research article long-term effects of musical training and...

Research ArticleLong-Term Effects of Musical Training andFunctional Plasticity in Salience System

Cheng Luo1 Shipeng Tu12 Yueheng Peng1 Shan Gao1 Jianfu Li1

Li Dong1 Gujing Li1 Yongxiu Lai1 Hong Li3 and Dezhong Yao1

1 Key Laboratory for NeuroInformation of Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of China Chengdu China

2Medical Engineering Department PLA Chengdu Military Area Command General Hospital Chengdu China3 Key Laboratory of Cognition and Personality of Ministry of Education Southwest University Chongqing China

Correspondence should be addressed to Cheng Luo chengluouestceducn and Dezhong Yao dyaouestceducn

Received 15 August 2014 Revised 21 October 2014 Accepted 21 October 2014 Published 13 November 2014

Academic Editor Małgorzata Kossut

Copyright copy 2014 Cheng Luo et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Musicians undergoing long-term musical training show improved emotional and cognitive function which suggests the presenceof neuroplasticityThe structural and functional impacts of the human brain have been observed inmusicians In this study we useddata-driven functional connectivity analysis to map local and distant functional connectivity in resting-state functional magneticresonance imaging data from 28 professional musicians and 28 nonmusicians Compared with nonmusicians musicians exhibitedsignificantly greater local functional connectivity density in 10 regions including the bilateral dorsal anterior cingulate cortexanterior insula and anterior temporoparietal junction A distant functional connectivity analysis demonstrated that most of theseregions were included in salience system which is associated with high-level cognitive control and fundamental attentional processAdditionally musicians had significantly greater functional integration in this system especially for connections to the left insulaIncreased functional connectivity between the left insula and right temporoparietal junctionmay be a response to long-termmusicaltraining Our findings indicate that the improvement of salience network is involved in musical training The salience systemmay represent a new avenue for exploration regarding the underlying foundations of enhanced higher-level cognitive processesin musicians

1 Introduction

The human brain is a complex and efficient informationsystem Learning changes this system as we adapt to infor-mation in the environment Professional musicians comprisea special population for studying learning because they oftenbegin intensive music training early in life and excel invarious cognitive tasks such as precise acoustic identificationof musical sounds Thus musicians are an appropriate focusfor neuroplasticity research about changes in functional andstructural brain organization [1 2] In the 1990s Schlaug andhis colleagues showed neuroanatomical evidence of changesin brain structure associated with musical expertise andmusicianship [3] Since then a growing number of structuralimaging studies have reported increased volume of graymatter and altered diffusion parameters in the cerebellum

frontotemporoparietal cortex corticospinal tract and supe-rior longitudinal fasciculus of musicians [2 4] Researchershave used a variety of functional neuroimaging tools to inves-tigate neural plasticity inmusiciansThese studies support theidea of transfer effects in which finely tuned gestural motorskills and heightened auditory perception acquired throughyears of training transfer to other domains such as auditoryprocessing language emotion and attention [5ndash7] More-over many studies have provided evidence for multidomainfunctional improvement in musicians including enhancedmotor skills acoustic perception emotion and cognition[2 8] Thus there may be a common foundation for theimprovement of various cognitive functions in musicians

The salience network comprised of the bilateral anteriorinsula anterior temporoparietal junction (TPJ) and dorsalanterior cingulated cortex (ACC) has been observed in

Hindawi Publishing CorporationNeural PlasticityVolume 2014 Article ID 180138 13 pageshttpdxdoiorg1011552014180138

2 Neural Plasticity

resting-state fMRI studies The salience system is associatedwith detection of relevance among several interoceptive andexteroceptive stimuli and guides behavior while updatingexpectations about the internal and external environment[9 10]This systemmay play a role for fundamental cognitiveand behavioral functions As a result it has become a topicof intensive research in recent years [10ndash12] Of particularinterest is coactivation of the insula and dorsal ACC whichhas been observed during a variety of cognitive tasks Menonand Uddin [12] recently proposed a model in which thesalience network enables switching between the default modenetwork (DMN) and task-related brain networks Once asalient stimulus is detected the salience network facilitatestask-related information processing by initiating appropriatetransient control signals to engage brain areas mediatingattention working memory and higher code cognitive pro-cesses and disengage DMN Thus we hypothesized thatimproved integration in the salience network would correlatewith enhanced higher-level cognitive processes in musicians

Recently investigations of intrinsic functional connec-tivity based on resting-state functional magnetic resonanceimaging (fMRI) have revealed several stable and reliablefunctional brain networks Moreover altered functional con-nectivity has been considered a potential biomarker forneuropsychiatric diseases such as schizophrenia [13] andepilepsy [14] Functional connectivity has also been used todescribe plasticity induced by advanced level skill trainingsuch as that required to achieve mastery of the game of chess[15] as well as long-termmotor training [16 17] Our previousstudy found improved functional and effective connectivityof spontaneous intrinsic activity among multisensory andmotor systems during the resting state in musicians [18]These findings suggest that task-free analyses of intrinsicfunctional networks are useful for investigating the neuralarchitecture relevant to musical training

Information processing in the brain is dependent oninteractions both between adjacent regions (local interac-tions) and between distant areas (distant interactions) Bal-ance between the two types of interactions may contributeto the high efficiency of information processing in thebrain [19] Using local and global functional connectivitydensity (FCD) mapping known as a data-driven methodTomasi and Volkow found that the strongest hubs in restingconditions were located in the DMN and sensory cortices[20] Furthermore strong local functional connectivity wasobserved in the primary sensory regions motor regions andthe DMN while preferential distant functional connectivitywas observed in the DMN and heteromodal association area[19] The nature of local cortical functional connectivity inmusicians has been elusive We hypothesized that local anddistant functional connectivity would be enhanced by musi-cal training as this improvement might benefit performancewhile playing a music instrument

To test this hypothesis we evaluated the local and distantresting-state functional connectivity features by using a data-driven method First the FCDmapping was used to examinelocal functional connectivity associatedwith long-termmusi-cal training in musicians Then we assessed the distant func-tional connectivity of those regions in which we observed a

significantly different local FCD We predicted that regionsassociated with the salience system would show distinctlocal and distant functional connectivity If confirmed thispattern would support the role of the salience network inmusical training and provide evidence for improved networkintegration as an underlyingmechanism of enhanced higher-level cognitive processes in musicians

2 Materials and Methods

21 Participants Twenty-eight (20 female) musicians and 28(20 female) nonmusicians participated in the study after pro-viding informedwritten consentThese participants includedtwo parts in which one was from the previous study [18]and the other was newly recruited in 2013 The experimentalprotocol was approved by the Research Ethics Review Boardof Southwest University China All participants in the musi-cian group were either music majors at Southwest UniversityChina or professional musicians who possessed an academicdegree inmusic Seventeenmusicians had received long-termtraining in piano (6ndash20 years) and some of these individualshad also received training in either the Chinese zither oraccordion Eleven musicians primarily focused on violin (6ndash16 years) All nonmusicians were students at the University ofSouthwest China and reported that they had never receivedformal musical training or played any musical instrumentThere was no significant difference in the years of educationbetween the two groups (two-sample two-tailed 119905-test 119879 =

0571 119875 = 0182) All participants in both groups were right-handed and healthy with normal brain structure normalhearing and no history of neurological or psychiatric deficitsAll participants received monetary compensation for theirinvolvement

22 Image Acquisition All participants were scanned in a3T Siemens Trio Tim MRI scanner (Siemens ErlangenGermany) with an eight-channel phased array head coil Theexperiments were conducted in the Key Laboratory of Cog-nition and Personality of Ministry of Education SouthwestUniversity ChinaThe functional images were acquired using2D gradient echo-planar imaging (EPI) sequences with thefollowing imaging parameters thickness = 3mm (with 1mmgap) repetition time (TR) = 2000ms echo time (TE) =30ms field of view (FOV) = 22 times 22 cm flip angle = 90∘and matrix = 64 times 64 A total of 205 volumes (32 slicesper volume) were acquired during 410 seconds To ensuresteady-state longitudinalmagnetization the first five volumeswere discarded During data acquisition participants wereinstructed to relax with their eyes closed without fallingasleep Anatomical T1-weighted images were acquired using athree-dimensional- (3D-) spoiled gradient recalled sequencegenerating 176 axial slices (thickness = 1mm (no gap) TR =85ms TE = 34ms FOV = 24 times 24 cm flip angle = 12∘ andmatrix = 512 times 512)

23 Data Preprocessing Analysis Preprocessing and analysesof fMRI data were carried out using SPM8 software (Statis-tical Parametric Mapping httpwwwfilionuclacukspm)

Neural Plasticity 3

We conducted slice time correction 3D motion detectionand correction and spatial normalization to the SPM EPItemplate and resampled the data (3 times 3 times 3mm) Data wereexcluded if head motion exceeded 1mm and 1∘ during fMRIacquisition In addition we also assessed group differences intranslation and rotation of head motion using the followingformula

head motionrotation

=1

119872 minus 1

119872

sum

119894=2

radic10038161003816100381610038161003816Δ119889119909119894

10038161003816100381610038161003816

2

+10038161003816100381610038161003816Δ119889119910119894

10038161003816100381610038161003816

2

+10038161003816100381610038161003816Δ119889119911119894

10038161003816100381610038161003816

2

(1)

where 119872 is the length of the time series (119872 = 200 in thisstudy) 119909

119894 119910119894 and 119911

119894are translationsrotations at the 119894th time

point in the 119909 119910 and 119911 directions respectively and Δ119889119909119894=

119909119894minus 119909119894minus1

and similarly for the other head motionrotationparametersTherewere no significant differences between thetwo groups in head motion and rotation (two-sample two-tailed 119905-test 119879 = 0601 119875 = 0275 for translational motionand 119879 = 0372 119875 = 0644 for rotational motion) Theaveraged signals from white matter and cerebrospinal fluidas well as the headmotion parameters (three translations andthree rotations) were corrected using amultilinear regressionapproach to minimize motion related fluctuations and othernoise in the fMRI signals We conducted temporal band-passfiltering (pass band 001ndash008Hz) using a phase-insensitivefilter which served to reduce the effects of low-frequency driftand high-frequency physiological noise

24 Local Functional Connectivity Analysis We used degreecentrality (or degree) which is a network measure to mapthe local FCD for each voxel In other words the localFCD represented the number of voxels with significantconnections in the local cluster around a given voxel Weused a key parameter Tc the threshold of the correlationcoefficient to determine significant connections Specificallywe conducted the following for a given target voxel Firstwe calculated Pearsonrsquos linear correlation between the targetvoxel and its immediate neighborsThevoxelswith significantlinks to the target were added to a cluster with the target voxelat the center Next we evaluated the relationship betweenthe neighbors of the cluster and the target voxel using thesame threshold Tc We used this process to extend the clusteruntil the correlation coefficient between the neighbors of thecluster and the target was less than Tc Thus the functionalcluster had been established when the boundaries around thetarget voxel had been determined All the voxels in the clusterwere considered to have a significant connection with thetarget voxel We then used the number of voxels (119870) in thecluster surrounding the target voxel to map the local FCD foreach target voxel

We used the threshold Tc as a key parameter in ourcalculations Although no gold standard exists in the previousliterature Tc = 06 is a common choice [20 21] Many brainnetwork studies choose to use a dynamic threshold rangeor multithreshold because this produces more reliable androbust findings We hypothesized that too small Tc wouldlead to increased type I error and too large Tc would lead toincreased type II errorThus our thresholds ranged from 045to 085 in 005 steps such that nine thresholds were used in

the current study To address variability in the local functionalconnectivity strength of the voxels across participants wenormalized each individual local FCD map by 1119896

0 where

1198960represented the mean value across voxels in a given

participant We created nine normalized maps for the ninethresholds Tc for each participant Finally we performedspatial smoothing using a Gaussian kernel of full-width half-maximum (FWHM) 6mm

25 Group Analyses of Local Functional Connectivity Groupanalyses of local FCD were conducted using 119905-tests Foreach local FCD map for each threshold Tc we conducteda two-sample 119905-test comparing musicians and nonmusicians(119875 lt 0005) Correction for multiple comparisons wasapplied at the cluster level following Monte Carlo sim-ulations conducted in the AlphaSim program [httpafninimhnihgovpubdistdocmanualAlphaSimpdf] To bet-ter detect differences between the two groups we onlyincluded the clusters which were found significantly differentbetween the two groups after five consecutive Tc valuesof FCD comparisons in the following analysis Then foreach cluster in which a significant difference was foundthe intersection was used as region of interest (ROI) in thesubsequent functional connectivity analyses

26 Functional Connectivity among ROIs We adopted twostrategies to evaluate the relationships among the regionswith different local FCD between musicians and nonmusi-cians functional connectivity analysis and global functionalconnectivity analysis

First we identified the time-courses of activation forthe ROIs and calculated Pearsonrsquos linear correlation amongthese ROIs After Fisher-z-transformation one-sample 119905-tests were conducted within the musician and nonmusiciangroups Then we performed a univariate covariate analysis(ANCOVA) to detect the difference between the musicianand nonmusician groups for all possible connections and weadjusted the results for effects of age and gender

Second we conducted a functional connectivity analysisfor the ROIs to investigate the functional connectivity inwhole brain The procedure was the same as in our previousstudies [14] In short the regions that showed significantdifferences in local connectivity were selected as seedsWe calculated Pearsonrsquos correlation coefficients between thetime-courses from each voxel and the averaged time-courseof each seed and the Fisher-z-transformation for correlationcoefficients

As mentioned above we conducted a one-sample 119905-testfor each group and each seed To compare the functionalconnectivity maps between the ROIs in the two groups wecalculated an 119890119905119886

2 coefficient for each seed-map pair The1198901199051198862 represented the fraction of the variance in one signal

accounted for by the variance in a second signal where thecomparisons were done on a voxel by voxel basis [22]

1198901199051198862

= 1 minus

sum119899

119894=1

[(119886119894minus 119898119894)2

+ (119887119894minus 119898119894)2

]

sum119899

119894=1

[(119886119894minus119872)2

+ (119887119894minus119872)2

]

(2)

4 Neural Plasticity

Musician

Nonmusician

z = minus1 z = 28 z = 35 z = 60

0 7

z = 20 x = 0

L R

Figure 1 The local FCDmaps in two groups (TC = 06) The upperpart represents the averaged local FCD in musiciansrsquo group and thebottom part represents averaged local FCD in nonmusiciansrsquo groupR right L left

where 119886119894and 119887119894represent the values at voxel 119894 in functional

connectivity maps 119886 and 119887 respectively 119898119894is the averaged

value of the two images at voxel 119894 (119886119894+ 119887119894)2 119872-bar is

the grand mean value across all voxels in the mean imageAlthough the correlation coefficient 119903 is often used forsimilarity descriptions of functional connectivity maps wechose to use 1198901199051198862 because it focuses on the value of eachvoxel in the map and provides a better measure of the overallsimilarity or difference between two maps

To compare the functional connectivity maps in terms ofROIs we conducted a two-sample 119905-test on the maps whichunited the thresholded functional connectivity maps fromthe two groups for each ROI

27 Correlation Analyses between Functional Connectivity andMusic Training To further explore the relationship betweenintrinsic functional connectivity and long-term music train-ing the further association analysis was performed Weused a partial correlation analysis to assess the relationshipbetween extent of musical training and the functional con-nectivity features including the local FCD of each ROI andthe functional connectivity between ROIs controlling for theeffects of age and gender

3 Results

We excluded three of the musicians and two of the nonmu-sicians owing to excessive head motion Thus 25 musiciansand 26 nonmusicians were included in the final analysis Theaverage age in the musician group was 2313 years (SD = 238)and 2193 years (SD = 205) in the nonmusician group Wefound no significant difference (119875 = 015) in age between thetwo groups

31 Local Functional Connectivity Analysis Because ldquoTc =

06rdquo is often chosen as a threshold when determining localFCD [20 21] we first calculated the averaged distribution ofthe local FCD for ldquoTc = 06rdquo in the two groups (musicians andnonmusicians see Figure 1) We found the highest local FCDin the bilateral cuneus precuneus inferior occipital gyrus

L

045

050

055

060

0 7

065

070

075

080

085

0 14

z = minus1 z = 20 z = 28 z = 35 z = 60 x = 0

R

Figure 2 The mean local FCD maps across all subjects for 9 Tcthresholds

cingulate cortex precentral gyrus middle temporal gyrusmiddle frontal gyrus inferior frontal gyrus cerebellumthalamus and putamen These findings were consistent withprevious studies [20 21] Figure 2 shows the mean local FCDmaps across all participants (musicians and nonmusicians)for every Tc threshold For almost all Tc thresholds we foundthe highest local FCD in the bilateral visual cortex cuneusand the medial prefrontal cortex The patterns of local FCDwere similar across Tc thresholds although the size of theregions grew smaller as the Tc threshold increased

We conducted a two-sample 119905-test to compare data frommusicians and nonmusicians for each threshold (119875 lt 0005AlphaSim corrected) In order to get a stable differencebetween groups we summarized all differences resultingfrom the comparison of FCD with nine Tc thresholds Wefound that the local FCD had significantly increased formorethan half of the thresholds (five of nine Tc values) at tenclusters in musicians compared with nonmusicians Theseregions included the bilateral insula bilateral TPJ bilateraldorsal ACC right striatum right superior fontal gyrus left

Neural Plasticity 5

Table 1 Ten ROIs determined in local FCD analysis

Regions Abbreviation Size of ROIs Brodmann MNI coordinate119883 119884 119885

Right insula RIns 73 48 42 12 minus9Left insula LIns 42 48 minus43 16 minus8Right temporoparietal junction RTPJ 258 4048 60 minus22 23Left temporoparietal junction LTPJ 161 4048 minus65 minus33 28Anterior cingulate cortex ACC 113 24 6 0 30Right striatum RStr 70 20 11 minus9Left amygdala LAmy 31 minus27 6 minus18Left middle frontal gyrus LMFG 23 46 minus29 53 32Left superior parietal lobule LSPL 42 740 minus21 minus48 72Right superior frontal gyrus RSFG 21 946 18 60 27

middle frontal gyrus left superior parietal lobule and leftamygdala (Figure 3) We also found a decreased local FCDin musicians at the occipital cortex in three Tc thresholdconditions (Tc = 06 065 and 07) In total we identified10 clusters for which there was a significantly increased localFCD in the musician group These were identified as ROIswith distinct effects of musical training and included in thesubsequent functional connectivity analysis The center ofthese ROIs is shown in Table 1

32 Functional Connectivity Analysis among ROIs Weassessed the region-wise functional connectivity among tenROIs in the two groups We found that most of the ten ROIswere connected with each other in both participant groups(119875 lt 005 FDR-corrected) We found full connections inthe subnetwork comprising the dorsal ACC insula andTPJ which have been identified as the salience networkin previous resting-state fMRI studies [10 12] We thenevaluated differences in this subnetwork between musiciansand nonmusicians using a univariate ANCOVA Aftercontrolling for the effects of age and gender we found thatthree functional connections had significantly increasedin musicians compared with nonmusicians (119875 lt 005FDR-corrected Figure 4(a)) including one between the leftinsula and dorsal ACC (119865(1 46) = 2154) one between theleft insula and left TPJ (119865(1 46) = 791) and one betweenthe left insula and right TPJ (119865(1 46) = 886) We also testedthe symmetry of the edge of the subnetwork constructedby 5 regions (Figure 4(a)) A paired 119905-test was conductedfor three pairs of bilateral symmetry connections includingthe connection between the TPJ and insula the connectionbetween the TPJ and ACC and the connection betweenthe insula and ACC For example for the connectionbetween the TPJ and insula the sample of matched pairsincluded the connection between left TPJ and left insulaand the connection between right TPJ and right insulaWe found significantly enhanced connection between rightTPJ and right insula compared to that in left hemispherein both groups (119875 lt 00001 in both groups) It meansthe significant right-lateralized connectivity between theTPJ and insula However we found no difference in thelateralized index between the groups (119875 = 0366) We found

neither a lateralized predomination nor a difference betweenthe musicians and nonmusicians in terms of connectionsbetween the TPJ and ACC or connections between the insulaand ACC

We generated a whole brain functional connectivity mapfor each group and each seed using a one-sample 119905-test inSPM8 These connectivity patterns were shown in Figures5 and 6 Interestingly these patterns were similar by visualinspection The regions that showed a positive correlationwith the seeds included the bilateral insula TPJ dorsal ACCmiddle frontal gyrus and supplementarymotor area In somecases the bilateral thalamus and striatum were also includedThe regions that showed a negative correlation with theseeds included the bilateral posterior cingulate cortex medialprefrontal cortex angular gyrus and superior frontal gyruswhich were included in the DMN [23] The 1198901199051198862 coefficientwas used to assess the similarity of any two functionalconnectivity patterns at a voxel level The result is shownin Table 2 we found a high similarity between connectivitypatterns among all seeds with the exception of two frontalseeds

Compared with nonmusicians musicians showed signifi-cantly enhanced functional connectivity in six ROIs (bilateralinsula bilateral TPJ dorsal ACC and right striatum shownin Figure 5) and no significant differences in the other fourROIs (left amygdala left middle frontal gyrus left superiorparietal lobule and right superior fontal gyrus shown inFigure 6) Table 3 shows these findings in detail In shortwe observed increased functional connectivity among thebilateral TPJ middle frontal gyrus insulafrontal operculumand ACC We did not find decreased functional connectivityin any seeds These findings from the seed-based functionalconnection analysis reflect increased connections among themajor nodes of the salience network (ACC TPJ and insula)

33 Results of Correlation Analyses For three connectionswith significant difference between groups the correlationanalyses showed that the functional connectivity betweenthe left anterior insula and the right anterior TPJ waspositively related to duration of musical training (119903 = 0534119875 = 0009) when we controlled for the effects of age andgender (Figure 4(b)) For 10 ROIs we did not find significant

6 Neural Plasticity

Tc 045 050 055 060 065 070 075 080 085

RIns

LIns

RTPJ

LTPJ

ACC

LAmy

LSPL

RStr

RSFG

ROI

LMFG

minus4

minus8

minus14

minus6 minus6 minus6 minus6 minus6 minus6 minus6 minus6 minus6

minus14 minus14 minus14 minus14 minus14 minus14 minus14 minus14

26

23

33

72 72 72 72 72 72 72 72 72

33 33 33 33 33 33 33 33

24

32 32 32 32 32 32 32 32 32

24 24 24 24 24 24 24 24

23 23 23 23 23 23 23 23

26 26 26 26 26 26 26 26



0 7

Figure 3 Significantly increased local FCD in musicians compared with nonmusicians The left column shows 10 ROIsrsquo position The rightpart shows significantly increased local FCD in 10 axis images and 9Tc values separately ROIsrsquo abbreviations are consistent with those shownin Table 1

Neural Plasticity 7

ACC

LIns

RTPJLTPJ

RIns

(a)

5 10 15 20

0

01

02

03

04

05

06

07

08R = 0534P = 0009

Musical training (years)

CC b

etw

een

RTP

J and

LIn

s

minus01

(b)

Figure 4 Significantly increased functional connectivity between ROIs in musicians compared with nonmusicians (a) and the relationshipbetween the functional connectivity and musical training duration (b) ROIsrsquo abbreviations are consistent with those shown in Table 1 andthe abbreviation ldquoCCrdquo meant correlation coefficient

Table 2 The 1198901199051198862 coefficient between functional connectivity maps seeded at 10 ROIs in musicians and nonmusicians

RIns LIns RTPJ LTPJ ACC RStr LAmy LMFG LSPL RSFGRIns 0851 0879 0910 0900 0834 0756 0537 0725 0293LIns 0895 0726 0775 0723 0699 0672 0670 0618 0450RTPJ 0926 0854 0963 0849 0758 0704 0379 0875 0158LTPJ 0941 0901 0957 0869 0769 0712 0403 0856 0175ACC 0859 0851 0822 0861 0848 0748 0541 0773 0318RStr 0862 0820 0839 0844 0823 0885 0556 0662 0371LAmy 0743 0754 0763 0746 0742 0920 0455 0558 0364LMFG 0529 0670 0424 0480 0640 0506 0461 0373 0820LSPL 0720 0685 0856 0819 0721 0684 0641 0341 0208RSFG 0319 0414 0220 0240 0398 0322 0319 0830 0179Notes ROIsrsquo abbreviations are consistent with those shown in Table 1 Upper-right part values result from the nonmusicians and bottom-left part values frommusicians The bold values cover five major nodes of salience network with high 1198901199051198862 value (1198901199051198862 gt 08)

correlationship between duration of musical training andlocal FCD in all ROIs

4 Discussion

We used resting-state fMRI to explore intrinsic functionalconnectivity in the brain ofmusicians Combining local FCDregion-wise and global functional connectivity analyses weobserved a distinct increase in the integration of the saliencesystem in musicians We found both a marked enhancementin local region functional connectivity and a significantincrease in functional integration in the salience networkof musicians Components of the salience network seemedto be affected by musical training especially the anteriorinsula which has a critical and causal role in activating central

executive networks and deactivating the DMN in response tosalient stimuli [12 24] Considering these results we proposethat changes in the salience system trigger an improvementin higher-level cognitive processes in musicians To the bestof our knowledge this is the first time that the saliencesystem has been associated with musical training Moregenerally our findings indicate that a data-driven approachto interpretation of resting-state functional connectivity datacould be useful for evaluating cortical neuroplasticity relatedwith musical training

41 Local Functional Plasticity inMusicians Several previousstudies have reported structural alteration of brain tissueinduced by musical training [25] Specifically increasedvolume of gray matter was found in motor auditory and

8 Neural Plasticity

ROI Musician Musician gt nonmusician Nonmusician

RL RL RL

RIns

LIns

RTPJ

LTPJ

ACC

RStr

z = 30 z = 37z = 26

z = 32 z = 34z = 0

z = 0

z = 1

z = 46

z = 46

z = minus5

z = minus8

z = minus4 z = 42

z = 61z = 57z = 39

0 7

z = minus6

minus10 minus4 4 25 4 25minus10 minus4

Figure 5 The group-level functional connectivity maps seeded at 6 ROIs and their difference between musicians and nonmusicians Thefirst column shows the seeds the second and the third column illustrated the positive (hot color) and negative (cool color) functionalconnectivity with the seeds rendered onto a three-dimension brain reconstruction The last column (axis images) represents significantlyincreased functional connectivity in musicians compared with nonmusicians ROIsrsquo abbreviations are consistent with those shown in Table 1

visuospatial regions which is reflective of different elementsof musical experience such as processing musical soundsand playing an instrument [4 5] Our FCD analysis revealedsignificantly enhanced local functional connectivity in thebilateral anterior TPJ which is located at the ventral-anteriorsection of the inferior parietal lobule and surrounds theposterior end of the Sylvian fissure The ventral intraparietal

region is thought to contain many multimodal representa-tions including visual auditory and somatosensory infor-mation [26 27] As in our previous study [18] the currentfindings indicate thatmusicians possess increased integrationin brain regions underlying motor and multiperceptionalfunction A previous study reported that musicians demon-strated greater activation in the anterior TPJ associated with

Neural Plasticity 9

Table 3 The significantly increased functional connectivity between musicians and nonmusicians in 6 seed-maps

Seeds Regions MNI coordinates Cluster 119879 value119883 119884 119885

RIns

RSupramarginal 55 minus25 26 40 486RMFG 35 32 37 38 434

LSupramarginal minus60 minus23 30 60 425LMFG minus34 30 36 22 384

LIns

dACC minus1 12 32 208 654RMFG 28 34 34 53 618LMFG minus28 35 34 97 535LIns minus41 11 0 22 450

RTPJLInsfrontal operculum minus40 3 0 66 579

RIns 31 29 minus5 31 535LMFG minus24 33 44 26 479

LTPJLCaudate minus9 17 1 40 564RIns 32 25 minus8 25 461LMFG minus35 28 48 46 444

ACC LInsfrontal operculum minus51 17 minus6 74 539LMFG minus34 30 42 78 505

RStr

RPrecentral 41 minus7 61 95 674LPrecentral minus47 minus5 57 27 569RMFG 37 32 39 39 558LMFG minus36 30 39 40 445

Notes ROIsrsquo abbreviations are consistent with those shown in Table 1

ROI Musician NonmusicianRL RL

minus10 minus4 minus10 minus44 25 4 25

LAmy

LSPL

RSFG

LMFG

Figure 6 The group-level functional connectivity maps seededat 4 ROIs The first column shows the seeds the second and thethird column illustrated the positive (hot color) and negative (coolcolor) functional connectivity with the seed rendered onto a three-dimension brain reconstruction ROIsrsquo abbreviations are consistentwith those shown in Table 1

auditory working memory compared with nonmusiciansThus the TPJ is implicated in auditory memory which iscrucial for learning music [28] Our findings provide supportfor the notion that increased functional connectivity at theanterior TPJ is related with musical training

The prefrontal cortex receives projections from bothauditory and visual cortices and is known to play a role in var-ious types of cognition including temporal integration [29]Although we observed an increase in local FCD rather thanin the connectivity between ROIs in the bilateral prefrontalcortex we suggest that the local functional improvements atthese regions may be relevant to multiperceptional functionin musicians

Emotions are a key element in our understanding ofmusic Previous fMRI studies have demonstrated that lis-tening to music can affect the activity of many limbic andparalimbic structures [5 30] We found the increased FCD atamygdala dorsal ACC anterior insula and ventral striatumThese regions are among the most commonly activated infunctional neuroimaging experiments across both affectiveand cognitive domains [31 32] such as in one study aboutmusic-evoked ldquochillrdquo [7] Here we provide resting-state fMRIevidence illustrating improvements in functional connec-tivity in brain regions related to emotional processing inmusicians These brain areas may also play a consistentrole in the emotional processing of music A more recentmeta-analysis showed leftward lateralization in the insulaassociated with affective processing [33] The connectivitybetween the left insula and the amygdala has also been linked

10 Neural Plasticity

to anxiety levels in healthy controls [34] We propose thatthe observed increase in local FCD at the left amygdalaand the increase in functional connectivity related to theleft insula are functionally coupled with respect to emotionprocessing related to themusicTherefore our findingsmightcontribute to understanding of the emotion modulation inmusic therapy

42 The Salience Network Potential Target of Musical Train-ing In the current study we found not only enhanced localfunctional connectivity but also increased distant functionalconnectivity among the regions that constitute the saliencenetwork The salience network is considered to play impor-tant roles that are fundamental to cognition and behavior [10ndash12] The often-observed coactivation of the insula and ACCacross a variety of cognitive tasks suggests the existence of afunctional network [35] The amygdala is known to react toemotional and novel stimulation suggesting a crucial role insalience processing [36] In particular connectivity betweenthe anterior TPJ and the insula and cingulate cortex has beenestablished These regions are thought to comprise an exter-nally oriented stimulus-driven network that may modulateattention during salient events in our environment and guideour reactions [37ndash39] In line with previous observationsour findings illustrate increased local functional connectivitybetween the components of the salience network

The comparison between two groups revealed strongerconnectivity between the left anterior insula and the bilateralanterior TPJ ACC in musicians Indeed previous studieshave reported rightward lateralization of the anterior insulaand anterior TPJ in the salience- and attention-related net-works [12 38] In line with previous findings [38] we foundsignificant right-lateralized connectivity between the TPJand insula in both groups This finding suggests that right-lateralized ventral attention is strongly retained in musiciansMoreover we found significantly increased connectionsbetween the left insula and left anterior TPJ in musiciansThis is concordant with previous findings which suggest thatmusical training leads to improved left anterior TPJ functionboth in cross-sectional and in longitudinal design [40]Therefore the observed increase in functional connections tothe left insula implicates the left insula along with the rightinsula in salience detection in musicians which could leadto enhanced efficacy of the salience network In addition ourobservation of a positive correlation between the duration ofmusic training and the connectivity between the left insulaand right anterior TPJ further supports the hypothesis thatmusical training enhances functional integration of the leftinsula which increases the efficiency of the salience networkTherefore our findings reflect neural plasticity in musiciansat a network level and implicate the salience network inmusical training

In general the salience network works to identify impor-tant information from the vast and continuous incomingstream of sensory stimuli [12] It partly overlaps with theright-lateralized ventral attention system which is composedof the TPJ ventral frontal cortex and anterior insula [41]This system shows increased activation upon detection of

salient targets [42] Once a stimulus is detected the ante-rior insula facilitates task-related information processingby initiating appropriate transient control signals whichengage brain areas mediating attention working memoryand higher order cognitive processes while disengaging theDMN The anterior TPJ has been proposed as the maincomponent in this system In addition the right anteriorinsula enables switching between the default and task-relatedstates of brain connectivity [24] Considering the criticalrole of the anterior insula and anterior TPJ in high-levelcognitive control and attentional processes we suggest thatthe functional improvement in the salience network observedin musicians may contribute to the ability to rapidly relaybottom-up environmental information and intensify synergyof the salience network enabling musicians to quickly detectrelevant stimuli and produce appropriate behaviors

There are two other possible interpretations regarding therole of the salience system in musical training First recentstructural and functional network studies have revealed thatthe insula is rich club organization of human brain connec-tome [43] The so-called rich club phenomenon in networksis said to be present when the highly connected (high-degree) hubs of a network aremore densely connected amongthemselves than predicted on the basis of their high degreealone [44] Attacks that specifically target richly connectedbrain areas might impair the global efficiency of a networkmore than those that affect random targets Thus we suggestthat music training-induced changes in the salience systemmay be of low cost and highly efficient The other possiblereason concerns cross modal transfer effect plasticity Thetransfer effects of years of musical training may result inenhanced processing in multiple domains that are not exclu-sively related to music [5] Wan and Schlaug argued that theplasticity in regions of the parietal lobe in which multimodalintegration takes place such as the intraparietal sulcus hasan effect on related cognitive domains [8] This is consistentwith our finding of improved functional connectivity at thebilateral anterior TPJ mainly at the intraparietal lobule Wepropose to extend this view to apply to a network insteadof one region Considering the above we suggest that thesalience system would be an optimal way for the humanbrain to respond to musical experience The increased localand remote functional connectivity enabled by the saliencenetwork may contribute to the underlying mechanisms ofenhanced higher-level cognitive processes in musicians

43Methodological Considerations Weapplied a data-drivenmethod to resting-state functional connectivity data to assesscortical neuroplasticity associated with musical trainingAlthough local functional connectivity has been assessedin various studies [19ndash21 45] the threshold of functionalconnection has yet to be concretely determined Here weused a set of successive thresholds ranging from 045 to 085in 005 steps in the hope that this approach might yield morestable findings We observed enhanced distant connectivitybetween the regions with increased local connectivity in themusician group Superficially these findings are inconsistentin terms of system balance However similar preferential

Neural Plasticity 11

local and distant connectivity profiles have been reported inseveral cortical regions such as theDMN [19 45] A potentialinterpretation of our findings is that salience informationprocessing requires not only high local connectivity to sustainstrong sensory constraints but also a set of modular tightlycoupled areas to modulate efficient local processing like thatfound in musicians In other words when salient informa-tion is detected the processing system can simultaneouslywork on in situ information while associating distributedinformation with multiple regions On the contrary thecombining two types of functional connectivity analysis forthe same dataset may suffer from circular analysis [46] In thefuture study we will pay special attention to the underlyingdistortions

One limitation of our study is that the number ofparticipants was relatively small The age and gender of theparticipants may have influenced our measure of functionalconnectivity especially in terms of the local FCD [21] Arecent study of factors influencing maturational and musicaltraining found age-related effects at the left TPJ ventralpremotor cortex and intraparietal sulcus during musicprocessing [47] Our findings indicate increased functionalconnections with the left insula including the connectionbetween the left insula and left anterior TPJ while controllingfor the effects of age and gender Future studies with a largersample population are necessary to corroborate our findingsand to detect the influence from gender Another limitationis that changes in the salience network which we have identi-fied may simply reflect altered coherence in the resting stateand may not predict behavioral responses This is certainlyan issue for all resting-state studies and requires furtherinvestigation Although there are some parallels between ourfindings and previous reports of stimulus-evoked changesin the regions of salience network in musicians [7 40]multimodal designs may be useful in future investigations

The individual variability related with the training espe-cially the variability in the level of expertise should also betaken into account The duration of musical training rangedfrom 6 to 20 years in this study The large variability ofexpertise across subjects might lead to discrepant change ofplasticity associated with training The ongoing experiencemight aggravate the individual differences However it wasdifficult to group these subjects according to the durationof training The correlation analysis strategy was adopted toidentify the feature of plasticity within group Moreover thegrowing selection pressure promoting musicians to a moretalented and conscientious stage would be another possiblefactor to encourage the individual variabilityThe interactionbetween the individual variability and training effects wouldbe considered as a confounding factor in this study Forexample it has been found that the age of onset of trainingacross the musicians affected the plasticity of brain [48] Thelongitudinal further researchwould be included in the future

5 Conclusion

In summary we have demonstrated that data-drivenmethodsapplied to resting-state functional connectivity analyses can

yield new data regarding cortical neuroplasticity in responseto musical training Our findings demonstrate enhancedfunctional connectivity in local regions and increased func-tional integration of the salience network in musicians Inaddition the observed increase in functional connectivitybetween the left insula and right anterior TPJ in musiciansmay be in response to long-term musical training Ourstudy provides the first evidence for the role of the saliencesystem in musical training We propose that improved inte-gration in the salience system contributes to the underlyingmechanisms of enhanced higher-level cognitive processes inmusicians

The further studies with multimodal and longitudinaldesigns are included in the future to yield the comprehensiveunderstanding of brain related with musical experience Inaddition the alteration in salience system was also observedin neuropsychiatric disorders [49 50] Our findings theimprovement of salience system in musicians may implythe role of the salience system in music therapy The clinicalresearch of music therapy should be included in the future toinvestigate our speculation

Conflict of Interests

Theauthors confirm that they have read the journalrsquos positionon issues involved in ethical publication and affirm that thisreport is consistent with those guidelines None of the authorshas any conflict of interests to disclose

Acknowledgments

This work was supported by grants from the 973 Project(no 2011CB707803) the National Nature Science Foundationof China (nos 81271547 81201159 81330032 81471638 and91232725) Application and Fundamental Research Funds forthe Sichuan Province (no 2013JY0189) and the Chinese Fun-damental Research Funding for Central Universities (nosZYGX 2011J097 ZYGX2012J110)

References

[1] S CHerholz andR J Zatorre ldquoMusical training as a frameworkfor brain plasticity behavior function and structurerdquo Neuronvol 76 no 3 pp 486ndash502 2012

[2] R J Zatorre R D Fields and H Johansen-Berg ldquoPlasticity ingray andwhite neuroimaging changes in brain structure duringlearningrdquo Nature Neuroscience vol 15 no 4 pp 528ndash536 2012

[3] G Schlaug L Jancke Y Huang J F Staiger and H SteinmetzldquoIncreased corpus callosum size in musiciansrdquo Neuropsycholo-gia vol 33 no 8 pp 1047ndash1055 1995

[4] C Gaser and G Schlaug ldquoBrain structures differ betweenmusicians and non-musiciansrdquoThe Journal of Neuroscience vol23 no 27 pp 9240ndash9245 2003

[5] N Kraus and B Chandrasekaran ldquoMusic training for thedevelopment of auditory skillsrdquo Nature Reviews Neurosciencevol 11 no 8 pp 599ndash605 2010

[6] A S Chan Y-C Ho and M-C Cheung ldquoMusic trainingimproves verbal memoryrdquo Nature vol 396 no 6707 p 1281998


[7] A J Blood and R J Zatorre ldquoIntensely pleasurable responsesto music correlate with activity in brain regions implicated inreward and emotionrdquo Proceedings of the National Academy ofSciences of theUnited States of America vol 98 no 20 pp 11818ndash11823 2001

[8] C Y Wan and G Schlaug ldquoMusic making as a tool forpromoting brain plasticity across the life spanrdquo Neuroscientistvol 16 no 5 pp 566ndash577 2010

[9] K S Taylor D A Seminowicz and K D Davis ldquoTwo systemsof resting state connectivity between the insula and cingulatecortexrdquo Human Brain Mapping vol 30 no 9 pp 2731ndash27452009

[10] W W Seeley V Menon A F Schatzberg et al ldquoDissociableintrinsic connectivity networks for salience processing andexecutive controlrdquoThe Journal of Neuroscience vol 27 no 9 pp2349ndash2356 2007

[11] N U Dosenbach D A Fair F M Miezin et al ldquoDistinctbrain networks for adaptive and stable task control in humansrdquoProceedings of the National Academy of Sciences of the UnitedStates of America vol 104 no 26 pp 11073ndash11078 2007

[12] V Menon and L Q Uddin ldquoSaliency switching attention andcontrol a network model of insula functionrdquo Brain structure ampfunction vol 214 no 5-6 pp 655ndash667 2010

[13] Y Liu M Liang Y Zhou et al ldquoDisrupted small-worldnetworks in schizophreniardquo Brain vol 131 no 4 pp 945ndash9612008

[14] C Luo Q Li Y Lai et al ldquoAltered functional connectivity indefault mode network in absence epilepsy a resting-state fMRIstudyrdquo Human Brain Mapping vol 32 no 3 pp 438ndash449 2011

[15] X Duan S He W Liao et al ldquoReduced caudate volume andenhanced striatal-DMN integration in chess expertsrdquoNeuroIm-age vol 60 no 2 pp 1280ndash1286 2012

[16] M Taubert G Lohmann D S Margulies A Villringer andP Ragert ldquoLong-term effects of motor training on resting-statenetworks and underlying brain structurerdquo NeuroImage vol 57no 4 pp 1492ndash1498 2011

[17] A C Vidal P Banca A G Pascoal G Cordeiro J Sargento-Freitas and M Castelo-Branco ldquoModulation of cortical inter-hemispheric interactions by motor facilitation or restraintrdquoNeural Plasticity vol 2014 Article ID 210396 8 pages 2014

[18] C Luo Z-W Guo Y-X Lai et al ldquoMusical training inducesfunctional plasticity in perceptual andmotor networks insightsfrom resting-state fMRIrdquo PLoS ONE vol 7 no 5 Article IDe36568 2012

[19] J Sepulcre H Liu T Talukdar I Martincorena B T Yeo andR L Buckner ldquoThe organization of local and distant functionalconnectivity in the human brainrdquo PLoS Computational Biologyvol 6 no 6 Article ID e1000808 2010

[20] D Tomasi and N D Volkow ldquoFunctional connectivity densitymappingrdquo Proceedings of the National Academy of Sciences of theUnited States of America vol 107 no 21 pp 9885ndash9890 2010

[21] D Tomasi and N D Volkow ldquoGender differences in brainfunctional connectivity densityrdquoHuman BrainMapping vol 33no 4 pp 849ndash860 2012

[22] A L Cohen D A Fair N U F Dosenbach et al ldquoDefiningfunctional areas in individual human brains using restingfunctional connectivityMRIrdquoNeuroImage vol 41 no 1 pp 45ndash57 2008

[23] M E Raichle A M MacLeod A Z Snyder W J Powers D AGusnard andG L Shulman ldquoAdefaultmode of brain functionrdquoProceedings of the National Academy of Sciences of the UnitedStates of America vol 98 no 2 pp 676ndash682 2001

[24] D Sridharan D J Levitin and V Menon ldquoA critical role forthe right fronto-insular cortex in switching between central-executive and default-mode networksrdquo Proceedings of theNational Academy of Sciences of the United States of Americavol 105 no 34 pp 12569ndash12574 2008

[25] J Li C Luo Y Peng et al ldquoProbabilistic diffusion tractographyreveals improvement of structural network in musiciansrdquo PLoSONE vol 9 no 8 Article ID e105508 2014

[26] M Avillac S Deneve E Olivier A Pouget and J-R DuhamelldquoReference frames for representing visual and tactile locationsin parietal cortexrdquo Nature Neuroscience vol 8 no 7 pp 941ndash949 2005

[27] A Schlack S J Sterbing-DrsquoAngelo KHartungK-PHoffmannand F Bremmer ldquoMultisensory space representations in themacaque ventral intraparietal areardquo Journal of Neuroscience vol25 no 18 pp 4616ndash4625 2005

[28] E Zimmerman and A Lahav ldquoThe multisensory brain andits ability to learn musicrdquo Annals of the New York Academy ofSciences vol 1252 no 1 pp 179ndash184 2012

[29] L M Romanski J F Bates and P S Goldman-Rakic ldquoAuditorybelt and parabelt projections to the prefrontal cortex in therhesus monkeyrdquo Journal of Comparative Neurology vol 403 no2 pp 141ndash157 1999

[30] V N Salimpoor and R J Zatorre ldquoNeural interactions that giverise tomusical pleasurerdquoPsychology of Aesthetics Creativity andthe Arts vol 7 no 1 pp 62ndash75 2013

[31] S Koelsch ldquoTowards a neural basis of music-evoked emotionsrdquoTrends in Cognitive Sciences vol 14 no 3 pp 131ndash137 2010

[32] A D Craig ldquoHow do you feelmdashnow The anterior insula andhuman awarenessrdquo Nature Reviews Neuroscience vol 10 no 1pp 59ndash70 2009

[33] E G Duerden M Arsalidou M Lee and M J Taylor ldquoLater-alization of affective processing in the insulardquo NeuroImage vol78 pp 159ndash175 2013

[34] V Baur J Hanggi N Langer and L Jancke ldquoResting-state func-tional and structural connectivity within an insula-amygdalaroute specifically index state and trait anxietyrdquo Biological Psy-chiatry vol 73 no 1 pp 85ndash92 2013

[35] M Fabri and G Polonara ldquoFunctional topography of humancorpus callosum an FMRI mapping studyrdquo Neural Plasticityvol 2013 Article ID 251308 15 pages 2013

[36] A Santos D Mier P Kirsch and A Meyer-LindenbergldquoEvidence for a general face salience signal in human amygdalardquoNeuroImage vol 54 no 4 pp 3111ndash3116 2011

[37] D Bzdok R Langner L Schilbach et al ldquoCharacterization ofthe temporo-parietal junction by combining data-driven par-cellation complementary connectivity analyses and functionaldecodingrdquo NeuroImage vol 81 pp 381ndash392 2013

[38] A Kucyi M Hodaie and K D Davis ldquoLateralization in intrin-sic functional connectivity of the temporoparietal junctionwith salience- and attention-related brain networksrdquo Journal ofNeurophysiology vol 108 no 12 pp 3382ndash3392 2012

[39] R B Mars J Sallet U Schuffelgen S Jbabdi I Toni and M FS Rushworth ldquoConnectivity-based subdivisions of the humanright ldquotemporoparietal junction areardquo evidence for differentareas participating in different cortical networksrdquo CerebralCortex vol 22 no 8 pp 1894ndash1903 2012

[40] R J Ellis B Bruijn A C Norton E Winner and G SchlaugldquoTraining-mediated leftward asymmetries during music pro-cessing a cross-sectional and longitudinal fMRI analysisrdquoNeuroImage vol 75 pp 97ndash107 2013


[41] M Corbetta G Patel and G L Shulman ldquoThe reorientingsystem of the human brain from environment to theory ofmindrdquo Neuron vol 58 no 3 pp 306ndash324 2008

[42] M Corbetta J M Kincade J M Ollinger M P McAvoyand G L Shulman ldquoVoluntary orienting is dissociated fromtarget detection in human posterior parietal cortexrdquo NatureNeuroscience vol 3 no 3 pp 292ndash297 2000

[43] M P van den Heuvel and O Sporns ldquoRich-club organization ofthe human connectomerdquo Journal of Neuroscience vol 31 no 44pp 15775ndash15786 2011

[44] V Colizza A Flammini M A Serrano and A VespignanildquoDetecting rich-club ordering in complex networksrdquo NaturePhysics vol 2 no 2 pp 110ndash115 2006

[45] D Tomasi and N D Volkow ldquoAssociation between functionalconnectivity hubs and brain networksrdquo Cerebral Cortex vol 21no 9 pp 2003ndash2013 2011

[46] N Kriegeskorte W K Simmons P S F Bellgowan and C IBaker ldquoCircular analysis in systems neuroscience the dangersof double dippingrdquo Nature neuroscience vol 12 no 5 pp 535ndash540 2009

[47] R J Ellis A CNorton KOvery EWinner D C Alsop andGSchlaug ldquoDifferentiating maturational and training influenceson fMRI activation during music processingrdquo NeuroImage vol60 no 3 pp 1902ndash1912 2012

[48] C J Steele J A Bailey R J Zatorre and V B PenhuneldquoEarly musical training and white-matter plasticity in thecorpus callosum evidence for a sensitive periodrdquo Journal ofNeuroscience vol 33 no 3 pp 1282ndash1290 2013

[49] C Luo T Yang S Tu et al ldquoAltered intrinsic functionalconnectivity of the salience network in childhood absenceepilepsyrdquo Journal of the Neurological Sciences vol 339 no 1-2pp 189ndash195 2014

[50] L Palaniyappan T P White and P F Liddle ldquoThe conceptof salience network dysfunction in schizophrenia from neu-roimaging observations to therapeutic opportunitiesrdquo CurrentTopics in Medicinal Chemistry vol 12 no 21 pp 2324ndash23382012

Submit your manuscripts athttpwwwhindawicom

Neurology Research International

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Alzheimerrsquos DiseaseHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


BioMed Research International


Research and TreatmentSchizophrenia

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Neural Plasticity


Parkinsonrsquos Disease


Research and TreatmentAutism

Sleep DisordersHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Neuroscience Journal

Epilepsy Research and TreatmentHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Psychiatry Journal


Computational and Mathematical Methods in Medicine

Depression Research and TreatmentHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Brain ScienceInternational Journal of

StrokeResearch and TreatmentHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Neurodegenerative Diseases


Journal of

Cardiovascular Psychiatry and NeurologyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

2 Neural Plasticity

resting-state fMRI studies The salience system is associatedwith detection of relevance among several interoceptive andexteroceptive stimuli and guides behavior while updatingexpectations about the internal and external environment[9 10]This systemmay play a role for fundamental cognitiveand behavioral functions As a result it has become a topicof intensive research in recent years [10ndash12] Of particularinterest is coactivation of the insula and dorsal ACC whichhas been observed during a variety of cognitive tasks Menonand Uddin [12] recently proposed a model in which thesalience network enables switching between the default modenetwork (DMN) and task-related brain networks Once asalient stimulus is detected the salience network facilitatestask-related information processing by initiating appropriatetransient control signals to engage brain areas mediatingattention working memory and higher code cognitive pro-cesses and disengage DMN Thus we hypothesized thatimproved integration in the salience network would correlatewith enhanced higher-level cognitive processes in musicians

Recently investigations of intrinsic functional connec-tivity based on resting-state functional magnetic resonanceimaging (fMRI) have revealed several stable and reliablefunctional brain networks Moreover altered functional con-nectivity has been considered a potential biomarker forneuropsychiatric diseases such as schizophrenia [13] andepilepsy [14] Functional connectivity has also been used todescribe plasticity induced by advanced level skill trainingsuch as that required to achieve mastery of the game of chess[15] as well as long-termmotor training [16 17] Our previousstudy found improved functional and effective connectivityof spontaneous intrinsic activity among multisensory andmotor systems during the resting state in musicians [18]These findings suggest that task-free analyses of intrinsicfunctional networks are useful for investigating the neuralarchitecture relevant to musical training

Information processing in the brain is dependent oninteractions both between adjacent regions (local interac-tions) and between distant areas (distant interactions) Bal-ance between the two types of interactions may contributeto the high efficiency of information processing in thebrain [19] Using local and global functional connectivitydensity (FCD) mapping known as a data-driven methodTomasi and Volkow found that the strongest hubs in restingconditions were located in the DMN and sensory cortices[20] Furthermore strong local functional connectivity wasobserved in the primary sensory regions motor regions andthe DMN while preferential distant functional connectivitywas observed in the DMN and heteromodal association area[19] The nature of local cortical functional connectivity inmusicians has been elusive We hypothesized that local anddistant functional connectivity would be enhanced by musi-cal training as this improvement might benefit performancewhile playing a music instrument

To test this hypothesis we evaluated the local and distantresting-state functional connectivity features by using a data-driven method First the FCDmapping was used to examinelocal functional connectivity associatedwith long-termmusi-cal training in musicians Then we assessed the distant func-tional connectivity of those regions in which we observed a

significantly different local FCD We predicted that regionsassociated with the salience system would show distinctlocal and distant functional connectivity If confirmed thispattern would support the role of the salience network inmusical training and provide evidence for improved networkintegration as an underlyingmechanism of enhanced higher-level cognitive processes in musicians

2 Materials and Methods

21 Participants Twenty-eight (20 female) musicians and 28(20 female) nonmusicians participated in the study after pro-viding informedwritten consentThese participants includedtwo parts in which one was from the previous study [18]and the other was newly recruited in 2013 The experimentalprotocol was approved by the Research Ethics Review Boardof Southwest University China All participants in the musi-cian group were either music majors at Southwest UniversityChina or professional musicians who possessed an academicdegree inmusic Seventeenmusicians had received long-termtraining in piano (6ndash20 years) and some of these individualshad also received training in either the Chinese zither oraccordion Eleven musicians primarily focused on violin (6ndash16 years) All nonmusicians were students at the University ofSouthwest China and reported that they had never receivedformal musical training or played any musical instrumentThere was no significant difference in the years of educationbetween the two groups (two-sample two-tailed 119905-test 119879 =

0571 119875 = 0182) All participants in both groups were right-handed and healthy with normal brain structure normalhearing and no history of neurological or psychiatric deficitsAll participants received monetary compensation for theirinvolvement

22 Image Acquisition All participants were scanned in a3T Siemens Trio Tim MRI scanner (Siemens ErlangenGermany) with an eight-channel phased array head coil Theexperiments were conducted in the Key Laboratory of Cog-nition and Personality of Ministry of Education SouthwestUniversity ChinaThe functional images were acquired using2D gradient echo-planar imaging (EPI) sequences with thefollowing imaging parameters thickness = 3mm (with 1mmgap) repetition time (TR) = 2000ms echo time (TE) =30ms field of view (FOV) = 22 times 22 cm flip angle = 90∘and matrix = 64 times 64 A total of 205 volumes (32 slicesper volume) were acquired during 410 seconds To ensuresteady-state longitudinalmagnetization the first five volumeswere discarded During data acquisition participants wereinstructed to relax with their eyes closed without fallingasleep Anatomical T1-weighted images were acquired using athree-dimensional- (3D-) spoiled gradient recalled sequencegenerating 176 axial slices (thickness = 1mm (no gap) TR =85ms TE = 34ms FOV = 24 times 24 cm flip angle = 12∘ andmatrix = 512 times 512)

23 Data Preprocessing Analysis Preprocessing and analysesof fMRI data were carried out using SPM8 software (Statis-tical Parametric Mapping httpwwwfilionuclacukspm)

Neural Plasticity 3


head motionrotation

=1

119872 minus 1

119872

sum

119894=2

radic10038161003816100381610038161003816Δ119889119909119894

10038161003816100381610038161003816

2

+10038161003816100381610038161003816Δ119889119910119894

10038161003816100381610038161003816

2

+10038161003816100381610038161003816Δ119889119911119894

10038161003816100381610038161003816

2

(1)


119894 119910119894 and 119911



119909119894minus 119909119894minus1





0 where










1198901199051198862

= 1 minus

sum119899

119894=1

[(119886119894minus 119898119894)2

+ (119887119894minus 119898119894)2

]

sum119899

119894=1

[(119886119894minus119872)2

+ (119887119894minus119872)2

]

(2)

4 Neural Plasticity

Musician

Nonmusician

z = minus1 z = 28 z = 35 z = 60

0 7

z = 20 x = 0

L R








3 Results




L

045

050

055

060

0 7

065

070

075

080

085

0 14

z = minus1 z = 20 z = 28 z = 35 z = 60 x = 0

R




Neural Plasticity 5










6 Neural Plasticity

Tc 045 050 055 060 065 070 075 080 085

RIns

LIns

RTPJ

LTPJ

ACC

LAmy

LSPL

RStr

RSFG

ROI

LMFG

minus4

minus8

minus14



26

23

33

72 72 72 72 72 72 72 72 72

33 33 33 33 33 33 33 33

24

32 32 32 32 32 32 32 32 32

24 24 24 24 24 24 24 24

23 23 23 23 23 23 23 23

26 26 26 26 26 26 26 26



0 7


Neural Plasticity 7

ACC

LIns

RTPJLTPJ

RIns

(a)

5 10 15 20

0

01

02

03

04

05

06

07

08R = 0534P = 0009


CC b

etw

een

RTP

J and

LIn

s

minus01

(b)





4 Discussion




8 Neural Plasticity


RL RL RL

RIns

LIns

RTPJ

LTPJ

ACC

RStr

z = 30 z = 37z = 26

z = 32 z = 34z = 0

z = 0

z = 1

z = 46

z = 46

z = minus5

z = minus8

z = minus4 z = 42

z = 61z = 57z = 39

0 7

z = minus6





Neural Plasticity 9



RIns



LIns






RStr





LAmy

LSPL

RSFG

LMFG

















5 Conclusion






Acknowledgments


References

































































Neural Plasticity










Psychiatry Journal









Journal of


Neural Plasticity 3


head motionrotation

=1

119872 minus 1

119872

sum

119894=2

radic10038161003816100381610038161003816Δ119889119909119894

10038161003816100381610038161003816

2

+10038161003816100381610038161003816Δ119889119910119894

10038161003816100381610038161003816

2

+10038161003816100381610038161003816Δ119889119911119894

10038161003816100381610038161003816

2

(1)


119894 119910119894 and 119911



119909119894minus 119909119894minus1





0 where










1198901199051198862

= 1 minus

sum119899

119894=1

[(119886119894minus 119898119894)2

+ (119887119894minus 119898119894)2

]

sum119899

119894=1

[(119886119894minus119872)2

+ (119887119894minus119872)2

]

(2)

4 Neural Plasticity

Musician

Nonmusician

z = minus1 z = 28 z = 35 z = 60

0 7

z = 20 x = 0

L R








3 Results




L

045

050

055

060

0 7

065

070

075

080

085

0 14

z = minus1 z = 20 z = 28 z = 35 z = 60 x = 0

R




Neural Plasticity 5










6 Neural Plasticity

Tc 045 050 055 060 065 070 075 080 085

RIns

LIns

RTPJ

LTPJ

ACC

LAmy

LSPL

RStr

RSFG

ROI

LMFG

minus4

minus8

minus14



26

23

33

72 72 72 72 72 72 72 72 72

33 33 33 33 33 33 33 33

24

32 32 32 32 32 32 32 32 32

24 24 24 24 24 24 24 24

23 23 23 23 23 23 23 23

26 26 26 26 26 26 26 26



0 7


Neural Plasticity 7

ACC

LIns

RTPJLTPJ

RIns

(a)

5 10 15 20

0

01

02

03

04

05

06

07

08R = 0534P = 0009


CC b

etw

een

RTP

J and

LIn

s

minus01

(b)





4 Discussion




8 Neural Plasticity


RL RL RL

RIns

LIns

RTPJ

LTPJ

ACC

RStr

z = 30 z = 37z = 26

z = 32 z = 34z = 0

z = 0

z = 1

z = 46

z = 46

z = minus5

z = minus8

z = minus4 z = 42

z = 61z = 57z = 39

0 7

z = minus6





Neural Plasticity 9



RIns



LIns






RStr





LAmy

LSPL

RSFG

LMFG

















5 Conclusion






Acknowledgments


References

































































Neural Plasticity










Psychiatry Journal









Journal of


4 Neural Plasticity

Musician

Nonmusician

z = minus1 z = 28 z = 35 z = 60

0 7

z = 20 x = 0

L R








3 Results




L

045

050

055

060

0 7

065

070

075

080

085

0 14

z = minus1 z = 20 z = 28 z = 35 z = 60 x = 0

R




Neural Plasticity 5










6 Neural Plasticity

Tc 045 050 055 060 065 070 075 080 085

RIns

LIns

RTPJ

LTPJ

ACC

LAmy

LSPL

RStr

RSFG

ROI

LMFG

minus4

minus8

minus14



26

23

33

72 72 72 72 72 72 72 72 72

33 33 33 33 33 33 33 33

24

32 32 32 32 32 32 32 32 32

24 24 24 24 24 24 24 24

23 23 23 23 23 23 23 23

26 26 26 26 26 26 26 26



0 7


Neural Plasticity 7

ACC

LIns

RTPJLTPJ

RIns

(a)

5 10 15 20

0

01

02

03

04

05

06

07

08R = 0534P = 0009


CC b

etw

een

RTP

J and

LIn

s

minus01

(b)





4 Discussion




8 Neural Plasticity


RL RL RL

RIns

LIns

RTPJ

LTPJ

ACC

RStr

z = 30 z = 37z = 26

z = 32 z = 34z = 0

z = 0

z = 1

z = 46

z = 46

z = minus5

z = minus8

z = minus4 z = 42

z = 61z = 57z = 39

0 7

z = minus6





Neural Plasticity 9



RIns



LIns






RStr





LAmy

LSPL

RSFG

LMFG

















5 Conclusion






Acknowledgments


References

































































Neural Plasticity










Psychiatry Journal









Journal of


Neural Plasticity 5










6 Neural Plasticity

Tc 045 050 055 060 065 070 075 080 085

RIns

LIns

RTPJ

LTPJ

ACC

LAmy

LSPL

RStr

RSFG

ROI

LMFG

minus4

minus8

minus14



26

23

33

72 72 72 72 72 72 72 72 72

33 33 33 33 33 33 33 33

24

32 32 32 32 32 32 32 32 32

24 24 24 24 24 24 24 24

23 23 23 23 23 23 23 23

26 26 26 26 26 26 26 26



0 7


Neural Plasticity 7

ACC

LIns

RTPJLTPJ

RIns

(a)

5 10 15 20

0

01

02

03

04

05

06

07

08R = 0534P = 0009


CC b

etw

een

RTP

J and

LIn

s

minus01

(b)





4 Discussion




8 Neural Plasticity


RL RL RL

RIns

LIns

RTPJ

LTPJ

ACC

RStr

z = 30 z = 37z = 26

z = 32 z = 34z = 0

z = 0

z = 1

z = 46

z = 46

z = minus5

z = minus8

z = minus4 z = 42

z = 61z = 57z = 39

0 7

z = minus6





Neural Plasticity 9



RIns



LIns






RStr





LAmy

LSPL

RSFG

LMFG

















5 Conclusion






Acknowledgments


References

































































Neural Plasticity










Psychiatry Journal









Journal of


6 Neural Plasticity

Tc 045 050 055 060 065 070 075 080 085

RIns

LIns

RTPJ

LTPJ

ACC

LAmy

LSPL

RStr

RSFG

ROI

LMFG

minus4

minus8

minus14



26

23

33

72 72 72 72 72 72 72 72 72

33 33 33 33 33 33 33 33

24

32 32 32 32 32 32 32 32 32

24 24 24 24 24 24 24 24

23 23 23 23 23 23 23 23

26 26 26 26 26 26 26 26



0 7


Neural Plasticity 7

ACC

LIns

RTPJLTPJ

RIns

(a)

5 10 15 20

0

01

02

03

04

05

06

07

08R = 0534P = 0009


CC b

etw

een

RTP

J and

LIn

s

minus01

(b)





4 Discussion




8 Neural Plasticity


RL RL RL

RIns

LIns

RTPJ

LTPJ

ACC

RStr

z = 30 z = 37z = 26

z = 32 z = 34z = 0

z = 0

z = 1

z = 46

z = 46

z = minus5

z = minus8

z = minus4 z = 42

z = 61z = 57z = 39

0 7

z = minus6





Neural Plasticity 9



RIns



LIns






RStr





LAmy

LSPL

RSFG

LMFG

















5 Conclusion






Acknowledgments


References

































































Neural Plasticity










Psychiatry Journal









Journal of


Neural Plasticity 7

ACC

LIns

RTPJLTPJ

RIns

(a)

5 10 15 20

0

01

02

03

04

05

06

07

08R = 0534P = 0009


CC b

etw

een

RTP

J and

LIn

s

minus01

(b)





4 Discussion




8 Neural Plasticity


RL RL RL

RIns

LIns

RTPJ

LTPJ

ACC

RStr

z = 30 z = 37z = 26

z = 32 z = 34z = 0

z = 0

z = 1

z = 46

z = 46

z = minus5

z = minus8

z = minus4 z = 42

z = 61z = 57z = 39

0 7

z = minus6





Neural Plasticity 9



RIns



LIns






RStr





LAmy

LSPL

RSFG

LMFG

















5 Conclusion






Acknowledgments


References

































































Neural Plasticity










Psychiatry Journal









Journal of


8 Neural Plasticity


RL RL RL

RIns

LIns

RTPJ

LTPJ

ACC

RStr

z = 30 z = 37z = 26

z = 32 z = 34z = 0

z = 0

z = 1

z = 46

z = 46

z = minus5

z = minus8

z = minus4 z = 42

z = 61z = 57z = 39

0 7

z = minus6





Neural Plasticity 9



RIns



LIns






RStr





LAmy

LSPL

RSFG

LMFG

















5 Conclusion






Acknowledgments


References

































































Neural Plasticity










Psychiatry Journal









Journal of


Neural Plasticity 9



RIns



LIns






RStr





LAmy

LSPL

RSFG

LMFG

















5 Conclusion






Acknowledgments


References

































































Neural Plasticity










Psychiatry Journal









Journal of














5 Conclusion






Acknowledgments


References

































































Neural Plasticity










Psychiatry Journal









Journal of




























































Neural Plasticity










Psychiatry Journal









Journal of


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