Neural Correlates Of Attentional Expertise In Long-term .
Neural correlates of attentional expertisein long-term meditation practitionersJ. A. Brefczynski-Lewis*†, A. Lutz*, H. S. Schaefer‡, D. B. Levinson*, and R. J. Davidson*§*W.M. Keck Laboratory for Functional Brain Imaging and Behavior, Medical College of Wisconsin, University of Wisconsin, Madison, WI 53226; †Departmentof Radiology, West Virginia University, Morgantown, WV 26506; and ‡Department of Psychology, University of Virginia, Charlottesville, VA 22904Edited by Edward E. Smith, Columbia University, New York, NY, and approved May 29, 2007 (received for review August 3, 2006)attention 兩 frontal 兩 parietal 兩 response inhibitionIn recent years interest has been growing regarding the neural andpsychological effects of meditation. The present experimentexamined the neural basis of ‘‘one-pointed concentration,’’ which ispracticed to strengthen attentional focus and achieve a tranquilstate in which preoccupation with thoughts and emotions is gradually reduced (1, 2). In this meditation one sustains concentrationon a small object or the breath without succumbing to distractions(3). In addition, one engages in a process of self-monitoring, inwhich one notes mental states contrary to concentration, such assleepiness or ‘‘mental chatter.’’Studies have shown expertise-related changes in those proficientin meditation and other skills. Concentration meditation has beenreported to improve performance on multiple components ofattention (4), decrease attentional blink (5), and improve the abilityto control perceptual rivalry (6). In addition, changes in electroencephalogram and cortical thickness have been reported in longterm meditation practitioners of compassion (7) and insight meditation (8). For other types of expertise, functional MRI findingsvary depending on training. For example, a study of short-termobject discrimination training showed increased activation in theworking-memory network (9), whereas studies of long-term expertsshowed either increased [musicians (10)] or decreased [golfers (11)]activation. Other studies showed an inverted u-shaped curve inwhich those learning a skill initially had increased activation yeteventually showed less activation (12, 13).We studied expert meditators (EMs) with 10,000–54,000 h ofpractice in two similar schools of the Tibetan Buddhist tradition.EMs were compared with age-matched novice meditators (NMs)with an interest in meditation but no prior experience except in theweek before the scanning session, in which they were given instructions. To control for motivation, a second NM group, the incentiveNMs (INMs), were offered a financial bonus if they were among thebest activators of attention regions. Participants alternated a stateof concentration meditation (Med.) with a focus on a small fixationdot on a screen, with a neutral resting state (Rest) in a standardblock paradigm. To probe the meditation, we presented as.0606552104external stimuli (positive, neutral, or negative sounds) during partsof the Med. and Rest blocks in an event-related design.Because concentration involves focusing attention, our first hypothesis was that Med. vs. Rest would result in activation ofattention-related networks and visual cortex to maintain focus onthe fixation dot (14–17). We further hypothesized that activationwould vary among participants according to a skill-related invertedu-shaped function in which NMs would have less activation thanEMs with moderate levels of practice, but those EMs with the mostpractice would show less sustained activation because of lessrequired effort (12, 13). Next, we predicted that, in Med., EMswould be less perturbed by external stimuli (sounds in Med.) andshow less activation compared with NMs and INMs in brain regionsthat are associated with task-unrelated thoughts (18), daydreams(19), and emotional processing (20). Similarly we predicted that adecrease in distraction-related regions would correlate with EMs’hours of practice.ResultsConcentration Meditation Block Data. In the Med. block paradigm,participants performed concentration meditation, focusing on asimple visual stimulus, alternating with a specific form of a neutral,resting state while brain function was recorded with functionalMRI. The patterns of significant activation for the Med. blocks vs.the Rest blocks are shown for EMs (see Fig. 1A) and NMs (Fig. 1B)on cortical surface models (21). EMs and NMs activated a largeoverlapping network of attention-related brain regions, includingfrontal parietal regions, lateral occipital (LO), insula (Ins), multiplethalamic nuclei, basal ganglia, and cerebellar regions (Tables 1 and2). Only NMs showed negative activation (Rest Med.) in anteriortemporal lobe bilaterally (blue hues in Fig. 1B).As predicted in our hypothesis, in Med. vs. Rest, EMs showedgreater activation than NMs in multiple attentional and otherregions including frontoparietal regions, cerebellar, temporal, parahippocampal, and posterior (P.) occipital cortex, likely including thefoveal visual cortex of the attended dot (red in Fig. 1C and Tables1 and 2). NMs showed more activation than EMs in medial frontalgyrus (MeFG)/anterior cingulate (Acc) and in the right mid-Ins toAuthor contributions: J.A.B.-L. and A.L. contributed equally to this work; A.L., H.S.S., andR.J.D. designed research; J.A.B.-L., A.L., and R.J.D. performed research; J.A.B.-L., A.L., andD.B.L. analyzed data; and J.A.B.-L. and R.J.D. wrote the paper.The authors declare no conflict of interest.This article is a PNAS Direct Submission.Freely available online through the PNAS open access option.Abbreviations: Amyg, amygdala; DLPFC, dorsal lateral prefrontal cortex; EM, expert meditator; NM, novice meditator; INM, incentive NM; IFG, inferior frontal gyrus; Ins, insula; IPS,intraparietal sulcus; LO, lateral occipital; LHEMs, EMs with the least hours of practice;MHEM, EMs with the least hours of practice; MeFG, medial frontal gyrus; Acc, anteriorcingulate; Med., meditation; P., posterior; P. Cing, P. cingulate; ROI, region of interest; SFG,superior frontal gyrus; MFG, middle frontal gyrus.§Towhom correspondence should be addressed at: W. M. Keck Laboratory for FunctionalBrain Imaging and Behavior, Waisman Center, University of Wisconsin, 1500 HighlandAvenue, Madison, WI 53705. E-mail: rjdavids@wisc.edu.This article contains supporting information online at www.pnas.org/cgi/content/full/0606552104/DC1. 2007 by The National Academy of Sciences of the USAPNAS 兩 July 3, 2007 兩 vol. 104 兩 no. 27 兩 11483–11488NEUROSCIENCEMeditation refers to a family of mental training practices that aredesigned to familiarize the practitioner with specific types ofmental processes. One of the most basic forms of meditation isconcentration meditation, in which sustained attention is focusedon an object such as a small visual stimulus or the breath. Inage-matched participants, using functional MRI, we found thatactivation in a network of brain regions typically involved insustained attention showed an inverted u-shaped curve in whichexpert meditators (EMs) with an average of 19,000 h of practicehad more activation than novices, but EMs with an average of44,000 h had less activation. In response to distracter sounds usedto probe the meditation, EMs vs. novices had less brain activationin regions related to discursive thoughts and emotions and moreactivation in regions related to response inhibition and attention.Correlation with hours of practice suggests possible plasticity inthese mechanisms.
Fig. 1. Meditation block data. Activation in concentration meditation block(Med.) vs. resting state block (Rest) for 12 EMs (A), 12 age-matched NMs (B), andt test subtraction of EMs (C) (red hues reflect greater activation in EMs vs. NMs) vs.regular NMs (blue hues reflect greater activation in NMs vs. EMs). Alpha mapsranging from P 0.001 (orange, positive activation; medium blue, negativeactivation) to P 0.01 (orange/medium blue) to P 0.05, corrected (red, positiveactivation; dark blue, negative activation) are overlaid on inflated populationaverage, landmark- and surface-based atlas cortical model brains and an axialslice at z 11 to show midbrain regions. ‡, smaller than corrected for multiplecomparisons. (D) Activation in attention-shifting metaanalysis ROIs. Color scale isthe same for all panels (see key). (E) Response over time (seconds) for left DLPFC.Start of the meditation block is indicated by an orange line at 80 sec. Standarderror bars are shown for every 10 sec. (F) Bar graphs for amplitude of activationin DLPFC in the ‘‘early’’ part of the meditation block (the first 10 sec, excluding thefirst 2 sec because of hemodynamic delay) and the ‘‘late’’ part of the meditationblock (120 sec to 200 sec).P. Ins (Fig. 1C and Table 3), regions that have been shown tonegatively correlate with performance in a sustained attention task(16, 22).We were concerned that these differences may have resulted inpart from structural differences between participant-group brains,because seven of 12 EMs were Asian (five Caucasian), and all NMswere Caucasian. Therefore, we performed a separate analysis inwhich structural differences were taken to account by using probability of gray matter maps as voxel-wise covariates in a t testcomparison between groups (23). All significant regions remainedsignificant in this analysis, and several regions just below thresholdbecame larger and thus survived multiple correction [supportinginformation (SI) Fig. 3A]. In addition, we were concerned withpossible motivation differences between groups. Therefore, tobetter match motivational arousal, we collected data from a set of10 INMs who were told they would receive a monetary award ( 50)if they were in the top one-third of the INMs in activatingattention-related regions.We examined all participant groups, including the INMs, using apriori regions of interest (ROIs) from a metaanalysis of 31 studiesinvolving attention-shifting paradigms (24). The EMs showed significantly more activation (two-tailed t test) than NMs in allattention ROIs except the thalamus (red vs. dark blue in Fig. 1D).However, the INMs (light blue) showed more activation than theNMs and were not significantly different from the EMs in theseROIs. In the t test of EMs vs. INMs, EMs had more activation in11484 兩 04the left superior frontal gyrus (SFG)/middle frontal gyrus (MFG),and INMs had more activation in left P. Ins, left inferior frontalgyrus (IFG), and LO (SI Fig. 3 B and C and Table 2).Next, because we predicted that these results would correlatewith hours of practice, we split the EM group into those with themost hours of practice (top four MHEMs, mean hours 44,000,range 37,000–52,000, mean age 52.3 years) and those with the leasthours of practice (lower four LHEMs, mean hours 19,000, range10,000–24,000, mean age 48.8 years, youngest participant notincluded to ensure age-matching). Two Asians and two Caucasianswere in each group. Consistent with an inverted u-shaped function,we found that the LHEMs (brown) had the strongest activation,significantly higher than both sets of NMs in all attention ROIsexcept left intraparietal sulcus (IPS) and LO (SI Fig. 3D) andsignificantly higher than MHEMs (orange) in all ROIs except LO.Results were not significantly different when the top five MHEMswere used (rather than the top one-third; data not shown) nor whenthe youngest LHEM was used (making mean age 42.3 years), exceptin thalamus ROI, in which LHEMs were not significantly different(same trend) from INMs or MHEMs (the thalamus ROI was moreposterior than the thalamus cluster activated in our study).In addition, we performed correlations with hours of practicewithin the EM group. Because age was a potential confound, wecalculated the correlation between a participant’s age and hours ofpractice. This was not significant (r 0.22, P 0.44), but it had apositive trend of older participants having more hours. Thus, we listpartial r values for activation vs. hours of practice, accounting forage. Many regions, including those in the attention network, showedsignificant negative correlation with hours, whereas no regionsshowed positive correlation with hours (see last columns of Tables1 and 2, SI Table 4, and SI Fig. 4A), consistent with the view thatexpertise may lead to decreased activation, possibly because ofincreased processing efficiency. The notion of increased processingefficiency in long-term practitioners is consistent with recent evidence from our laboratory using another task, the attentional blinktask, where we found that a 3-month period of intensive meditationled to decreased amplitude of the late component of the eventrelated potential to an initial target, a marker of increased processing efficiency that predicted improved behavioral performanceon a subsequent target (5).We reasoned that if these results could be explained by differences in the amount of effort required to maintain attentional focuswith expertise, one should see differences in the time courses of thehemodynamic response. In the left dorsal lateral prefrontal cortex(DLPFC) ROI, MHEMs had only a short activation period at thebeginning of the Med. block (P 0.02) that returned to baselinewithin the first 10–20 sec (significantly less than the LHEMs; P 0.001). In contrast, LHEMs had a larger, sustained response overthe duration of the block (Fig. 1E). This short vs. sustained responsecontributed in part to the decreased activation for MHEMs vs.LHEMs in the attention-related ROIs (Fig. 1D) because thehemodynamic response function we used in our analysis modeleda continuous response over the entire block. ‘‘Meditation startup’’increases occurred in most attention ROIs except for the thalamusand left anterior Ins and were also seen in right fusiform gyrus andbilateral caudate. Several other types of responses were seen inMHEMs, including suppression in regions like MeFG/Acc and P.Ins and more sustained responses in IPS, LO, inferior occipital,SFG, and MFG (regions with activation in last 80 sec of Med.) [SIFig. 3E (P 0.05 uncorrected); also see representative time courseplots in SI Fig. 4B]. The left SFG/MFG region overlapped with theonly region that was significantly greater in the 12 EMs vs. INMs(compare SI Fig. 3 C and E; see also Table 2).If the hemodynamic time course is influenced by effort, oneshould also see a more sustained response in the highly motivatedINMs compared with the regular NMs. Indeed, INMs had a greatersustained response than the NMs in which activation at times fellwithin baseline levels. However, both NM groups had reducedBrefczynski-Lewis et al.
Table 1. Meditation block data: Brain regions differentially activated for EMs vs. NMsROIEMs NMsFrontalLeft MFG/IFG, BA45, 46Right SFG, BA9Left supplementary motor area, MFG, DLPFC, BA9, BA32Left rectal gyrus, BA11Left precentral, DLPFC, BA6Parietal/posteriorLeft IPS, superior parietal, supramarginal gyrus, BA7Right superior parietal, BA7Occipital/temporalRight cuneus, BA17Left middle temporal gyrus, IFG, BA20, BA21Right middle temporal gyrus, BA21, BA22Fusiform, BA37NoncorticalLeft putamenRight lentiform, parahippocampus, BA28Cerebellum, declive, culmenLeft cerebellar tonsilNM EMLeft medial front/Acc, BA6, BA32Right Ins, BA13Volume,mm3Talairachcoordinates,x, y, and zEMt value1,3551,0099248111,535 49, 29, 1931, 42, 31 21, 6, 50 0.5, 43, 26 34, 2, 364.4**2.9**3.3**3.8***4.2*** 0.770.021.0 1.91.33.2***2.4*2.5*3.4***3.0** 0.72** 0.47 0.63* 0.32 0.72**7,4001,359 24, 61, 4614, 62, 543.6***4.8***-.46 1.33.2***3.8*** 0.71** 0.62*1,7921,9387863,27222, 85, 11 38, 7, 2654, 12, 8 42, 55, 163.7***4***1.74.5*** 1.6 3.2*** 2.7*0.164***5.1***3.2***3.5*** 0.52 0.53 0.63* 0.61*8082,98922,0821,944 30, 20, 329, 42, 11 4, 56, 14 22, 39, 403.9***4.0***4.4***4.0***0.83 0.410.27 0.332.8**2.9**3.3***3.3*** 0.61* 0.60 0.68* 0.67* 10, 39, 2639, 13, 15941851NMt value 1.4 2.2*EM vs. NMt valueEM hourspartial r value 2.5* 3.0**2.2*2.1 0.32 0.21sustained activation over time compared with LHEMs and alsoshowed a delay in the amount of time it took to reach maximumactivation in these regions, typically 10–20 sec longer. These resultsare presented for the DLPFC ROI in Fig. 1F. All groups hadsignificant (NMs and LHEMs) or near significant (INM andMHEMs; P 0.06) activation in the first 10 sec of the meditationblock (LHEMs significantly greater than all other groups). However, for the last 80 sec of the block, there was an inverted u-shapedcurve in which activation for NM INM LHEM MHEM (allgroups significantly different from each other; P 0.001). However, whether these activation differences are due to skill learningor strategy and task performance differences cannot be definitelyresolved in this study.Because MHEMs may have been able to reach a less effortfultranquil meditation state within these short blocks, it is possible thatregions that remained active in the latter part (last 80 sec) of themeditation block for the MHEMs may be the minimal brain regionsnecessary to sustain attention on a visual object.Distracting Sound Data. In addition to looking at the brain regionsinvolved in generating and sustaining the meditation state, weexamined event-related neural responses during presentation ofdistracting sounds, presented at 2-sec intervals during the lasttwo-thirds of the Med. and Rest blocks. These sounds could beneutral (restaurant ambiance), positive (baby cooing), or negative(woman screaming) and were contrasted with randomly presentedsilent, null events with the same timing. In this paradigm, 13 EMs,13 NMs, and 10 INMs were included (see Methods). Generalauditory processing pathways (temporal cortex and Ins) werecommonly activated for all participant groups in response todistracting sounds during both states (data not shown). A stateANOVA (sounds in Med. vs. Rest) revealed that participantsshowed an overall ‘‘active response’’ (no suppressed regions) inresponse to the sounds in Med., involving regions such as rightintraparietal lobule/temporal parietal junction, bilateral pre- andpost-central sulci, DLPFC, Ins, and anterior SFG (see SI Table 5 forstate effects for all three groups; also see SI Fig. 5 A–C).Next we looked for differences between the groups. Our hypothesis predicted that NMs would be more distracted by the sounds andthus would show more activation in default-mode regions related totask-irrelevant thoughts and in emotion regions. First, NMs did nothave any regions that were more active than either the EMs orINMs [SI Fig. 5C vs. A and B; see also state-by-group (EM vs. NM)ANOVA in SI Table 6]. These reduced differences for NMs mayhave been due to the greater similarity between Med. and Reststates for these participants, as we saw in the Med. block data.Table 2. Meditation block data: Regions differentially activated for EMs vs. INMsROIEM INMLeft anterior MFGINM EMLeft IFG/anterior superior temporal gyrusSuperior P. central/BA4†LO/medial occipital†Right P. tes,x, y, and zEMt value 26, 43, 72.85* 36, 12, 1634, 26, 59 39, 62, 340, 33, 18 1.70 1.724.04**0.84INMt value 1.942.91*4.59**6.32***6.80***EM vs.INM tvalueEM hourspartial r value 3.17** 0.203.40**4.56***3.41**3.83** 0.210.05 0.27 0.15Data are from a t test subtraction (significantly different between groups at P 0.05 corrected; smaller clusters marked with †).*, P 0.05; **, P 0.01; ***, P 0.005.Brefczynski-Lewis et al.PNAS 兩 July 3, 2007 兩 vol. 104 兩 no. 27 兩 11485NEUROSCIENCEData are from a t test subtraction (significantly different between groups at P 0.05 corrected). *, P 0.05; **, P 0.01; ***, P 0.005.
Table 3. Distracting sound dataROIINM EMLeft MFGRight anterior cingulateRight culmenLeft pulvinar†Right caudate†Right cerebellum†Left P. Cing†EM INMLeft central sulcus/parietalRight central sulcus/parietalRight SFGRight central sulcusLeft visual c
Concentration meditation has been reported to improve performance on multiple components of etheability to control perceptual rivalry (6). In addition, changes in electro-encephalogram and cortical thickness have been reported in long-term meditation practitioners of compassion (7) and insight med-
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ability (GMA), personality, and experience to job performance. For example, Schmidt and Hunter (2004) report that GMA correlates above 0.5 with job performance, personality correlates at about 0.3, and experience correlates at about 0.2. There is much less data about the impact of motivation
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