Changes in Neural Activity Underlying Working Memory after Computerized Cognitive Training in Older Adults

Erich S Tusch, Brittany R Alperin, Eliza Ryan, Phillip J Holcomb, Abdul H Mohammed, Kirk R Daffner, Erich S Tusch, Brittany R Alperin, Eliza Ryan, Phillip J Holcomb, Abdul H Mohammed, Kirk R Daffner

Abstract

Computerized cognitive training (CCT) may counter the impact of aging on cognition, but both the efficacy and neurocognitive mechanisms underlying CCT remain controversial. In this study, 35 older individuals were randomly assigned to Cogmed adaptive working memory (WM) CCT or an active control CCT, featuring five weeks of five ∼40 min sessions per week. Before and after the 5-week intervention, event-related potentials were measured while subjects completed a visual n-back task with three levels of demand (0-back, 1-back, 2-back). The anterior P3a served as an index of directing attention and the posterior P3b as an index of categorization/WM updating. We hypothesized that adaptive CCT would be associated with decreased P3 amplitude at low WM demand and increased P3 amplitude at high WM demand. The adaptive CCT group exhibited a training-related increase in the amplitude of the anterior P3a and posterior P3b in response to target stimuli across n-back tasks, while subjects in the active control CCT group demonstrated a post-training decrease in the anterior P3a. Performance did not differ between groups or sessions. Larger overall P3 amplitudes were strongly associated with better task performance. Increased post-CCT P3 amplitude correlated with improved task performance; this relationship was especially robust at high task load. Our findings suggest that adaptive WM training was associated with increased orienting of attention, as indexed by the P3a, and the enhancement of categorization/WM updating processes, as indexed by the P3b. Increased P3 amplitude was linked to improved performance; however. there was no direct association between adaptive training and improved performance.

Keywords: ERPs (Event-Related Potentials); P3; P3a; P3b; cognitive aging; computerized cognitive training; n-back task; working memory.

Figures

FIGURE 1
FIGURE 1
Hypothetical task-demand/resource utilization curves. (A) Downward shift of hypothetical task demand-activation curve. (B) Upward shift of hypothetical task demand-activation curve. (C) Rightward shift of hypothetical task demand-activation curve.
FIGURE 2
FIGURE 2
Subject flow chart.
FIGURE 3
FIGURE 3
N-Back task illustration.
FIGURE 4
FIGURE 4
Electrode montage with regions of interest (ROIs) highlighted.
FIGURE 5
FIGURE 5
Average waveforms plotted at Pz ROI.
FIGURE 6
FIGURE 6
Topographic scalp plots of P3 mean amplitude.
FIGURE 7
FIGURE 7
Amplitude at three ROI during three tasks.
FIGURE 8
FIGURE 8
N-Back performance and target P3 amplitude.
FIGURE 9
FIGURE 9
Hypothetical task-demand/resource utilization curve.

References

    1. Alperin B. R., Mott K. K., Rentz D. M., Holcomb P. J., Daffner K. R. (2014). Investigating the age-related “anterior shift” in the scalp distribution of the P3b component using principal component analysis. Psychophysiology 51 620–633. 10.1111/psyp.12206
    1. American Psychiatric Association (1994). Diagnostic and Statistical Manual of Mental Disorders. Washington, DC: American Psychological Association.
    1. Barcelo F., Escera C., Corral M. J., Perianez J. A. (2006). Task switching and novelty processing activate a common neural network for cognitive control. J. Cogn. Neurosci. 18 1734–1748. 10.1162/jocn.2006.18.10.1734
    1. Barcelo F., Perianez J. A., Knight R. T. (2002). Think differently: a brain orienting response to task novelty. Neuroreport 13 1887–1892. 10.1097/00001756-200210280-00011
    1. Barnes D. E., Covinsky K. E., Whitmer R. A., Kuller L. H., Lopez O. L., Yaffe K. (2009). Predicting risk of dementia in older adults: the late-life dementia risk index. Neurology 73 173–179. 10.1212/WNL.0b013e3181a81636
    1. Bassett S. S., Folstein M. F. (1993). Memory complaint, memory performance, and psychiatric diagnosis: a community study. J Geriatr.Psychiatry Neurol. 6 105–111. 10.1177/089198879300600207
    1. Belleville S., Mellah S., de Boysson C., Demonet J.-F., Bier B. (2014). The pattern and loci of training-induced brain changes in healthy older adults are predicted by the nature of the intervention. PLoS ONE 9:e102710 10.1371/journal.pone.0102710
    1. Brehmer Y., Rieckmann A., Bellander M., Westerberg H., Fischer H., Backman L. (2011). Neural correlates of training-related working-memory gains in old age. Neuroimage 58 1110–1120. 10.1016/j.neuroimage.2011.06.079
    1. Brehmer Y., Westerberg H., Backman L. (2012). Working-memory training in younger and older adults: training gains, transfer, and maintenance. Front. Hum. Neurosci. 6:63 10.3389/fnhum.2012.00063
    1. Commissaris C. J., Ponds R. W., Jolles J. (1998). Subjective forgetfulness in a normal Dutch population: possibilities for health education and other interventions. Patient Educ Couns 34 25–32. 10.1016/S0738-3991(98)00040-8
    1. Daffner K. R., Chong H., Sun X., Tarbi E. C., Riis J. L., McGinnis S. M., et al. (2011a). Mechanisms underlying age- and performance-related differences in working memory. J. Cogn. Neurosci. 23 1298–1314. 10.1162/jocn.2010.21540
    1. Daffner K. R., Mesulam M. M., Scinto L. F., Cohen L. G., Kennedy B. P., West W. C., et al. (1998). Regulation of attention to novel stimuli by frontal lobes: an event-related potential study. Neuroreport 9 787–791. 10.1097/00001756-199803300-00004
    1. Daffner K. R., Scinto L. F., Weitzman A. M., Faust R., Rentz D. M., Budson A. E., et al. (2003). Frontal and parietal components of a cerebral network mediating voluntary attention to novel events. J. Cogn. Neurosci. 15 294–313. 10.1162/089892903321208213
    1. Daffner K. R., Sun X., Tarbi E. C., Rentz D. M., Holcomb P. J., Riis J. L. (2011b). Does compensatory neural activity survive old-old age? Neuroimage 54 427–438. 10.1016/j.neuroimage.2010.08.006
    1. Daselaar S. M., Cabeza R. (2005). “Age-related changes in hemispheric organization,” in Cognitive Neuroscience of Aging eds Cabeza R., Nyberg L., Park D. (New York, NY: Oxford University Press; ) 325–353.
    1. De Sanctis P., Gomez-Ramirez M., Sehatpour P., Wylie G. R., Foxe J. J. (2009). Preserved executive function in high-performing elderly is driven by large-scale recruitment of prefrontal cortical mechanisms. Hum. Brain Mapp. 30 4198–4214. 10.1002/hbm.20839
    1. Delorme A., Makeig S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134 9–21. 10.1016/j.jneumeth.2003.10.009
    1. Dien J., Spencer K. M., Donchin E. (2004). Parsing the late positive complex: mental chronometry and the ERP components that inhabit the neighborhood of the P300. Psychophysiology 41 665–678. 10.1111/j.1469-8986.2004.00193.x
    1. Donchin E. (1981). Presidential address, 1980. Surprise!.Surprise? Psychophysiology 18 493–513. 10.1111/j.1469-8986.1981.tb01815.x
    1. Donchin E., Kramer A. F., Wickens C. D. (1986). “Applications of brain event-related potentials to problems in engineering psychology,” in Psychophysiology: Systems, Processes and Applications eds Donchin E., Coles M. G. H., Porges S. (New York, NY: Guilford Press; ) 702–778.
    1. Doyon J., Benali H. (2005). Reorganization and plasticity in the adult brain during learning of motor skills. Curr. Opin. Neurobiol. 15 161–167. 10.1016/j.conb.2005.03.004
    1. Edwards J. D., Hauser R. A., O’Connor M. L., Valdes E. G., Zesiewicz T. A., Uc E. Y. (2013). Randomized trial of cognitive speed of processing training in Parkinson disease. Neurology 81 1284–1290. 10.1212/WNL.0b013e3182a823ba
    1. Edwards J. D., Xu H., Clark D., Ross L. A., Unverzagt F. W. (2016). “The ACTIVE study: what we have learned and what is next? Cognitive training reduces incident dementia across ten years,” in Proceeding of the Alzheimer’s Association International Conference Toronto, ON.
    1. Federal Trade Comission (2016). “Lumosity to Pay $2 Million to Settle FTC Deceptive Advertising Charges for Its “Brain Training” Program”. Washington, DC: Federal Trade Comission.
    1. Fisher M., Holland C., Subramaniam K., Vinogradov S. (2009). Neuroplasticity-based cognitive training in schizophrenia: an interim report on the effects 6 months later. Schizophr. Bull 36 869–879. 10.1093/schbul/sbn170
    1. 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. J. Psychiatr. Res. 12 189–198. 10.1016/0022-3956(75)90026-6
    1. Friedman D., Cycowicz Y. M., Gaeta H. (2001). The novelty P3: an event-related brain potential (ERP) sign of the brain’s evaluation of novelty. Neurosci. Biobehav. Rev. 25 355–373. 10.1016/S0149-7634(01)00019-7
    1. Green C. S., Strobach T., Schubert T. (2014). On methodological standards in training and transfer experiments. Psychol. Res. 78 756–772. 10.1007/s00426-013-0535-3
    1. Grier J. B. (1971). Nonparametric indexes for sensitivity and bias: computing formulas. Psychol. Bull. 75 424–429. 10.1037/h0031246
    1. Hannay H. (1988). “Psychophysical measurement techniques and their application to neuropsychology,” in Experimental Techniques in Human Neuropsychology ed. Hannay H. (New York, NY: Oxford University Press Inc.) 45–94.
    1. Heinzel S., Lorenz R. C., Brockhaus W. R., Wustenberg T., Kathmann N., Heinz A., et al. (2014). Working memory load-dependent brain response predicts behavioral training gains in older adults. J. Neurosci. 34 1224–1233. 10.1523/jneurosci.2463-13.2014
    1. Huckans M., Hutson L., Twamley E., Jak A., Kaye J., Storzbach D. (2013). Efficacy of cognitive rehabilitation therapies for mild cognitive impairment (MCI) in older adults: working toward a theoretical model and evidence-based interventions. Neuropsychol. Rev. 23 63–80. 10.1007/s11065-013-9230-9
    1. Ivnik R. J., Malec J. F., Smith G. E., Tangalos E. G., Petersen R. C. (1996). Neuropsychological tests’ norms above age 55: COWAT, BNT, MAE token, WRAT-R reading, AMNART, stroop, TMT, and JLO. Clin. Neuropsychol. 10 262–278. 10.1080/13854049608406689
    1. Jaeggi S. M., Buschkuehl M., Jonides J., Perrig W. J. (2008). Improving fluid intelligence with training on working memory. Proc. Natl. Acad. Sci. U.S.A. 105 6829–6833. 10.1073/pnas.0801268105
    1. Karbach J., Kray J. (2009). How useful is executive control training? Age differences in near and far transfer of task-switching training. Dev. Sci. 12 978–990. 10.1111/j.1467-7687.2009.00846.x
    1. Klingberg T. (2010). Training and plasticity of working memory. Trends Cogn. Sci. 14 317–324. 10.1016/j.tics.2010.05.002
    1. Knight R. T., Scabini D. (1998). Anatomic bases of event-related potentials and their relationship to novelty detection in humans. J. Clin. Neurophysiol. 15 3–13. 10.1097/00004691-199801000-00003
    1. Kok A. (2001). On the utility of P3 amplitude as a measure of processing capacity. Psychophysiology 38 557–577. 10.1017/S0048577201990559
    1. Kueider A. M., Parisi J. M., Gross A. L., Rebok G. W. (2012). Computerized cognitive training with older adults: a systematic review. PLoS ONE 7:e40588 10.1371/journal.pone.0040588
    1. Kutas M., McCarthy G., Donchin E. (1977). Augmenting mental chronometry: the P300 as a measure of stimulus evaluation time. Science 197 792–795. 10.1126/science.887923
    1. Lampit A., Hallock H., Valenzuela M. (2014). Computerized cognitive training in cognitively healthy older adults: a systematic review and meta-analysis of effect modifiers. PLoS Med. 11:e1001756 10.1371/journal.pmed.1001756
    1. Lansing A. E., Ivnik R. J., Cullum C. M., Randolph C. (1999). An empirically derived short form of the Boston Naming Test. Arch. Clin. Neuropsychol. 14 481–487. 10.1016/S0887-6177(98)00022-5
    1. Lopez-Calderon J., Luck S. J. (2014). ERPLAB: an open-source toolbox for the analysis of event-related potentials. Front. Hum. Neurosci. 8:213 10.3389/fnhum.2014.00213
    1. Max Planck Institute (2014). A Consensus on the Brain Training Industry from the Scientific Community. Available at:
    1. McNamara D. S., Scott J. L. (2001). Working memory capacity and strategy use. Mem. Cognit. 29 10–17. 10.3758/BF03195736
    1. Melby-Lervag M., Hulme C. (2013). Is working memory training effective? A meta-analytic review. Dev. Psychol. 49 270–291. 10.1037/a0028228
    1. O’Brien J. L., Edwards J. D., Maxfield N. D., Peronto C. L., Williams V. A., Lister J. J. (2013). Cognitive training and selective attention in the aging brain: an electrophysiological study. Clin. Neurophysiol. 124 2198–2208. 10.1016/j.clinph.2013.05.012
    1. Olesen P. J., Westerberg H., Klingberg T. (2004). Increased prefrontal and parietal activity after training of working memory. Nat. Neurosci. 7 75–79. 10.1038/nn1165
    1. Petersen R. C., Smith G. E., Waring S. C., Ivnik R. J., Tangalos E. G., Kokmen E. (1999). Mild cognitive impairment: clinical characterization and outcome. Arch. Neurol. 56 303–308. 10.1001/archneur.56.3.303
    1. Polich J. (1996). Meta-analysis of P300 normative aging studies. Psychophysiology 33 334–353. 10.1111/j.1469-8986.1996.tb01058.x
    1. Polich J. (2007). Updating P300: an integrative theory of P3a and P3b. Clin. Neurophysiol. 118 2128–2148. 10.1016/j.clinph.2007.04.019
    1. Polich J., Kok A. (1995). Cognitive and biological determinants of P300: an integrative review. Biol. Psychol. 41 103–146. 10.1016/0301-0511(95)05130-9
    1. Ponds R. W., Commissaris K. J., Jolles J. (1997). Prevalence and covariates of subjective forgetfulness in a normal population in The Netherlands. Int. J Aging Hum. Dev. 45 207–221. 10.2190/MVQ1-WB58-875H-Y4X0
    1. Reitan R., Wolfson D. (1985). The Halstead-Reitan Neuropsychological Test Battery: Theory and Clinical Interpretation. Tucson, AZ: Neuropsychology Press.
    1. Reuter-Lorenz P. A., Cappell K. A. (2008). Neurocognitive aging and the compensation hypothesis. Curr. Dir. Psychol. Sci. 17 177–182. 10.1111/j.1467-8721.2008.00570.x
    1. Riis J. L., Chong H., Ryan K. K., Wolk D. A., Rentz D. M., Holcomb P. J., et al. (2008). Compensatory neural activity distinguishes different patterns of normal cognitive aging. Neuroimage 39 441–454. 10.1016/j.neuroimage.2007.08.034
    1. Rose N. S., Rendell P. G., Hering A., Kliegel M., Bidelman G. M., Craik F. I. (2015). Cognitive and neural plasticity in older adults’ prospective memory following training with the Virtual Week computer game. Front. Hum. Neurosci. 9:592 10.3389/fnhum.2015.00592
    1. Ryan J., Paolo A. (1992). A screening procedure for estimating premorbid intelligence in the elderly. Clin. Neuropsychol. 6 53–62. 10.1080/13854049208404117
    1. Schneider-Garces N. J., Gordon B. A., Brumback-Peltz C. R., Shin E., Lee Y., Sutton B. P., et al. (2010). Span, CRUNCH, and beyond: working memory capacity and the aging brain. J. Cogn. Neurosci. 22 655–669. 10.1162/jocn.2009.21230
    1. Schulz K. F., Altman D. G., Moher D., CONSORT Group (2010). CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials. Trials 11:32 10.1186/1745-6215-11-32
    1. Shinaver C. S., III, Entwistle P. C., Soderqvist S. (2014). Cogmed WM training: reviewing the reviews. Appl. Neuropsychol. Child 3 163–172. 10.1080/21622965.2013.875314
    1. Shipstead Z., Redick T. S., Engle R. W. (2012). Is working memory training effective? Psychol. Bull. 138 628–654. 10.1037/a0027473
    1. Simon S. S., Yokomizo J. E., Bottino C. M. (2012). Cognitive intervention in amnestic Mild Cognitive Impairment: a systematic review. Neurosci. Biobehav. Rev. 36 1163–1178. 10.1016/j.neubiorev.2012.01.007
    1. Snyder E., Hillyard S. A. (1976). Long-latency evoked potentials to irrelevant, deviant stimuli. Behav. Biol. 16 319–331. 10.1016/S0091-6773(76)91447-4
    1. Spencer-Smith M., Klingberg T. (2015). Benefits of a working memory training program for inattention in daily life: a systematic review and meta-analysis. PLoS ONE 10:e0119522 10.1371/journal.pone.0119522
    1. The-Economist (2013). “Commercialising neuroscience: brain sells,” in The Economist. Business ed. (New York, NY: The Economist Newspaper Limited; ).
    1. United Nations (2013). Department of Economic and Social Affairs Population Division: World Population Ageing 2013. New York, NY: United Nations.
    1. Verleger R. (1997). On the utility of P3 latency as an index of mental chronometry. Psychophysiology 34 131–156. 10.1111/j.1469-8986.1997.tb02125.x
    1. Verleger R., Jaskowski P., Wascher E. (2005). Evidence for an integrative role of P3b in linking reaction to perception. J. Psychophysiol. 20 165–181. 10.1027/0269-8803.19.3.165
    1. Vermeij A., Kessels R. P., Heskamp L., Simons E. M., Dautzenberg P. L., Claassen J. A. (2016). Prefrontal activation may predict working-memory training gain in normal aging and mild cognitive impairment. Brain Imaging Behav. 10.1007/s11682-016-9508-7 [Epub ahead of print].
    1. Von Ah D., Carpenter J. S., Saykin A., Monahan P., Wu J., Yu M., et al. (2012). Advanced cognitive training for breast cancer survivors: a randomized controlled trial. Breast Cancer Res. Treat. 135 799–809. 10.1007/s10549-012-2210-6
    1. Wechsler D. (1997). Wechsler Memory Scale. WMS-III. Administration and Scoring Manual. San Antonio, TX: The Psychological Corporation.
    1. Wechsler D. (2008). Wechsler Adult Intelligence Scale. San Antonio, TX: Pearson.
    1. Wickens C., Kramer A., Vanasse L., Donchin E. (1983). Performance of concurrent tasks: a psychophysiological analysis of the reciprocity of information-processing resources. Science 221 1080–1082. 10.1126/science.6879207
    1. Zinke K., Zeintl M., Eschen A., Herzog C., Kliegel M. (2012). Potentials and limits of plasticity induced by working memory training in old-old age. Gerontology 58 79–87. 10.1159/000324240

Source: PubMed

스폰서 및 공동 작업자

건강 상태

약물 개입

3
구독하다