0038

Grey matter changes in adolescents participating in a meditation training
Justin P Yuan1, Colm G Connolly2, Eva Henje Blom3,4, Leo P Sugrue1, Tony T Yang4, Duan Xu1, and Olga Tymofiyeva1

1Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, 2Department of Biomedical Sciences, Florida State University College of Medicine, Tallahassee, FL, United States, 3Department of Clinical Science/Child- and Adolescent Psychiatry, Umeå University, Umeå, Sweden, 4Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States

Synopsis

Meditation is a popular mind-body practice with numerous benefits, but its neural mechanism remains unclear. Previous MRI studies have shown morphometric changes associated with meditation, such as increased grey matter volume (GMV). However, these findings were in adults and adolescents remained understudied. Using voxel-based morphometry, we assessed GMV changes in adolescents participating in a training containing elements from mindfulness meditation and yoga-based practices. We found a significant GMV decrease in the left posterior insula, a key homeostatic awareness and interoception region. The decreasing GMV opposes previous findings of structural increase in adults, which may be due to adolescence-specific developmental processes.

Introduction

Meditation is a popular mind-body practice that has become increasingly popular both recreationally and clinically.1 Despite its well-documented health benefits,2 meditation’s underlying neural mechanisms are not fully understood.3,4 Voxel-based morphometry (VBM), a neuroimaging method that measures local grey matter volume (GMV) differences, has been previously utilized to study mechanisms of meditation.5,6,7 These findings however are limited to adults, and meditation’s effects in adolescence, a period of crucial maturation, remained understudied.3 In this study, we examined GMV changes in adolescents participating in a 12-week meditation training. We also examined GMV changes during a control period without training.

Methods

Thirty-eight adolescent volunteers (mean±SD=16.48±1.29 years, 24F; Table 1) underwent the Training for Awareness, Resilience, and Action (TARA).8,9 This training program integrates elements from mindfulness meditation, interoceptive practices, yoga-based movements, and breathing exercises. The participants attended weekly 1.5-hour sessions for 12 weeks and were encouraged to practice the exercises at home daily.

The adolescents were scanned pre- and post-training on a 3T MRI scanner using an IR-SPGR T1 sequence with TR/TI/TE=10.2s/450ms/4.2s, flip angle=15°, matrix=256x256, FOV=25.6cm, slice thickness=1mm, acceleration=2.00, Tacq=3min50s.

VBM analysis10 was conducted using FSLv5.0.811 to assess GMV differences between pre- and post-training. Images were skull-stripped (bet-FSL, 3dUnifize and 3dSkullStrip-AFNIv18.1.18)12 and segmented into grey matter (GM), white matter, and cerebrospinal fluid. GM was registered to the MNI152 templates using affine13,14 and nonlinear transformations.15,16 A study-specific template was created, and all GM images were nonlinearly reregistered to it. GM images were modulated by the Jacobian of the warp field17 and smoothed with a Gaussian kernel (σ=2mm≈4.7mm FWHM).

GMV changes were assessed using nonparametric, voxel-wise permutation tests18 using a repeated-measures paired t-test design, controlling for gender. The permutation tests incorporated threshold-free cluster enhancement19 (TFCE) and had 5000 permutations. This yielded two GMV contrast maps, pre > post or pre < post. The maps were thresholded at p=0.025 to account for the two contrasts.The VBM analysis was repeated with a subset of the subjects (n=21, 16.70±1.07 yrs., 14F) who received a third, control MRI scan twelve weeks before the training.

Results

VBM analysis identified one cluster centered in the left posterior insula that showed significant GMV decreases (p=0.019). The cluster extended medially into the left thalamus and putamen, showing significant (p<0.025) GMV decreases in both areas. No areas showed significant GMV increase. There were no significant changes in either direction in the control analysis. See Figure 1 and Table 2 for results.

Discussion

The VBM analysis identified a left posterior insula (PI) cluster showing significant GMV decrease after meditation training. Structural insular changes are a consistent finding in studies of meditation’s neural effects.5,6,20,21,22 The PI provides the primary interoceptive representation of one’s physiological condition,23 processing temperature, pain, itch, and respiration stimuli from lamina I spinothalamic neurons.24,25 The homeostatic information is sent to the anterior insula where it is assigned emotional relevance, thus underlying emotional awareness.26,27 The PI findings may reflect the training’s emphasis on interoceptive physical awareness practices, such as breathing exercises and body-scan meditations.

The significant cluster overlapped with the left thalamus and putamen. The thalamus relays information to the cortex, and it is particularly crucial for the transfer of sensorimotor information.28 GMV changes could reflect this specific function, as participants repeatedly practiced exercises involving sensory awareness and physical movement. The thalamus also sends viscero-somatic stimuli to the PI.23,26 Structural changes in both regions further support an awareness-based mechanism of meditation.

The putamen is associated with refinement and control of motor movement29 and reinforcement of learning.30 It is also a key component of the corticostriatal motor circuit. GMV decreases here could reflect yoga-based movement practices involved in the training. Subjects learned coordinated motor movements that were elaborated upon weekly.

Most meditation studies have found morphometric increases with practice, but we observed GMV decreases. One explanation for this discrepancy is that our sample used adolescents, whereas past studies used adults. Adolescent brain maturation is marked by an inverted-U shaped trajectory, peaking early and then declining towards young adulthood.31-34 This is distinct from adults. Additionally, the relationship between GM structure and functional ability changes throughout the lifespan.35 Cellularly, GMV decreases could be due to dendritic spine pruning that is thought to improve synaptic efficiency and solidify experience-dependent learning.36,37 We speculate that our decreased GMV findings could be explained by such a process: meditation practices could have bolstered the maturation of neuronal connections in regions associated with physical awareness (posterior insula), along with the structures required to communicate and learn such practices (thalamus and putamen).

Acknowledgements

NCCIH R21AT009173, NICHD R01HD072074, UCSF Research Evaluation and Allocation Committee (REAC) and J. Jacobson Fund, and UCSF Radiology Seed Grant #14-31.

References

  1. Goyal, M., Singh, S., Sibinga, E. M. S., Gould, N. F., Rowland-Seymour, A., Sharma, R.,... Haythornthwaite, J. A. (2014). Meditation Programs for Psychological Stress and Well-being: A Systematic Review and Meta-analysis. JAMA Internal Medicine, 174(3), 357–368.
  2. Grossman, P., Niemann, L., Schmidt, S., & Walach, H. (2004). Mindfulness-based stress reduction and health benefits. Journal of Psychosomatic Research, 57(1), 35–43.
  3. Fox, K. C. R., Nijeboer, S., Dixon, M. L., Floman, J. L., Ellamil, M., Rumak, S. P., … Christoff, K. (2014). Is meditation associated with altered brain structure? A systematic review and meta-analysis of morphometric neuroimaging in meditation practitioners. Neuroscience & Biobehavioral Reviews, 43, 48–73.
  4. Tang, Y.-Y., Hölzel, B. K., & Posner, M. I. (2015). The neuroscience of mindfulness meditation. Nature Reviews Neuroscience, 16(4), 213–225.
  5. Hölzel, B. K., Ott, U., Gard, T., Hempel, H., Weygandt, M., Morgen, K., & Vaitl, D. (2008). Investigation of mindfulness meditation practitioners with voxel-based morphometry. Social Cognitive and Affective Neuroscience, 3(1), 55–61.
  6. Luders, E., Toga, A. W., Lepore, N., & Gaser, C. (2009). The underlying anatomical correlates of long-term meditation: Larger hippocampal and frontal volumes of gray matter. NeuroImage, 45(3), 672–678.
  7. Pagnoni, G., & Cekic, M. (2007). Age effects on gray matter volume and attentional performance in Zen meditation. Neurobiology of Aging, 28(10), 1623–1627.
  8. Henje Blom, E., Duncan, L. G., Ho, T. C., Connolly, C. G., LeWinn, K. Z., Chesney, M., … Yang, T. T. (2014). The development of an RDoC-based treatment program for adolescent depression: “Training for Awareness, Resilience, and Action” (TARA). Frontiers in Human Neuroscience, 8.
  9. Henje Blom, E., Tymofiyeva, O., Chesney, M. A., Ho, T. C., Moran, P., Connolly, C. G., … Yang, T. T. (2017). Feasibility and Preliminary Efficacy of a Novel RDoC-Based Treatment Program for Adolescent Depression: “Training for Awareness Resilience and Action” (TARA)—A Pilot Study. Frontiers in Psychiatry, 7.
  10. Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N., Friston, K. J., & Frackowiak, R. S. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage, 14(1 Pt 1), 21–36.
  11. Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., … Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(Supplement 1), S208–S219.
  12. Cox, R. W. (1996). AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages. Computers and Biomedical Research, 29(3), 162–173.
  13. Jenkinson, M., & Smith, S. (2001). A global optimisation method for robust affine registration of brain images. Medical Image Analysis, 5(2), 143–156.
  14. Jenkinson, Mark, Bannister, P., Brady, M., & Smith, S. (2002). Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. NeuroImage, 17(2), 825–841.
  15. Andersson, J. L., Jenkinson, M., & Smith, S. (2007). Non-linear optimisation. FMRIB technical report TR07JA1. University of Oxford FMRIB Centre: Oxford, UK. Retrieved from
  16. Andersson, J. L., Jenkinson, M., Smith, S., & others. (2007). Non-linear registration, aka Spatial normalisation FMRIB technical report TR07JA2. FMRIB Analysis Group of the University of Oxford, 2.
  17. Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N., Friston, K. J., & Frackowiak, R. S. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage, 14(1 Pt 1), 21–36.
  18. Nichols, T. E., & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human Brain Mapping, 15(1), 1–25.
  19. Smith, S. M., & Nichols, T. E. (2009). Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage, 44(1), 83–98.
  20. Lazar, S. W., Kerr, C. E., Wasserman, R. H., Gray, J. R., Greve, D. N., Treadway, M. T., … Fischl, B. (2005). Meditation experience is associated with increased cortical thickness. Neuroreport, 16(17), 1893–1897.
  21. Murakami, H., Nakao, T., Matsunaga, M., Kasuya, Y., Shinoda, J., Yamada, J., & Ohira, H. (2012). The Structure of Mindful Brain. PLOS ONE, 7(9), e46377.
  22. Friedel, S., Whittle, S. L., Vijayakumar, N., Simmons, J. G., Byrne, M. L., Schwartz, O. S., & Allen, N. B. (2015). Dispositional mindfulness is predicted by structural development of the insula during late adolescence. Developmental Cognitive Neuroscience, 14, 62–70.
  23. Craig, A. D. (2002). How do you feel? Interoception: the sense of the physiological condition of the body. Nature Reviews Neuroscience, 3(8), 655–666.
  24. Brooks, D. J. (2006). Dopaminergic action beyond its effects on motor function: imaging studies. Journal of Neurology, 253 Suppl 4, IV8-15.
  25. Strigo, I. A., & Craig, A. D. (Bud). (2016). Interoception, homeostatic emotions and sympathovagal balance. Phil. Trans. R. Soc. B, 371(1708), 20160010.
  26. Craig, A. D. (Bud). (2009). How do you feel — now? The anterior insula and human awareness. Nature Reviews Neuroscience, 10(1), 59–70.
  27. Fox, K. C. R., Andrews-Hanna, J. R., Mills, C., Dixon, M. L., Markovic, J., Thompson, E., & Christoff, K. (2018). Affective neuroscience of self-generated thought. Annals of the New York Academy of Sciences.
  28. Vertes, R. P., Linley, S. B., & Hoover, W. B. (2015). Limbic circuitry of the midline thalamus. Neuroscience & Biobehavioral Reviews, 54, 89–107.
  29. Balleine, B. W., Delgado, M. R., & Hikosaka, O. (2007). The Role of the Dorsal Striatum in Reward and Decision-Making. Journal of Neuroscience, 27(31), 8161–8165.
  30. Lanciego, J. L., Luquin, N., & Obeso, J. A. (2012). Functional Neuroanatomy of the Basal Ganglia. Cold Spring Harbor Perspectives in Medicine, 2(12).
  31. Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., … Rapoport, J. L. (1999). Brain development during childhood and adolescence: a longitudinal MRI study. Nature Neuroscience, 2(10), 861–863.
  32. Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., … Giedd, J. (2006). Intellectual ability and cortical development in children and adolescents. Nature, 440(7084), 676–679.
  33. Shaw, Philip, Kabani, N. J., Lerch, J. P., Eckstrand, K., Lenroot, R., Gogtay, N., … Wise, S. P. (2008). Neurodevelopmental Trajectories of the Human Cerebral Cortex. Journal of Neuroscience, 28(14), 3586–3594.
  34. Narvacan, K., Treit, S., Camicioli, R., Martin, W., & Beaulieu, C. (2017). Evolution of deep gray matter volume across the human lifespan. Human Brain Mapping, 38(8), 3771–3790.
  35. Schnack, H. G., Haren, V., E.m, N., Brouwer, R. M., Evans, A., Durston, S., … E, H. (2015). Changes in Thickness and Surface Area of the Human Cortex and Their Relationship with Intelligence. Cerebral Cortex, 25(6), 1608–1617.
  36. Drzewiecki, C. M., Willing, J., & Juraska, J. M. (2016). Synaptic number changes in the medial prefrontal cortex across adolescence in male and female rats: A role for pubertal onset. Synapse, 70(9), 361–368. https://doi.org/10.1002/syn.21909
  37. Anderson, S. A., Classey, J. D., Condé, F., Lund, J. S., & Lewis, D. A. (1995). Synchronous development of pyramidal neuron dendritic spines and parvalbumin-immunoreactive chandelier neuron axon terminals in layer III of monkey prefrontal cortex. Neuroscience, 67(1), 7–22.

Figures

Fig. 1 Three-plane view of the region showing significant grey matter volume decrease after meditation training. The cluster showed the most significant change at the left posterior insula (p ≤ 0.025, TFCE-corrected for family-wise errors). The cluster also overlapped with the left thalamus and left putamen. The cluster is overlaid over the MNI 2mm brain template, and coordinates are in MNI standard space. The right-side color bar corresponds to the cluster’s significance (1-p).

Table 1. Demographic information of the participating sample. Subjects were adolescent volunteers drawn from the community. Two VBM analyses were conducted: 1) primary, 2) control composed of subjects with an additional MRI scan 12 weeks prior to training. ADD/ADHD – attention deficit disorder/attention deficit hyperactivity disorder, MDD – major depressive disorder, GAD – generalized anxiety disorder.

Table 2. Primary VBM results of posterior insula grey matter volume decrease pre- to post-training. Peak significance is reported, at the α=0.025 level (TFCE-corrected for family-wise errors). The cluster’s peak coordinates are in MNI standard space. L – left, Sig. – significance, (-) – decreasing direction, (+) – increasing direction.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
0038