Skip to main content
Download PDF

MRI assessment of cortical thickness and functional activity changes in adolescent girls following three months of practice on a visual-spatial task

BMC Research Notes volume 2, Article number: 174 (2009) Cite this article

Abstract

Background

Neuro-imaging studies demonstrate plasticity of cortical gray matter before and after practice for some motor and cognitive tasks in adults. Other imaging studies show functional changes after practice, but there is not yet direct evidence of how structural and functional changes may be related. A fundamental question is whether they occur at the same cortical sites, adjacent sites, or sites in other parts of a network.

Findings

Using a 3 T MRI, we obtained structural and functional images in adolescent girls before and after practice on a visual-spatial problem-solving computer game, Tetris. After three months of practice, compared to the structural scans of controls, the group with Tetris practice showed thicker cortex, primarily in two areas: left BAs 6 and 22/38. Based on fMRI BOLD signals, the Tetris group showed cortical activations throughout the brain while playing Tetris, but significant BOLD decreases, mostly in frontal areas, were observed after practice. None of these BOLD decreases, however, overlapped with the cortical thickness changes.

Conclusion

Regional cortical thickness changes were observed after three months of Tetris practice. Over the same period, brain activity decreases were observed in several other areas. These data indicate that structural change in one brain area does not necessarily result in functional change in the same location, at least on the levels assessed with these MRI methods.

Findings

Plasticity of gray matter (GM) in adults has been demonstrated before and after practice of both motor and cognitive tasks [1–3]. Evidence points to plasticity effects for both novel learning of a task and for continued practice of a task once learned [4]. A fundamental question is whether GM increases after practice in a specific brain area are concomitant with functional changes in the same area (site specific), and/or whether different areas in the same network show changes. A secondary question is whether GM increases are associated with increased or decreased brain activity. Functional imaging studies show both directions [5–11]. With two exceptions, none of these included structural assessments so any overlap of functional and structural changes could not be determined. One exception [12] reported limited overlap for a mirror reading task; the other [13] reported no overlap for a simple visual-motor task.

We conducted a straightforward test of the site-specific hypothesis. We first examined whether cortical thickness (CT) increased after three months of practicing a visual-spatial game, Tetris. Then, in the same subjects, we determined blood oxygen level dependent (BOLD) changes after practice. Finally, we examined the overlap of the structural and functional results. Since Tetris involves rapid visual-spatial rotation problem-solving and motor co-ordination, we hypothesized GM increases in pre-motor and post-central gyri and parts of the parietal and occipital lobes as shown in previous studies [5, 14–16].

Methods

Ethics Statement

Each participant gave written assent and a parent gave written informed consent as approved by the University of New Mexico Institutional Review Board. This research was conducted in accord with the Helsinki Declaration.

Subjects and Procedure

Because developing brains are most likely to show potential changes, we studied adolescents. We chose girls to minimize bias based on previous video game experience.

Twenty-six girls aged 12-15 were recruited from advertisements and visits to schools. All were familiar with computers but none reported significant interest in computer game play (defined as playing any games requiring rapid visual-spatial puzzle solving on a regular basis) or significant exposure to Tetris (defined as playing more than one time, or watching someone else play more than three times). None had medical illness, brain injury, psychiatric history or were taking prescription medications. Girls were randomly assigned to either the Tetris practice group (n = 15) or the control group (n = 11). The groups did not differ on age (mean 13.1 +/- 1.1 vs. 12.9 +/- 1.0, respectively, t = .51, NS), or FSIQ (113.8 +/- 13.4 vs. 115.7 +/- 11.4, t = .40, NS).

The Tetris group got online access to a controlled web site where they logged in to play. Each login was tracked for duration and scores. Each girl received Tetris instruction and 15 minutes of practice when they entered the study. They were instructed to practice as their schedules permitted during a three-month period. On average, the girls practiced 1.5 hours per week (individual games take only a few minutes). The Control group had no access to the Tetris site and was instructed not to play Tetris during the three-month period. Both groups had a structural MRI at the start and end of the study. Both groups also had fMRI scans, while playing Tetris, at both MRI sessions. Before beginning fMRI, both groups received Tetris instructions and practiced 15 minutes.

Tetris Task

In random order, pieces with different shapes constructed from 4 squares, appear one at a time at the top of the computer screen. As the piece falls to the bottom, the player uses four buttons to move and/or rotate the piece so it fits in with previous pieces to form rows with no gaps. When a row is completed, it disappears from the screen, and all the remaining pieces drop down, changing the configuration of the play space. This requires rapid analysis of the best way to place each piece into the existing pattern. The version used here was scored as time it took to complete 40 rows.

MRI Acquisition

Images were obtained on a 3 T Siemens TrioTim scanner. Structural (sMRI) images were collected using a 5 echo mprage sequence [TE1 = 1.64 ms, TE2 = 3.5 ms, TE3 = 5.36 ms, TE4 = 7.22 ms, TE5 = 9.08 ms; TR = 2530 ms; FOV = 256 mm; total acquisition time = 6:03]. 192 3D 1 mm thick slices (0 mm skip) were selected to provide coverage of the entire brain (voxel size: 1 mm × 1 mm × 1 mm). Functional echo planar (fMRI) images were collected using a single-shot sequence [TE = 29 ms; TR = 2000 ms; FOV = 24 cm; matrix size = 64 × 64; 32 axial 3.5 mm slices (1.05 mm skip); voxel size: 3.75 × 3.75 × 4.55].

MR Image Processing

Cortical thickness

Structural images were submitted to the CIVET pipeline (1.1.9) http://wiki.bic.mni.mcgill.ca/index.php/CIVET for fully automated analysis. Steps, detailed elsewhere [17], include: 1) Registering to MNI space. 2) Producing high-resolution hemispheric surfaces with 40962 vertices each. 3) Registering surfaces to a high-resolution template. 4) Applying a reverse of step 1 allowing CT estimations in native space of each subject. 5) Smoothing using 20-milimeter kernel [18]. 6) Calculating CT at each vertex.

Brain function

Subjects alternated between playing Tetris (ON) for 30 seconds using an MRI compatible device and keeping their eyes fixed (OFF) on a (+) sign for 20 seconds. Each run consisted of 11 ON/OFF cycles totalling 9 minutes 10 seconds per run. On both visits, subjects performed two runs, averaged together for analyses. fMRI images were motion corrected, and normalized to the MNI stereotaxic space using SPM http://www.fil.ion.ucl.ac.uk and re-sampled to 4 × 4 × 4 mm. Data for which motion parameters deviated >3 S.D. from the mean were excluded leaving 11 Tetris and 9 control subjects. Deconvolution determined the hemodynamic response function (HRF) corresponding to the 50-second ON/OFF period and was converted to percent signal change versus baseline. Empirical examination of this 50 second HRF in responsive regions showed large percent signal changes from 4 to 36 seconds into the ON/OFF period. The mean percent signal change from 4-36 seconds, encompassing the theoretical peak of the hemodynamic response [19], was used as a single measure of percent signal change per voxel.

Statistical Analyses

sMRI

Analyses used SurfStat created for MATLAB 7 (The MathWorks, Inc.). Baseline cortical thickness maps were subtracted, for each subject, from the 3-month cortical thickness maps, yielding subject-specific maps of change in cortical thickness. These maps for the Tetris group were compared to those for the Control group. Results are shown at p < .05 using the Family Wise Error (FWE) correction (Figure 1); trends are noted at p < .005, uncorrected. For all tables, Brodmann areas (BA) were determined as best estimates based on the Talairach and Tourneau atlas [20, 21].

Figure 1
figure1

Cortical Thickness Changes. Cortical thickness changes after practice showing Tetris follow-up minus baseline versus Controls follow-up minus baseline, p < .005. Upper left is left hemisphere; arrows show cluster level p < .05 FWE corrected.

Full size image

fMRI

Individual subject voxel-wise percent BOLD signal change in response to Tetris was passed on to level 2 group analysis. One sample t-tests (df = 10 for Tetris group, df = 8 for controls, SPM) against the null hypothesis showed areas significantly modulated by Tetris (p < 0.05; Figure 2, top two panels). Practice effects in the Tetris group were assessed using a paired two-sample t-test contrasting the follow-up vs. baseline visits (Figure 2, lower panel). Group-by-visit interactions were assessed using an unpaired t-test (df = 18) contrasting (Percent signal change at 3 months - Baseline Percent signal change in Experimental Group) Minus (Percent signal change at 3 months - Baseline Percent signal change in Experimental Group). Stereotaxic coordinates of peak statistical significance were converted from ICBM-152 to Talairach compatible coordinates [21].

Figure 2
figure2

BOLD Signal Changes. BOLD signal increases (green) and decreases (blue) in cortex during the Tetris task (top row is at baseline; middle row is at follow-up; p < .05, FWE) and after practice (follow-up minus baseline; bottom row; p < .05, FDR). Left column is left hemisphere. Template is ICBM-152.

Full size image

We limit discussion to the more conservative FWE results since the chance of Type I error is greater for results based on more lenient threshold methods of FDR or uncorrected p values. Nonetheless, we included these findings in the tables to increase the chance of replication by other studies.

Results

Changes in CT following practice led to a significantly thicker cortex in the experimental compared to the control group [Tetris follow-up minus baseline] minus [control follow-up minus baseline], in two Brodmann areas (BA), left BA 6 and left BA 22/38, (p < .05, FWE corrected); these are shown in Figure 1 along with other areas where there was a trend of increases after practice (p < .005, uncorrected). These areas were distributed throughout the brain (Table 1) and were most significant (p < .0005, uncorrected) in BA 6 and 44 (left frontal lobe), BA 40 (bilateral parietal lobe), BA 4 (left paracentral lobule), BA 20 and 22 (left temporal lobe), and BA 38 (right temporal pole). There was no evidence of any areas of thinner cortex after practice.

Table 1 Peak vertex coordinates (MNI) and brain areas where change over three months showed greater cortical thickness in the Tetris group than in the controls (p < 0.005, uncorrected; * p < .05 cluster level, FWE corrected).
Full size table

Tetris-induced BOLD signal increases (p < 0.05, FWE corrected) are similar for both visits (Figure 2, top two panels; Additional Files 1, 2, 3 &4). Most are in frontal and parietal lobes including parts of BAs 6, 7, 40, although the follow-up visit showed additional activations in occipital BAs 17, 18, and 19. For both visits, only a few small clusters (<20 voxels each) showed a significantly decreased BOLD signal during task performance. The same comparisons for the control group showed similar results.

For the Tetris group, no activation changes over the 3-month practice period were significant after FWE correction. However, changes based on statistical false discovery rate control (FDR) [22, 23] are shown in Figure 2 (lower panel) and Table 2. These were all decreases after practice and were mostly in the right hemisphere; the most significant were in frontal BAs 32, 8, 9, 6, 46, and in parietal BA 40.

Table 2 Brain areas with decreases in BOLD signal after practice (follow-up minus baseline; p < .05 FDR).
Full size table

Figure 3 shows both CT change and BOLD signal during Tetris play. For baseline and follow-up, overlap was limited to a few small areas of BAs 6, 7, 22, and 19 (arrows in Figure 3, top and middle rows). The areas with decreased activation over the practice period (follow-up minus baseline) showed no overlap with CT increases (bottom row). To increase statistical power, we did additional analyses, first restricting the CT analyses to regions that showed BOLD changes; and second, restricting the BOLD analyses to regions that showed CT changes. Neither analysis identified any new regions of overlap (p < .005, uncorrected).

Figure 3
figure3

Cortical Thickness and BOLD Changes. Both cortical thickness change after practice (red) and BOLD signal increases (green) or decreases (blue) during Tetris task are shown. Top row is at baseline, middle row is at follow-up, bottom row is follow-up minus baseline. Left column is left hemisphere. Template is Surfstat MNI. Arrows indicate small areas of overlap.

Full size image

Discussion

We found no apparent overlap between structural and functional findings to support the site-specific hypothesis. Interpreting fMRI data has important limitations. For example, the BOLD signal reflects neuronal mass activity. It does not distinguish between excitatory and inhibitory activity, and a marked change in processing strategies involving an anatomical area does not necessitate a change in BOLD [24]. It is possible that practiced-subjects developed a more sophisticated approach, but if this did not invoke novel brain areas, it might not be detectable using fMRI.

The frontal eye fields in left BA 6 showed the most significant CT change, consistent with other imaging studies of visual-spatial tasks. The same area also showed increased activity while the subjects played at baseline, but there was no additional change after practice. Notably, BA 6 is at a critical juncture between "intention" and "action" [25], linking memory of previous responses and the rewards associated with them [26], and is active when motor plans are altered [27]. The left temporal pole (BA 22/38) also showed change. The temporal poles integrate visual, auditory, tactile, and internal physiological information, suggesting a critical role in multimodal perceptual analysis [28]. Moreover, there are lateralizing differences, with right more associated with emotion and socially relevant episodic memories, and left associated with semantic memory[29]. We interpret the temporal pole changes as reflecting increased multisensory integration to support visual, spatial, and tactile input necessary to succeed at Tetris. That greater cortical change was seen in the left temporal pole, suggests Tetris play is processed as a general cognitive puzzle rather than a discrete episodic memory. Whether practiced Tetris play and associated cortical changes generalize to performance changes in other cognitive domains (e.g. working memory, processing speed, spatial reasoning) is not yet determined.

Conclusion

These data indicate that structural and functional brain changes after practice do not necessarily occur in the same location, at least on the levels assessed with these MRI methods.

References

  1. 1.

    Boyke J, Driemeyer J, Gaser C, Buechel C, May A: Training-induced brain structure changes in the elderly. Journal of Neuroscience. 2008, 28 (28): 7031-7035. 10.1523/JNEUROSCI.0742-08.2008.

    Article  CAS  PubMed  Google Scholar 

  2. 2.

    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. 10.1038/427311a.

    Article  CAS  PubMed  Google Scholar 

  3. 3.

    Draganski B, Gaser C, Kempermann G, Kuhn HG, Winkler J, Buchel C, May A: Temporal and Spatial Dynamics of Brain Structure Changes during Extensive Learning. J Neurosci. 2006, 26 (23): 6314-6317. 10.1523/JNEUROSCI.4628-05.2006.

    Article  CAS  PubMed  Google Scholar 

  4. 4.

    Driemeyer J, Boyke J, Gaser C, Buchel C, May A: Changes in gray matter induced by learning--revisited. PLoS ONE. 2008, 3 (7): e2669-10.1371/journal.pone.0002669.

    PubMed Central  Article  PubMed  Google Scholar 

  5. 5.

    Haier RJ, Siegel BV, MacLachlan A, Soderling E, Lottenberg S, Buchsbaum MS: Regional glucose metabolic changes after learning a complex visuospatial/motor task: a positron emission tomographic study. Brain Res. 1992, 570 (1-2): 134-143. 10.1016/0006-8993(92)90573-R.

    Article  CAS  PubMed  Google Scholar 

  6. 6.

    McNab F, Varrone A, Farde L, Jucaite A, Bystritsky P, Forssberg H, Klingberg T: Changes in cortical dopamine D1 receptor binding associated with cognitive training. Science. 2009, 323 (5915): 800-802. 10.1126/science.1166102.

    Article  CAS  PubMed  Google Scholar 

  7. 7.

    Goodyear BG, Douglas EA: Decreasing task-related brain activity over repeated functional MRI scans and sessions with no change in performance: implications for serial investigations. Exp Brain Res. 2009, 192 (2): 231-239. 10.1007/s00221-008-1574-7.

    Article  PubMed  Google Scholar 

  8. 8.

    Little DM, Klein R, Shobat DM, McClure ED, Thulborn KR: Changing patterns of brain activation during category learning revealed by functional MRI. Brain Res Cogn Brain Res. 2004, 22 (1): 84-93. 10.1016/j.cogbrainres.2004.07.011.

    Article  PubMed  Google Scholar 

  9. 9.

    Landau SM, Schumacher EH, Garavan H, Druzgal TJ, D'Esposito M: A functional MRI study of the influence of practice on component processes of working memory. Neuroimage. 2004, 22 (1): 211-221. 10.1016/j.neuroimage.2004.01.003.

    Article  PubMed  Google Scholar 

  10. 10.

    Wu T, Kansaku K, Hallett M: How self-initiated memorized movements become automatic: a functional MRI study. J Neurophysiol. 2004, 91 (4): 1690-1698. 10.1152/jn.01052.2003.

    Article  PubMed  Google Scholar 

  11. 11.

    Karni A, Meyer G, Jezzard P, Adams MM, Turner R, Ungerleider LG: Functional MRI evidence for adult motor cortex plasticity during motor skill learning. Nature. 1995, 377 (6545): 155-158. 10.1038/377155a0.

    Article  CAS  PubMed  Google Scholar 

  12. 12.

    Ilg R, Wohlschlager AM, Gaser C, Liebau Y, Dauner R, Woller A, Zimmer C, Zihl J, Muhlau M: Gray matter increase induced by practice correlates with task-specific activation: a combined functional and morphometric magnetic resonance imaging study. J Neurosci. 2008, 28 (16): 4210-4215. 10.1523/JNEUROSCI.5722-07.2008.

    Article  CAS  PubMed  Google Scholar 

  13. 13.

    Thomas AG, Marrett S, Saad ZS, Ruff DA, Martin A, Bandettini PA: Functional but not structural changes associated with learning: An exploration of longitudinal Voxel-Based Morphometry (VBM). Neuroimage. 2009, 48 (1): 117-125. 10.1016/j.neuroimage.2009.05.097.

    PubMed Central  Article  PubMed  Google Scholar 

  14. 14.

    Alivisatos B, Petrides M: Functional activation of the human brain during mental rotation. Neuropsychologia. 1997, 35 (2): 111-118. 10.1016/S0028-3932(96)00083-8.

    Article  CAS  PubMed  Google Scholar 

  15. 15.

    Cohen MS, Kosslyn SM, Breiter HC, DiGirolamo GJ, Thompson WL, Anderson AK, Bookheimer SY, Rosen BR, Belliveau JW: Changes in cortical activity during mental rotation - A mapping study using functional MRI. Brain. 1996, 119: 89-100. 10.1093/brain/119.1.89.

    Article  PubMed  Google Scholar 

  16. 16.

    O'Boyle MW, Cunnington R, Silk TJ, Vaughan D, Jackson G, Syngeniotis A, Egan GF: Mathematically gifted male adolescents activate a unique brain network during mental rotation. Cognitive Brain Research. 2005, 25 (2): 583-587. 10.1016/j.cogbrainres.2005.08.004.

    Article  PubMed  Google Scholar 

  17. 17.

    Karama S, Ad-Dab'bagh Y, Haier RJ, Deary IJ, Lyttelton OC, Lepage C, Evans AC: Positive association between cognitive ability and cortical thickness in a representative US sample of healthy 6 to 18 year-olds. Intelligence. 2009, 37: 145-155. 10.1016/j.intell.2008.09.006.

    Article  Google Scholar 

  18. 18.

    Lerch JP, Evans AC: Cortical thickness analysis examined through power analysis and a population simulation. Neuroimage. 2005, 24 (1): 163-173. 10.1016/j.neuroimage.2004.07.045.

    Article  PubMed  Google Scholar 

  19. 19.

    Cohen MS: Parametric analysis of fMRI data using linear systems methods. NeuroImage. 1997, 6 (2): 93-103. 10.1006/nimg.1997.0278.

    Article  CAS  PubMed  Google Scholar 

  20. 20.

    Talairach J, Tournoux P: Co-planar stereotaxic atlas of the human brain: a 3-dimensional proportional system, an approach to cerebral imaging. 1988, Stuttgart; New York: New York: G. Thieme; Thieme Medical Publishers

    Google Scholar 

  21. 21.

    Lancaster JL, Tordesillas-Gutierrez D, Martinez M, Salinas F, Evans A, Zilles K, Mazziotta JC, Fox PT: Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template. Hum Brain Mapp. 2007, 28 (11): 1194-1205. 10.1002/hbm.20345.

    Article  PubMed  Google Scholar 

  22. 22.

    Chumbley JR, Friston KJ: False discovery rate revisited: FDR and topological inference using Gaussian random fields. Neuroimage. 2009, 44 (1): 62-70. 10.1016/j.neuroimage.2008.05.021.

    Article  PubMed  Google Scholar 

  23. 23.

    Genovese CR, Lazar NA, Nichols T: Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage. 2002, 15 (4): 870-878. 10.1006/nimg.2001.1037.

    Article  PubMed  Google Scholar 

  24. 24.

    Logothetis NK: What we can do and what we cannot do with fMRI. Nature. 2008, 453 (7197): 869-878. 10.1038/nature06976.

    Article  CAS  PubMed  Google Scholar 

  25. 25.

    Lau HC, Rogers RD, Haggard P, Passingham RE: Attention to intention. Science. 2004, 303 (5661): 1208-1210. 10.1126/science.1090973.

    Article  CAS  PubMed  Google Scholar 

  26. 26.

    Nakamura K, Sakai K, Hikosaka O: Neuronal activity in medial frontal cortex during learning of sequential procedures. Journal of Neurophysiology. 1998, 80 (5): 2671-2687.

    CAS  PubMed  Google Scholar 

  27. 27.

    Nachev P, Rees G, Parton A, Kennard C, Husain M: Volition and conflict in human medial frontal cortex. Current Biology. 2005, 15 (2): 122-128. 10.1016/j.cub.2005.01.006.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  28. 28.

    Olson IR, Plotzker A, Ezzyat Y: The Enigmatic temporal pole: a review of findings on social and emotional processing. Brain. 2007, 130 (Pt 7): 1718-1731. 10.1093/brain/awm052.

    Article  PubMed  Google Scholar 

  29. 29.

    Markowitsch HJ: Which brain regions are critically involved in the retrieval of old episodic memory?. Brain Res Brain Res Rev. 1995, 21 (2): 117-127. 10.1016/0165-0173(95)00007-0.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank Ranee Barrow (partial salary supported by the BPS funding) for invaluable help in data collection and Alan Evans for supporting cortical thickness analyses done at MNI.

Author information

Affiliations

  1. School of Medicine (Emeritus), University of California, Irvine, CA, USA

    Richard J Haier

  2. McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Canada

    Sherif Karama

  3. Mind Research Network, Albuquerque, New Mexico, USA

    Leonard Leyba & Rex E Jung

  4. Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico, USA

    Rex E Jung

Authors
  1. Richard J Haier
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sherif Karama
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leonard Leyba
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rex E Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Richard J Haier.

Additional information

Competing interests

This project was funded at the MIND Research Network, Albuquerque, N.M. by Blue Planet Software (BPS), Inc., the company holding exclusive licensing rights to Tetris. REJ was the PI and LL received partial salary support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. RJH is a paid consultant to Blue Planet Software.

Authors' contributions

RJH and REJ conceived and designed the experiment. REJ performed the experiment. SK and LL analyzed the data. All authors approved and helped write the manuscript.

Electronic supplementary material

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Authors’ original file for figure 3

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Haier, R.J., Karama, S., Leyba, L. et al. MRI assessment of cortical thickness and functional activity changes in adolescent girls following three months of practice on a visual-spatial task. BMC Res Notes 2, 174 (2009). https://doi.org/10.1186/1756-0500-2-174

Download citation

Keywords

  • Blood Oxygen Level Dependent
  • Brodmann Area
  • Blood Oxygen Level Dependent Signal
  • Percent Signal Change
  • Brodmann Area

BMC Research Notes

ISSN: 1756-0500

Contact us