The Exercising Brain: Changes in Functional Connectivity Induced by an Integrated Multimodal Cognitive and Whole-Body Coordination Training

Traute Demirakca, Vita Cardinale, Sven Dehn, Matthias Ruf, Gabriele Ende, Traute Demirakca, Vita Cardinale, Sven Dehn, Matthias Ruf, Gabriele Ende

Abstract

This study investigated the impact of "life kinetik" training on brain plasticity in terms of an increased functional connectivity during resting-state functional magnetic resonance imaging (rs-fMRI). The training is an integrated multimodal training that combines motor and cognitive aspects and challenges the brain by introducing new and unfamiliar coordinative tasks. Twenty-one subjects completed at least 11 one-hour-per-week "life kinetik" training sessions in 13 weeks as well as before and after rs-fMRI scans. Additionally, 11 control subjects with 2 rs-fMRI scans were included. The CONN toolbox was used to conduct several seed-to-voxel analyses. We searched for functional connectivity increases between brain regions expected to be involved in the exercises. Connections to brain regions representing parts of the default mode network, such as medial frontal cortex and posterior cingulate cortex, did not change. Significant connectivity alterations occurred between the visual cortex and parts of the superior parietal area (BA7). Premotor area and cingulate gyrus were also affected. We can conclude that the constant challenge of unfamiliar combinations of coordination tasks, combined with visual perception and working memory demands, seems to induce brain plasticity expressed in enhanced connectivity strength of brain regions due to coactivation.

Figures

Figure 1
Figure 1
Greater connectivity increases in trainees compared to controls subjects; seed regions are represented in the small brain images; seeds determine the colour of the result region: (a) BA4 left: red and BA1 left: cyan; (b) and (c) visual cortex: BA17 right: violet and BA18 right: blue; (d) and (e) auditory cortex: BA41 right: green, BA41 left: red, BA42 right: violet, and BA42 left: blue; (f) dorsolateral prefrontal cortex: right: red, FEF left: blue, and midcingulate cortex: violet.

References

    1. Hebb D. O. The Organization of Behavior: A Neuropsychological Theory. New York, NY, USA: Psychology Press; 1949.
    1. Kempermann G., Kuhn H. G., Gage F. H. More hippocampal neurons in adult mice living in an enriched environment. Nature. 1997;386(6624):493–495. doi: 10.1038/386493a0.
    1. Green C. S., Bavelier D. Exercising your brain: a review of human brain plasticity and training-induced learning. Psychology and Aging. 2008;23(4):692–701. doi: 10.1037/a0014345.
    1. Bavelier D., Levi D. M., Li R. W., Dan Y., Hensch T. K. Removing brakes on adult brain plasticity: from molecular to behavioral interventions. Journal of Neuroscience. 2010;30(45):14964–14971. doi: 10.1523/jneurosci.4812-10.2010.
    1. Gutchess A. Plasticity of the aging brain: new directions in cognitive neuroscience. Science. 2014;346(6209):579–582. doi: 10.1126/science.1254604.
    1. Zatorre R. J., Fields R. D., Johansen-Berg H. Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nature Neuroscience. 2012;15(4):528–536. doi: 10.1038/nn.3045.
    1. May A. Experience-dependent structural plasticity in the adult human brain. Trends in Cognitive Sciences. 2011;15(10):475–482. doi: 10.1016/j.tics.2011.08.002.
    1. Spolidoro M., Sale A., Berardi N., Maffei L. Plasticity in the adult brain:lessons from the visual system. Experimental Brain Research. 2009;192(3):335–341. doi: 10.1007/s00221-008-1509-3.
    1. Guerra-Carrillo B., MacKey A. P., Bunge S. A. Resting-state fMRI: a window into human brain plasticity. Neuroscientist. 2014;20(5):522–533. doi: 10.1177/1073858414524442.
    1. Kelly C., Castellanos F. X. Strengthening connections: functional connectivity and brain plasticity. Neuropsychology Review. 2014;24(1):63–76. doi: 10.1007/s11065-014-9252-y.
    1. Biswal B., Yetkin F. Z., Haughton V. M., Hyde J. S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine. 1995;34(4):537–541. doi: 10.1002/mrm.1910340409.
    1. Lowe M. J. The emergence of doing ‘nothing’ as a viable paradigm design. NeuroImage. 2012;62(2):1146–1151. doi: 10.1016/j.neuroimage.2012.01.014.
    1. Snyder A. Z., Raichle M. E. A brief history of the resting state: the Washington University perspective. NeuroImage. 2012;62(2):902–910. doi: 10.1016/j.neuroimage.2012.01.044.
    1. Fox M. D., Raichle M. E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience. 2007;8(9):700–711. doi: 10.1038/nrn2201.
    1. Albert N. B., Robertson E. M., Miall R. C. The resting human brain and motor learning. Current Biology. 2009;19(12):1023–1027. doi: 10.1016/j.cub.2009.04.028.
    1. Yoo K., Sohn W. S., Jeong Y. Tool-use practice induces changes in intrinsic functional connectivity of parietal areas. Frontiers in Human Neuroscience. 2013;7, article 49 doi: 10.3389/fnhum.2013.00049.
    1. Ma L., Narayana S., Robin D. A., Fox P. T., Xiong J. Changes occur in resting state network of motor system during 4 weeks of motor skill learning. NeuroImage. 2011;58(1):226–233. doi: 10.1016/j.neuroimage.2011.06.014.
    1. Vahdat S., Darainy M., Milner T. E., Ostry D. J. Functionally specific changes in resting-state sensorimotor networks after motor learning. Journal of Neuroscience. 2011;31(47):16907–16915. doi: 10.1523/jneurosci.2737-11.2011.
    1. Taubert M., Lohmann G., Margulies D. S., Villringer A., Ragert P. Long-term effects of motor training on resting-state networks and underlying brain structure. NeuroImage. 2011;57(4):1492–1498. doi: 10.1016/j.neuroimage.2011.05.078.
    1. Voss M. W., Prakash R. S., Erickson K. I., et al. Plasticity of brain networks in a randomized intervention trial of exercise training in older adults. Frontiers in Aging Neuroscience. 2010;2, article 32 doi: 10.3389/fnagi.2010.00032.
    1. Takeuchi H., Taki Y., Nouchi R., et al. Effects of working memory training on functional connectivity and cerebral blood flow during rest. Cortex. 2013;49(8):2106–2125. doi: 10.1016/j.cortex.2012.09.007.
    1. Jolles D. D., van Buchem M. A., Crone E. A., Rombouts S. A. R. B. Functional brain connectivity at rest changes after working memory training. Human Brain Mapping. 2013;34(2):396–406. doi: 10.1002/hbm.21444.
    1. Takeuchi H., Taki Y., Nouchi R., et al. Effects of multitasking-training on gray matter structure and resting state neural mechanisms. Human Brain Mapping. 2014;35(8):3646–3660. doi: 10.1002/hbm.22427.
    1. Mackey A. P., Singley A. T. M., Bunge S. A. Intensive reasoning training alters patterns of brain connectivity at rest. Journal of Neuroscience. 2013;33(11):4796–4803. doi: 10.1523/jneurosci.4141-12.2013.
    1. Martínez K., Solana A. B., Burgaleta M., et al. Changes in resting-state functionally connected parietofrontal networks after videogame practice. Human Brain Mapping. 2013;34(12):3143–3157. doi: 10.1002/hbm.22129.
    1. Harmelech T., Preminger S., Wertman E., Malach R. The day-after effect: long term, hebbian-like restructuring of resting-state fMRI patterns induced by a single epoch of cortical activation. Journal of Neuroscience. 2013;33(22):9488–9497. doi: 10.1523/jneurosci.5911-12.2013.
    1. Megumi F., Yamashita A., Kawato M., Imamizu H. Functional MRI neurofeedback training on connectivity between two regions induces long-lasting changes in intrinsic functional network. Frontiers in Human Neuroscience. 2015;9, article 160 doi: 10.3389/fnhum.2015.00160.
    1. Kraft E. Cognitive function, physical activity, and aging: possible biological links and implications for multimodal interventions. Aging, Neuropsychology, and Cognition. 2012;19(1-2):248–263. doi: 10.1080/13825585.2011.645010.
    1. Hötting K., Röder B. Beneficial effects of physical exercise on neuroplasticity and cognition. Neuroscience and Biobehavioral Reviews. 2013;37(9):2243–2257. doi: 10.1016/j.neubiorev.2013.04.005.
    1. Li R., Zhu X., Yin S., et al. Multimodal intervention in older adults improves resting-state functional connectivity between the medial prefrontal cortex and medial temporal lobe. Frontiers in Aging Neuroscience. 2014;6, article 39 doi: 10.3389/fnagi.2014.00039.
    1. Holzschneider K., Wolbers T., Röder B., Hötting K. Cardiovascular fitness modulates brain activation associated with spatial learning. NeuroImage. 2012;59(3):3003–3014. doi: 10.1016/j.neuroimage.2011.10.021.
    1. Lewis C. M., Baldassarre A., Committeri G., Romani G. L., Corbetta M. Learning sculpts the spontaneous activity of the resting human brain. Proceedings of the National Academy of Sciences of the United States of America. 2009;106(41):17558–17563. doi: 10.1073/pnas.0902455106.
    1. Behrens T. E. J., Johansen-Berg H., Woolrich M. W., et al. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nature Neuroscience. 2003;6(7):750–757. doi: 10.1038/nn1075.
    1. Fama R., Sullivan E. V. Thalamic structures and associated cognitive functions: relations with age and aging. Neuroscience & Biobehavioral Reviews. 2015;54:29–37. doi: 10.1016/j.neubiorev.2015.03.008.
    1. Zhou B., Liu Y., Zhang Z., et al. Impaired functional connectivity of the thalamus in Alzheimer' s disease and mild cognitive impairment: a resting-state FMRI study. Current Alzheimer Research. 2013;10(7):754–766. doi: 10.2174/15672050113109990146.
    1. Wang Z., Jia X., Liang P., et al. Changes in thalamus connectivity in mild cognitive impairment: evidence from resting state fMRI. European Journal of Radiology. 2012;81(2):277–285. doi: 10.1016/j.ejrad.2010.12.044.
    1. Buckner R. L., Krienen F. M., Castellanos A., Diaz J. C., Thomas Yeo B. T. The organization of the human cerebellum estimated by intrinsic functional connectivity. Journal of Neurophysiology. 2011;106(5):2322–2345. doi: 10.1152/jn.00339.2011.
    1. O'Reilly J. X., Beckmann C. F., Tomassini V., Ramnani N., Johansen-Berg H. Distinct and overlapping functional zones in the cerebellum defined by resting state functional connectivity. Cerebral Cortex. 2010;20(4):953–965. doi: 10.1093/cercor/bhp157.
    1. Pa J., Dutt S., Mirsky J. B., et al. The functional oculomotor network and saccadic cognitive control in healthy elders. NeuroImage. 2014;95:61–68. doi: 10.1016/j.neuroimage.2014.03.051.
    1. Hutchison R. M., Gallivan J. P., Culham J. C., Gati J. S., Menon R. S., Everling S. Functional connectivity of the frontal eye fields in humans and macaque monkeys investigated with resting-state fMRI. Journal of Neurophysiology. 2012;107(9):2463–2474. doi: 10.1152/jn.00891.2011.
    1. Anderson E. J., Jones D. K., O'Gorman R. L., Leemans A., Catani M., Husain M. Cortical network for gaze control in humans revealed using multimodal MRI. Cerebral Cortex. 2012;22(4):765–775. doi: 10.1093/cercor/bhr110.
    1. Ashburner J. A fast diffeomorphic image registration algorithm. NeuroImage. 2007;38(1):95–113. doi: 10.1016/j.neuroimage.2007.07.007.
    1. Whitfield-Gabrieli S., Nieto-Castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connectivity. 2012;2(3):125–141. doi: 10.1089/brain.2012.0073.
    1. Shirer W. R., Jiang H., Price C. M., Ng B., Greicius M. D. Optimization of rs-fMRI pre-processing for enhanced signal-noise separation, test-retest reliability, and group discrimination. NeuroImage. 2015;117:67–79. doi: 10.1016/j.neuroimage.2015.05.015.
    1. Behzadi Y., Restom K., Liau J., Liu T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage. 2007;37(1):90–101. doi: 10.1016/j.neuroimage.2007.04.042.
    1. Qing Z., Dong Z., Li S., Zang Y., Liu D. Global signal regression has complex effects on regional homogeneity of resting state fMRI signal. Magnetic Resonance Imaging. 2015 doi: 10.1016/j.mri.2015.07.011.
    1. Agcaoglu O., Miller R., Mayer A. R., Hugdahl K., Calhoun V. D. Lateralization of resting state networks and relationship to age and gender. NeuroImage. 2015;104:310–325. doi: 10.1016/j.neuroimage.2014.09.001.
    1. Luo C., Guo Z.-W., Lai Y.-X., et al. Musical training induces functional plasticity in perceptual and motor networks: insights from resting-state fMRI. PLoS ONE. 2012;7(5) doi: 10.1371/journal.pone.0036568.e36568
    1. Sampaio-Baptista C., Khrapitchev A. A., Foxley S., et al. Motor skill learning induces changes in white matter microstructure and myelination. Journal of Neuroscience. 2013;33(50):19499–19503. doi: 10.1523/JNEUROSCI.3048-13.2013.
    1. Sampaio-Baptista C., Filippini N., Stagg C. J., Near J., Scholz J., Johansen-Berg H. Changes in functional connectivity and GABA levels with long-term motor learning. NeuroImage. 2015;106:15–20. doi: 10.1016/j.neuroimage.2014.11.032.
    1. Draganski B., Gaser C., Busch V., Schuierer G., Bogdahn U., May A. Neuroplasticity: changes in grey matter induced by training. Nature. 2004;427(6972):311–312. doi: 10.1038/427311a.
    1. Culham J. C., Kanwisher N. G. Neuroimaging of cognitive functions in human parietal cortex. Current Opinion in Neurobiology. 2001;11(2):157–163. doi: 10.1016/S0959-4388(00)00191-4.
    1. Simon O., Mangin J.-F., Cohen L., Le Bihan D., Dehaene S. Topographical layout of hand, eye, calculation, and language-related areas in the human parietal lobe. Neuron. 2002;33(3):475–487. doi: 10.1016/S0896-6273(02)00575-5.
    1. Bartels A., Zeki S. Brain dynamics during natural viewing conditions—a new guide for mapping connectivity in vivo. NeuroImage. 2005;24(2):339–349. doi: 10.1016/j.neuroimage.2004.08.044.
    1. Herrero M.-T., Barcia C., Navarro J. M. Functional anatomy of thalamus and basal ganglia. Child's Nervous System. 2002;18(8):386–404. doi: 10.1007/s00381-002-0604-1.
    1. Hearne L., Cocchi L., Zalesky A., Mattingley J. B. Interactions between default mode and control networks as a function of increasing cognitive reasoning complexity. Human Brain Mapping. 2015;36(7):2719–2731. doi: 10.1002/hbm.22802.
    1. Bonzano L., Palmaro E., Teodorescu R., Fleysher L., Inglese M., Bove M. Functional connectivity in the resting-state motor networks influences the kinematic processes during motor sequence learning. European Journal of Neuroscience. 2015;41(2):243–253. doi: 10.1111/ejn.12755.
    1. Andreasen N. C. The role of the thalamus in schizophrenia. Canadian Journal of Psychiatry. 1997;42(1):27–33.
    1. Schmahmann J. D., Pandya D. N. Disconnection syndromes of basal ganglia, thalamus, and cerebrocerebellar systems. Cortex. 2008;44(8):1037–1066. doi: 10.1016/j.cortex.2008.04.004.
    1. Wang H. S., Rau C., Li Y., Chen Y., Yu R. Disrupted thalamic resting-state functional networks in schizophrenia. Frontiers in Behavioral Neuroscience. 2015;9, article 45 doi: 10.3389/fnbeh.2015.00045.
    1. Solé-Padullés C., Bartrés-Faz D., Junqué C., et al. Brain structure and function related to cognitive reserve variables in normal aging, mild cognitive impairment and Alzheimer's disease. Neurobiology of Aging. 2009;30(7):1114–1124. doi: 10.1016/j.neurobiolaging.2007.10.008.
    1. Sylvester C.-Y. C., Wager T. D., Lacey S. C., et al. Switching attention and resolving interference: fMRI measures of executive functions. Neuropsychologia. 2003;41(3):357–370. doi: 10.1016/s0028-3932(02)00167-7.
    1. Raichle M. E. The restless brain: how intrinsic activity organizes brain function. Philosophical Transactions of the Royal Society of London Series B: Biological Sciences. 2015;370(1668) doi: 10.1098/rstb.2014.0172.
    1. Zhang D., Raichle M. E. Disease and the brain's dark energy. Nature Reviews Neurology. 2010;6(1):15–28. doi: 10.1038/nrneurol.2009.198.
    1. Solesio-Jofre E., Serbruyns L., Woolley D. G., Mantini D., Beets I. A. M., Swinnen S. P. Aging effects on the resting state motor network and interlimb coordination. Human Brain Mapping. 2014;35(8):3945–3961. doi: 10.1002/hbm.22450.
    1. Seidler R., Erdeniz B., Koppelmans V., Hirsiger S., Mérillat S., Jäncke L. Associations between age, motor function, and resting state sensorimotor network connectivity in healthy older adults. NeuroImage. 2015;108:47–59. doi: 10.1016/j.neuroimage.2014.12.023.
    1. Ajilore O., Lamar M., Kumar A. Association of brain network efficiency with aging, depression, and cognition. The American Journal of Geriatric Psychiatry. 2014;22(2):102–110. doi: 10.1016/j.jagp.2013.10.004.
    1. Zheng Z., Zhu X., Yin S., et al. Combined cognitive-psychological-physical intervention induces reorganization of intrinsic functional brain architecture in older adults. Neural Plasticity. 2015;2015:11. doi: 10.1155/2015/713104.713104
    1. Lampit A., Hallock H., Suo C., Naismith S. L., Valenzuela M. Cognitive training-induced short-term functional and long-term structural plastic change is related to gains in global cognition in healthy older adults: a pilot study. Frontiers in Aging Neuroscience. 2015;7, article 014 doi: 10.3389/fnagi.2015.00014.
    1. Cole M. W., Yarkoni T., Repovš G., Anticevic A., Braver T. S. Global connectivity of prefrontal cortex predicts cognitive control and intelligence. The Journal of Neuroscience. 2012;32(26):8988–8999. doi: 10.1523/jneurosci.0536-12.2012.
    1. Sheline Y. I., Price J. L., Yan Z., Mintun M. A. Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proceedings of the National Academy of Sciences of the United States of America. 2010;107(24):11020–11025. doi: 10.1073/pnas.1000446107.
    1. Voss M. W., Erickson K. I., Prakash R. S., et al. Functional connectivity: a source of variance in the association between cardiorespiratory fitness and cognition? Neuropsychologia. 2010;48(5):1394–1406. doi: 10.1016/j.neuropsychologia.2010.01.005.
    1. Ben-Soussan T. D., Berkovich-Ohana A., Glicksohn J., Goldstein A. A suspended act: increased reflectivity and gender-dependent electrophysiological change following Quadrato Motor Training. Frontiers in Psychology. 2014;5, article 55 doi: 10.3389/fpsyg.2014.00055.
    1. Bennett C. M., Miller M. B. How reliable are the results from functional magnetic resonance imaging? Annals of the New York Academy of Sciences. 2010;1191:133–155. doi: 10.1111/j.1749-6632.2010.05446.x.
    1. Gorgolewski K. J., Storkey A. J., Bastin M. E., Whittle I., Pernet C. Single subject fMRI test-retest reliability metrics and confounding factors. NeuroImage. 2013;69:231–243. doi: 10.1016/j.neuroimage.2012.10.085.
    1. Maitra R., Roys S. R., Gullapalli R. P. Test-retest reliability estimation of functional MRI data. Magnetic Resonance in Medicine. 2002;48(1):62–70. doi: 10.1002/mrm.10191.
    1. Raemaekers M., du Plessis S., Ramsey N. F., Weusten J. M. H., Vink M. Test-retest variability underlying fMRI measurements. NeuroImage. 2012;60(1):717–727. doi: 10.1016/j.neuroimage.2011.11.061.
    1. Shehzad Z., Kelly A. M. C., Reiss P. T., et al. The resting brain: unconstrained yet reliable. Cerebral Cortex. 2009;19(10):2209–2229. doi: 10.1093/cercor/bhn256.
    1. Murphy K., Birn R. M., Bandettini P. A. Resting-state fMRI confounds and cleanup. NeuroImage. 2013;80:349–359. doi: 10.1016/j.neuroimage.2013.04.001.
    1. Zuo X.-N., Xing X.-X. Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: a systems neuroscience perspective. Neuroscience and Biobehavioral Reviews C. 2014;45:100–118. doi: 10.1016/j.neubiorev.2014.05.009.

Source: PubMed

3
Suscribir