Dynamic properties of human brain structure - CiteSeerX

Author Manuscript Preprint submitted to Journal of Neuroscience (2010).

Author Manuscript

Published in final edited form as: Journal of Neuroscience. 2010 30: 11688-11695. doi:10.1523/JNEUROSCI.2567-10.2010

Journal section: Behavioral/Systems/Cognitive

Dynamic properties of human brain structure: learning-related changes in cortical areas and associated fibre connections Temporal dynamics of grey and white matter changes

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Marco Taubert, Bogdan Draganski*, Alfred Anwander*, Karsten Mueller, Annette Horstmann, Arno Villringer & Patrick Ragert# Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany *contributed equally #

Corresponding author:

Dr. Patrick Ragert (PhD) Max Planck Institute for Human Cognitive and Brain Sciences Stephanstrasse 1a, D-04103 Leipzig, Germany

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http://www.cbs.mpg.de/~ragert Figures: Tables: Supplemental material: Pages : Words: Keywords:

5 2 Figures (3) and discussion 30 Abstract (171), Introduction (481), Discussion (1493) learning, structural plasticity, grey matter, white matter, voxel-based morphometry, diffusion tensor imaging

Preprint submitted to Journal of Neuroscience (2010). Initial submission: 15 January 2010

ABSTRACT

Recent findings in neuroscience suggest that adult brain structure changes in response to environmental alterations and skill learning. Whereas much is known about structural changes after intensive training for several months, little is known about the effects of single practice sessions on macroscopic brain structure and about progressive (dynamic) morphological alterations relative to improved task proficiency during learning. Using magnetic resonance and diffusion tensor imaging in humans, we showed that only 90 minutes of practice in a complex whole-body balancing task affects grey matter structure in frontal and parietal brain areas. Grey matter dynamics in the prefrontal cortex positively correlated with performance improvements during a six weeks practice period. In addition, we found that microstructural changes (decreased FA and parallel diffusivity) in connected white matter regions followed the same temporal dynamic in relation to task performance. The results make clear how marginal alterations in our ever changing environment affect adult brain structure and elucidate the interrelated reorganization in cortical areas and associated fibre connections in correlation with improvements in task performance.

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INTRODUCTION

The adult brain shows a remarkable capacity for morphological alterations during learning or adaption to a changing environment (Markham and Greenough, 2004; Adkins et al., 2006; Draganski and May, 2008). In human subjects, grey and white matter changes can be observed after intensive long-term motor skill learning for several months (Draganski et al., 2004; Boyke et al., 2008; Scholz et al., 2009). Even though we face a consistently changing environment in our daily life and the need to rapidly adapt to such changes, less is known about the capability of the adult human brain for structural alterations in response to slight environmental changes. Animal studies suggest that the formation of new synaptic connections by dendritic spine growth and remodelling of axons is associated with learning (Trachtenberg et al., 2002; Chklovskii et al., 2004; Markham and Greenough, 2004; DeBello, 2008; Butz et al., 2009; Xu et al., 2009). For example, motor skill learning rapidly forms and eliminates dendritic spines in response to short practice sessions (Xu et al., 2009). Behavioural studies of motor skill learning indicate that individuals pass through different phases during the time course of skill acquisition (Zanone and Kelso, 1992; Lee and Swinnen, 1993; Newell, 1996; Karni et al., 1998). Functional neuroimaging studies found that specific brain networks are recruited during early and late periods of skill learning demonstrating specific dynamic patterns of neural activity (Karni et al., 1995; Floyer-Lea and Matthews, 2005; Luft and Buitrago, 2005). So far, however, the structural implementation of such behavioural and functional adaptations within distinct brain areas and their associated structural connectivity patterns is largely unexplored. Furthermore, it still remains elusive if the temporal dynamics of such changes in the human brain are directly linked with improvements in motor performance over time. Motor skill learning and the organization of goal-directed 3

behaviour has been associated with functional changes in cortical brain areas such as premotor, parietal and prefrontal cortex as well as in their functional connectivity patterns (Passingham, 1993; Andres et al., 1999; Koechlin et al., 1999; Swinnen and Wenderoth, 2004; Koechlin and Hyafil, 2007; Sun et al., 2007; Nachev et al., 2008; Boorman et al., 2009). In the present study, we hypothesized (1) that a short period of practice in a complex motor task substantially changes brain structure and (2) that long-term motor skill learning will induce dynamic patterns of structural alterations in functionally relevant brain areas and their associated anatomical connections in correlation with improvements in task performance during motor skill learning. Using structural magnetic resonance (MRI) and diffusion tensor imaging (DTI) in young healthy volunteers in a multi-level longitudinal design, we observed macroscopic structural changes in frontal and parietal brain areas as early as after 2 x 45 minutes of learning a complex whole-body balancing task. Furthermore, dynamic structural alterations in cortical areas and associated fibre connections correlated with improvements in motor performance during a learning period of six consecutive weeks.

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MATERIALS AND METHODS

Subjects Twenty-eight healthy right-handed (Oldfield, 1971) subjects (mean age = 25.9 years; SD = 2.8 years; 14 females) with normal or corrected-to-normal vision were recruited for his study after obtaining written informed consent approved by the local ethics 4

committee. All subjects underwent a neurological examination prior to participation. Subjects were naïve to the experimental setup with no prior experience of other highly coordinative balancing skills.

Experimental overview 14 subjects were asked to learn a whole-body dynamic balancing task (DBT) over six consecutive weeks with one training day (TD) in each week (see Fig. 1B). TD’s as well as the time schedule in each week was kept constant (±1 day) across the whole learning period. On each TD, subjects performed the DBT for approximately 45 minutes. During task performance, EMG activity of the left and right soleus muscle was recorded continuously using surface electromyography (EMG) in order to capture possible changes in muscle activity pattern. MR data acquisition was performed as follows: baseline scan prior to learning (s1, pre), two intermittent scans after two and four weeks (s2 and s3) and final scan one week after completion of the learning period (s4; see Fig. 1B). Importantly, MR scanning was performed prior to the learning session on TD 1, TD 3 and TD 5 (see Fig. 1B). The control group, consisting of 14 age- and gender-matched subjects scanned at baseline (s1, pre) and two weeks later (s2), did not receive any balance training.

Whole-body dynamic balancing task (DBT) The whole body dynamic balancing task (DBT) was performed on a movable platform with a maximum deviation of 26° to each side (Stability platform, Model 16030L, Lafayette Instrument, US). Subjects were instructed to stand with both feet on the platform and to keep it in a horizontal position as long as possible during a trial length of 30 seconds.

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To familiarize subjects with the task and to prevent falls in the initial three trials on TD 1, we allowed using supporting hand rail. The familiarization trials were excluded from the analysis. The behavioural outcome measure was the time (in seconds) in which subjects kept the platform in a horizontal position (BAL), within a deviation range of ± 3° to each side, out of the total trial length of 30 seconds. We used a discovery learning approach (Wulf et al., 2003; Orrell et al., 2006) in which no information about the performance strategy was provided during learning. After each trial, subjects were only given verbal feedback about their time in balance (BAL). Therefore, subjects had to discover their optimal strategy to improve task performance based on trial outcome on a trial-and-error basis. On each of the six TD`s, 15 trials had to be performed with an intertrial-interval of 2 minutes to avoid fatigue. Thus, the time to complete the DBT on each TD was approximately 45 minutes. Three month after the end of the learning period, the stability of the acquired motor skill was re-assessed in a retention test in 13 subjects (same procedure as on each TD).

Surface electromyographic recordings (EMG) Ag-AgCl surface electrodes were positioned bilaterally on the skin overlying the soleus muscle (SM) of the right and left leg in a bipolar montage (inter-electrode distance, approx. 5 cm). Electrode positions were carefully determined and kept constant to ensure identical recording sites during the learning period. The signal was amplified using a Counterpoint EMG device (Digitimer D360, Digitimer Ltd., UK) with band-pass filtering between 50 and 2000 Hz, digitized at a frequency of 5000 Hz and fed off-line to a data acquisition system for further analysis (CED 1401 system, Spike2 software, Cambridge Electronic Devices (CED), Cambridge, UK). EMG activity was recorded continuously during DBT and subjects were instructed to relax 6

as much as possible during the rest periods of the task. EMG signals were processed offline with a low pass filter. Rectified EMG activities for right and left SM were calculated as a mean average voltage starting from the EMG onset of each trial. Then muscular imbalances were calculated as the ratio between left and right SM EMG activity for each trial (where a value of 1 indicates no EMG difference and values >1 or s2, s3, s4) independent from improvements in motor performance. (B) Negative linear correlation between average improvements in motor performance and FA changes in left prefrontal and right parietal white matter regions during the six weeks of learning. Left rendered brain represents right hemisphere and right rendered brain represents left hemisphere (see also Fig. S3). (C) Diagram shows absolute FA signal change (error bars indicate s.e.m.) during learning in peak voxel in left inferior prefrontal and right parietal white matter regions derived from the correlation analysis (see above). Images are shown at p < 0.05 (corrected).

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Figure 5. Topographical relationship between parallel diffusivity and grey matter changes in left frontopolar region. Cyan regions represent negative linear correlation between changes in parallel diffusivity and average improvements in motor performance during the learning period. Yellow regions represent positive linear correlation between grey matter increase in left FPC and average improvements motor performance during learning (see also Fig. 3B). Structural changes are superimposed on a coronal (left) and axial (right) slice of a MNI template brain. Images are shown at p < 0.001 (uncorrected).

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Table 1A: Effects of motor skill learning on grey matter Analysis/brain region

hemisphere

MNI coordinates (x, y, z)

Z

cluster size k

p (corrected)

L

-12, 13, 64

4,35

1684