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Merging Clinical Neuropsychology and Functional Neuroimaging to Evaluate the Construct Validity and Neural Network Engagement of the n-Back Task

Published online by Cambridge University Press:  25 June 2014

Tonisha E. Kearney-Ramos ,
Jennifer S. Fausett ,
Jennifer L. Gess ,
Ashley Reno ,
Jennifer Peraza ,
Clint D. Kilts  and
G. Andrew James
Tonisha E. Kearney-Ramos*
Affiliation:
Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
Jennifer S. Fausett
Affiliation:
Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
Jennifer L. Gess
Affiliation:
Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
Ashley Reno
Affiliation:
University of Virginia School of Medicine, Charlottesville, Virginia
Jennifer Peraza
Affiliation:
New Mexico VA Health Care System, Albuquerque, New Mexico
Clint D. Kilts
Affiliation:
Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
G. Andrew James
Affiliation:
Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
*
Correspondence and reprint requests to: Tonisha E. Kearney-Ramos, University of Arkansas for Medical Sciences, Psychiatric Research Institute, 4301 W. Markham Street, #554, Little Rock, AR 72205-7199. E-mail: [email protected]
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Abstract

The n-back task is a widely used neuroimaging paradigm for studying the neural basis of working memory (WM); however, its neuropsychometric properties have received little empirical investigation. The present study merged clinical neuropsychology and functional magnetic resonance imaging (fMRI) to explore the construct validity of the letter variant of the n-back task (LNB) and to further identify the task-evoked networks involved in WM. Construct validity of the LNB task was investigated using a bootstrapping approach to correlate LNB task performance across clinically validated neuropsychological measures of WM to establish convergent validity, as well as measures of related but distinct cognitive constructs (i.e., attention and short-term memory) to establish discriminant validity. Independent component analysis (ICA) identified brain networks active during the LNB task in 34 healthy control participants, and general linear modeling determined task-relatedness of these networks. Bootstrap correlation analyses revealed moderate to high correlations among measures expected to converge with LNB (|ρ|≥0.37) and weak correlations among measures expected to discriminate (|ρ|≤0.29), controlling for age and education. ICA identified 35 independent networks, 17 of which demonstrated engagement significantly related to task condition, controlling for reaction time variability. Of these, the bilateral frontoparietal networks, bilateral dorsolateral prefrontal cortices, bilateral superior parietal lobules including precuneus, and frontoinsular network were preferentially recruited by the 2-back condition compared to 0-back control condition, indicating WM involvement. These results support the use of the LNB as a measure of WM and confirm its use in probing the network-level neural correlates of WM processing. (JINS, 2014, 20, 1–15)

Keywords

Working memoryConvergent validityDiscriminant validityDivergent validityReliability and validityBootstrapfMRIIndependent component analysisFrontoparietalLag task
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 20 , Supplement 7 , August 2014 , pp. 736 - 750
Copyright
Copyright © The International Neuropsychological Society 2014 

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References

Abou-Elseoud, A., Starck, T., Remes, J., Nikkinen, J., Tervonen, O., & Kiviniemi, V. (2010). The effect of model order selection in group PICA. Human Brain Mapping, 31(8), 1207–1216. doi:10.1002/hbm.20929 CrossRefGoogle ScholarPubMed
Allison, J., Meader, K., Loring, D., Figueroa, R., & Wright, J. (2000). Functional MRI cerebral activation and deactivation during finger movement. Neurology, 54(1), 135–142.Google Scholar
Andrews-Hanna, J., Reidler, J., Huang, C., & Buckner, R. (2010). Evidence for the default network’s role in spontaneous cognition. Journal of Neurophysiology, 104(1), 322–335. doi:10.1152/jn.00830.2009 Google Scholar
Arsalidou, M., Pascal-Leone, J., Johnson, J., Morris, D., & Taylor, M.J. (2013). A balancing act of the brain: Activations and deactivations driven by cognitive load. Brain and Behavior, 3, 273–285.Google Scholar
Baddeley, A. (1986). Working memory. Oxford: Clarendon Press.Google ScholarPubMed
Baddeley, A. (2012). Working memory: Theories, models, and controversies. In S.T. Fiske, D.L. Schacter & S.E. Taylor (Eds.), Annual review of psychology, (Vol. 63, pp 1–29). Palo Alto: Annual Reviews.Google Scholar
Baddeley, A., Banse, R., Huang, Y.M., & Page, M. (2012). Working memory and emotion: Detecting the hedonic detector. Journal of Cognitive Psychology, 24(1), 6–16. doi:10.1080/20445911.2011.613820 CrossRefGoogle Scholar
Beckmann, C.F. (2012). Modelling with independent components. Neuroimage, 62(2), 891–901.Google Scholar
Bennett, C.L., Petros, T.V., Johnson, M., & Ferraro, F.R. (2008). Individual differences in the influence of time of day on executive functions. Am J Psychol, 121(3), 349–361. doi:10.2307/20445471.Google Scholar
Bledowski, C., Kaiser, J., & Rahm, B. (2010). Basic operations in working memory: Contributions from functional imaging studies. Behavioural Brain Research, 214(2), 172–179.CrossRefGoogle ScholarPubMed
Braver, T.S., Cohen, J.D., Nystrom, L.E., Jonides, J., Smith, E.E., & Noll, D.C. (1997). A parametric study of prefrontal cortex involvement in human working memory. Neuroimage, 5(1), 49–62. doi:10.1006/nimg.1996.0247 CrossRefGoogle ScholarPubMed
Buckner, R., Andrews-Hanna, J., Schacter, D., Kingstone, A., & Miller, M. (2008). The brain’s default network - Anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences, 1124, 1–38. doi:10.1196/annals.1440.011 CrossRefGoogle ScholarPubMed
Burton, D.B., Ryan, J.J., Axlerod, B.N., Schellenberger, T., & Richards, H.M. (2003). A confirmatory factor analysis of the WMS-III in a clinical sample with crossvalidation in the standardization sample. Archives of Clinical Neuropsychology, 18(6), 629–641.CrossRefGoogle Scholar
Calhoun, V.D., Adali, T., Pearlson, G.D., & Pekar, J.J. (2001). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping, 14(3), 140–151. doi:10.1002/hbm.1048 Google Scholar
Calhoun, V.D., Adali, T., Pearlson, G.D., & Pekar, J.J. (2002). On complex infomax applied to functional MRI data. Paper presented at the International Conference on Acoustics, Speech, and Signal Processing, Orlando, Florida, May 13–17, 2002.Google Scholar
Champod, A.S., & Petrides, M. (2010). Dissociation within the frontoparietal network in verbal working memory: A parametric functional magnetic resonance imaging study. Journal of Neuroscience, 30(10), 3849–3856. doi:10.1523/jneurosci.0097-10.2010 CrossRefGoogle ScholarPubMed
Ciesielski, K.T., Lesnik, P.G., Savoy, R.L., Grant, E.P., & Ahlfors, S.P. (2006). Developmental neural networks in children performing a Categorical N-Back Task. Neuroimage, 33(3), 980–990.Google Scholar
Cohen, J.D., Perlstein, W.M., Braver, T.S., Nystrom, L.E., Noll, D.C., Jonides, J., & Smith, E.E. (1997). Temporal dynamics of brain activation during a working memory task. Nature, 386(6625), 604–608. doi:10.1038/386604a0 CrossRefGoogle ScholarPubMed
Colom, R., Abad, F.J., Quiroga, M.A., Shih, P.C., & Flores-Mendoza, C. (2008). Working memory and intelligence are highly related constructs, but why? Intelligence, 36, 584–606.Google Scholar
Congdon, E., Mumford, J., Cohen, J., Galvan, A., Aron, A., Xue, G., Poldrack, R. (2010). Engagement of large-scale networks is related to individual differences in inhibitory control. Neuroimage, 53(2), 653–663. doi:10.1016/j.neuroimage.2010.06.062 Google Scholar
Conway, A.R.A., Jarrold, C., Kane, M.J., Miyake, A., & Towse, J.N. (2007). Variation in working memory. New York, NY: Oxford University Press.Google Scholar
Conway, A.R.A., Kane, M., Bunting, M., Hambrick, D., Wilhelm, O., & Engle, R. (2005). Working memory span tasks: A methodological review and user's guide. Psychonomic Bulletin & Review, 12(5), 769–786. doi:10.3758/BF03196772 Google Scholar
Cousijn, J., Wiers, R.W., Ridderinkhof, K.R., van den Brink, W., Veltman, D.J., & Goudriaan, A.E. (2014). Effect of baseline cannabis use and working-memory network function on changes in cannabis use in heavy cannabis users: A prospective fMRI study. Human Brain Mapping, 35, 2470–2482. doi:10.1002/hbm.22342 CrossRefGoogle ScholarPubMed
Cox, R.W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162–173.Google Scholar
Cumming, G. (2008). Replication and p intervals: p values predict the future only vaguely, but confidence intervals do much better. Perspectives on Psychological Science, 3(4), 286–300. doi:10.1111/j.1745-6924.2008.00079.x Google Scholar
D’Esposito, M. (2007). From cognitive to neural models of working memory. Philosophical Transactions of the Royal Society B-Biological Sciences, 362(1481), 761–772. doi:10.1098/rstb.2007.2086 CrossRefGoogle ScholarPubMed
Delis, D.C., Kaplan, E., & Kramer, J.H. (2001). Delis-Kaplan Executive Function System. San Antonio, TX: The Psychological Corporation.Google Scholar
Desmond, J.E., & Fiez, J.A. (1998). Neuroimaging studies of the cerebellum: Language, learning and memory. Trends in Cognitive Sciences, 2(9), 355–362.CrossRefGoogle ScholarPubMed
Desmond, J.E., Gabrieli, J.D., Wagner, A.D., Ginier, B.L., & Glover, G.H. (1997). Lobular patterns of cerebellar activation in verbal working-memory and finger-tapping tasks as revealed by functional MRI. Journal of Neuroscience, 17(24), 9675–9685.CrossRefGoogle ScholarPubMed
Dosenbach, N.U., Fair, D.A., Miezin, F.M., Cohen, A.L., Wenger, K.K., Dosenbach, R.A., Petersen, S.E. (2007). Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences of the United States of America, 104(26), 11073–11078. doi:10.1073/pnas.0704320104 Google Scholar
Durisko, C., & Fiez, J.A. (2010). Functional activation in the cerebellum during working memory and simple speech tasks. Cortex, 46(7), 896–906. doi:10.1016/j.cortex.2009.09.009 Google Scholar
Efron, B., & Tibshirani, R. (1993). An introduction to the bootstrap. New York: Chapman and Hall.Google Scholar
Esposito, F., Aragri, A., Latorre, V., Popolizio, T., Scarabino, T., Cirillo, S., Di Salle, F. (2009). Does the default-mode functional connectivity of the brain correlate with working-memory performances? Archives Italiennes de Biologie, 147(1-2), 11–20.Google ScholarPubMed
Esposito, F., Bertolino, A., Scarabino, T., Latorre, V., Blasi, G., Popolizio, T., Di Salle, F. (2006). Independent component model of the default-mode brain function: Assessing the impact of active thinking. Brain Research Bulletin, 70(4-6), 263–269. doi:10.1016/j.brainresbull.2006.06.012 Google Scholar
First, M.B., Spitzer, R.L., Gibbon, M., & Williams, J.B.W. (2002). Structured clinical interview for DSM-IV-TR Axis I disorders, research version, non-patient edition (SCID-I/NP). New York: Biometrics Research, New York State Psychiatric Institute.Google Scholar
Fox, P.T., Laird, A.R., Fox, S.P., Fox, P.M., Uecker, A.M., Crank, M., Lancaster, J.L. (2005). BrainMap taxonomy of experimental design: Description and evaluation. Human Brain Mapping, 25(1), 185–198. doi:10.1002/hbm.20141 Google Scholar
Friedman, N.P., Miyake, A., Corley, R.P., Young, S.E., Defries, J.C., & Hewitt, J.K. (2006). Not all executive functions are related to intelligence. Psychological Science, 17(2), 172–179. doi:10.1111/j.1467-9280.2006.01681.x Google Scholar
Gagnon, L.G., & Belleville, S. (2011). Working memory in mild cognitive impairment and Alzheimer’s disease: Contribution of forgetting and predictive value of complex span tasks. Neuropsychology, 25(2), 226–236. doi:10.1037/a0020919 Google Scholar
Gardner, M.J., & Altman, D.G. (1986). Confidence intervals rather than P values: Estimation rather than hypothesis testing. British Medical Journal (Clinical Research Ed), 292(6522), 746–750.Google Scholar
Gevins, A., & Smith, M.E. (2000). Neurophysiological measures of working memory and individual differences in cognitive ability and cognitive style. Cerebral Cortex, 10(9), 829–839.CrossRefGoogle ScholarPubMed
Gorman, S., Barnes, M.A., Swank, P.R., Prasad, M., & Ewing-Cobbs, L. (2012). The effects of pediatric traumatic brain injury on verbal and visual-spatial working memory. Journal of the International Neuropsychological Society, 18(1), 29–38. doi:10.1017/S1355617711001251 Google Scholar
Gray, J.R., Chabris, C.F., & Braver, T.S. (2003). Neural mechanisms of general fluid intelligence. Nature Neuroscience, 6(3), 316–322. doi:10.1038/nn1014 Google Scholar
Greicius, M.D., Krasnow, B., Reiss, A.L., & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the United States of America, 100(1), 253–258. doi:10.1073/pnas.0135058100 Google Scholar
Greicius, M.D., Srivastava, G., Reiss, A.L., & Menon, V. (2004). Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI. Proceedings of the National Academy of Sciences of the United States of America, 101(13), 4637–4642. doi:10.1073/pnas.0308627101 CrossRefGoogle ScholarPubMed
Gusnard, D.A., & Raichle, M.E. (2001). Searching for a baseline: Functional imaging and the resting human brain. Nature Reviews. Neuroscience, 2(10), 685–694. doi:10.1038/35094500 CrossRefGoogle ScholarPubMed
Haller, S., Homola, G.A., Scheffler, K., Beckmann, C.F., & Bartsch, A.J. (2009). Background MR gradient noise and non-auditory BOLD activations: A data-driven perspective. Brain Research, 1282, 74–83. doi:10.1016/j.brainres.2009.05.094 CrossRefGoogle ScholarPubMed
Hautzel, H., Mottaghy, F.M., Specht, K., Müller, H.W., & Krause, B.J. (2009). Evidence of a modality-dependent role of the cerebellum in working memory? An fMRI study comparing verbal and abstract n-back tasks. Neuroimage, 47(4), 2073–2082. doi:10.1016/j.neuroimage.2009.06.005 CrossRefGoogle ScholarPubMed
Jackson, M.C., Morgan, H.M., Shapiro, K.L., Mohr, H., & Linden, D.E. (2011). Strategic resource allocation in the human brain supports cognitive coordination of object and spatial working memory. Human Brain Mapping, 32(8), 1330–1348. doi:10.1002/hbm.21112 CrossRefGoogle ScholarPubMed
Jaeggi, S.M., Buschkuehl, M., Perrig, W.J., & Meier, B. (2010). The concurrent validity of the N-back task as a working memory measure. Memory, 18(4), 394–412. doi:10.1080/09658211003702171 CrossRefGoogle ScholarPubMed
Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., & Smith, S.M. (2012). FSL. Neuroimage, 62(2), 782–790. doi:10.1016/j.neuroimage.2011.09.015 CrossRefGoogle ScholarPubMed
Jonides, J., Schumacher, E.H., Smith, E.E., Lauber, E.J., Awh, E., Minoshima, S., & Koeppe, R.A. (1997). Verbal working memory load affects regional brain activation as measured by PET. Journal of Cognitive Neuroscience, 9(4), 462–475. doi:10.1162/jocn.1997.9.4.462 Google Scholar
Just, M.A., & Carpenter, P.A. (1992). A capacity theory of comprehension: Individual differences in working memory. Psychology Review, 99(1), 122–149.CrossRefGoogle ScholarPubMed
Kane, M.J., Conway, A.R., Miura, T.K., & Colflesh, G.J. (2007). Working memory, attention control, and the N-back task: A question of construct validity. Journal of Expimental Psychology . Learning, Memory, and Cognition, 33(3), 615–622. doi:10.1037/0278-7393.33.3.615 Google Scholar
Kane, M.J., & Engle, R.W. (2002). The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: An individual-differences perspective. Psychonomic Bulletin & Review, 9(4), 637–671.Google Scholar
Kaplan, E., Goodglass, H., & Weintraub, S. (2001). The Boston Naming Test (2nd ed.). Philadelphia: Lippincott Williams & Wilkins.Google Scholar
Kessels, R.P., van den Berg, E., Ruis, C., & Brands, A.M. (2008). The backward span of the Corsi Block-Tapping Task and its association with the WAIS-III Digit Span. Assessment, 15(4), 426–434. doi:10.1177/1073191108315611 CrossRefGoogle ScholarPubMed
Kim, D.I., Manoach, D.S., Mathalon, D.H., Turner, J.A., Mannell, M., Brown, G.G., Calhoun, V.D. (2009). Dysregulation of working memory and default-mode networks in schizophrenia using independent component analysis, an fBIRN and MCIC study. Human Brain Mapping, 30(11), 3795–3811. doi:10.1002/hbm.20807 Google Scholar
Koziol, L.F., Budding, D.E., & Chidekel, D. (2012). From movement to thought: Executive function, embodied cognition, and the cerebellum. Cerebellum, 11(2), 505–525. doi:10.1007/s12311-011-0321-y CrossRefGoogle ScholarPubMed
Laird, A.R., Eickhoff, S.B., Li, K., Robin, D.A., Glahn, D.C., & Fox, P.T. (2009). Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling. Journal of Neuroscience, 29(46), 14496–14505. doi:10.1523/JNEUROSCI.4004-09.2009 Google Scholar
Leech, R., Kamourieh, S., Beckmann, C.F., & Sharp, D.J. (2011). Fractionating the default mode network: Distinct contributions of the ventral and dorsal posterior cingulate cortex to cognitive control. Journal of Neuroscience, 31(9), 3217–3224. doi:10.1523/JNEUROSCI.5626-10.2011 Google Scholar
Leiner, H.C., Leiner, A.L., & Dow, R.S. (1986). Does the cerebellum contribute to mental skills? Behavioral Neuroscience, 100(4), 443–454.CrossRefGoogle ScholarPubMed
Li, S.C., & Lewandowsky, S. (1995). Forward and backward recall: Different retrieval processes. Journal of Experimental Psychology. Learning, Memory, and Cognition, 21(4), 837–847.CrossRefGoogle Scholar
Li, Y.O., Adali, T., & Calhoun, V. (2007). Estimating the number of independent components for functional magnetic resonance imaging data. Human Brain Mapping, 28, 1251–1266.CrossRefGoogle ScholarPubMed
Liu, Y., Shen, H., Zhou, Z., & Hu, D. (2011). Sustained negative BOLD response in human fMRI finger tapping task. Plos One, 6(8), e23839. doi:10.1371/journal.pone.0023839 CrossRefGoogle ScholarPubMed
Marien, P., Engelborghs, S., & De Deyn, P.P. (2001). Cerebellar neurocognition: A new avenue. Acta Neurologica Belgica, 101(2), 96–109.Google ScholarPubMed
Mayer, J.S., Roebroeck, A., Maurer, K., & Linden, D.E. (2010). Specialization in the default mode: Task-induced brain deactivations dissociate between visual working memory and attention. Human Brain Mapping, 31(1), 126–139. doi:10.1002/hbm.20850 Google Scholar
McKeown, M.J., Jung, T.P., Makeig, S., Brown, G., Kindermann, S.S., Lee, T.W., & Sejnowski, T.J. (1998). Spatially independent activity patterns in functional MRI data during the stroop color-naming task. Proceedings of the National Academy of Sciences of the United States of America, 95(3), 803–810.CrossRefGoogle ScholarPubMed
McKiernan, K.A., Kaufman, J.N., Kucera-Thompson, J., & Binder, J.R. (2003). A parametric manipulation of factors affecting task-induced deactivation in functional neuroimaging. Journal of Cognitive Neuroscience, 15(3), 394–408. doi:10.1162/089892903321593117 Google Scholar
Menon, V., & Uddin, L.Q. (2010). Saliency, switching, attention and control: A network model of insula function. Brain Structure & Function, 214(5-6), 655–667. doi:10.1007/s00429-010-0262-0 CrossRefGoogle ScholarPubMed
Miller, K.M., Price, C.C., Okun, M.S., Montijo, H., & Bowers, D. (2009). Is the n-back task a valid neuropsychological measure for assessing working memory? Archives of Clinical Neuropsychology, 24(7), 711–717. doi:10.1093/arclin/acp063 CrossRefGoogle ScholarPubMed
Missonnier, P., Leonards, U., Gold, G., Palix, J., Ibáñez, V., & Giannakopoulos, P. (2003). A new electrophysiological index for working memory load in humans. Neuroreport, 14(11), 1451–1455. doi:10.1097/01.wnr.0000080101.90506.9b Google Scholar
Miyake, A., & Shah, P. (1999). Models of working memory: Mechanisms of active maintenance and executive control. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Nejad, A.B., Madsen, K.H., Ebdrup, B.H., Siebner, H.R., Rasmussen, H., Aggernæs, B., Baaré, W.F. (2013). Neural markers of negative symptom outcomes in distributed working memory brain activity of antipsychotic-naive schizophrenia patients. International Journal of Neuropsychopharmacology, 16(6), 1195–1204. doi:10.1017/S1461145712001253 CrossRefGoogle ScholarPubMed
Oberauer, K. (2005). Binding and inhibition in working memory: Individual and age differences in short-term recognition. Journal of Expimental Psychology . General, 134(3), 368–387. doi:10.1037/0096-3445.134.3.368 Google Scholar
Owen, A.M., McMillan, K.M., Laird, A.R., & Bullmore, E. (2005). N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies. Human Brain Mapping, 25(1), 46–59. doi:10.1002/hbm.20131 Google Scholar
Palacios, E.M., Sala-Llonch, R., Junque, C., Roig, T., Tormos, J.M., Bargallo, N., & Vendrell, P. (2012). White matter integrity related to functional working memory networks in traumatic brain injury. Neurology, 78(12), 852–860. doi:10.1212/WNL.0b013e31824c465a Google Scholar
Pamilo, S., Malinen, S., Hlushchuk, Y., Seppä, M., Tikka, P., & Hari, R. (2012). Functional subdivision of group-ICA results of fMRI data collected during cinema viewing. Plos One, 7(7), e42000. doi:10.1371/journal.pone.0042000 CrossRefGoogle ScholarPubMed
Penadés, R., Pujol, N., Catalán, R., Massana, G., Rametti, G., García-Rizo, C., Junqué, C. (2013). Brain effects of cognitive remediation therapy in schizophrenia: A structural and functional neuroimaging study. Biological Psychiatry, 73(10), 1015–1023. doi:10.1016/j.biopsych.2013.01.017 CrossRefGoogle ScholarPubMed
Raichle, M.E., MacLeod, A.M., Snyder, A.Z., Powers, W.J., Gusnard, D.A., & Shulman, G.L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 676–682. doi:10.1073/pnas.98.2.676 CrossRefGoogle ScholarPubMed
Ray, K.L., McKay, D.R., Fox, P.M., Riedel, M.C., Uecker, A.M., Beckmann, C.F., Laird, A.R. (2013). ICA model order selection of task co-activation networks. Frontiers in Neuroscience, 7, 237. doi:10.3389/fnins.2013.00237 Google Scholar
Reitan, R.M., & Wolfson, D. (1985). The Halstead-Reitan Neuropsychological Test Battery: Theory and interpretation. Tucson, AZ: Neuropsychology Press.Google Scholar
Roberts, R., & Gibson, E. (2002). Individual differences in sentence memory. Journal of Psycholinguistic Research, 31(6), 573–598.CrossRefGoogle ScholarPubMed
Robertson, I.H., Ward, T., Ridgeway, V., & Nimmo-Smith, I. (1994). The test of everyday attention. Bury St. Edmunds: Thames Valley Test Company.Google Scholar
Robertson, I.H., Ward, T., Ridgeway, V., & Nimmo-Smith, I. (1996). The structure of normal human attention: The test of everyday attention. Journal of the International Neuropsychological Society, 2(6), 525–534.Google Scholar
Sambataro, F., Blasi, G., Fazio, L., Caforio, G., Taurisano, P., Romano, R., Bertolino, A. (2010). Treatment with olanzapine is associated with modulation of the default mode network in patients with Schizophrenia. Neuropsychopharmacology, 35(4), 904–912. doi:10.1038/npp.2009.192 Google Scholar
Sattler, J.M., & Ryan, J.J. (2009). Assessment with the WAIS-IV. San Diego, CA: Jerome M. Sattler.Google Scholar
Schmiedek, F., Hildebrandt, A., Lövdén, M., Wilhelm, O., & Lindenberger, U. (2009). Complex span versus updating tasks of working memory: The gap is not that deep. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(4), 1089–1096. doi:10.1037/a0015730 Google Scholar
Schweitzer, J.B., Faber, T.L., Grafton, S.T., Tune, L.E., Hoffman, J.M., & Kilts, C.D. (2000). Alterations in the functional anatomy of working memory in adult attention deficit hyperactivity disorder. American Journal of Psychiatry, 157(2), 278–280.CrossRefGoogle ScholarPubMed
Shamosh, N.A., Deyoung, C.G., Green, A.E., Reis, D.L., Johnson, M.R., Conway, A.R., Gray, J.R. (2008). Individual differences in delay discounting: Relation to intelligence, working memory, and anterior prefrontal cortex. Psychological Science, 19(9), 904–911. doi:10.1111/j.1467-9280.2008.02175.x Google Scholar
Shelton, J.T., Elliott, E.M., Hill, B.D., Calamia, M.R., & Gouvier, W.D. (2009). A comparison of laboratory and clinical working memory tests and their prediction of fluid intelligence. Intelligence, 37(3), 283. doi:10.1016/j.intell.2008.11.005 Google Scholar
Shelton, J.T., Elliott, E.M., Matthews, R.A., Hill, B.D., & Gouvier, W.D. (2010). The relationships of working memory, secondary memory, and general fluid intelligence: Working memory is special. Journal of Experimental Psychology . Learning, Memory, and Cognition, 36(3), 813–820. doi:10.1037/a0019046 Google Scholar
Shelton, J.T., Metzger, R.L., & Elliott, E.M. (2007). A group-administered lag task as a measure of working memory. Behavior Research Methods, 39(3), 482–493.Google Scholar
Smith, E.E., & Jonides, J. (1997). Working memory: A view from neuroimaging. Cognitive Psychology, 33(1), 5–42. doi:10.1006/cogp.1997.0658 Google Scholar
Smith, S., Fox, P., Miller, K., Glahn, D., Fox, P., Mackay, C., Beckmann, C. (2009). Correspondence of the brain's functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America, 106(31), 13040–13045. doi:10.1073/pnas.0905267106 Google Scholar
Smyth, M.M., & Scholey, K.A. (1992). Determining spatial span memory: The role of movement time and articulation rate. The Quarterly Journal of Experimental Psychology, 45A(3), 479–501. doi:10.1080/02724989208250624 CrossRefGoogle Scholar
Spreng, R.N., & Grady, C.L. (2010). Patterns of brain activity supporting autobiographical memory, prospection, and theory of mind, and their relationship to the default mode network. Journal of Cognitive Neuroscience, 22(6), 1112–1123. doi:10.1162/jocn.2009.21282 CrossRefGoogle ScholarPubMed
Sridharan, D., Levitin, D.J., & Menon, V. (2008). A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proceedings of the National Academy of Sciences of the United States of America, 105(34), 12569–12574. doi:10.1073/pnas.0800005105 Google Scholar
Strick, P.L., Dum, R.P., & Fiez, J.A. (2009). Cerebellum and nonmotor function. Annual Review of Neuroscience, 32, 413–434. doi:10.1146/annurev.neuro.31.060407.125606 CrossRefGoogle ScholarPubMed
Tang, Y.Y., Rothbart, M.K., & Posner, M.I. (2012). Neural correlates of establishing, maintaining, and switching brain states. Trends in Cognitive Sciences, 16(6), 330–337. doi:10.1016/j.tics.2012.05.001 Google Scholar
Tohka, J., Foerde, K., Aron, A.R., Tom, S.M., Toga, A.W., & Poldrack, R.A. (2008). Automatic independent component labeling for artifact removal in fMRI. Neuroimage, 39(3), 1227–1245. doi:10.1016/j.neuroimage.2007.10.013 Google Scholar
Uddin, L.Q., Kelly, A.M., Biswal, B.B., Castellanos, F.X., & Milham, M.P. (2009). Functional connectivity of default mode network components: Correlation, anticorrelation, and causality. Human Brain Mapping, 30(2), 625–637. doi:10.1002/hbm.20531 Google Scholar
Wager, T.D., & Smith, E.E. (2003). Neuroimaging studies of working memory: A meta-analysis. Cognitive Affective & Behavioral Neuroscience, 3(4), 255–274.Google Scholar
Was, C.A., Dunlosky, J., Bailey, H., & Rawson, K.A. (2012). The unique contributions of the facilitation of procedural memory and working memory to individual differences in intelligence. Acta Psychologica (Amst), 139(3), 425–433. doi:10.1016/j.actpsy.2011.12.016 CrossRefGoogle ScholarPubMed
Wechsler, D. (1997). Wechsler Memory Scale-Third Edition (3rd ed.). San Antonio, TX: The Psychological Corporation.Google Scholar
Wechsler, D. (2008). Wechsler Adult Intelligence Scale-Fourth Edition: Technical and interpretive manual. San Antonio, TX: Pearson.Google Scholar
Wilde, N., & Strauss, E. (2002). Functional equivalence of WAIS-III/WMS-III digit and spatial span under forward and backward recall conditions. The Clinical Neuropsychologist, 16(3), 322–330. doi:10.1076/clin.16.3.322.13858 Google Scholar
Wilks, D.S. (2011). Forecast verification. In: Statistical methods in the atmospheric sciences (3rd ed., p 309). Oxford, UK: Academic Press.Google Scholar
Xu, J., Potenza, M.N., & Calhoun, V.D. (2013). Spatial ICA reveals functional activity hidden from traditional fMRI GLM-based analyses. Frontiers in Neuroscience, 7, 154. doi: 10.3389/fnins.2013.00154 Google Scholar
Xu, J., Zhang, S., Calhoun, V.D., Monterosso, J., Li, C.S., Worhunsky, P.D., Potenza, M.N. (2013). Task-related concurrent but opposite modulations of overlapping functional networks as revealed by spatial ICA. Neuroimage, 79, 62–71. doi:10.1016/j.neuroimage.2013.04.038 Google Scholar
Young, K.D., & Lewis, R.J. (1997). What is confidence? Part 1: The use and interpretation of confidence intervals. Annals of Emergency Medicine, 30(3), 307–310.Google Scholar
Zeharia, N., Hertz, U., Flash, T., & Amedi, A. (2012). Negative blood oxygenation level dependent homunculus and somatotopic information in primary motor cortex and supplementary motor area. Proceedings of the National Academy of Sciences of the United States of America, 109(45), 18565–18570. doi:10.1073/pnas.1119125109 Google Scholar
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