Search

SPRINT is a longitudinal intervention study investigating the effects of language learning in old age. Participants between 65 and 75 years of age were randomized to receive an 11-week long language course (experimental group) or an 11-week long relaxation course (control group). Before and after the 11-week intervention period, all participants completed a comprehensive cognitive battery and a subset of the participants completed magnetic resonance imaging. The sub-project (SPRINT-BRAIN) will investigate the effect of language learning on grey matter.

In the proposed investigation we intend to investigate to what extent cardiorespiratory fitness and physical activity can predict learning in a working memory training intervention in healthy older adults, when such working memory training is performed in isolation or in close temporal succession with aerobic exercise. Cardiorespiratory fitness refers to the ability of the circulatory and respiratory systems to supply oxygen to skeletal muscles during sustained physical activity. It is best measured by maximal oxygen consumption, which reflects the greatest amount of oxygen uptake a subject can use in an incremental exercise test, determined by a plateau of values at a high exercise intensity (Ross et al., 2016). Cardiorespiratory fitness decreases with increasing age and is moderately related to self-reported and device-measured levels of physical activity (Jackson et al., 2009; Kulinski et al., 2014). Pulse pressure, which refers to the gradient from systolic to diastolic blood pressure and reflects stiffness of larger arteries, is a known risk factor for cardiovascular disease and increases with age (Dart et al., 2001). The role of cardiorespiratory fitness, physical activity and pulse pressure in physiological health is well-established, with low levels being associated with high risk of cardiovascular disease and all-cause mortality (Laukkanen et al., 2004; Blair et al., 1989; Domanski et al., 2002), and evidence is now emerging in support of beneficial effects also for brain and cognition (Barnes & Corkery, 2018; Chaddock et al., 2011; Cotman et al., 2007; Thorin-Trescases et al., 2018). About half of the variance in cardiorespiratory fitness and also in how it responds to physical exercise is considered to be attributable to heritable factors (Bouchard et al., 1998; Bouchard et al., 1985), leaving a sizeable portion of variance attributable to environmental factors. In aging, improving cardiovascular fitness with physical activity has been proposed as a promising preventative intervention that can help reverse or buffer age-related brain atrophy and cognitive decline (d’Arbeloff et al, 2020) Cross-sectional evidence supports an association between better cognition and greater cardiorespiratory fitness as well as higher level of physical activity (Freudenberger et al., 2016; Hayes et al., 2016; Zhu et al., 2018) and longitudinal studies have demonstrated an association between higher cardiorespiratory fitness and higher levels of physical activity, and reduced age-related cognitive decline as well as a reduced risk for dementia (Barnes et al., 2003; Wendell et al., 2013; Sofi et al., 2011; Pentikainen et al., 2019; Hörder et al., 2008; Nyberg et al., 2014; Hamer & Chida., 2009). Recent meta-analytic evidence of randomized controlled trials has supported a small but significant effect of regular physical exercise on cognition in older adults, across cognitive domains and training type (Ludyga et al., 2020; Northey et al., 2019; Falck et al., 2019). However, somewhat surprisingly, the presumed role of cardiorespiratory fitness for the effect of regular physical exercise interventions on cognition has yet to be demonstrated in a meta-analytical context (Young et al., 2015). Beyond regular physical exercise, several meta-analyses have concluded that a single session of physical exercise can acutely improve cognitive performance (Liprinzi et al., 2020; Roig et al., 2013; Liprinzi et al., 2020; but see Rathore et al., 2017), with some indication that individuals with high cardiovascular fitness may benefit more (Chang et al., 2012). The mechanisms underpinning the presumed effect of cardiorespiratory fitness and physical activity on cognition is not well understood. It has been proposed that individuals with greater cardiorespiratory fitness may be better adapted to the physiological changes triggered by exercise and thereby receive greater benefits to cognition (Dinoff et al., 2017; Chang et al., 2012). Evidence in support of this can be found in the context of brain-derived neurotrophic factor (BDNF), which is known to be essential for experience-dependent plasticity and learning from studies in rodents (Voss et al., 2013). In humans, BDNF has consistently been shown to increase acutely and transiently in peripheral blood following a single session of physical exercise (Kneapen et al., 2010; Szuhany et al., 2015; Dinoff et al., 2017), more so for individuals with high cardiorespiratory fitness (Dinoff et al., 2017). It has consequently been suggested that physical exercise prior to cognitive engagement would be optimal since this would allow the exercise-induced BDNF increase to directly coincide with cognitive engagement (Walsh & Tschakovsky, 2018; Chang et al., 2014), although such BDNF increases may also facilitate subsequent consolidation processes (Loprinzi et al., 2019). In regards to regular physical exercise, meta-analytic evidence suggest that regular aerobic exercise can increase resting concentrations of peripheral BDNF (Dinoff et al., 2016). In summary, individuals who are more physically active and have higher cardiorespiratory fitness may be better adapted to the physiological changes induced by exercise, allowing for improved cognitive capacity and learning. In the proposed investigation, we will investigate whether cardiorespiratory fitness, physical activity and pulse pressure predict learning in a working memory training intervention, when it is performed in isolation and when it is performed in close temporal succession with aerobic exercise. The primary aim will be to test the hypotheses that higher cardiorespiratory fitness, a higher level of moderate-vigorous physical activity and lower pulse pressure are associated with improved working memory training outcome, particularly when working memory training is preceded by aerobic exercise (confirmatory analyses). We will also test the hypothesis that acute increases in BDNF following physical exercise partially mediates the hypothesized relationship between higher cardiorespiratory fitness and working memory training outcome (confirmatory analysis). A secondary aim will be to investigate how cardiorespiratory fitness, physical activity and pulse pressure may interact with each other and with peripheral BDNF, at rest and following physical exercise, in predicting working memory outcome (exploratory analyses). Age, sex and BMI will be investigated as possible confounders in all hypothesized relationships (sensitivity analyses), with the exception of BMI for models that include cardiorespiratory fitness in relative units (mL/kg/min). The proposed investigation will be based on an already completed randomized-controlled intervention trial, in which healthy older participants received either aerobic exercise immediately before working memory training, aerobic exercise immediately after working memory training, aerobic training only, or working memory training only during a 12-week intervention period involving 30 intervention visits (Nilsson et al., 2020; pre-registration for original study available at 10.31219/osf.io/rkf5t). Contrary to our hypotheses, combining working memory training with aerobic exercise in close temporal succession, irrespective of order, did not result in greater cognitive improvements at the group level. However, we demonstrated that acute increases in peripheral BDNF following physical exercise were predictive of working memory training outcome, but only in the group that received physical exercise prior to cognitive training, suggesting that administration order may be of importance at an interindividual level (Nilsson et al., 2020). The proposed investigation will continue the search for interindividual factors that predict learning in a working memory training intervention when it is combined with aerobic training, focusing on cardiorespiratory fitness and physical activity.

Aim To investigate whether plastic changes in brain structure and function assessed with MRI following training of a motor task agree with the predictions of the exploration-selection-refinement model (Makino et al. 2016; Lindenberger and Lövdén 2019). Background Understanding the process of skill acquisition, such as learning to play a musical instrument, is a long-standing topic in psychology and cognitive neuroscience. Building on theoretical work (Edelman 1987; Changeux 1989; Kilgard 2012; Dhawale et al. 2017), evidence from animal models (Molina-Luna et al. 2008; Xu et al. 2009; Yang et al. 2009; Reed et al. 2011; Makino et al. 2016), and our own work using repeated imaging of the human brain’s gray matter structure during motor learning (Wenger et al. 2016a), we have in recent years developed a model of the neural process of human skill acquisition (Wenger et al. 2017; Lindenberger and Lövdén 2019). According to this model, during learning the brain initially tries out numerous candidate circuits (exploration). Based on reinforcement, the best candidate circuit is then chosen (selection) for further local fine-tuning (refinement). During the exploration phase, this process is hypothesized to be associated with the growth of regional brain structure (expansion), which partially retracts (renormalization) after the best circuit for the task has been selected. The main purpose of the project is to empirically test an ensemble of predictions from this model. The microstructural alterations underlying plastic changes are possibly diverse, including dendritic branching and synaptogenesis, axon sprouting and glial changes (Zatorre et al. 2012). In particular, it is now well established that myelination is under the influence of neural activity (Ullén 2009; Fields 2015; Jensen and Yong 2016), and recent investigations have shown that production of new oligodendrocytes is necessary for optimal motor learning (Mckenzie et al. 2014; Xiao et al. 2016) and a requirement for memory consolidation in general (Fields and Bukalo 2020). Work linking myelination and cortical thickness assessed with MRI (Natu et al. 2018) opens the possibility that experience-dependent morphometric alterations seen in longitudinal studies (Draganski et al. 2004, 2006; Scholz et al. 2009; Engvig et al. 2010; Lövdén et al. 2010; Mårtensson et al. 2012; Kühn et al. 2014; Wenger et al. 2016b; de Lange et al. 2017) respond to myelin reorganization in the cortex. In the present study, we randomly allocated 70 healthy, right-handed younger adults (20-30 y. o.) to 2 groups of equal size. Subjects in the first group trained a motor task, consisting of typing predefined sequences of key combinations with the fingers of their left-hand on a keyboard, akin to playing short piano chord sequences. Over a period of 6 weeks, they practiced at home 5 days a week for about 20 minutes on a laptop that was provided. The second group did not practice the task, and subjects in both groups were scanned with MRI once a week. Subjects with previous experience in similar motor tasks (e.g. playing an instrument) were not included in the experiment. The data for this study have already been acquired and partially preprocessed. We have used a collection of MRI measurements to characterize the structural and functional changes triggered by the practice of the task. From MP2RAGE (Marques et al. 2010) and T2-w scans, we have derived cortical thickness (CT), gray matter volumes (GMV), and approximate T1 relaxation time values, which have been shown to have good sensitivity for myelin (Sereno et al. 2013). We will compare alterations over time in these measures in areas related to the execution of the motor task between the 2 groups. Based on our model of skill acquisition, we predict a pattern of expansion followed by renormalization of the structural measures in the group undergoing motor practice, whereas these measures will remain stable in the group not practicing the motor skill. REFERENCES Changeux J-P. 1989. Neuronal models of cognitive functions. Cognition. 33:63–109. de Lange AMG, Bråthen ACS, Rohani DA, Grydeland H, Fjell AM, Walhovd KB. 2017. The effects of memory training on behavioral and microstructural plasticity in young and older adults. Hum Brain Mapp. 38:5666–5680. Dhawale AK, Smith MA, Ölveczky BP. 2017. The Role of Variability in Motor Learning. Annu Rev Neurosci. 40:479–498. Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A. 2004. Neuroplasticity: changes in grey matter induced by training. Nature. 311–312. Draganski B, Gaser C, Kempermann G, Kuhn HG, Bu C. 2006. Temporal and Spatial Dynamics of Brain Structure Changes during Extensive Learning. 26:6314–6317. Edelman GM. 1987. Neural Darwinism: The theory of neuronal group selectio. New York, NY, US: Basic books. Engvig A, Fjell AM, Westlye LT, Moberget T, Sundseth O, Larsen VA, Walhovd KB. 2010. Effects of memory training on cortical thickness in the elderly. Neuroimage. 52:1667–1676. Fields RD. 2015. A new mechanism of nervous system plasticity: Activity-dependent myelination. Nat Rev Neurosci. 16:756–767. Fields RD, Bukalo O. 2020. Myelin makes memories. Nat Neurosci. Jensen SK, Yong VW. 2016. Activity-Dependent and Experience-Driven Myelination Provide New Directions for the Management of Multiple Sclerosis. Trends Neurosci. 39:356–365. Kilgard MP. 2012. Harnessing plasticity to understand learning and treat disease. Trends Neurosci. 35:715–722. Kühn S, Gleich T, Lorenz RC, Lindenberger U, Gallinat J. 2014. Playing Super Mario induces structural brain plasticity: gray matter changes resulting from training with a commercial video game. Mol Psychiatry. 19:265–271. Lindenberger U, Lövdén M. 2019. Brain Plasticity in Human Lifespan Development : The Exploration – Selection – Refinement Model. Annu Rev Dev Psychol. 1:197–222. Lövdén M, Bodammer NC, Kühn S, Kaufmann J, Schütze H, Tempelmann C, Heinze HJ, Düzel E, Schmiedek F, Lindenberger U. 2010. Experience-dependent plasticity of white-matter microstructure extends into old age. Neuropsychologia. 48:3878–3883. Makino H, Hwang EJ, Hedrick NG, Komiyama T. 2016. Circuit Mechanisms of Sensorimotor Learning. Neuron. 92:705–721. Marques JP, Kober T, Krueger G, Zwaag W Van Der. 2010. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T 1 -mapping at high field. Neuroimage. 49:1271–1281. Mårtensson J, Eriksson J, Bodammer NC, Lindgren M, Johansson M, Nyberg L, Lövdén M. 2012. Growth of language-related brain areas after foreign language learning. Neuroimage. 63:240–244. Mckenzie IA, Ohayon D, Li H, Faria JP De, Emery B, Tohyama K, Richardson WD. 2014. Motor skill learning requires active central myelination. Science (80- ). 346:318–323. Molina-Luna K, Hertler B, Buitrago MM, Luft AR. 2008. Motor learning transiently changes cortical somatotopy. Neuroimage. 40:1748–1754. Natu VS, Gomez J, Barnett M, Jeska B, Kirilina E, Jaeger C, Zhen Z, Cox S, Weiner KS, Weiskopf N, Grill-Spector K. 2018. Apparent thinning of visual cortex during childhood is associated with myelination, not pruning. bioRxiv. 368274. Reed A, Riley J, Carraway R, Carrasco A, Perez C, Jakkamsetti V, Kilgard MP. 2011. Cortical Map Plasticity Improves Learning but Is Not Necessary for Improved Performance. Neuron. 70:121–131. Scholz J, Klein MC, Behrens TEJ, Johansen-Berg H. 2009. Training induces changes in white-matter architecture. Nat Neurosci. 12:1370–1371. Sereno MI, Lutti A, Weiskopf N, Dick F. 2013. Mapping the human cortical surface by combining quantitative T1 with retinotopy. Cereb Cortex. 23:2261–2268. Ullén F. 2009. Is activity regulation of late myelination a plastic mechanism in the human nervous system? Neuron Glia Biol. 5:29–34. Wenger E, Brozzoli C, Lindenberger U, Lövdén M. 2017. Expansion and Renormalization of Human Brain Structure During Skill Acquisition. Trends Cogn Sci. 21:930–939. Wenger E, Kühn S, Verrel J, Mårtensson J, Bodammer NC, Lindenberger U, Lövdén M. 2016a. Repeated Structural Imaging Reveals Nonlinear Progression of Experience-Dependent Volume Changes in Human Motor Cortex. 1–15. Wenger E, Kühn S, Verrel J, Mårtensson J, Bodammer NC, Lindenberger U, Lövdén M. 2016b. Repeated Structural Imaging Reveals Nonlinear Progression of Experience-Dependent Volume Changes in Human Motor Cortex. Cereb Cortex. 1–15. Xiao L, Ohayon D, Mckenzie IA, Sinclair-wilson A, Wright JL, Fudge AD, Emery B, Li H, Richardson WD. 2016. Rapid production of new oligodendrocytes is required in the earliest stages of motor-skill learning. Nat Neurosci. 19. Xu T, Yu X, Perlik AJ, Tobin WF, Zweig JA, Tennant K, Jones T, Zuo Y. 2009. Rapid formation and selective stabilization of synapses for enduring motor memories. Nature. 462:915–919. Yang G, Pan F, Gan WB. 2009. Stably maintained dendritic spines are associated with lifelong memories. Nature. 462:920–924. Zatorre RJ, Fields RD, Johansen-berg H. 2012. Plasticity in gray and white : neuroimaging changes in brain structure during learning. Nat Publ Gr. 15.

Aging impacts negatively the microstructural properties of white matter (WM) fiber tracts and the integrity of the dopaminergic system, two factors associated with decline in cognitive functioning (Bäckman et al. 2006; Bennett & Madden, n.d.; Oschwald et al., 2019). Due to the burden of age-related cognitive decline and dementia for individuals and healthcare in general, interventions are designed to improve cognitive abilities. Studies have reported an effect of dopamine signaling in learning and neurobiological plasticity, including WM plasticity, as well as in modulating cognitive training-related improvements (e.g. Bäckman, 2011, 2017; Lindholm & Jazin, 2007; Pendolino et al., 2014). However, a recent study by Lebedev et al. (2020) showed that older adults undergoing cognitive training combined with dopamine intake (L-DOPA) did not improve, and even performed worse on tests of spatial intelligence, compared to controls that were given a placebo during their training. In the present follow-up study, we aim at better understanding the impact of dopamine, given in its precursor form L-DOPA, on WM microstructural properties in older participants undergoing cognitive training. The second aim of this study is to gain a better understanding of the relationship between L-DOPA-related changes in WM tract integrity and cognitive performance. WM tracts microstructure will be assessed using mean diffusion tensor imaging (DTI) metrics such as fractional anisotropy (FA) and mean diffusivity (MD) while cognitive abilities will be assessed through a standard cognitive testing battery.

The project focuses on associations between acute changes in BDNF, cortisol and proBDNF concentrations in plasma, and their role in predicting cognitive training outcome in older adults.

To investigate whether plastic changes in brain structure assessed with MRI following training of a motor task agree with the predictions of the exploration-selection-refinement model (Makino et al. 2016; Lindenberger and Lövdén 2019).

This project aims at investigating the effect of acute physical exercise at moderate intensity on cognitive performance, cerebral blood flow, brain structure, and brain activity in older adults.