Salthouse, T.A., & Coon, V.E. (1993). Influence of task-specific processing speed on age differences in memory. Journal of Gerontology, 48, 245–255.CrossRefGoogle ScholarPubMed
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), 91–110. doi:10.1037/0882-7974.18.1.91 CrossRefGoogle ScholarPubMed
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), 139–144.CrossRefGoogle ScholarPubMed
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), 281–294. doi:10.1017/S1041610205002693 CrossRefGoogle ScholarPubMed
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), 16–23. doi:10.1016/j.neurobiolaging.2004.12.008 CrossRefGoogle ScholarPubMed
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, 1275–1282. doi:10.1093/brain/awf125 CrossRefGoogle ScholarPubMed
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, 471–476. doi:10.1017/S1355617705050575 CrossRefGoogle ScholarPubMed
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), 149–153. doi:10.1136/jnnp.2005.070193 CrossRefGoogle Scholar
Wechsler, D. (1997). Wechsler Adult Intelligence Scale, Third Edition. New York: Pearson.Google Scholar
Wechsler, D. (2001). Wechsler Test of Adult Reading. New York: Pearson.Google Scholar
Wechsler, D. (2008). Wechsler Adult Intelligence Scale, Fourth Edition. New York: Pearson.Google Scholar
Wen, W., & Sachdev, P. (2004). The topography of white matter hyperintensities on brain MRI in healthy 60- to 64-year-old individuals. Neuroimage, 22, 144–154. doi:10.1016/j.neuroimage.2003.12.027 CrossRefGoogle ScholarPubMed
Whiting, W.L. IV, & Smith, A.D. (1997). Differential age-related processing limitation in recall and recognition tasks. Psychology and Aging, 12(2), 216–224. doi:10.1037/0882-7974.12.2.216 CrossRefGoogle ScholarPubMed