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BRAIN PROCESSES DURING MEDITATION AND OTHER TASKS EXPLORED WITH FMRI References 1. Alexandrov, Yu. & T. Jarvilehto (1993) Activity Versus Reactivity in Psychology and Neurophysiology. Ecological Psychology. 5(1):85-103. 2. Anokhin, P. K. (1974) Biology and neurophysiology of the conditioned reflex and its role in adaptive behavior. Oxford. Pergamon Press 3. Beckmann, C. F. & S. M. Smith (2004) Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging. 23:137-152. 4. Beckmann, C. F. & S. M. Smith (2005) Tensorial extensions of independent component analysis for multisub- ject fMRI analysis. Neuroimage, 25:294–311. 5. Beckmann, C. F.; M. DeLuca, J. T. Devlin & S. M. Smith (2005) Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci, 360:1001-1013. 6. Bullmore, E. & O. Sporns (2009) Complex brain networks: graph theoretical analysis of structural and func- tional systems. Nature, 10:186-198. 7. Bærentsen, K. et al. (2010) An investigation of brain processes supporting meditation. Cognitive Processing, 11(1):57-84. 8. Cole, D. M. ; S. M. Smith & C. F. Beckmann (2010) Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Frontiers In Systems Neuroscience. 4(8):1-15. doi: 10.3389/fnsys.2010.00008. 9. Edelman, G. M. & G. Tononi (2000): A Universe of Consciousness. How Matter becomes Imagination. Basic Books, New York. 10. Freeman, W. J. (2000): How Brains Make up their Minds. Columbia University Press, N. Y. 11. Hyvärinen, A. (1999) Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw, 10:626–634. 12. Lancaster, J. Et al. (2000) Automated Talairach Atlas labels for functional brain mapping. Hum Brain Mapp. 10:120-131. 13. Lancaster, J. Et al. (2007) Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template. Hum Brain Mapp. 28(11):1194-1205. 14. Leont’ev, A. N. (1989) The Problem of Activity in Psychology. In: Wertsch, J. V. (ed.) The Concept of Activity in Soviet Psychology. Sharp, N.Y. (pp. 37-71). 15. Luria, A. R. (1972) The Working Brain. Introduction to Neuropsychology. Penguin, Harmondsworth etc. 16. Patanjali (2008) Yoga Sutras. In: Ranganathan, S. Patanjalis Yoga Sutra. Penguin, New Delhi etc. 17. www.fmrib.ox.ac.uk/fsl. 16TH ANNUAL MEETING OF THE ORGANIZATION FOR HUMAN BRAIN MAPPING. HBM 2010. BARCELONA, SPAIN. JUNE 6-10, 2010. ABSTRACT NO: 3122. POSTER NUMBER: 99 MT-AM. C o n t a c t : K l a u s B . B æ r e n t s e n . E m a i l : k l a u s . b a e r e n t s e n @ p s y . a u . d k Klaus Bærentsen 1 , Bo Sommerlund 1 , Johannes Damsgaard-Bruhn 1 , Mark Fosnæs 1 , Cecilie Møller 1 , Kirstine Sonne Berg 1 , Hans Stødkilde-Jørgensen 2 1 Department of Psychology, University of Aarhus, Aarhus, Denmark, 2 Aarhus University Hospital, Skejby, Aarhus, Denmark PARTICIPANTS The participants were 11 females and 11 males (mean age 45 years, range 24- 61). All participants belong to two Danish schools of meditation, inspired by Yoga and Tibetan Tantric Buddhism. The level of experience with meditation varied from 1.5 to 25 years, (average 12 years). SCANNING The participants were scanned during fingertapping, resting and meditation on- off, followed by continuous meditation, meditation on-off and resting. The finger- tapping, resting and meditation on-off sessions each lasted 4½ min. The continu- ous meditation session consisted of ½ minute relaxation followed by 14½ minutes uninterrupted meditation utilizing specialise imaginative mantras created by the respective teachers for the occasion. Participants meditated in accordance with their particular traditions. Fingertapping and meditation on-off followed a stand- ard block design with 45 sec. epochs of Task versus resting state. The scans were made on a GE Signa 1.5 T scanner, (Gradient EPI, TE=40 msec., TR=3.5 sec., 34 or 35 axial slices); see (7) for further details. DATA ANALYSIS Data analysis was carried out using Probabilistic Independent Component Analy- sis (FSL 4.0, MELODIC 3.05). The analysis followed the standards as described in (3, 4, 5, 11, 17). The group analyses of scans during each task were carried out using a Concatenation design with Hpf cutoff at 250 secs. Anatomical localisation of components were found by first recovering voxel co- ordinates of local maxima from thresholded z stats images of the independent components. Anatomical labels were then identified for local maxima using the Talairach Daemon (12), after conversion of MNI coordinates to Talairach space (13). On the basis of visual inspection of the data components similar to the resting-state network (RSN) -components described in (5) were identi- fied in the different tasks, and their anatomical similarity across the dif - ferent tasks were assessed. Further their temporal relationships to ideal- ized BOLD response time series representing the experimental designs, and to other RSN components were analyzed. - In order to estimate the anatomical similarity between components from differ- ent tasks, the distances in mm between the voxel coordinates of local maxima from the “similar” components in different tasks were calculated. For this calcula- tion a component was selected from one task which appeared to be most similar to the corresponding RSN component in (5). This was used as a common refer- ence (“best fit reference”) for the calculation. - The temporal correlation of RSN component time series describing the eigen- vectors and the experimental designs were calculated. For the on-off designs (fingertapping and meditation on-off) a simple repeated boxcar convolved with the canonical BOLD response function was used as regressor. For the continuous meditation, the regressor incorporated one prolonged BOLD response function starting at 30 secs. with an approximated exponential decay spanning the entire duration of the scanning session. For the baseline scans both types of regressors were used, as well as a completely linear time series without any variation. The temporal correlation between RSN component time series in the same tasks were calculated for each subject, and across different tasks for group results (eigen- vectors) of the ICA. All reported temporal correlations are significant at a level corresponding to at least p<0.001 (uncorrected for multiple comparisons). M E T H O D S Table 2. Correlation of RSN component eigenvector time series vs experimental design. Upper line: Correlation in phase. Lower line: maximal correlation and phase delay of experimental design. All correlation coefficients are significant at p<0.001 (uncorrected for multiple comparisons. Continuous meditation df=255; other scans df=75). (*) Indicate correlation with OnOff experimental design. RSN Finger Base1 OnOff 1 Cont. Medi. OnOff 2 Base 2 OnOff 1+2 Base 1+2 a) -0.39 -0.50; -2 ns -0.43 -0.52; +4 ns +0.75 +0.77; +1 ns -0.67 -0.83; 3 ns (*) b) -0.39 -0.50; -2 ns -0.43 -0.52; +4 ns -0.75 -0.85; +2 ns -0.67 -0.83; 3 ns (*) c) ns +0.54; +4 ns (*) -0.60 -0.79; +3 +0.22 +0.25; -2 -0.63 -0.78; +3 ns +0.62 +0.89; +3 ns (*) d) +0.82 +0.92; +1 ns (*) +0.50 +0.65; +2 ns +0.81 +0.86; +1 ns +0.77 +0.84; +1 ns (*) e) -0.85 -0.86; +1 ns (*) -0.67 -0.79; +2 ns -0.68 -0.85; +3 ns -0.69 -0.76; +2 ns (*) f) ns ns +0.39; +3 -0.60 -0.79; +3 ns +0.68 +0.79; +2 ns +0.62 +0.89; +3 ns (*) g) ns -0.38; ±5 ns -0.45 -0.75; +4 ns -0.53 -0.77; +3 ns (*) -0.61 -0.74; +2 ns h) ns +0.40; ±5 ns -0.45 -0.75; +4 ns +0.69 +0.84; +3 +0.37 -0.47; -3 -0.56 -0.85; +4 ns -0.39; -3 TEMPORAL CORRELATIONS WITH EXPERIMENTAL DESIGNS The temporal correlations of component time series (eigen- vectors) to idealized BOLD response time series is shown in (TABLE 2 & FIG 2). In fingertapping the RSN d) (somatomo- tor) is positively correlated with the ex- perimental design, whereas RSN a), b) (visual), and e) (default mode) are nega- tively correlated. RSN c), f), g) and h) are uncorrelated. In the baseline scans, only RSN h) show a minimal level of correlation with a “design” regressor consisting of an initial BOLD re- sponse with exponential decay in baseline 2. There is no correlation to a completely linear design without any variation, but some RSN’s reveal small correlations with the on-off design (marked by (*) in the table), which is most pronounced when all baseline scans are analysed in combina- tion. All RSN’s are correlated with the experi- mental design in the meditation on-off scans. RSN b), e), and g) show consistent negative correlation, whereas d) is posi- tively correlated. RSN a), f) and h) reveal opposite correlations to the design and in the case of RSN c), on-off 1 and 2 are neg- atively correlated with the design when an- alysed separately, but positively correlated when they are analysed in combination. Only RSN c) is positively correlated with the design in the continuous meditation scan. No other RSN’s show any correlation, and no RSN’s are correlated with a completely linear design without any variation. Other components in the scans display cor- relations with the experimental designs, but since they are not part of the set of RSN’s, they are left out of consideration. TEMPORAL CORRELATIONS BETWEEN SELECTED RSN COMPONENTS The temporal correlations between the RSN a) to h) com- ponent time series (eigenvectors) in different tasks may be seen in (FIG 3). During fingertapping RSN a) and b) are represented in one component, and thus highly positively correlated. They are also positively correlated with RSN h). RSN c) is positively cor- related with RSN g), which is also positively correlated with RSN h). RSN d) show a highly negative cor- relation with RSN e). The patterns of correlation between components are rather different and to a large extend opposite in the two baseline-resting state scans, with two exceptions. During both, the RSN a) and b) make up one component, which is also the case for RSN g) and h) in baseline 1, but not in baseline 2, although they are still positively correlated. During baseline 1, RSN c), d) & f) are positively correlated, but uncor- related in baseline 2. Further, RSN d) is positively correlated with g) & h), but uncorrelated in baseline 2. On the opposite, RSN a) & c) are negatively correlated, but positively correlated in baseline 2. RSN b) is negatively correlated with c), and d), as well as with RSN f), but both of these sets are uncorrelated in baseline 2. During baseline 2, RSN a) and b) are both positively correlated with d), and c) with f), but these are all un- correlated in baseline 1. During the meditation on-off scans most RSN’s are either positively or negatively correlated, in fact, during on-off 2, all components show mutu- al correlations. But they also display the highest level of inconsistency. In on-off 1 most correlations are posi- tive, and most of the rest are uncor- related. In on-off 2, most correlations are negative, and the remaining are positive. In both scans, RSN’s a) & h); b) & g); c) & e) & g); e) & g), and f) & g) are positively correlated. During on-off 1, a number of RSN’s are positively correlated, but negatively correlated in on-off 2. This concerns RSN a) & b) & h); b) & h); c) & f) & h); d) & e) & g); e) & f) & h); f) & g), and g) & h). In on-off 1, RSN c) & f) & h), as well as d) & f) & h) are negatively correlated, but positively correlated in on-off 2. In on-off 1, RSN a) & d) &f); b) & e) are uncorrelated, but positively correlated in on-off 2. RSN a) & c) & e), and b) & d) & f); and d) & e) are uncorrelated, but negatively correlated in on-off 2. In on-off 1 a few uncorrelated RSN’s are almost reaching negative corre- lations. In the continuous meditation, RSN a) is positively correlated with c), d) and e); RSN b) with d) and d) with e), whereas RSN b) and f), and RSN d) and f) are negatively correlated. All other pairs of RSN’s are uncorre- lated. This pattern of correlations is unlike the patterns in all other scans, but most similar to the pattern dis- played in baseline 2. FIGURE 3 RESTING-STATE NETWORKS The definition of the resting-state networks is based on (5, 8), see also (7). RSN a) and b) represent visual functions. RSN a) consist of the primary visual cortical areas (striate cortex), whe- reas b) contain the extrastriate areas. RSN c) is consisting of auditory areas, and other sensory asso- ciation areas. RSN d) cover primary the somatotor system, and RSN e) constitutes what has been described as the “default mode network”, mainly localized in the posterior cingulate, medial pre- frontal, and parietal association areas. The RSN f) is related to executive control, and mainly localized in medial prefrontal and cingulate areas. RSN g) and h) corresponds to the right and left aspects of the dorsal visual stream, and form the lateralized parts of a bilateral dorsal attention system. ANATOMICAL SIMILARITY The anatomical similarity of RSN-Components varies between 2% and84% of local maxima lying within 10 mm distance of corre- sponding local maxima across tasks, and between 15% and 94 % within 20 mm. On average 34% were within 10 mm and 65% within 20 mm distance. In general the local maxima in the best fit reference scan (BF) is in better agreement with local maxima in other target scans (TG), than the opposite comparison of TG to BF. The most similar components were the RSN’s e), f), g), and h), whereas the lowest level of agreement was found in RSN a). (TABLE 1 with FIG 1). Two examples of RSN’s are shown. It should be noted that the similarity of the RSN-Components are evaluated on the basis of distances between local maxima coordinates only, whereas the actual overlap of the corresponding clusters are not known. The assessment of the ana- tomical similarity is thus only a crude estimate. Table 1. Similarity of resting-state networks across different tasks. Anatomical localization of components in different tasks corresponding to resting-state networks (RSN). For each RSN a “best fit reference” (BF) was selected from one task on the basis of visual comparison with RSN components (a-h) as reported by Beckman et al. (2005) & Cole et al. (2010). Two examples of similar RSN across tasks are shown with slice localization in MNI coordinates. Right is to the left in coronal and horizontal views. Numbers indicate the percentage of local maxima in the selected components (TG) which are localized within a distance of 10 mm (upper line) or 20 mm (lower line) of local maxima in the best fit reference component (BF), as well as the opposite comparison (BF to TG). RSN-components Finger Tg vs BF BF vs Tg Base 1 Tg vs BF BF vs Tg OnOff 1 Tg vs BF BF vs Tg Cont. Medi. Tg vs BF BF vs Tg OnOff 2 Tg vs BF BF vs Tg Base 2 Tg vs BF BF vs Tg OnOff 1+2 Tg vs BF BF vs Tg Base 1+2 Tg vs BF BF vs Tg a) Primary visual cortical areas 5% 24 % 22 % 44 % 6% 23 % 17 % 33 % 4% 15 % 17 % 39 % 100 % (best fit reference) 3% 26 % 11 % 50 % 2% 24 % 6% 39 % 3% 23 % 11 % 50 % 5% 24 % 11 % 22 % b) Extrastriate visual cortex 15 % 34 % 50 % 79 % 13 % 40 % 29% 61 % 12 % 39 % 39 % 82 % 100 % (best fit reference) 11 % 40 % 36 % 79 % 22 % 41 % 46 % 64 % 12 % 36 % 36 % 75 % 24 % 45 % 32 % 50 % c) Auditory and other sensory association cortices Shown at: x=3, y=-17, z=1.5 13 % 48 % 15 % 35 % 16 % 57 % 31 % 69 % 10 % 53 % 17 % 73 % 100 % (best fit reference) 47 % 52 % 2% 21 % 15 % 68 % 38 % 62 % 20 % 58 % 29 % 65 % 20 % 57 % 15 % 44 % d) Somatomotor system 29 % 66 % 16 % 58 % 50 % 85 % 49 % 89 % 54 % 88 % 66 % 95 % 45 % 69 % 22 % 63 % 20 % 50 % 47 % 73 % 100 % (best fit reference) 50 % 79 % 30 % 56 % 63 % 91 % 48 % 78 % e) Visuo-spatial system (Default Mode Network) 57 % 96 % 28 % 59 % 48 % 86 % 43 % 73 % 52 % 94 % 30 % 71 % 41 % 84 % 21 % 48 % 60 % 93 % 40 % 69 % 100 % (best fit reference) 61 % 82 % 32 % 60 % 55 % 88 % 38 % 80 % f) Executive control 8% 50 % 10 % 41 % 39 % 68 % 84 % 96 % 40 % 73 % 65 % 92 % 33 % 70 % 45 % 86 % 33 % 73 % 45 % 82 % 16 % 53 % 45 % 78 % 42 % 63 % 59 % 82 % 100 % (best fit reference) g) dorsal visual stream - right. Right lateralised part of bilateral dorsal attention network. Shown at: x=45, y=-42, z=47 54 % 85 % 52 % 86 % 48 % 77 % 43 % 73 % 45 % 78 % 44 % 75 % 49 % 86 % 41 % 72 % 38 % 71 % 57 % 85 % 56 % 89 % 43 % 66 % 44 % 78 % 41 % 71 % 100 % (best fit reference) h) dorsal visual stream - left 45 % 74 % 41 % 72 % 40 % 68 % 39 % 72 % 35 % 83 % 43 % 87 % 33 % 69 % 30 % 69 % 37 % 76 % 67 % 95 % 34 % 68 % 49 % 85 % 50 % 85 % 44 % 79 % 100 % (best fit reference) R E S U L T S FIGURE 4 a b c d e f g h a b c d e f g h IDENTIFICATION OF RSN’S ACROSS TASKS It is possible to identify known resting-state networks as compo- nents in all tasks, but a more precise assessment of the degree of similarity calls for more sophisticated methods than those em- ployed here. It is evident, that component networks may be more or less cor- related during different tasks, and even to the extent that they are forming one component during one task, and two or more separate components in other tasks, as well as the other way around. The inclusion or division of resting-state networks into task-related networks does thus not appear to be a matter of linear algebraic recombinations if fixed entities, but must rather be understood as guided by the requirements of the situation, i.e. the present goals, motivations and constraints determined by situational conditions (2, 9, 14, 15). RSN CORRELATIONS WITH DESIGN VS THEIR MUTUAL CORRELATIONS It is not surprising that processes in the somatomotor cortex re- veal a highly positive correlation with the experimental design during fingertapping, wheras the default mode network show a highly negative correlation with the task. This pattern is also seen in the pattern of mutual correlations between components, which however also reveal an independent pattern of positively corre- lated components consisting of areas related to visual perception and attention. During the baseline resting-state tasks a real experimental de- sign is lacking, and no correlations were found with any simple regressor expressing a linearly stable state. In the two sessions inconsistent patterns of correlations were found between the com- ponents, with the only exception, that the areas related to visual perception and attention show positive mutual correlations. During meditation on-off it is notable that the somatomotor areas are positively correlated with the experimental design, whereas the auditory and related sensory association areas and the de- fault mode network are negatively correlated with the design. This pattern is consistent with that found by correlating the com- ponents directly. It is further notable, that the executive control network, and the visual areas, and the dorsal attention areas re- veal a rather inconsistent pattern of correlations in both kinds of analysis. During continuous meditation only the auditory and related sen- sory association network (RSN c)) revealed a positive correlation with the experimental design. In the analysis of correlations be- tween components this RSN was also positively correlated with the primary visual areas. Since these were positively correlated with the somatomotor network and the default mode network, some of which are negatively correlated with the executive con- trol network (RSN f)), it might thus be supposed that the auditory and related association areas were negatively correlated with the executive control network. RSN c) was previously established as being of primary interest in relation to meditation (7). Visual inspection indicated that simi- lar components were present during the meditation on-off task and baseline resting-state, although here including anatomical areas located in a separate component (RSN f) during continuous meditation. As documented here, these components seem to be represented in other tasks as well, although to different degrees, and in varying combinations. The corresponding components in the meditation on-off 1 and on-off 2 sessions display opposite correlations with the experi- mental design, being positively correlated in one, and negatively correlated in the other. During the continuous meditation the two components are nega- tively correlated in approximately 1/3, and positively correlated in another 1/3 of the subjects, and the corresponding eigenvec- tors reveal no significant correlation. When the time series of the eigenvectors for RSN c) and f) are plotted against each other, they appear to form a limit cycle attractor which becomes three dimensional, when RSN e) is included (FIG 4). D I S C U S S I O N Previously we identified brain processes supporting the onset of meditation, as well as continuous meditation (7). We here present analyses of the consistency of the anatomical localization of identified brain processes constitutingresting-statenetworksacrossdifferenttasks (Meditation, fingertapping and resting state). We also present analyses of the varying temporal relationships between processes across different tasks. I N T R O D U C T I O N C O N C L U S I O N The results demonstrate that similar brain networks (components) subserving various functions are in- volved in such diverse tasks as meditation, fingertap- ping and resting-states. Their general presence, and the seemingly inconsistent patterns of combinations across similar as well as different tasks raise some questions about the localization of brain areas sub- serving specific tasks. The common basis for realiza- tion of similar tasks does not appear to be consistent combinations of fixed networks but rather situation- ally determined interactions (temporal correlations) of component processes and the involved brain areas. A focus on such time varying combinations of networks has long since been suggested by theories of function- al systems (1, 2, 15), and more recently by theories related to dynamical self-organization and small-world networks (6, 9, 10). Components (RSN c) and f)) which were previously (7) found related to executive control of attention dur- ing continuous meditation are similar to components identified during resting-state, meditation on-off, and to a lesser degree during fingertapping. Together with the “default mode network” (RSN e)), they appear to form a limit cycle attractor, which may be involved in achieving the stability of mind sought after during meditation (16).
