- Email: [email protected]
The neuroanatomy of long-term meditators
Available online at www.sciencedirect.com
ScienceDirect The neuroanatomy of long-term meditators Eileen Luders1,2 and Florian Kurth1 Meditating is an active mental process that has been proposed to lead to structural changes in the brain, especially if occurring repeatedly, regularly, and over longer periods of time. Thus, meditators might present with a distinctive brain anatomy detectable via modern imaging technologies. This article summarizes findings as reported in the imaging literature when comparing long-term meditators with controls. The morphometric analyses applied include global, regional, and local measures, such as voxel-wise or point-wise estimates. Overall, long-term meditators present with larger (rather than smaller) anatomical measures than controls, which may be indicative of actual meditation-induced changes, pre-existing differences in meditators’ brains, or a combination of both. Addresses 1 School of Psychology, University of Auckland, Auckland, New Zealand 2 Laboratory of Neuro Imaging, School of Medicine, University of Southern California, Los Angeles, USA Corresponding author: Luders, Eileen ([email protected])
Current Opinion in Psychology 2019, 28:172–178 This review comes from a themed issue on Mindfulness Edited by Amit Bernstein, Dave Vago and Thorsten Barnhofer For a complete overview see the Issue and the Editorial Available online 27th December 2018 https://doi.org/10.1016/j.copsyc.2018.12.013 2352-250X/ã 2018 Published by Elsevier Ltd.
(long-term) meditation may change and shape the brain, all with the caveat that we cannot reliably disentangle the effects of nature and nurture. The aim of this article is to summarize the observed differences in brain anatomy between long-term meditators and control samples discriminating between effects on a global, regional, and local level, while providing insights into the diverse morphometric measurements applied. Images of meditators’ brains have been acquired using both, functional and structural imaging techniques. Since the focus of this article is on the neuroanatomy of long-term meditators, the findings presented here are based on data acquired using structural MRI and DTI in meditator samples with an average practice duration of at least ten years. Meditation studies that focused on age effects, gender effects, and/or links between brain anatomy and other measures are omitted to keep this review as concise as possible. Similarly, studies of ‘mixed’ samples (e.g., those including yoga practitioners who did not necessarily practice meditation) are beyond the scope of this review. Altogether, this left nine samples, which were analyzed in twelve studies that compared long-term meditators and controls with respect to brain anatomy. Some of the meditation samples overlap across studies, a natural consequence of data sharing and/or collecting data over time and adding new brain images to an already existing pool of images. Table 1 describes each study sample in terms of meditation practice years, number of subjects, meditation style, as well as the anatomical measurement(s) applied in each study. Study outcomes
Introduction Engaging in meditation is an active mental process that, depending on the technique, incorporates efforts to exercise awareness, attention, concentration, focus, and so on. If occurring regularly, such intense brain processes are likely to leave an imprint on the microstructure as well as macrostructure of the brain and as such should be particularly evident in the brains of long-term meditation practitioners. Modern imaging technologies, such as magnetic resonance imaging (MRI) or diffusion tensor imaging (DTI), are suitable to detect deviations in brain anatomy, either over time or between groups, using high-resolution assessment techniques. Over the last decade, a solid body of research has emerged comparing long-term practitioners with control samples at a single point in time. These cross-sectional studies have uncovered a wide array of distinctive brain features in long-term practitioners, which offer an initial glimpse into how Current Opinion in Psychology 2019, 28:172–178
To organize the outcomes, findings are classified as global, regional, and local (see Box 1). In addition, since studies have captured different attributes of brain anatomy, findings are presented as outcomes of specific morphometric measures (see Box 2). The approximate location of study-specific findings is illustrated in Figure 1. For all reported effects, the references to the original study are provided.1 Global and regional analyses
To our knowledge, there are no reports of significant group differences with respect to total intracranial volume but, interestingly, meditators have been reported to show more whole-brain gray matter [11]. In addition to those global measures, significant effects (and trends toward significant effects) have also been revealed at the regional 1
For an interpretation of structural effects in terms of their function, please refer to the original publications. www.sciencedirect.com
The neuroanatomy of long-term meditators Luders and Kurth 173
Table 1 Study-specific meditation samples Ref.
Practice years mean [SD]; range
N of MED
N of CTL
Style
[1]
30.0 [7.0]; 20–41
17
15
LK
[2]
24.2 [12.4]; 5–46
22
22
S, V, Z
[3] [4]
23.3 [12.2]; 5–46 20.2 [12.2]; 5–46
27 30
27 30
S, V, Z S, V, Z
[5]
20.2 [12.2]; 5–46
30
30
S, V, Z
[6] [7]
19.8 [11.4]; 4–46 19.8 [11.4]; 4–46
50 50
50 50
S, V, Z S, V, Z
[8] [9] [10] [11] [12]
19.8 [11.4]; 4–46 16.5 [5.1]; 14–31 14.4 [8.39]; 2–30 14.1 [6.1]; 5–26 10 [n/a]; 10-40
50 10 19 23 6
50 10 20 23 67
S, V, Z D Z SA D, V, Z
Anatomical measurement (G = global, R = regional, L = local) G: mean cortical thickness over all voxels (asymmetry analysis) L: point-wise cortical thickness (regular + asymmetry analysis) G: total brain / gray matter volume R: volumes (various ROIs) L: voxel-wise gray matter R: fractional anisotropy (various ROIs) R: areas (corpus callosum) R: distances to core (corpus callosum) R: fractional anisotropy (corpus callosum) R: volumes (hippocampus) R: distances to core (hippocampus) L: point-wise cortical gyrification R: gray matter volumes (hippocampus) L: voxel-wise gray matter L: voxel-wise gray matter (asymmetry) L: voxel-wise gray matter R: point-wise cortical thickness (various ROIs) L: voxel-wise gray matter L: voxel-wise gray matter
Studies/meditation samples are listed in the order of the mean duration of their meditation practice. MED = meditators; CTL = controls; n/a = not available; ROIs = regions of interest; Meditation styles: D = Dzogchen, LK = Loving-kindness meditation, SA = Sahaja yoga meditation, S = Shamatha, V = Vipassana, Z = Zen.
level using both MRI-based and DTI-based data. More specifically, using MRI-based volume and distance-tocore measures, larger values were reported in meditators for the left and right hippocampus [2,5,7]. Similarly, meditators also had larger distance-to-core measures in some sections of the corpus callosum [4], but there were no significant group differences in callosal area measures [4]. Moreover, region-of-interest (ROI) analyses of MRIbased measures of cortical thickness revealed larger values in meditators within the left and right secondary somatosensory cortices [10] as well as in the right cingulate in a section adjacent to the rostral body and anterior midbody of the corpus callosum [10]. Similarly, using DTI-based measures of fractional anisotropy, larger values were reported in meditators within the anterior thalamic radiation [3], cingulum-hippocampus bundle [3], cortico-spinal tract [3], inferior fronto-occipital fasciculus [3], inferior longitudinal fasciculus [3], superior longitudinal fasciculus (main tract and temporal component) [3], uncinate fasciculus [3], and the forceps minor [3], the latter constituting a fiber bundle which connects the two frontal lobes via the genu of the corpus callosum. Significant group effects with a higher fractional anisotropy in meditations were also reported in other subregions of the corpus callosum, such as the rostral body of the anterior third, the anterior midbody, and the posterior midbody [4]. Local analyses
Significant group differences in MRI-based voxel-wise gray matter (and trends toward significant effects), with more gray matter in meditators, were reported within the www.sciencedirect.com
right orbito-frontal cortex [1,11,12], the left orbitofrontal cortex [11], the right thalamus [1], the left inferior temporal gyrus [1], the right inferior temporal gyrus [11], the left hippocampus [7], the left superior frontal gyrus [9], the left inferior frontal gyrus [9], the left fusiform gyrus [9], the left and right insula [11,12], the right angular gyrus [11], the left ventrolateral prefrontal cortex [11], the left and right anterior cingulate cortex [12], the left and right temporal and parietal operculum [12], the left and right anterior lobes of the cerebellum [9], as well as the brain stem [9]. Last but not least, when specifically focusing on differences between the left and right hemisphere, the precuneus was reported to show a decreased rightward asymmetry of voxel-wise gray matter in meditators compared to controls [8]. Significant group differences in MRI-based point-wise cortical thickness (and trends toward significant effects), with thicker cortices in meditators, were detected in a right prefrontal cluster encompassing the frontopolar cortex [1], the left ventrolateral prefrontal cortex [1] as well as the right fronto-insular cortex [1]. When explicitly focusing on brain asymmetry, no differences between meditators and controls were detected in cortical thickness, regardless of whether the analysis was directed at the mean cortical thickness (averaged over all vertices) or the cortical thickness at each vertex [1]. Significant group differences in MRI-based point-wise cortical gyrification (and trends toward significant effects), were detected. More specifically, when using uncorrected significance thresholds at p 0.01, Current Opinion in Psychology 2019, 28:172–178
174 Mindfulness
Box 1 Degrees of regional specificity
Global
Regional
Study outcomes differ in terms of their regional specificity, where measurements can be roughly classified as global, regional, and local. Global measures offer a good starting point to explore if there are any differences between groups and/or over time at all (e.g., with respect to wholebrain gray matter volume). However, as the name implies, global measures are severely limited in their spatial sensitivity and unlikely to produce any significant effects if group differences, etc. are spatially restricted.
For greater specificity, regional measures may be obtained as a follow-up to global effects. Nevertheless, regional approaches may also stand alone. Socalled region-of-interest (ROI) analyses constitute a regional approach and focus on a specific brain structure (e.g., the corpus callosum). Ultimately, local approaches (see below) may also be applied to a region of interest (e.g., point-wise callosal distances), thus creating a hybrid between regional and local, but for the sake of convenience will be treated here as a regional approach.
Local
Regional vs. Global vs. Local
For even more detail, local measures may be obtained as a follow-up to global effects. Nevertheless, local approaches may also stand alone. So-called voxelwise analyses constitute a local approach and are designed to map significant effects across an entire search volume, such as the entire brain or the entire cortex, with an extremely high regional specificity (e.g., voxel-wise gray matter). Other examples for local approaches are vertex-wise analyses or point- wise analyses (e.g., point-wise cortical thickness).
Regional approaches outperform global approaches in terms of regional specificity. However, they may also offer an advantage over local approaches as they entail fewer statistical tests and, as such, require fewer corrections for multiple comparisons. However, unlike local approaches, regional approaches may be compromised by limiting a study to pre-determined areas possibly overlooking effects in other parts of the brain; they are also prone to user bias if it is impossible to identify (or define) unambiguous boundaries.
Current Opinion in Psychology
meditators presented with higher degrees of gyrification than controls within the left and right anterior dorsal insula [6], left precentral and postcentral gyrus [6], left central sulcus [6], left inferior and middle temporal gyrus [6], left angular gyrus [6], left parieto-occipital fissure [6] as well as in the right parietal operculum [6], right fusiform gyrus [6], and right cuneus [6]. Interestingly, when relaxing statistical thresholds even further ( p 0.05, uncorrected), group differences manifested in both directions, with more gyrification in meditators than in controls in some regions, but less gyrification in other regions. Figure 1 provides an overview of brain structures where meditation effects (i.e., cross-sectional differences Current Opinion in Psychology 2019, 28:172–178
between meditators and controls) have been detected using structural MRI and DTI.
Discussion While there are some exceptions, overall, long-term meditators present with larger (rather than smaller) anatomical measures than controls (e.g., more gray matter, thicker cortices, a higher fractional anisotropy). Distinctive anatomical features in the brains of long-term meditation practitioners seem not confined only to a particular core region (or a few specific key regions) but rather involve large-scale brain networks that include the cerebral cortex, subcortical gray and white matter, and even brain stem and cerebellum. Thus, meditation might be a www.sciencedirect.com
The neuroanatomy of long-term meditators Luders and Kurth 175
Box 2 Morphometric measures
Whole-brain Gray Matter and Total Intracranial Volumes. Brain tissue can be automatically classified as gray matter (GM), white matter (WM), and cerebrospinal fluid (CFS). Gray matter volume (total intracranial volume, respectively) can be obtained (in mm3) by adding up the voxel-wise GM content (the voxel-wise GM, WM, and CFS content, respectively) and multiplying it with the voxel dimensions. Distances, Areas, and Volumes. Brains can be parcellated, either manually or automatically, into two-dimensional (e.g., corpus callosum) or three-dimensional (e.g., hippocampus) regions of interest (ROI). Various ROI-specific measurements can then be obtained, such as distances (in mm), areas (in mm2), or volumes (in mm3). Voxel- or point-wise measures (see below) can also be averaged within a ROI. Fractional Anisotropy. Fractional anisotropy (FA) reflects the degree of unequal spatial restriction (or degree of direction) in diffusion. Within each voxel, FA is calculated based on the magnitude of diffusion in each of the three principal directions. Once established, FA values are either analyzed on a voxel-by-voxel basis or averaged within white matter fiber tracts (e.g., cortico-spinal tract).
Voxel-wise Gray Matter. Voxel-wise “gray matter volume”, sometimes also referred to as voxel-wise “gray matter concentration”, reflects the local amount of gray matter. It is calculated as the relative amount of gray matter within a voxel at thousands of voxels across the entire brain. It is often examined using a method called “voxel-based morphometry” (or short “VBM”). Point-wise Cortical Thickness. Cortical thickness, as the name implies, reflects the thickness of the cerebral cortex (i.e., the outer mantle of the brain consisting of gray matter). It is calculated as the distance (in mm) between its inner boundary (e.g., GM/WM) and outer boundary (e.g., GM/CSF) at thousands of points (or so-called vertices) across the cerebral cortex.
Point-wise cortical Gyrification. Cortical gyrification, sometimes also referred to as “cortical convolution” or “cortical complexity”, reflects the magnitude and frequency of local folding of the cerebral cortex. It is often calculated (in degrees or radians) as the point-wise mean curvature (or fractal dimension) at thousands of points across the cerebral cortex. Current Opinion in Psychology
powerful mental exercise with the potential to change the physical structure of the brain at large. Indeed, it is tempting to assume that the observed group differences constitute true meditation effects, especially given the accumulating evidence for training-induced neuroplasticity, both with respect to short-term meditation [cf. Refs. 13,14] as well as outside the framework of meditation [15–19]. Possible mechanisms and underlying processes for such changes are discussed in detail elsewhere [20,21,22,23]. However, given the cross-sectional design of the studies summarized here, this www.sciencedirect.com
assumption is merely conjecture. It is equally possible that meditators, especially long-term practitioners, have brains that were fundamentally different prior to the practice of meditation, perhaps even from birth. A particular brain anatomy might be linked to specific personality profiles, particular interests, or certain mental capacities, which may have drawn an individual to meditation in the first place. In addition, or alternatively, such a particular brain anatomy may have equipped that person with the required prerequisites to reach desired states during meditation and/or experience rewarding effects (e.g., calmness, patience, clarity, focus, joy, loving kindness) Current Opinion in Psychology 2019, 28:172–178
176 Mindfulness
Figure 1
pre- / postcentral gyrus, central sulcus [6]
parietal / temporal operculum, posterior insula [6,10,12]
superior frontal gyrus [9]
orbitofrontal cortex [1,11,12]
angular gyrus [6,11]
frontopolar cortex [1]
inferior / middle temporal gyrus [1,6,11]
ventrolateral prefrontal cortex, inferior frontal gyrus [1,9,11]
anterior insula [1,6,11,12]
precuneus [8]
fusiform gyrus [9,6] corpus callosum [4]
cuneus, parietooccipital fissure [6]
thalamus [1]
anterior / mid cingulate [10,12] hippocampus [2,5,7]
anterior thalamic radiation [3]
anterior cerebellum [9]
brainstem [9]
superior longitudinal fasciculus [3]
cingulum-hippocampus bundle [3]
inferior fronto-occipital fasciculus [3]
corpus callosum / forceps minor [3,4]
uncinate fasciculus [3]
inferior longitudinal fasciculus [3]
cortico-spinal tract [3] Current Opinion in Psychology
Significant differences between long-term meditators and controls based on MRI data (top) and DTI data (bottom). Regions reported in one study are indicated in blue, regions reported in two studies in yellow, and regions reported in three or more studies in pink. All findings are displayed in the left hemisphere of the brain, regardless of whether the effect occurred in the left and/or right hemisphere (for information on laterality, please refer to the main text). The numbers in brackets refer to the original publication.
Current Opinion in Psychology 2019, 28:172–178
www.sciencedirect.com
The neuroanatomy of long-term meditators Luders and Kurth 177
that linger after meditation, thus providing an incentive to keep meditating over years. Of course, it might as well be a combined effect of nature and nurture, where any preexisting differences might strongly interact with actual changes. For example, if a specific brain architecture facilitates a successful or rewarding meditation experience, it probably plays a significant role in maintaining a regular and long-term practice — via intrinsic positive reinforcement — and may subsequently lead to further changes in the brain. Altogether, since there is a lack of longitudinal long-term studies, it remains to be established whether the distinctive brain anatomy in long-term meditators is indeed a consequence of their practices or rather a predisposing feature. However, while longitudinal long-term meditation research is clearly desirable, it poses several challenges [cf. Ref. 24]: Ideally, longitudinal research should follow practitioners over years (or even decades), which might not be feasible due to budgetary limitations or subject attrition, etc. In the same vein, the inclusion of an active control condition, with random assignment to experimental (meditation) and control condition, is a desirable goal, but seems to be overly ambitious and unrealistic for practical or ethical reasons. Short-term longitudinal studies have already enhanced this field of research and will continue to do so, but may only uncover regions involved in skill acquisition, rather than regions changed due to skill practice or maintenance over time. Thus, cross-sectional studies in long-term meditators will remain a valuable source of information, not only with respect to the phenomenon of brain plasticity, but also as an explanation for unique characteristics of meditators in terms of cognitive, emotional, self-related, and interpersonal processes, as reviewed in other chapters of this special issue.
Funding This article did not receive any specific grant from funding agencies in the public, commercial, or not-forprofit sectors.
Conflict of interest statement Nothing declared.
Acknowledgements The authors wish to warmly thank Professor Michael Corballis for his valuable feedback on the article.
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest 1.
Engen HG, Bernhardt BC, Skottnik L, Ricard M, Singer T: Structural changes in socio-affective networks: multi-modal MRI findings in long-term meditation practitioners. Neuropsychologia 2018, 116:26-33.
www.sciencedirect.com
2.
Luders E, Toga AW, Lepore N, Gaser C: The underlying anatomical correlates of long-term meditation: larger hippocampal and frontal volumes of gray matter. Neuroimage 2009, 45:672-678.
3.
Luders E, Clark K, Narr KL, Toga AW: Enhanced brain connectivity in long-term meditation practitioners. Neuroimage 2011, 57:1308-1316.
4.
Luders E, Phillips OR, Clark K, Kurth F, Toga AW, Narr KL: Bridging the hemispheres in meditation: thicker callosal regions and enhanced fractional anisotropy (FA) in long-term practitioners. Neuroimage 2012, 61:181-187.
5.
Luders E, Thompson PM, Kurth F, Hong JY, Phillips OR, Wang Y, Gutman BA, Chou YY, Narr KL, Toga AW: Global and regional alterations of hippocampal anatomy in long-term meditation practitioners. Hum Brain Mapp 2013, 34:3369-3375.
6.
Luders E, Kurth F, Mayer EA, Toga AW, Narr KL, Gaser C: The unique brain anatomy of meditation practitioners: alterations in cortical gyrification. Front Hum Neurosci 2012, 6:34.
7.
Luders E, Kurth F, Toga AW, Narr KL, Gaser C: Meditation effects within the hippocampal complex revealed by voxel-based morphometry and cytoarchitectonic probabilistic mapping. Front Psychol 2013, 4:398.
8.
Kurth F, MacKenzie-Graham A, Toga AW, Luders E: Shifting brain asymmetry: the link between meditation and structural lateralization. Soc Cogn Affect Neurosci 2015, 10:55-61.
9.
Vestergaard-Poulsen P, van BM, Skewes J, Bjarkam CR, Stubberup M, Bertelsen J, Roepstorff A: Long-term meditation is associated with increased gray matter density in the brain stem. Neuroreport 2009, 20:170-174.
10. Grant JA, Courtemanche J, Duerden EG, Duncan GH, Rainville P: Cortical thickness and pain sensitivity in zen meditators. Emotion 2010, 10:43-53. 11. Hernandez SE, Suero J, Barros A, Gonzalez-Mora JL, Rubia K: Increased grey matter associated with long-term sahaja yoga meditation: a voxel-based morphometry study. PLoS One 2016, 11:e0150757. 12. Chetelat G, Mezenge F, Tomadesso C, Landeau B, ArenazaUrquijo E, Rauchs G, Andre C, de Flores R, Egret S, Gonneaud J et al.: Reduced age-associated brain changes in expert meditators: a multimodal neuroimaging pilot study. Sci Rep 2017, 7:10160. 13. Fox KC, Nijeboer S, Dixon ML, Floman JL, Ellamil M, Rumak SP, Sedlmeier P, Christoff K: Is meditation associated with altered brain structure? A systematic review and meta-analysis of morphometric neuroimaging in meditation practitioners. Neurosci Biobehav Rev 2014, 43C:48-73. 14. Tang YY, Holzel BK, Posner MI: The neuroscience of mindfulness meditation. Nat Rev Neurosci 2015, 16:213-225. 15. Taubert M, Draganski B, Anwander A, Muller K, Horstmann A, Villringer A, Ragert P: Dynamic properties of human brain structure: learning-related changes in cortical areas and associated fiber connections. J Neurosci 2010, 30:1167011677. 16. 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, 26:6314-6317. 17. Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A: Neuroplasticity: changes in grey matter induced by training. Nature 2004, 427:311-312. 18. Boyke J, Driemeyer J, Gaser C, Buchel C, May A: Traininginduced brain structure changes in the elderly. J Neurosci 2008, 28:7031-7035. 19. Bezzola L, Merillat S, Gaser C, Jancke L: Training-induced neural plasticity in golf novices. J Neurosci 2011, 31:12444-12448. 20. Kurth F, Cherbuin N, Luders E: Promising links between meditation and reduced (Brain) aging: an attempt to bridge some gaps between the alleged fountain of youth and the youth of the field. Front Psychol 2017, 8:860. Current Opinion in Psychology 2019, 28:172–178
178 Mindfulness
This review article outlines theories and mechanisms pertaining to normal (brain) aging, summarizes findings in the field of meditation research, and discusses how meditation may affect aging processes. 21. Luders E, Cherbuin N, Gaser C: Estimating brain age using highresolution pattern recognition: younger brains in long-term meditation practitioners. Neuroimage 2016, 134:508-513. 22. Luders E, Cherbuin N, Kurth F: Forever Young(er): potential agedefying effects of long-term meditation on gray matter atrophy. Front Psychol 2014, 5:1551.
Current Opinion in Psychology 2019, 28:172–178
23. Boldrini M, Fulmore CA, Tartt AN, Simeon LR, Pavlova I, Poposka V, Rosoklija GB, Stankov A, Arango V, Dwork AJ et al.: Human hippocampal neurogenesis persists throughout. Aging Cell Stem Cell 2018, 22:589-599 e585. Using autopsy hippocampi in humans aged 14 to 79 years, the study revealed evidence for preserved neurogenesis in healthy older subjects. 24. Luders E: Exploring age-related brain degeneration in meditation practitioners. Ann N Y Acad Sci 2014, 1307:82-88.
www.sciencedirect.com
ScienceDirect The neuroanatomy of long-term meditators Eileen Luders1,2 and Florian Kurth1 Meditating is an active mental process that has been proposed to lead to structural changes in the brain, especially if occurring repeatedly, regularly, and over longer periods of time. Thus, meditators might present with a distinctive brain anatomy detectable via modern imaging technologies. This article summarizes findings as reported in the imaging literature when comparing long-term meditators with controls. The morphometric analyses applied include global, regional, and local measures, such as voxel-wise or point-wise estimates. Overall, long-term meditators present with larger (rather than smaller) anatomical measures than controls, which may be indicative of actual meditation-induced changes, pre-existing differences in meditators’ brains, or a combination of both. Addresses 1 School of Psychology, University of Auckland, Auckland, New Zealand 2 Laboratory of Neuro Imaging, School of Medicine, University of Southern California, Los Angeles, USA Corresponding author: Luders, Eileen ([email protected])
Current Opinion in Psychology 2019, 28:172–178 This review comes from a themed issue on Mindfulness Edited by Amit Bernstein, Dave Vago and Thorsten Barnhofer For a complete overview see the Issue and the Editorial Available online 27th December 2018 https://doi.org/10.1016/j.copsyc.2018.12.013 2352-250X/ã 2018 Published by Elsevier Ltd.
(long-term) meditation may change and shape the brain, all with the caveat that we cannot reliably disentangle the effects of nature and nurture. The aim of this article is to summarize the observed differences in brain anatomy between long-term meditators and control samples discriminating between effects on a global, regional, and local level, while providing insights into the diverse morphometric measurements applied. Images of meditators’ brains have been acquired using both, functional and structural imaging techniques. Since the focus of this article is on the neuroanatomy of long-term meditators, the findings presented here are based on data acquired using structural MRI and DTI in meditator samples with an average practice duration of at least ten years. Meditation studies that focused on age effects, gender effects, and/or links between brain anatomy and other measures are omitted to keep this review as concise as possible. Similarly, studies of ‘mixed’ samples (e.g., those including yoga practitioners who did not necessarily practice meditation) are beyond the scope of this review. Altogether, this left nine samples, which were analyzed in twelve studies that compared long-term meditators and controls with respect to brain anatomy. Some of the meditation samples overlap across studies, a natural consequence of data sharing and/or collecting data over time and adding new brain images to an already existing pool of images. Table 1 describes each study sample in terms of meditation practice years, number of subjects, meditation style, as well as the anatomical measurement(s) applied in each study. Study outcomes
Introduction Engaging in meditation is an active mental process that, depending on the technique, incorporates efforts to exercise awareness, attention, concentration, focus, and so on. If occurring regularly, such intense brain processes are likely to leave an imprint on the microstructure as well as macrostructure of the brain and as such should be particularly evident in the brains of long-term meditation practitioners. Modern imaging technologies, such as magnetic resonance imaging (MRI) or diffusion tensor imaging (DTI), are suitable to detect deviations in brain anatomy, either over time or between groups, using high-resolution assessment techniques. Over the last decade, a solid body of research has emerged comparing long-term practitioners with control samples at a single point in time. These cross-sectional studies have uncovered a wide array of distinctive brain features in long-term practitioners, which offer an initial glimpse into how Current Opinion in Psychology 2019, 28:172–178
To organize the outcomes, findings are classified as global, regional, and local (see Box 1). In addition, since studies have captured different attributes of brain anatomy, findings are presented as outcomes of specific morphometric measures (see Box 2). The approximate location of study-specific findings is illustrated in Figure 1. For all reported effects, the references to the original study are provided.1 Global and regional analyses
To our knowledge, there are no reports of significant group differences with respect to total intracranial volume but, interestingly, meditators have been reported to show more whole-brain gray matter [11]. In addition to those global measures, significant effects (and trends toward significant effects) have also been revealed at the regional 1
For an interpretation of structural effects in terms of their function, please refer to the original publications. www.sciencedirect.com
The neuroanatomy of long-term meditators Luders and Kurth 173
Table 1 Study-specific meditation samples Ref.
Practice years mean [SD]; range
N of MED
N of CTL
Style
[1]
30.0 [7.0]; 20–41
17
15
LK
[2]
24.2 [12.4]; 5–46
22
22
S, V, Z
[3] [4]
23.3 [12.2]; 5–46 20.2 [12.2]; 5–46
27 30
27 30
S, V, Z S, V, Z
[5]
20.2 [12.2]; 5–46
30
30
S, V, Z
[6] [7]
19.8 [11.4]; 4–46 19.8 [11.4]; 4–46
50 50
50 50
S, V, Z S, V, Z
[8] [9] [10] [11] [12]
19.8 [11.4]; 4–46 16.5 [5.1]; 14–31 14.4 [8.39]; 2–30 14.1 [6.1]; 5–26 10 [n/a]; 10-40
50 10 19 23 6
50 10 20 23 67
S, V, Z D Z SA D, V, Z
Anatomical measurement (G = global, R = regional, L = local) G: mean cortical thickness over all voxels (asymmetry analysis) L: point-wise cortical thickness (regular + asymmetry analysis) G: total brain / gray matter volume R: volumes (various ROIs) L: voxel-wise gray matter R: fractional anisotropy (various ROIs) R: areas (corpus callosum) R: distances to core (corpus callosum) R: fractional anisotropy (corpus callosum) R: volumes (hippocampus) R: distances to core (hippocampus) L: point-wise cortical gyrification R: gray matter volumes (hippocampus) L: voxel-wise gray matter L: voxel-wise gray matter (asymmetry) L: voxel-wise gray matter R: point-wise cortical thickness (various ROIs) L: voxel-wise gray matter L: voxel-wise gray matter
Studies/meditation samples are listed in the order of the mean duration of their meditation practice. MED = meditators; CTL = controls; n/a = not available; ROIs = regions of interest; Meditation styles: D = Dzogchen, LK = Loving-kindness meditation, SA = Sahaja yoga meditation, S = Shamatha, V = Vipassana, Z = Zen.
level using both MRI-based and DTI-based data. More specifically, using MRI-based volume and distance-tocore measures, larger values were reported in meditators for the left and right hippocampus [2,5,7]. Similarly, meditators also had larger distance-to-core measures in some sections of the corpus callosum [4], but there were no significant group differences in callosal area measures [4]. Moreover, region-of-interest (ROI) analyses of MRIbased measures of cortical thickness revealed larger values in meditators within the left and right secondary somatosensory cortices [10] as well as in the right cingulate in a section adjacent to the rostral body and anterior midbody of the corpus callosum [10]. Similarly, using DTI-based measures of fractional anisotropy, larger values were reported in meditators within the anterior thalamic radiation [3], cingulum-hippocampus bundle [3], cortico-spinal tract [3], inferior fronto-occipital fasciculus [3], inferior longitudinal fasciculus [3], superior longitudinal fasciculus (main tract and temporal component) [3], uncinate fasciculus [3], and the forceps minor [3], the latter constituting a fiber bundle which connects the two frontal lobes via the genu of the corpus callosum. Significant group effects with a higher fractional anisotropy in meditations were also reported in other subregions of the corpus callosum, such as the rostral body of the anterior third, the anterior midbody, and the posterior midbody [4]. Local analyses
Significant group differences in MRI-based voxel-wise gray matter (and trends toward significant effects), with more gray matter in meditators, were reported within the www.sciencedirect.com
right orbito-frontal cortex [1,11,12], the left orbitofrontal cortex [11], the right thalamus [1], the left inferior temporal gyrus [1], the right inferior temporal gyrus [11], the left hippocampus [7], the left superior frontal gyrus [9], the left inferior frontal gyrus [9], the left fusiform gyrus [9], the left and right insula [11,12], the right angular gyrus [11], the left ventrolateral prefrontal cortex [11], the left and right anterior cingulate cortex [12], the left and right temporal and parietal operculum [12], the left and right anterior lobes of the cerebellum [9], as well as the brain stem [9]. Last but not least, when specifically focusing on differences between the left and right hemisphere, the precuneus was reported to show a decreased rightward asymmetry of voxel-wise gray matter in meditators compared to controls [8]. Significant group differences in MRI-based point-wise cortical thickness (and trends toward significant effects), with thicker cortices in meditators, were detected in a right prefrontal cluster encompassing the frontopolar cortex [1], the left ventrolateral prefrontal cortex [1] as well as the right fronto-insular cortex [1]. When explicitly focusing on brain asymmetry, no differences between meditators and controls were detected in cortical thickness, regardless of whether the analysis was directed at the mean cortical thickness (averaged over all vertices) or the cortical thickness at each vertex [1]. Significant group differences in MRI-based point-wise cortical gyrification (and trends toward significant effects), were detected. More specifically, when using uncorrected significance thresholds at p 0.01, Current Opinion in Psychology 2019, 28:172–178
174 Mindfulness
Box 1 Degrees of regional specificity
Global
Regional
Study outcomes differ in terms of their regional specificity, where measurements can be roughly classified as global, regional, and local. Global measures offer a good starting point to explore if there are any differences between groups and/or over time at all (e.g., with respect to wholebrain gray matter volume). However, as the name implies, global measures are severely limited in their spatial sensitivity and unlikely to produce any significant effects if group differences, etc. are spatially restricted.
For greater specificity, regional measures may be obtained as a follow-up to global effects. Nevertheless, regional approaches may also stand alone. Socalled region-of-interest (ROI) analyses constitute a regional approach and focus on a specific brain structure (e.g., the corpus callosum). Ultimately, local approaches (see below) may also be applied to a region of interest (e.g., point-wise callosal distances), thus creating a hybrid between regional and local, but for the sake of convenience will be treated here as a regional approach.
Local
Regional vs. Global vs. Local
For even more detail, local measures may be obtained as a follow-up to global effects. Nevertheless, local approaches may also stand alone. So-called voxelwise analyses constitute a local approach and are designed to map significant effects across an entire search volume, such as the entire brain or the entire cortex, with an extremely high regional specificity (e.g., voxel-wise gray matter). Other examples for local approaches are vertex-wise analyses or point- wise analyses (e.g., point-wise cortical thickness).
Regional approaches outperform global approaches in terms of regional specificity. However, they may also offer an advantage over local approaches as they entail fewer statistical tests and, as such, require fewer corrections for multiple comparisons. However, unlike local approaches, regional approaches may be compromised by limiting a study to pre-determined areas possibly overlooking effects in other parts of the brain; they are also prone to user bias if it is impossible to identify (or define) unambiguous boundaries.
Current Opinion in Psychology
meditators presented with higher degrees of gyrification than controls within the left and right anterior dorsal insula [6], left precentral and postcentral gyrus [6], left central sulcus [6], left inferior and middle temporal gyrus [6], left angular gyrus [6], left parieto-occipital fissure [6] as well as in the right parietal operculum [6], right fusiform gyrus [6], and right cuneus [6]. Interestingly, when relaxing statistical thresholds even further ( p 0.05, uncorrected), group differences manifested in both directions, with more gyrification in meditators than in controls in some regions, but less gyrification in other regions. Figure 1 provides an overview of brain structures where meditation effects (i.e., cross-sectional differences Current Opinion in Psychology 2019, 28:172–178
between meditators and controls) have been detected using structural MRI and DTI.
Discussion While there are some exceptions, overall, long-term meditators present with larger (rather than smaller) anatomical measures than controls (e.g., more gray matter, thicker cortices, a higher fractional anisotropy). Distinctive anatomical features in the brains of long-term meditation practitioners seem not confined only to a particular core region (or a few specific key regions) but rather involve large-scale brain networks that include the cerebral cortex, subcortical gray and white matter, and even brain stem and cerebellum. Thus, meditation might be a www.sciencedirect.com
The neuroanatomy of long-term meditators Luders and Kurth 175
Box 2 Morphometric measures
Whole-brain Gray Matter and Total Intracranial Volumes. Brain tissue can be automatically classified as gray matter (GM), white matter (WM), and cerebrospinal fluid (CFS). Gray matter volume (total intracranial volume, respectively) can be obtained (in mm3) by adding up the voxel-wise GM content (the voxel-wise GM, WM, and CFS content, respectively) and multiplying it with the voxel dimensions. Distances, Areas, and Volumes. Brains can be parcellated, either manually or automatically, into two-dimensional (e.g., corpus callosum) or three-dimensional (e.g., hippocampus) regions of interest (ROI). Various ROI-specific measurements can then be obtained, such as distances (in mm), areas (in mm2), or volumes (in mm3). Voxel- or point-wise measures (see below) can also be averaged within a ROI. Fractional Anisotropy. Fractional anisotropy (FA) reflects the degree of unequal spatial restriction (or degree of direction) in diffusion. Within each voxel, FA is calculated based on the magnitude of diffusion in each of the three principal directions. Once established, FA values are either analyzed on a voxel-by-voxel basis or averaged within white matter fiber tracts (e.g., cortico-spinal tract).
Voxel-wise Gray Matter. Voxel-wise “gray matter volume”, sometimes also referred to as voxel-wise “gray matter concentration”, reflects the local amount of gray matter. It is calculated as the relative amount of gray matter within a voxel at thousands of voxels across the entire brain. It is often examined using a method called “voxel-based morphometry” (or short “VBM”). Point-wise Cortical Thickness. Cortical thickness, as the name implies, reflects the thickness of the cerebral cortex (i.e., the outer mantle of the brain consisting of gray matter). It is calculated as the distance (in mm) between its inner boundary (e.g., GM/WM) and outer boundary (e.g., GM/CSF) at thousands of points (or so-called vertices) across the cerebral cortex.
Point-wise cortical Gyrification. Cortical gyrification, sometimes also referred to as “cortical convolution” or “cortical complexity”, reflects the magnitude and frequency of local folding of the cerebral cortex. It is often calculated (in degrees or radians) as the point-wise mean curvature (or fractal dimension) at thousands of points across the cerebral cortex. Current Opinion in Psychology
powerful mental exercise with the potential to change the physical structure of the brain at large. Indeed, it is tempting to assume that the observed group differences constitute true meditation effects, especially given the accumulating evidence for training-induced neuroplasticity, both with respect to short-term meditation [cf. Refs. 13,14] as well as outside the framework of meditation [15–19]. Possible mechanisms and underlying processes for such changes are discussed in detail elsewhere [20,21,22,23]. However, given the cross-sectional design of the studies summarized here, this www.sciencedirect.com
assumption is merely conjecture. It is equally possible that meditators, especially long-term practitioners, have brains that were fundamentally different prior to the practice of meditation, perhaps even from birth. A particular brain anatomy might be linked to specific personality profiles, particular interests, or certain mental capacities, which may have drawn an individual to meditation in the first place. In addition, or alternatively, such a particular brain anatomy may have equipped that person with the required prerequisites to reach desired states during meditation and/or experience rewarding effects (e.g., calmness, patience, clarity, focus, joy, loving kindness) Current Opinion in Psychology 2019, 28:172–178
176 Mindfulness
Figure 1
pre- / postcentral gyrus, central sulcus [6]
parietal / temporal operculum, posterior insula [6,10,12]
superior frontal gyrus [9]
orbitofrontal cortex [1,11,12]
angular gyrus [6,11]
frontopolar cortex [1]
inferior / middle temporal gyrus [1,6,11]
ventrolateral prefrontal cortex, inferior frontal gyrus [1,9,11]
anterior insula [1,6,11,12]
precuneus [8]
fusiform gyrus [9,6] corpus callosum [4]
cuneus, parietooccipital fissure [6]
thalamus [1]
anterior / mid cingulate [10,12] hippocampus [2,5,7]
anterior thalamic radiation [3]
anterior cerebellum [9]
brainstem [9]
superior longitudinal fasciculus [3]
cingulum-hippocampus bundle [3]
inferior fronto-occipital fasciculus [3]
corpus callosum / forceps minor [3,4]
uncinate fasciculus [3]
inferior longitudinal fasciculus [3]
cortico-spinal tract [3] Current Opinion in Psychology
Significant differences between long-term meditators and controls based on MRI data (top) and DTI data (bottom). Regions reported in one study are indicated in blue, regions reported in two studies in yellow, and regions reported in three or more studies in pink. All findings are displayed in the left hemisphere of the brain, regardless of whether the effect occurred in the left and/or right hemisphere (for information on laterality, please refer to the main text). The numbers in brackets refer to the original publication.
Current Opinion in Psychology 2019, 28:172–178
www.sciencedirect.com
The neuroanatomy of long-term meditators Luders and Kurth 177
that linger after meditation, thus providing an incentive to keep meditating over years. Of course, it might as well be a combined effect of nature and nurture, where any preexisting differences might strongly interact with actual changes. For example, if a specific brain architecture facilitates a successful or rewarding meditation experience, it probably plays a significant role in maintaining a regular and long-term practice — via intrinsic positive reinforcement — and may subsequently lead to further changes in the brain. Altogether, since there is a lack of longitudinal long-term studies, it remains to be established whether the distinctive brain anatomy in long-term meditators is indeed a consequence of their practices or rather a predisposing feature. However, while longitudinal long-term meditation research is clearly desirable, it poses several challenges [cf. Ref. 24]: Ideally, longitudinal research should follow practitioners over years (or even decades), which might not be feasible due to budgetary limitations or subject attrition, etc. In the same vein, the inclusion of an active control condition, with random assignment to experimental (meditation) and control condition, is a desirable goal, but seems to be overly ambitious and unrealistic for practical or ethical reasons. Short-term longitudinal studies have already enhanced this field of research and will continue to do so, but may only uncover regions involved in skill acquisition, rather than regions changed due to skill practice or maintenance over time. Thus, cross-sectional studies in long-term meditators will remain a valuable source of information, not only with respect to the phenomenon of brain plasticity, but also as an explanation for unique characteristics of meditators in terms of cognitive, emotional, self-related, and interpersonal processes, as reviewed in other chapters of this special issue.
Funding This article did not receive any specific grant from funding agencies in the public, commercial, or not-forprofit sectors.
Conflict of interest statement Nothing declared.
Acknowledgements The authors wish to warmly thank Professor Michael Corballis for his valuable feedback on the article.
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest 1.
Engen HG, Bernhardt BC, Skottnik L, Ricard M, Singer T: Structural changes in socio-affective networks: multi-modal MRI findings in long-term meditation practitioners. Neuropsychologia 2018, 116:26-33.
www.sciencedirect.com
2.
Luders E, Toga AW, Lepore N, Gaser C: The underlying anatomical correlates of long-term meditation: larger hippocampal and frontal volumes of gray matter. Neuroimage 2009, 45:672-678.
3.
Luders E, Clark K, Narr KL, Toga AW: Enhanced brain connectivity in long-term meditation practitioners. Neuroimage 2011, 57:1308-1316.
4.
Luders E, Phillips OR, Clark K, Kurth F, Toga AW, Narr KL: Bridging the hemispheres in meditation: thicker callosal regions and enhanced fractional anisotropy (FA) in long-term practitioners. Neuroimage 2012, 61:181-187.
5.
Luders E, Thompson PM, Kurth F, Hong JY, Phillips OR, Wang Y, Gutman BA, Chou YY, Narr KL, Toga AW: Global and regional alterations of hippocampal anatomy in long-term meditation practitioners. Hum Brain Mapp 2013, 34:3369-3375.
6.
Luders E, Kurth F, Mayer EA, Toga AW, Narr KL, Gaser C: The unique brain anatomy of meditation practitioners: alterations in cortical gyrification. Front Hum Neurosci 2012, 6:34.
7.
Luders E, Kurth F, Toga AW, Narr KL, Gaser C: Meditation effects within the hippocampal complex revealed by voxel-based morphometry and cytoarchitectonic probabilistic mapping. Front Psychol 2013, 4:398.
8.
Kurth F, MacKenzie-Graham A, Toga AW, Luders E: Shifting brain asymmetry: the link between meditation and structural lateralization. Soc Cogn Affect Neurosci 2015, 10:55-61.
9.
Vestergaard-Poulsen P, van BM, Skewes J, Bjarkam CR, Stubberup M, Bertelsen J, Roepstorff A: Long-term meditation is associated with increased gray matter density in the brain stem. Neuroreport 2009, 20:170-174.
10. Grant JA, Courtemanche J, Duerden EG, Duncan GH, Rainville P: Cortical thickness and pain sensitivity in zen meditators. Emotion 2010, 10:43-53. 11. Hernandez SE, Suero J, Barros A, Gonzalez-Mora JL, Rubia K: Increased grey matter associated with long-term sahaja yoga meditation: a voxel-based morphometry study. PLoS One 2016, 11:e0150757. 12. Chetelat G, Mezenge F, Tomadesso C, Landeau B, ArenazaUrquijo E, Rauchs G, Andre C, de Flores R, Egret S, Gonneaud J et al.: Reduced age-associated brain changes in expert meditators: a multimodal neuroimaging pilot study. Sci Rep 2017, 7:10160. 13. Fox KC, Nijeboer S, Dixon ML, Floman JL, Ellamil M, Rumak SP, Sedlmeier P, Christoff K: Is meditation associated with altered brain structure? A systematic review and meta-analysis of morphometric neuroimaging in meditation practitioners. Neurosci Biobehav Rev 2014, 43C:48-73. 14. Tang YY, Holzel BK, Posner MI: The neuroscience of mindfulness meditation. Nat Rev Neurosci 2015, 16:213-225. 15. Taubert M, Draganski B, Anwander A, Muller K, Horstmann A, Villringer A, Ragert P: Dynamic properties of human brain structure: learning-related changes in cortical areas and associated fiber connections. J Neurosci 2010, 30:1167011677. 16. 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, 26:6314-6317. 17. Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A: Neuroplasticity: changes in grey matter induced by training. Nature 2004, 427:311-312. 18. Boyke J, Driemeyer J, Gaser C, Buchel C, May A: Traininginduced brain structure changes in the elderly. J Neurosci 2008, 28:7031-7035. 19. Bezzola L, Merillat S, Gaser C, Jancke L: Training-induced neural plasticity in golf novices. J Neurosci 2011, 31:12444-12448. 20. Kurth F, Cherbuin N, Luders E: Promising links between meditation and reduced (Brain) aging: an attempt to bridge some gaps between the alleged fountain of youth and the youth of the field. Front Psychol 2017, 8:860. Current Opinion in Psychology 2019, 28:172–178
178 Mindfulness
This review article outlines theories and mechanisms pertaining to normal (brain) aging, summarizes findings in the field of meditation research, and discusses how meditation may affect aging processes. 21. Luders E, Cherbuin N, Gaser C: Estimating brain age using highresolution pattern recognition: younger brains in long-term meditation practitioners. Neuroimage 2016, 134:508-513. 22. Luders E, Cherbuin N, Kurth F: Forever Young(er): potential agedefying effects of long-term meditation on gray matter atrophy. Front Psychol 2014, 5:1551.
Current Opinion in Psychology 2019, 28:172–178
23. Boldrini M, Fulmore CA, Tartt AN, Simeon LR, Pavlova I, Poposka V, Rosoklija GB, Stankov A, Arango V, Dwork AJ et al.: Human hippocampal neurogenesis persists throughout. Aging Cell Stem Cell 2018, 22:589-599 e585. Using autopsy hippocampi in humans aged 14 to 79 years, the study revealed evidence for preserved neurogenesis in healthy older subjects. 24. Luders E: Exploring age-related brain degeneration in meditation practitioners. Ann N Y Acad Sci 2014, 1307:82-88.
www.sciencedirect.com