Cortical Thickness Changes After Computerized Working Memory Training in Patients With Mild Cognitive Impairment

Haakon R Hol, Marianne M Flak, Linda Chang, Gro Christine Christensen Løhaugen, Knut Jørgen Bjuland, Lars M Rimol, Andreas Engvig, Jon Skranes, Thomas Ernst, Bengt-Ove Madsen, Susanne S Hernes, Haakon R Hol, Marianne M Flak, Linda Chang, Gro Christine Christensen Løhaugen, Knut Jørgen Bjuland, Lars M Rimol, Andreas Engvig, Jon Skranes, Thomas Ernst, Bengt-Ove Madsen, Susanne S Hernes

Abstract

Background: Adaptive computerized working memory (WM) training has shown favorable effects on cerebral cortical thickness as compared to non-adaptive training in healthy individuals. However, knowledge of WM training-related morphological changes in mild cognitive impairment (MCI) is limited.

Objective: The primary objective of this double-blind randomized study was to investigate differences in longitudinal cortical thickness trajectories after adaptive and non-adaptive WM training in patients with MCI. We also investigated the genotype effects on cortical thickness trajectories after WM training combining these two training groups using longitudinal structural magnetic resonance imaging (MRI) analysis in Freesurfer.

Method: Magnetic resonance imaging acquisition at 1.5 T were performed at baseline, and after four- and 16-weeks post training. A total of 81 individuals with MCI accepted invitations to undergo 25 training sessions over 5 weeks. Longitudinal Linear Mixed effect models investigated the effect of adaptive vs. non-adaptive WM training. The LME model was fitted for each location (vertex). On all statistical analyzes, a threshold was applied to yield an expected false discovery rate (FDR) of 5%. A secondary LME model investigated the effects of LMX1A and APOE-ε4 on cortical thickness trajectories after WM training.

Results: A total of 62 participants/patients completed the 25 training sessions. Structural MRI showed no group difference between the two training regimes in our MCI patients, contrary to previous reports in cognitively healthy adults. No significant structural cortical changes were found after training, regardless of training type, across all participants. However, LMX1A-AA carriers displayed increased cortical thickness trajectories or lack of decrease in two regions post-training compared to those with LMX1A-GG/GA. No training or training type effects were found in relation to the APOE-ε4 gene variants.

Conclusion: The MCI patients in our study, did not have improved cortical thickness after WM training with either adaptive or non-adaptive training. These results were derived from a heterogeneous population of MCI participants. The lack of changes in the cortical thickness trajectory after WM training may also suggest the lack of atrophy during this follow-up period. Our promising results of increased cortical thickness trajectory, suggesting greater neuroplasticity, in those with LMX1A-AA genotype need to be validated in future trials.

Keywords: APOE genotype; LMX1A; MCI; cortical thickness; working memory training.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Hol, Flak, Chang, Løhaugen, Bjuland, Rimol, Engvig, Skranes, Ernst, Madsen and Hernes.

Figures

FIGURE 1
FIGURE 1
Overview of the patient inclusion process and visits during the study.
FIGURE 2
FIGURE 2
Top panels: Average cortical thickness maps of adaptive and non-adaptive training group, with mean differences from all three timepoints. Timepoint 1: Baseline, timepoint 2: 4 weeks after training cessation. Timepoint 3: 16 weeks after training cessation. Bottom panel: Cortical thickness analysis of training effect independent of training type (time, uncorrected), show no significant region after FDR correction (significance threshold of log10-p: 2.76 = 0.0017). Resampled surface maps, were smoothed with kernel factor of 30 mm (fwhm).
FIGURE 3
FIGURE 3
Top panels: Effect maps (Cohen’s D) illustrating training type group differences at each timepoint for the left and right hemispheres. Bottom row: Cortical thickness trajectory maps showing the interaction effect (Time × Training type, uncorrected). These interaction effects were no longer significant after FDR correction. Resampled surface maps, were smoothed with kernel factor of 30 mm (fwhm).
FIGURE 4
FIGURE 4
Regions with significant Time x LMX1A (AA vs. GA and GG) interaction on cortical thickness after FDR correction of the P-value, minimal significant FDR corrected P threshold is 10(– 2.58) = 0.00043. Resampled surface maps, were smoothed with kernel factor of 30 mm (fwhm). Brain regions showing significant group differences (after FDR correction) in the right hemisphere include the superior frontal region, and paracentral region. In the left hemisphere, there was no significant group difference in any region.

References

    1. Bellander M., Brehmer Y., Westerberg H., Karlsson S., Furth D., Bergman O., et al. (2011). Preliminary evidence that allelic variation in the LMX1A gene influences training-related working memory improvement. Neuropsychologia 49 1938–1942. 10.1016/j.neuropsychologia.2011.03.021
    1. Belleville S., Bherer L. (2012). Biomarkers of cognitive training effects in aging. Curr. Transl. Geriatr. Exp. Gerontol. Rep. 1 104–110. 10.1007/s13670-012-0014-5
    1. Belleville S., Clement F., Mellah S., Gilbert B., Fontaine F., Gauthier S. (2011). Training-related brain plasticity in subjects at risk of developing Alzheimer’s disease. Brain 134 1623–1634. 10.1093/brain/awr037
    1. Belleville S., Gilbert B., Fontaine F., Gagnon L., Menard E., Gauthier S. (2006). Improvement of episodic memory in persons with mild cognitive impairment and healthy older adults: evidence from a cognitive intervention program. Dement. Geriatr. Cogn. Disord. 22 486–499. 10.1159/000096316
    1. Benjamini Y., Krieger A. M., Yekutieli D. (2006). Adaptive linear step-up procedures that control the false discovery rate. Biometrika 93, 491–507. 10.1093/biomet/93.3.491
    1. Bernal-Rusiel J. L., Reuter M., Greve D. N., Fischl B., Sabuncu M. R. Alzheimer’s Disease Neuroimaging Initiative (2013). Spatiotemporal linear mixed effects modeling for the mass-univariate analysis of longitudinal neuroimage data. Neuroimage 81, 358–370. 10.1016/j.neuroimage.2013.05.049
    1. Butler M., McCreedy E., Nelson V. A., Desai P., Ratner E., Fink H. A., et al. (2018). Does cognitive training prevent cognitive decline: a systematic review. Ann. Intern. Med. 168 63–68. 10.7326/M17-1531
    1. Caeyenberghs K., Clemente A., Imms P., Egan G., Hocking D. R., Leemans A., et al. (2018). Evidence for training-dependent structural neuroplasticity in brain-injured patients: a critical review. Neurorehabil. Neural Repair 32 99–114. 10.1177/1545968317753076
    1. Chang L., Douet V., Bloss C., Lee K., Pritchett A., Jernigan T. L., et al. (2016). Gray matter maturation and cognition in children with different APOE epsilon genotypes. Neurology 87 585–594. 10.1212/WNL.0000000000002939
    1. Chang L., Lohaugen G. C., Andres T., Jiang C. S., Douet V., Tanizaki N., et al. (2017). Adaptive working memory training improved brain function in human immunodeficiency virus-seropositive patients. Ann.Neurol. 81 17–34. 10.1002/ana.24805
    1. Chang L., Wang G. J., Volkow N. D., Ernst T., Telang F., Logan J., et al. (2008). Decreased brain dopamine transporters are related to cognitive deficits in HIV patients with or without cocaine abuse. Neuroimage 42 869–878. 10.1016/j.neuroimage.2008.05.011
    1. Edmonds E. C., Weigand A. J., Hatton S. N., Marshall A. J., Thomas K. R., Ayala D. A., et al. (2020). Patterns of longitudinal cortical atrophy over 3 years in empirically derived MCI subtypes. Neurology 94 e2532–e2544. 10.1212/WNL.0000000000009462
    1. Engvig A., Fjell A. M., Westlye L. T., Moberget T., Sundseth O., Larsen V. A., et al. (2010). Effects of memory training on cortical thickness in the elderly. Neuroimage 52 1667–1676. 10.1016/j.neuroimage.2010.05.041
    1. Flak M. M., Hernes S. S., Chang L., Ernst T., Douet V., Skranes J., et al. (2014). The memory aid study: protocol for a randomized controlled clinical trial evaluating the effect of computer-based working memory training in elderly patients with mild cognitive impairment (MCI). Trials 15:156.
    1. Flak M. M., Hol H. R., Hernes S. S., Chang L., Engvig A., Bjuland K. J., et al. (2019). Adaptive computerized working memory training in patients with mild cognitive impairment. A randomized double-blind active controlled trial. Front. Psychol. 10:807. 10.3389/fpsyg.2019.00807
    1. Goldman-Rakic P. S. (1996). Regional and cellular fractionation of working memory. Proc. Natl. Acad. Sci. U.S.A. 93 13473–13480. 10.1073/pnas.93.24.13473
    1. Hampstead B. M., Stringer A. Y., Stilla R. F., Giddens M., Sathian K. (2012). Mnemonic strategy training partially restores hippocampal activity in patients with mild cognitive impairment. Hippocampus 22 1652–1658. 10.1002/hipo.22006
    1. Hernes S. S., Flak M. M., Lohaugen G. C. C., Skranes J., Hol H. R., Madsen B. O., et al. (2021). Working memory training in amnestic and Non-amnestic patients with mild cognitive impairment: preliminary findings from genotype variants on training effects. Front. Aging Neurosci. 13:624253. 10.3389/fnagi.2021.624253
    1. Hollingshead A. B., Redlich F. C. (2007). Social class and mental illness: a community study. 1958. Am. J. Public Health 97 1756–1757. 10.2105/ajph.97.10.1756
    1. Huang Y., Mucke L. (2012). Alzheimer mechanisms and therapeutic strategies. Cell 148 1204–1222. 10.1016/j.cell.2012.02.040
    1. Klingberg T. (2010). Training and plasticity of working memory. Trends Cogn. Sci. 14 317–324. 10.1016/j.tics.2010.05.002
    1. Klingberg T., Fernell E., Olesen P. J., Johnson M., Gustafsson P., Dahlstrom K., et al. (2005). Computerized training of working memory in children with ADHD–a randomized, controlled trial. J. Am. Acad. Child Adolesc. Psychiatry 44 177–186.
    1. Klingberg T., Forssberg H., Westerberg H. (2002). Training of working memory in children with ADHD. J. Clin. Exp. Neuropsychol. 24 781–791.
    1. Langa K. M., Levine D. A. (2014). The diagnosis and management of mild cognitive impairment: a clinical review. JAMA 312 2551–2561. 10.1001/jama.2014.13806
    1. Lauharatanahirun N., Bansal K., Thurman S. M., Vettel J. M., Giesbrecht B., Grafton S. T., et al. (2020). Flexibility of brain regions during working memory curtails cognitive consequences to lack of sleep. arXiv Neurons Cogn. [Preprint]. arXiv:2009.07233,
    1. Lawlor-Savage L., Clark C. M., Goghari V. M. (2019). No evidence that working memory training alters gray matter structure: a MRI surface -based analysis. Behav. Brain Res. 360 323–340. 10.1016/j.bbr.2018.12.008
    1. Lee H., Nakamura K., Narayanan S., Brown R. A., Arnold D. L. Alzheimer’s Disease Neuroimaging Initiative (2019). Estimating and accounting for the effect of MRI scanner changes on longitudinal whole-brain volume change measurements. Neuroimage 184 555–565. 10.1016/j.neuroimage.2018.09.062
    1. Lerch J. P., Evans A. C. (2005). Cortical thickness analysis examined through power analysis and a population simulation. Neuroimage 24 163–173. 10.1016/j.neuroimage.2004.07.045
    1. Li S. C., Lindenberger U., Backman L. (2010). Dopaminergic modulation of cognition across the life span. Neurosci. Biobehav. Rev. 34 625–630. 10.1016/j.neubiorev.2010.02.003
    1. Maas D. A., Angulo M. C. (2021). Can enhancing neuronal activity improve myelin repair in multiple sclerosis? Front. Cell Neurosci. 15:645240. 10.3389/fncel.2021.645240
    1. Metzler-Baddeley C., Caeyenberghs K., Foley S., Jones D. K. (2016). Task complexity and location specific changes of cortical thickness in executive and salience networks after working memory training. Neuroimage 130 48–62.
    1. Nee D. E., D’Esposito M. (2018). The representational basis of working memory. Curr. Top. Behav. Neurosci. 37 213–230. 10.1007/7854_2016_456
    1. Nee D. E., Brown J. W., Askren M. K., Berman M. G., Demiralp E., Krawitz A., et al. (2012). A meta-analysis of executive components of working memory. Cereb. Cortex 23 264–282. 10.1093/cercor/bhs007
    1. Nissim N. R., O’Shea A. M., Bryant V., Porges E. C., Cohen R., Woods A. J. (2016). Frontal structural neural correlates of working memory performance in older adults. Front. Aging Neurosci. 8:328. 10.3389/fnagi.2016.00328
    1. Nyberg C. K., Nordvik J. E., Becker F., Rohani D. A., Sederevicius D., Fjell A. M., et al. (2018). A longitudinal study of computerized cognitive training in stroke patients - effects on cognitive function and white matter. Top. Stroke Rehabil. 25 241–247. 10.1080/10749357.2018.1443570
    1. Petersen R. C. (2004). Mild cognitive impairment as a diagnostic entity. J. Intern. Med. 256 183–194.
    1. Petersen R. C. (2009). Early diagnosis of Alzheimer’s disease: is MCI too late? Curr. Alzheimer Res. 6 324–330. 10.2174/156720509788929237
    1. Petersen R. C., Smith G. E., Waring S. C., Ivnik R. J., Kokmen E., Tangelos E. G. (1997). Aging, memory, and mild cognitive impairment. Int. Psychogeriatr. 9(Suppl. 1) 65–69. 10.1017/s1041610297004717
    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. Prince M. J., Wu F., Guo Y., Gutierrez Robledo L. M., O’Donnell M., Sullivan R., et al. (2015). The burden of disease in older people and implications for health policy and practice. Lancet 385 549–562. 10.1016/S0140-6736(14)61347-7
    1. Puig M. V., Rose J., Schmidt R., Freund N. (2014). Dopamine modulation of learning and memory in the prefrontal cortex: insights from studies in primates, rodents, and birds. Front. Neural Circuits 8:93. 10.3389/fncir.2014.00093
    1. Rebok G. W., Ball K., Guey L. T., Jones R. N., Kim H. Y., King J. W., et al. (2014). Ten-year effects of the advanced cognitive training for independent and vital elderly cognitive training trial on cognition and everyday functioning in older adults. J. Am. Geriatr. Soc. 62 16–24. 10.1111/jgs.12607
    1. Reuter M., Fischl B. (2011). Avoiding asymmetry-induced bias in longitudinal image processing. Neuroimage 57 19–21. 10.1016/j.neuroimage.2011.02.076
    1. Reuter M., Rosas H. D., Fischl B. (2010). Highly accurate inverse consistent registration: a robust approach. Neuroimage 53 1181–1196. 10.1016/j.neuroimage.2010.07.020
    1. Reuter M., Tisdall M. D., Qureshi A., Buckner R. L., van der Kouwe A. J. W., Fischl B. (2015). Head motion during MRI acquisition reduces gray matter volume and thickness estimates. Neuroimage 107 107–115. 10.1016/j.neuroimage.2014.12.006
    1. Rottschy C., Langner R., Dogan I., Reetz K., Laird A. R., Schulz J. B., et al. (2012). Modelling neural correlates of working memory: a coordinate-based meta-analysis. Neuroimage 60 830–846. 10.1016/j.neuroimage.2011.11.050
    1. Salami A., Garrett D. D., Wahlin A., Rieckmann A., Papenberg G., Karalija N., et al. (2019). Dopamine D2/3 binding potential modulates neural signatures of working memory in a load-dependent fashion. J. Neurosci. 39 537–547. 10.1523/JNEUROSCI.1493-18.2018
    1. Salthouse T. A., Meinz E. J. (1995). Aging, inhibition, working memory, and speed. J. Gerontol. B Psychol. Sci. Soc. Sci. 50 297–306.
    1. Scheller E., Peter J., Schumacher L. V., Lahr J., Mader I., Kaller C. P., et al. (2017). APOE moderates compensatory recruitment of neuronal resources during working memory processing in healthy older adults. Neurobiol. Aging 56, 127–137. 10.1016/j.neurobiolaging.2017.04.015
    1. Segonne F., Dale A. M., Busa E., Glessner M., Salat D., Hahn H. K., et al. (2004). A hybrid approach to the skull stripping problem in MRI. Neuroimage 22 1060–1075. 10.1016/j.neuroimage.2004.03.032
    1. Sherman D. S., Mauser J., Nuno M., Sherzai D. (2017). The efficacy of cognitive intervention in mild cognitive impairment (MCI): a meta-analysis of outcomes on neuropsychological measures. Neuropsychol. Rev. 27 440–484. 10.1007/s11065-017-9363-3
    1. Simon S. S., Hampstead B. M., Nucci M. P., Duran F. L. S., Fonseca L. M., Martin M. D. G. M., et al. (2020). Training gains and transfer effects after mnemonic strategy training in mild cognitive impairment: a fMRI study. Int. J. Psychophysiol. 154 15–26. 10.1016/j.ijpsycho.2019.03.014
    1. Simon S. S., Tusch E. S., Holcomb P. J., Daffner K. R. (2016). Increasing working memory load reduces processing of cross-modal task-irrelevant stimuli even after controlling for task difficulty and executive capacity. Front. Hum. Neurosci. 10:380. 10.3389/fnhum.2016.00380
    1. Simons D. J., Boot W. R., Charness N., Gathercole S. E., Chabris C. F., Hambrick D. Z., et al. (2016). Do “Brain-Training” programs work? Psychol. Sci. Public Interest 17 103–186. 10.1177/1529100616661983
    1. Smith G. E., Housen P., Yaffe K., Ruff R., Kennison R. F., Mahncke H. W., et al. (2009). A cognitive training program based on principles of brain plasticity: results from the improvement in memory with plasticity-based adaptive cognitive training (IMPACT) study. J. Am. Geriatr. Soc. 57 594–603. 10.1111/j.1532-5415.2008.02167.x
    1. Soderqvist S., Bergman Nutley S., Peyrard-Janvid M., Matsson H., Humphreys K., Kere J., et al. (2012). Dopamine, working memory, and training induced plasticity: implications for developmental research. Dev. Psychol. 48 836–843. 10.1037/a0026179
    1. Stelzer J., Lohmann G., Mueller K., Buschmann T., Turner R. (2014). Deficient approaches to human neuroimaging. Front Hum Neurosci 8:462. 10.3389/fnhum.2014.00462
    1. Takeuchi H., Taki Y., Hashizume H., Sassa Y., Nagase T., Nouchi R., et al. (2011). Effects of training of processing speed on neural systems. J. Neurosci. 31 12139–12148. 10.1523/jneurosci.2948-11.2011
    1. Talassi E., Guerreschi M., Feriani M., Fedi V., Bianchetti A., Trabucchi M. (2007). Effectiveness of a cognitive rehabilitation program in mild dementia (MD) and mild cognitive impairment (MCI): a case control study. Arch. Gerontol. Geriatr. 44(Suppl. 1) 391–399. 10.1016/j.archger.2007.01.055
    1. Tomasi D., Caparelli E. C., Chang L., Ernst T. (2005). fMRI-acoustic noise alters brain activation during working memory tasks. Neuroimage 27 377–386. 10.1016/j.neuroimage.2005.04.010
    1. Tomasi D., Chang L., Caparelli E. C., Ernst T. (2007). Different activation patterns for working memory load and visual attention load. Brain Res. 1132 158–165. 10.1016/j.brainres.2006.11.030
    1. Tuminello E. R., Han S. D. (2011). The apolipoprotein e antagonistic pleiotropy hypothesis: review and recommendations. Int. J. Alzheimers Dis. 2011:726197. 10.4061/2011/726197
    1. Winblad B., Palmer K., Kivipelto M., Jelic V., Fratiglioni L., Wahlund L. O., et al. (2004). Mild cognitive impairment–beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J. Intern. Med. 256 240–246. 10.1111/j.1365-2796.2004.01380.x
    1. Winkler A. M., Greve D. N., Bjuland K. J., Nichols T. E., Sabuncu M. R., Haberg A. K., et al. (2018). Joint analysis of cortical area and thickness as a replacement for the analysis of the volume of the cerebral cortex. Cereb. Cortex 28 738–749. 10.1093/cercor/bhx308
    1. Wu Q., Ripp I., Emch M., Koch K. (2021). Cortical and subcortical responsiveness to intensive adaptive working memory training: an MRI surface-based analysis. Hum. Brain Mapp. 42 2907–2920. 10.1002/hbm.25412
    1. Zeighami Y., Evans A. C. (2021). Association vs. prediction: the impact of cortical surface smoothing and parcellation on brain age. Front. Big Data 4:637724. 10.3389/fdata.2021.637724
    1. Zelinski E. M., Spina L. M., Yaffe K., Ruff R., Kennison R. F., Mahncke H. W., et al. (2011). Improvement in memory with plasticity-based adaptive cognitive training: results of the 3-month follow-up. J. Am. Geriatr. Soc. 59 258–265. 10.1111/j.1532-5415.2010.03277.x
    1. Zhang H., Huntley J., Bhome R., Holmes B., Cahill J., Gould R. L., et al. (2019a). Effect of computerised cognitive training on cognitive outcomes in mild cognitive impairment: a systematic review and meta-analysis. BMJ Open 9:e027062. 10.1136/bmjopen-2018-027062
    1. Zhang H., Wang Z., Wang J., Lyu X., Wang X., Liu Y., et al. (2019b). Computerized multi-domain cognitive training reduces brain atrophy in patients with amnestic mild cognitive impairment. Transl. Psychiatry 9:48. 10.1038/s41398-019-0385-x

Source: PubMed

Sponsorer och medarbetare

Medicinska tillstånd

Läkemedelsinterventioner

3
Prenumerera