Sparse Representation of HCP Grayordinate Data Reveals. Novel Functional Architecture of Cerebral Cortex

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1 1 Sparse Representaton of HCP Grayordnate Data Reveals Novel Functonal Archtecture of Cerebral Cortex X Jang 1, Xang L 1, Jngle Lv 2,1, Tuo Zhang 2,1, Shu Zhang 1, Le Guo 2, Tanmng Lu 1* 1 Cortcal Archtecture Imagng and Dscovery Lab, Department of Computer Scence and Bomagng Research Center, The Unversty of Georga, Athens, GA; 2 School of Automaton, Northwestern Polytechncal Unversty, X an, P. R. Chna. *Correspondng author: telephone: ; Fax: ; Emal: tlu@cs.uga.edu Short ttle: Functonal Dfference between Gyr and Sulc Keywords: Task-based heterogeneous functonal regon, cortcal gyr and sulc, grayordnate, task-based fmri, sparse representaton Abstract The recently publcly released Human Connectome Project (HCP) grayordnate-based fmri data not only has hgh spatal and temporal resoluton, but also offers group-correspondng fmri sgnals across a large populaton for the frst tme n the bran magng feld, thus sgnfcantly facltatng mappng the functonal bran archtecture wth much hgher resoluton and n a groupwse fashon. In ths paper, we adopt the HCP grayordnate task-based fmri (tfmri) data to systematcally dentfy and characterze task-based heterogeneous functonal regons (THFRs) on cortcal surface,.e., the regons that are actvated durng multple tasks condtons and contrbute to multple task-evoked systems durng a specfc task performance, and to assess the spatal patterns of dentfed THFRs on cortcal gyr and sulc by applyng a computatonal framework of sparse representatons of grayordnate bran tfmri sgnals. Expermental results demonstrate that

2 2 both consstent task-evoked networks and ntrnsc connectvty networks across all subjects and tasks n HCP grayordnate data are effectvely and robustly reconstructed va the proposed sparse representaton framework. Moreover, t s found that there are relatvely consstent THFRs locatng at blateral paretal lobe, frontal lobe, and vsual assocaton cortces across all subjects and tasks. Partcularly, those dentfed THFRs locate sgnfcantly more on gyral regons than on sulcal regons. These results based on sparse representaton of HCP grayordnate data reveal novel functonal archtecture of cortcal gyr and sulc, and mght provde a foundaton to better understand functonal mechansms of the human cerebral cortex n the future. Keywords: Task-based heterogeneous functonal regon, cortcal gyr and sulc, grayordnate, task-based fmri, sparse representaton Introducton Studyng human bran functon usng n-vvo functonal neuromagng technques such as functonal magnetc resonance magng (fmri) (Logothets, 2008; Frston, 2009) has receved sgnfcant nterest n the bran mappng feld. Specfcally, task-based fmri (tfmri) has been wdely adopted to dentfy bran regons that are functonally nvolved n a specfc task performance (Logothets 2008; Frston 2009). To advance the understandng of functonal localzatons and nteractons wthn the human bran based on fmri data, there have been ncreasng efforts n acqurng and processng fmri data wth hgher spatal/temporal resoluton and correspondence across subjects and populatons to better characterze the regularty and varablty of human bran functon (Van Essen et al., 2013). One of such efforts s the recently publcly released Human Connectome Project (HCP) grayordnate-based fmri data (Van Essen et al., 2013; Smth et al., 2013; Barch et al., 2013; Glasser et al., 2013). The HCP grayordnate data models the gray matter as combned cortcal surface vertces and subcortcal voxels across subjects n the standard MNI152 space (Smth et al., 2013; Glasser et al., 2013). The HCP fmri

3 3 (ncludng tfmri and restng state fmri) data n the standard grayordnate space not only has both much hgher spatal and temporal resoluton, but also offers group-correspondng fmri sgnals across a large populaton for the frst tme n the bran magng feld. In short, the HCP grayordnate fmri data sgnfcantly facltates the mappng of functonal bran archtecture wth much hgher resoluton and n a group-wse fashon, wthout the need to average sgnals wthn bran regons and across subjects (Mkl et al., 2008; Yue et al., 2010). Based on tfmri data, varous studes (e.g., Huettel et al., 2004; Fox et al., 2005; Dosenbach et al., 2006; Bullmore and Sporns 2009; Lv et al., 2014a; Lv et al., 2014b) have demonstrated that there exst multple concurrent functonal networks that are spatally dstrbuted across bran regons and are nvolved and nteractng wth each other. Ths phenomenon s n agreement wth studes n the neuroscence feld whch have been wdely reported and argued that there are certan bran regons and networks that exhbt strong functonal heterogenety and dversty (Duncan 2010; Fedorenko et al., 2013; Pessoa 2012; Kanwsher 2010; Anderson et al., 2013; Gazzanga 2004). That s, a bran regon mght be nvolved n multple functonal processes smultaneously, and a functonal network mght recrut heterogeneous bran regons. For nstances, t was argued that neural bass of emoton and cognton should be vewed as governed less by propertes that are ntrnsc to specfc stes and more by nteractons among multple bran regons (Pessoa 2012) and that areas of the bran that have been assocated wth language processng appear to be recruted across other cogntve domans (Gazzanga 2004). In short, dentfyng and characterzng such meanngful task-based heterogeneous functonal regons (THFRs) on cerebral cortex,.e., the cortcal regons that are actvated durng multple tasks condtons and contrbute to multple task-evoked systems durng a specfc task performance, could be mportant to understandng the functonal archtecture of human cerebral cortex. However, those meanngful THFRs wth complex temporal patterns due to the complex composton of nvolved multple functonal networks/processes mght have been underestmated by tradtonal approaches whch

4 4 merely consder ndvdual tfmri sgnals based on model-drven subtracton procedures (Lv et al., 2014a; Lv et al., 2014b). The hghly convoluted cortcal foldng, whch s composed of convex gyr and concave sulc, s one of the most promnent features of human cerebral cortex (Rakc 1988; Welker 1990; Barron 1950). In recent years, there has been ncreasng nterest n human bran mappng from both mcro- and macro- scale to nvestgate the possble structural/functonal dfferences between gyr and sulc, and several nterestng fndngs have been reported (Ne et al., 2012; Takahash et al., 2012; Chen et al., 2013; Zhang et al., 2014; Zeng et al., 2014; Deng et al., 2014). For nstances, a recent mcro-scale study (Zeng et al., 2014) based on recently released Allen Mouse Bran Atlas demonstrated that the cerebellum gyr and sulc of rodent brans are sgnfcantly dfferent n both axonal connectvty and gene expresson patterns. For macro-scale data analyss, our recent studes (Ne et al., 2012; Chen et al., 2013) demonstrated that the termnaton of streamlne fbers derved from dffuson magnetc resonance magng (dmri) (e.g., dffuson tensor magng (DTI) and hgh angular resoluton dffuson magng (HARDI)) concentrate on gyrus n human, chmpanzee, and macaque brans. Ths phenomenon was also observed n another analyss on human fetus bran (Takahash et al., 2012). Another recent study (Zhang et al., 2014) dentfed and characterzed the U-shapes of streamlne fbers derved from dmri (e.g., DTI, HARDI, and dffuson spectrum magng (DSI)), and reported that most of the U-shaped streamlne fbers connect neghborng cortcal gyr and course along sulc n human, chmpanzee, and macaque brans. Moreover, nspred by those structural connectvty fndngs, our recent study (Deng et al., 2014) demonstrated that the functonal connectvty s strong between gyral-gyral regons, weak between sulcal-sulcal regons, and moderate between gyral-sulcal regons based on restng state fmri data. However, the functonal bran characterstcs (e.g., the possble dstrbuton dfference of THFRs n ths study) of cortcal gyral/sulcal regons durng a specfc task performance based on tfmri data are largely unknown.

5 5 Motvated by the above-mentoned reasons and based on the hgh-qualty HCP grayordnate tfmri data, ths study ams to dentfy and characterze the meanngful THFRs on cortcal surface durng a specfc task performance, and to assess the possble dstrbuton dfference of dentfed THFRs on cortcal gyral and sulcal regons. The recently publcly released HCP tfmri data n the standard grayordnate space (Van Essen et al., 2013; Glasser et al., 2013; Barch et al., 2013) s partcularly sutable for ths study due to the followng three reasons. 1) It s well demonstrated (e.g., Van Essen et al., 2013; Smth et al., 2013; Glasser et al., 2013) that t s of great mportance to analyze cortcal neuromagng data wth surface constrant nformaton snce the convoluted cortcal sheet and ts geometry nformaton s better represented n 2D surface space than n commonly adopted 3D volume space. 2) The HCP grayordnate tfmri data can suffcently dfferentate gyral/sulcal regons and relatvely relably map the tfmri tme seres on cortcal gyral and sulcal regons. Whle for the commonly used tfmri data n 3D volume space, the possble functonally dstnct regons across gyral blades or sulcal banks are only separated by a few voxels (mllmeters). It s possble that the tfmri tme seres from functonally dstnct gyral/sulcal regons are mxed when performng 3D volumetrc smoothng processng and thus naccurate for succeedng analyss (Glasser et al., 2013). 3) The HCP grayordnate tfmri data has both hgh spatal and temporal resoluton than the commonly used tfmri data, and the spatal correspondence of the standard grayordnate tfmri data s relatvely more precse than algned 3D volume data across dfferent subjects (Glasser et al., 2013), and the assocated tfmri sgnals of grayordnates also have relatvely precse correspondence across subjects, whch s sutable for cross-subject comparson and group-wse analyss. In short, usng HCP grayordnate-based tfmri data wll sgnfcantly beneft us to dentfy THFRs relably and assess ther spatal patterns on cortcal gyr and sulc accurately n ths study.

6 6 In general, our contrbutons n ths work are three fold: 1) We dentfy and characterze meanngful THFRs va our recent computatonal framework (Lv et al., 2014a; Lv et al., 2014b) of sparse representatons of whole-bran tfmri sgnals va an effectve onlne dctonary learnng algorthm (Maral et al., 2010). The ratonales of adoptng sparse representaton of whole-bran tfmri sgnals to dentfy meanngful functonal networks are explaned n two fold. Frst, based on the argument that a bran regon mght be nvolved n multple functonal processes smultaneously (Fedorenko et al., 2013; Duncan 2010; Pessoa 2012; Kanwsher 2010; Anderson et al., 2013; Gazzanga 2004), ts tfmri blood oxygen level dependent (BOLD) sgnal could be composed of varous components (functonal networks) smultaneously. Second, gven that dctonary learnng and sparse representaton approaches have been successfully adopted n machne learnng and pattern recognton felds to both represent sgnals accurately and compactly and extract meanngful patterns effectvely (e.g., Wrght et al., 2010), there have been several recent successes of adoptng dctonary learnng and sparse representaton for bran fmri sgnal analyss and actvaton/network detecton under the premse that each fmri sgnal s components are sparse and lnearly neural ntegrated (e.g., Lee et al., 2011; Okonomou et al., 2012; Abolghasem et al., 2013; Lv et al., 2014a; Lv et al., 2014b). Specfcally, our recent works (Lv et al., 2014a; Lv et al., 2014b) successfully performed sparse representaton of whole-bran fmri sgnals at voxel scale to nfer a comprehensve collecton of functonal networks n the whole bran concurrently, to characterze those functonal networks va spatal and temporal patterns, to assess the composton contrbutons of those functonal networks to whole-bran fmri sgnals, and to measure the spatal overlap patterns among functonal networks. A crtcal dfference between the dctonary learnng/sparse representaton approach and other decomposton approaches (e.g., ndependent component analyss (ICA) (McKeown et al., 1998)) s that the sparse representaton does not explctly assume the ndependence of fmri tme seres among dfferent functonal components, whle ICA does. It s more approprate to explore the THFRs wth concurrent functonal processes/networks based on the sparse representaton-based

7 7 components n ths study. 2) We apply our computatonal framework on the recently publcly released HCP grayordnate tfmri data (Van Essen et al., 2013; Glasser et al., 2013; Barch et al., 2013), makng the results relatvely relable, reproducble, and comparable for other studes. 3) It s the frst tme (as far as we know) to assess the spatal patterns of task-based heterogeneous functonal regons on cortcal gyr and sulc, the results of whch could provde a foundaton for future exploraton of functonal archtecture of the human cerebral cortex. The manuscrpt s organzed as follows. We frst brefly descrbe HCP grayordnate-based tfmri data and the assocated mnmal preprocessng ppelnes (Glasser et al., 2013). In the methods, we frst dentfy a comprehensve collecton of functonal networks of each subject durng specfc task performances va the recently developed computatonal framework (Lv et al., 2014a; Lv et al., 2014b) of sparse representatons of grayordnate bran tfmri sgnals. Then, we dentfy and quanttatvely characterze the meanngful task-evoked networks and ntrnsc connectvty networks n spatal and/or temporal domans. Fnally, we dentfy the task-based heterogeneous functonal regons (THFRs) nvolved n multple functonal networks, and assess ther spatal patterns on cortcal gyr/sulc. Expermental results and dscusson and concluson are also presented. Materals and Methods Grayordnate Data Acquston and Preprocessng We adopt the hgh-qualty task-based fmri (tfmri) data from the Human Connectome Project (HCP) (frst quarter (Q1) release) (Van Essen et al., 2013; Barch et al., 2013) n ths study. HCP provdes publcly avalable and easy-to-use mult-modalty MRI neuromagng datasets for multmodal analyss of bran structure, connectvty, and functon, as well as comparsons across subjects. Specfcally, the HCP tfmri datasets nclude seven dfferent task paradgms (emoton,

8 8 gamblng, language, motor, relatonal, socal, and workng memory) whch are adopted or desgned to dentfy core functonal nodes across a wde range of cerebral cortex, thus can be vewed as a comprehensve and systematc mappng of core functonal nodes and functonal networks across subjects (Barch et al., 2013). The detaled desgns of the seven task paradgms are referred to n (Barch et al., 2013). There are 68 subjects n the Q1 release of HCP tfmri datasets (Van Essen et al., 2013; Barch et al., 2013). The acquston parameters of tfmri data are as follows: matrx, 220mm FOV, 72 slces, TR=0.72s, TE=33.1ms, flp angle = 52, BW =2290 Hz/Px, n-plane FOV = mm, 2.0 mm sotropc voxels (Barch et al., 2013). We adopt the publcly released preprocessed tfmri data after the mnmal preprocessng ppelnes whch are especally defned for hgh spatal and temporal resoluton of HCP datasets (Glasser et al., 2013). The mnmal preprocessng ppelnes manly nclude spatal artfacts and dstortons removal, cortcal surfaces generaton, wthn-subject cross-modal regstraton, cross-subject regstraton to standard volume and surface spaces, and generaton of a CIFTI format of preprocessed data n the standard grayordnate space (Glasser et al., 2013). In bref, gray matter s modeled as combned cortcal surface vertces and subcortcal voxels, and the term grayordnates s adopted to descrbe the spatal dmenson of such combned coordnate system. The standard grayordnate space means that the cortcal surface mesh and subcortcal volume parcels are both n the MNI standard space (Fg. 1a). There are 91,282 maxmally algned grayordnates n total (ncludng the gray matter sampled at about 60k surface vertces n the standard 2 mm average vertex spacng on the cortcal surface and about 30k standard 2 mm voxels n subcortcal regons) across all subjects and data modaltes (Glasser et al., 2013). The grayordnate-based tfmri data s represented as a 2D matrx n CIFTI format, n whch one dmenson represents the standard grayordnates (spatal nformaton) whch have correspondence across subjects and the other dmenson represents the tfmri tme seres (Fg. 1b) (Barch et al., 2013; Glasser et al., 2013).

9 9 Sparse Representaton of Grayordnate-based Whole-bran TfMRI Sgnals We perform dctonary learnng and sparse representaton of grayordnate-based whole-bran tfmri data to obtan a comprehensve collecton of dctonary components (functonal networks) n the whole bran for each subject n each task data va our recently developed computatonal framework (Lv et al., 2014a; Lv et al., 2014b). All varables used n ths secton are summarzed n Supplemental Table 1. As llustrated n Fg. 1, for each subject n each task data, frst, we extract tfmri sgnals of whole-bran grayordnates (Fg. 1a). After normalzng to zero mean and standard devaton of 1, all tfmri sgnals are aggregated nto a 2D sgnal matrx,..., t n X x1 x n R (Fg.1b), where t s the tfmri tme ponts and n columns are n tfmri sgnals extracted from n grayordnates. Then X s factorzed nto an over-complete dctonary t k bass matrx D [ d1,..., d k ] R (Fg.1c, k s the dctonary component sze) and a sparse k n coeffcent weght matrx α [ α1,..., α n ] R (Fg.1c) va an effectve onlne dctonary learnng algorthm (Maral et al., 2010), n whch the tfmri sgnal vector x (=1,...n) n -th column of X s approxmately modeled as x D α, where α (=1,...n) s the -th column of α. Specfcally, each dctonary component can be vewed as a functonal network from bran scence perspectve,.e., the tme seres vector d (=1,...k) n -th column of D represents the functonal BOLD (blood-oxygen-level dependent) actvtes of -th functonal network (the blue curves n Fgs.1e-1f), whle d s correspondng sparse coeffcent weght vector α (=1,...k) n - th row of α can be mapped back to the cortcal surface to obtan the cortcal spatal pattern of the functonal network (Fgs.1e-1f). At the conceptual level, the computatonal framework of sparse representaton can not only accurately and compactly represent tfmri sgnals, but also effectvely dentfy a comprehensve collecton of functonal networks whose temporal ( d ) and spatal ( α )

10 10 patterns can be quanttatvely assessed (as detaled n Identfcaton of Functonal Networks n Sparse Representaton ). Fg. 1. Sparse representaton of grayordnate-based whole-bran tfmri sgnals. (a): The cortcal surface of an example subject n the MNI152 standard grayordnate space. Four example cortcal vertces (grayordnates) are hghlghted by four dfferent colors (red, blue, green and orange). (b): The grayordnatebased tfmri data of the subject n (a). It s represented as a 2D matrx X, n whch each row represents the standard grayordnates (spatal nformaton) and each column represents the tfmri tme seres. Four correspondng tfmri sgnals of the four example grayordnates n (a) are represented as straght lnes by the same color. (c): Sparse representaton of X as dctonary D sparse coeffcent weght matrx α. (d)-(f): Illustratons of D and α. The blue bars n (d) show dctonary components (ndexed horzontally) and the number of grayordnates that each dctonary component contans by countng the number of non-zero elements n each row of α (vertcal heght). (e)-(f) show spatal dstrbuton map on the cortcal surface (hghlghted by blue) and temporal tme seres (blue curve) of two example dctonary components, respectvely. We calculate D and α as follows. For sparse representaton of sgnal matrx learn an effectve over-complete dctonary t n X R, we am to t k D R whch satsfes the constrant that k>t and k<<n (Maral et al., 2010). Specfcally, the emprcal cost functon f ( D ) of n t n X R consderng the average loss of regresson to all n sgnal vectors usng D s

11 11 f D 1 x D 1 1 x Dα α (1) n n 2 n ( ), mnk 2 1 n 1 1 R n α 2 where the loss functon xd, s defned as the optmal value of sparse representaton: 1, mnk α R 2 2 x D x Dα α. Note that the value of, 2 1 xd should be small f sgnal x s reasonably well sparse represented by D. The l 1 regularzaton s used to yeld a sparse resoluton of α. λ s a regularzaton parameter between regresson resdual and sparsty level. Moreover, we have the constrant to prevent the elements n D from beng arbtrarly large, t k T C D R s. t. 1,.. k, d d 1 (2) So the problem of mnmzng Eq. (1) s rewrtten as a matrx factorzaton problem: mn k n D C, α R X Dα α (3) F 1,1 We adopt the effectve onlne dctonary learnng algorthm and the assocated publcly released onlne dctonary learnng toolbox (Maral et al., 2010) to solve Eq. (3) and to learn the dctonary D. We brefly demonstrate the onlne dctonary learnng approaches to solve Eq. (3) as follows. The core dea s that the two varables D and α n Eq. (3) are alternated and mnmzed over one whle keepng the other one fxed. Specfcally, we defne sgnal tranng set as samples of a dstrbuton p x. D m s defned as the updated dctonary at the teraton tme m. D 0 s the ntal dctonary and s randomly ntalzed from x. At the number of teratons m, for one element x m drawn from p x at a tme n stochastc gradent descent, the least angle regresson (LARS)-Lasso algorthm (Maral et al., 2010) s used to compute the decomposton α m of x m based on the dctonary D m 1 obtaned at the prevous teraton m-1. At the same tme, the dctonary D m 1 s updated as D m by mnmzng over Eq. (2) the functon n Eq. (1), where s computed durng prevous step. The block-coordnate descent wth warm starts algorthm s α m

12 12 used for dctonary update (Maral et al., 2010). It has been proven that the teratons of dctonary update can acheve convergence to learn an optmal D. More detaled equatons and solutons of Eq. (3) are referred to (Maral et al., 2010). Once D s learned and fxed n Eq. (3), the sparse representaton based on the learned D can be solved as an l 1 -regularzed lnear least-squares problem to learn an optmzed α (Maral et al., 2010). We select the value of regularzaton parameter λ and dctonary sze k va expermental results based on the crteron of group-wse consstency of the nferred functonal components across ndvdual subjects (Lv et al., 2014a; Lv et al., 2014b). More detals are n the supplemental materals. Identfcaton of Functonal Networks n Sparse Representaton Once we perform dctonary learnng and sparse representaton of tfmri sgnals to obtan a collecton of dctonary components for each subject n each task data, the next step s to dentfy and quanttatvely characterze the meanngful functonal networks (ncludng both task-evoked networks and ntrnsc connectvty networks) from the dctonary components as many as possble for each subject n each task data based on current bran scence knowledge, and to seek ther correspondences across ndvdual subjects. Specfcally, we dentfy the task-evoked networks from the dctonary components for each subject n each tfmri data as follows. After extensve vsual nspecton, t s found that certan networks (dctonary components) have smlar spatal and temporal patterns compared wth the actvaton maps derved from tradtonal general lnear model (GLM) (Frston et al., 1994) (Fg. 2 and Supplemental Fg. 2). In ths way, we adopt the tradtonal GLM to perform task actvaton detecton on tfmri data va FSL FEAT software. The resultng actvaton maps under specfc task contrast desgns as well as the nput task contrast paradgm curves n tradtonal GLM can be vewed as the references to dentfy and characterze task-evoked networks n sparse representaton. Smlar to the methods n (Lv et al., 2014a; Lv et al., 2014b), for -th dctonary component, we not only measure the temporal

13 13 smlarty (defned as Pearson's correlaton coeffcent) between ts temporal vector d (blue curve n Fg. 2a-2b) and each task contrast paradgm curve (black curve n Fg. 2a-2b), but also measure the spatal smlarty between ts spatal pattern α and the correspondng actvaton map obtaned by GLM (Fg. 2a). The spatal smlarty s defned as the spatal pattern overlap rate R between the dctonary component s spatal pattern (S) and the GLM-derved actvaton map (T) on cortcal surface: S T R( S, T) (4) T Note that we frstly convert S and T from contnuous values to dscrete labels (all values larger than 0 are labeled as 1, and others are labeled as 0) and then calculate the spatal overlap rate usng Eq. (4). For each subject, we select the top ten canddate dctonary components whch have hgh spatal smlarty wth correspondng GLM-derved actvaton map and have hgh temporal smlarty wth correspondng task contrast paradgm curve, respectvely usng the same methods n (Lv et al., 2014a; Lv et al., 2014b). Each component wth hgh sum of value n spatal and temporal smlarty s further examned by a group of seven experts separately to determne the fnal component wth the best match of both spatal and temporal patterns wth GLM based on the agreement reached by a votng procedure of all experts. Moreover, for a specfc task data, we examne the group consstency of each dentfed meanngful task-evoked network across all subjects by comparng the spatal and temporal patterns between group-averaged dentfed meanngful task-evoked networks and actvaton maps derved from group GLM (Fg. 2b), and only those consstent dctonary components across subjects are retaned as dentfed task-evoked networks n the specfc task data. More detals are n (Lv et al., 2014a; Lv et al., 2014b). Our ratonale s that snce temporal and spatal patterns provde crucal and complementary nformaton of functonal BOLD actvtes and neuroanatomc dstrbutons of a functonal network, respectvely, each dentfed meanngful task-evoked network n sparse representaton

14 14 should have both hgh temporal smlarty wth the task contrast paradgm curve and hgh spatal smlarty wth the actvaton map obtaned from tradtonal GLM (Lv et al., 2014a; Lv et al., 2014b). Fg. 2. Identfed task-evoked networks and ICNs n sparse representaton of emoton tfmri data. (a) Spatal and temporal patterns of the three dentfed task-evoked networks based on sparse representaton compared wth tradtonal GLM-derved actvaton maps and task contrast paradgm curves n one example subject. (b) Group-averaged spatal and temporal patterns of the three dentfed task-evoked networks compared wth group GLM-derved actvaton maps and task contrast paradgm curves across all subjects.

15 15 (c) Spatal patterns of nne dentfed ICNs n sparse representaton compared wth ICN templates n the example subject. (d) Group-averaged spatal patterns of the nne dentfed ICNs compared wth ICN templates across all subjects. Moreover, we dentfy the ntrnsc connectvty networks (ICNs) (Seeley et al., 2007; Rachle 2010; Smth et al., 2009; Lv et al., 2014a; Lv et al., 2014b) from the dctonary components for each subject n each task data as follows. We measure the spatal smlarty defned n Eq. (4) between dctonary components and prevously dentfed ICN templates (Fg. 2c) to determne the correspondence (Lv et al., 2014b). Specfcally, ten prevously well-defned ICNs (Smth et al., 2009) are adopted as ICN templates n ths study. We select the top ten canddate dctonary components whch have hgh spatal smlarty wth each ICN template, respectvely. Each component s further examned by a group of seven experts separately to determne the fnal component wth the best match of spatal pattern and ICN template based on the agreement reached by a votng procedure of all experts. Moreover, we examne the group consstency of each dentfed ICN across all subjects and all task data by comparng the spatal pattern between group-averaged dentfed ICN and correspondng ICN template (Fg. 2d), and only those consstent dctonary components across subjects and across task data are retaned as dentfed ICNs. More detals are n (Lv et al., 2014a; Lv et al., 2014b). Our ratonale s that each dentfed ICN n sparse representaton should have hgh spatal smlarty (overlap rate) wth the correspondng prevously dentfed ICN template across dfferent subjects. It should be noted that snce there s no quanttatve or effectve nterpretaton of the comprehensve collecton of all dctonary components dentfed by sparse representaton of tfmri sgnals, we adopt the ndependent tradtonal GLM-derved contrast maps and ICN templates as the references to dentfy those smlar meanngful functonal networks from the dctonary components (Lv et al., 2014a; Lv et al., 2014b), as well as to confrm that those

16 16 meanngful functonal networks ndeed exst n human bran no matter what methods are adopted based on current bran scence knowledge. It should also be noted that n the future, all of those hundreds of dctonary components (task-evoked or ntrnsc connectvty networks, or even nose and artfacts) should be dentfed wth future understandng of human bran functon and development of other effectve methodology. Identfcaton of THFRs and Assessment of Spatal Patterns on Gyr/Sulc After performng sparse representaton of tfmri sgnals and characterzng the meanngful functonal networks, we dentfy task-based heterogeneous functonal regons (THFRs) and assess ther spatal patterns on cortcal gyr/sulc for each subject n each task data n ths secton. Specfcally, snce the dctonary components can be vewed as functonal networks and the -th column α (=1,...n) of α represents the functonal network composton of grayordnate (=1,...n), we assess the number of nvolved functonal networks (dctonary components) of g g by countng the number of non-zero elements n α ( α 0 ). THFR s then defned as: THFR g s.. t α q (5) 0 In bref, THFR s composed of a collecton of grayordnate non-zero elements (.e., the number of nvolved functonal networks) n g (=1,...n) of whch the number of α s larger than a threshold q. Note that snce we defne q as the value of α at the top p% across all 0 grayordnates, the value of q s determned once value of p% s decded. The ratonale of choosng value of p% s that p% should be small enough to dentfy the THFRs from all grayordnates, whle p% should also not be too small to dentfy merely solated bran grayordnates nstead of contnues THFR regons. We test dfferent p% to examne the spatal pattern consstency of dentfed THFRs whch wll be detaled n Spatal Patterns of THFRs on Cortcal Gyr and Sulc secton. Note that we adopt a unform p% for all tfmri data and subjects

17 17 snce the values of α across all voxels/grayordnates are typcally normally dstrbuted wth 0 smlar mean and standard devaton across all subjects as llustrated n (Lv et al., 2014b) and Supplemental Fg. 1. It should also be noted that t s lkely that the dentfed THFRs contan specfc artfact or nose components whch cannot be well quanttatvely characterzed or modeled under current bran scence knowledge. However, the results n Results secton wll show that the dentfed THFRs are ndeed meanngful and contan multple dentfed functonal networks. After obtanng the spatal dstrbuton of THFRs on grayordnate cortcal surfaces, the further spatal pattern assessment of THFRs on gyr/sulc s straghtforward. As each grayordnate already has the gyr/sulc nformaton n each subject (Glasser et al., 2013), we could count the number of nvolved grayordnates n THFRs on gyral and sulcal regons respectvely, then assess the rato between the percentage of nvolved grayordnates n THFRs on gyr vs the percentage on sulc. Results Identfcaton of Meanngful Functonal Networks n Sparse Representaton We adopted the temporal/spatal smlarty measurement n Secton Identfcaton of Functonal Networks n Sparse Representaton to dentfy meanngful task-evoked networks and ICNs from tfmri data of each subject. In total, we dentfed 3, 2, 2, 5, 2, 3, and 6 task-evoked networks from the datasets of emoton, gamblng, language, motor, relatonal, socal, and workng memory task, respectvely. The detaled descrpton of the task-evoked networks s n Supplemental materals. Fgs. 2a-2b show an example consstng of the three dentfed task-evoked networks n emoton tfmri data, whle the results from the other sx tasks could be found n Supplemental Fg. 2. Specfcally, Fg. 2a shows the three task-evoked networks n emoton tfmri data of one

18 18 example subject. We can see that all three networks have smlar spatal patterns compared wth correspondng GLM-derved actvaton maps as well as smlar temporal patterns compared wth the correspondng task contrast paradgm curve. Quanttatvely, the spatal overlap rate R s 0.30, 0.33, and 0.29, and the temporal smlarty s 0.38, 0.30, and 0.37 for the three networks n the example subject, respectvely. Fg. 2b shows the group-averaged spatal maps and temporal curves of dentfed task-evoked networks across all subjects n emoton task. Table 1 provdes the spatal overlap rates and temporal smlarty values of all group-averaged task-evoked networks n seven tasks. Supplemental Tables 2 and 3 provde the spatal overlap rate and temporal smlarty value of all dentfed task-evoked networks n seven tasks across all ndvdual subjects. It s evdent that all dentfed task-evoked networks are consstent and have relatvely hgh spatal overlap rate and temporal smlarty across subjects. Table 1. Spatal overlap rate (S) and temporal smlarty (T) of all dentfed group-averaged task-evoked networks (N) comparng wth group GLM-derved actvaton maps. Emoton Gamblng Language Motor Relatonal Socal WM S T S T S T S T S T S T S T N# N# N# N# N# N# Moreover, we dentfed nne ICNs n all subjects and n all seven tasks. The detaled descrpton of the nne ICNs s n Supplemental materals. Fg. 2c shows the spatal maps of the nne dentfed ICNs compared wth ICN templates (Smth et al., 2009) n the emoton task of the example subject, whle the results from the other sx tasks are shown n Supplemental Fg. 2.

19 19 Quanttatvely, the spatal overlap rate s 0.34, 0.51, 0.37, 0.28, 0.27, 0.27, 0.23, 0.33, and 0.29 for the nne ICNs, respectvely. Fg. 2d shows the group-averaged spatal maps of dentfed ICNs n emoton data across all subjects. Table 2 provdes the spatal overlap rate of all group-averaged ICNs. Supplemental Table 4 provdes the spatal overlap rate of all nne dentfed ICNs n seven tasks across all subjects. We can see that all dentfed ICNs are consstent and have relatvely hgh spatal overlap rate across tasks and subjects. Table 2. Spatal overlap rates of all dentfed group-averaged ICNs. Emoton Gamblng Language Motor Relatonal Socal WM ICN# ICN# ICN# ICN# ICN# ICN# ICN# ICN# ICN# In summary, the dentfed task-evoked networks have smlar spatal patterns compared wth the tradtonal GLM-derved actvaton maps as well as smlar temporal patterns compared wth the task contrast paradgm curve, and the dentfed ICNs have smlar spatal patterns wth ICNs templates across all subjects and tasks based on HCP grayordnate tfmri data, ndcatng that HCP grayordnate tfmri data s suffcent to represent the whole-bran tfmri data (e.g., n volumetrc space), to reflect whole-bran functonal actvtes, and to dentfy meanngful wholebran functonal networks (Barch et al., 2013; Smth et al., 2013), whch s also the premse to explore functonal archtecture of cortcal gyr and sulc based on HCP grayordnate data n ths paper.

20 20 Identfcaton of THFRs n Sngle Task and Multple Tasks We dentfed task-based heterogeneous functonal regons (THFRs) n each sngle task. Fg.3 shows the dstrbuton densty map of dentfed THFRs across subjects n each sngle task. In bref, snce the grayordnates have correspondence across all subjects, for each task, we combne the dstrbuton map of dentfed THFRs of all subjects together by countng the number of each grayordnate n the THFRs of all subjects, and calculatng the mean number of each grayordnate n the THFRs to obtan the dstrbuton densty map of dentfed THFRs across subjects. We see that THFRs have hgher dstrbuton densty at the blateral paretal lobe, frontal lobe, and vsual assocaton cortces wthn each sngle task. Moreover, such hgh dstrbuton densty pattern s relatvely consstent across seven tasks (as hghlghted by the red arrows n Fg. 3). These fndngs are also consstent across all ndvdual subjects. More ndvdual examples are shown n Supplemental Fgs. 3 and 4.

21 21 Fg. 3. Dstrbuton densty map of dentfed THFRs across all subjects n each of the seven tfmri data. Those THFRs wth hgher dstrbuton densty and relatvely consstent across seven tasks are hghlghted by red arrows.

22 22 Moreover, we dentfed THFRs across multple tasks for each subject. Fg. 4 shows the dstrbuton densty map of dentfed THFRs of all subjects across multple tasks (at least 3-7 tasks), respectvely. In bref, snce the grayordnates have correspondence across all subjects, for each stuaton (across 3 to 7 tasks), we combne the dstrbuton map of dentfed THFRs of all subjects together by countng the number of each grayordnate n the THFRs of all subjects, and calculatng the mean number of each grayordnate n the THFRs to obtan the dstrbuton densty map of dentfed THFRs across all subjects. Interestngly, the THFRs also have hgher dstrbuton densty at the blateral paretal lobe, frontal lobe, and vsual assocaton cortces. Moreover, the hgher dstrbuton densty pattern s relatvely consstent across from at least three tasks to at least seven tasks (as hghlghted by red arrows n Fg. 4) as those n sngle task (Fg. 3). These fndngs are also consstent across all subjects. More ndvdual examples are shown n Supplemental Fgs. 5 and 6.

23 23 Fg. 4. Dstrbuton densty map of dentfed THFRs of all subjects across multple tasks (at least 3 of 7 tasks n HCP data (emoton, gamblng, language, motor, relatonal, socal, and workng memory)). Those THFRs wth hgher dstrbuton densty and relatvely consstent across multple tasks are hghlghted by red arrows. We quanttatvely characterzed the dentfed THFRs va two measurements. Frst, Fgs. 5a and 5c show the network hstograms (by countng the number of nvolved dctonary components

24 24 (functonal networks)) after normalzaton to the sum of 1 n THFRs and task-evoked networks of the example subject n emoton data, respectvely. We can see that the hstogram of THFRs (Fg. 5a) s complex and dstrbuted across all components, whle the hstogram of task-evoked networks hghly concentrates on the specfc components (hghlghted by black n Fg. 5c), as expected. More results are n Supplemental Fg. 7. Quanttatvely, the network hstogram concentraton (defned as summng the percentage of top three components n the hstogram) s 1.22% and 8.11% for THFRs and task-evoked networks n the example subject n emoton data, respectvely. Table 3 provdes the hstogram concentraton of THFRs and task-evoked networks n all seven tasks across all subjects. We can see that the hstogram concentraton value of THFRs s statstcally sgnfcantly smaller than that of task-evoked networks (p < 0.05) across all seven tasks and subjects by usng pared t-test. Moreover, the network hstogram entropy (defned as the entropy of all hstogram elements (Lv et al., 2014b)) s 8.62 and 8.34 for THFRs and task-evoked networks n the example subject n emoton data, respectvely. Table 4 shows the hstogram entropy of THFRs and task-evoked networks n all seven tasks across all subjects. We can see that the hstogram entropy of THFRs s statstcally sgnfcantly larger than that of task-evoked networks (p < 0.05) across all seven tasks and subjects by usng pared t-test. Second, we examned the temporal patterns of THFRs and compared wth those of task-evoked networks wthn the same subject. As shown n Fg. 5b, the temporal patterns of all THFRs as well as two example components (R4 and R5) n THFRs of the example subject n emoton data are complex and have much less smlarty wth the task contrast paradgm curves, whle the temporal patterns of the top three components n the hstogram of task-evoked networks have hgh smlarty wth the task contrast paradgm curves (Fg. 5d). Quanttatvely, the mean temporal smlarty for the temporal pattern of all THFRs and two example components (R4 and R5) s only 0.01, -0.06, and 0.01 compared wth the three task contrast paradgm curves, respectvely, whle 0.38, 0.30, and 0.37 for the top three components n the hstogram of task-evoked networks n the same subject, respectvely.

25 25 Fg. 5. Network hstogram and temporal pattern comparsons between THFRs and task-evoked networks. (a) Network hstogram (red) after normalzaton to the sum of 1 n THFRs of an example subject n emoton data. (b) Mean temporal pattern of dentfed THFRs (red) and temporal patterns of two example components dentfed as ICN #4 and ICN #5 n Fg. 2 (R4 and R5). The task contrast paradgm curve s shown n black. (c) Network hstogram (blue) after normalzaton to the sum of 1 n task-evoked networks of an example subject n emoton data. Here the task-evoked networks are the unon of three dentfed task-evoked networks. (d) Temporal patterns of three components dentfed as task-evoked networks n Fg. 2 (N1, N2, and N3). The task contrast paradgm curve s shown n black. Table 3. Network hstogram concentraton of THFRs and task-evoked networks (%) n seven tasks across all subjects. The value s represented as mean±standard devaton. Bold values ndcate p-values smaller than Emoton Gamblng Language Motor Relatonal Socal WM THFRs 1.44± ± ± ± ± ± ±0.09

26 26 Task-evoked 7.20± ± ± ± ± ± ±0.44 p-value 3.53E E E E E E E-48 Table 4. Network hstogram entropy of THFRs and task-evoked networks n seven tasks across all subjects. The value s represented as mean±standard devaton. Bold values ndcate p-values smaller than Emoton Gamblng Language Motor Relatonal Socal WM THFRs 8.61± ± ± ± ± ± ±0.00 Taskevoked 8.41± ± ± ± ± ± ±0.03 p-value 6.28E E E E E E E-28 We further justfed the dentfed THFRs from two perspectves. Frst, Fgs. 3-4 and Supplemental Fgs. 3-6 have llustrated that the spatal dstrbutons of dentfed THFRs are reasonably consstent (blateral paretal lobe, frontal lobe, and vsual assocaton cortces) across all subjects and tasks, and n agreement wth current neuroscence knowledge (Duncan 2010; Fedorenko et al., 2013; Anza et al., 2007), where t has been reported that the frontal and paretal lobes have multple-demand patterns assocated wth dverse cogntve demands (Duncan 2010; Fedorenko et al., 2013), and that vsual assocaton cortces s a heterogeneous collecton of vsual areas and s nvolved n hgher level of processng, e.g., respondng to vsual stmul whch have complex pattern or structure (Anza et al., 2007). Ths group-wse consstency and concdence wth current neuroscence knowledge s a reasonable verfcaton of dentfed THFRs as relable patterns whch can be adopted for further nvestgaton such as network dynamcs, gven the lack of ground-truth n bran mappng. Second, as shown n Fg. 5a, the components that are dentfed as task-evoked networks (N1, N2 and N3) and ICNs (R1 to R9) all have relatve hgh percentage n the hstogram of THFRs

27 27 (hghlghted by black n Fg. 5a) n the example subject, ndcatng that THFRs not only ndeed nvolve functonal networks (both task-evoked networks and ICNs), but also nvolve specfc regons that are partcpated n the task contrast paradgm (N1, N2, and N3). However, those regons wth complex temporal pattern due to the complex network composton (Fg. 5a) of THFRs may have been underestmated by tradtonal approaches whch merely consder ndvdual tfmri sgnals based on model-drven subtracton procedures (Lv et al., 2014a; Lv et al., 2014b). Those regons that were dentfed by our approach but were not by tradtonal approaches can be further nvestgated n the future, whch s another research topc besdes ths paper. Spatal Patterns of THFRs on Cortcal Gyr and Sulc We have shown the spatal dstrbuton of THFRs on the cortcal surface n each sngle task (Fg. 3) and across multple tasks (Fg. 4). In ths secton, we further nvestgate how the THFRs are dstrbuted over gyr/sulc. Frst, we assessed the spatal patterns of dentfed THFRs n each sngle task as llustrated n Fg. 3 on cortcal gyr and sulc. Fg. 6a shows the segmented gyr and sulc of one example subject whch s provded n HCP grayordnate data (Glasser et al., 2013). Fgs. 6b-6c show the spatal dstrbutons of THFRs on gyr and sulc of the example subject n emoton task data, respectvely. More examples are shown n Supplemental Fg. 8. Fg. 6d shows the percentage of nvolved grayordnates n THFRs on gyr and sulc across all seven tasks n the example subject. More subjects are shown n Fg. 7. Fg. 6e shows the mean percentage of nvolved grayordnates n THFRs on gyr and sulc n all seven tasks. We can see that the mean percentage of nvolved grayordnates n THFRs on gyr s consstently larger than that on sulc n all seven tasks and all subjects. Moreover, we calculated the rato of percentage of nvolved grayordnates n THFRs on gyr vs that on sulc across all subjects and tasks. Table 5 provdes the mean rato of percentage of nvolved grayordnates n THFRs on gyr vs that on sulc n seven tasks. We can see that the percentage of nvolved grayordnates n THFRs on gyr s statstcally

28 28 sgnfcantly larger than that on sulc (p < 0.05) across all subjects n all seven tasks by usng pared t-test. The mean rato of percentage of nvolved grayordnates n THFRs on gyr vs that on sulc s 2.22, 3.14, 2.63, 2.95, 3.38, 2.50, and 2.76 n seven tasks, respectvely. Supplemental Table 5 provdes the rato of percentage of nvolved grayordnates n THFRs on gyr vs that on sulc n ndvdual subjects. Supplemental Tables 6 and 7 provde mean rato and statstcal sgnfcance of percentage of nvolved grayordnates n THFRs on gyr vs that on sulc across all subjects n seven tasks usng dfferent threshold p% when dentfyng THFRs (Eq. (5)). We can see that the percentage of nvolved grayordnates n THFRs on gyr s consstently sgnfcantly larger than that on sulc under dfferent threshold p%, ndcatng the stablty of our fndngs when choosng p% n a reasonable range.

29 29 Fg. 6. Spatal patterns of THFRs n sngle task on gyr/sulc and the percentages of nvolved grayordnates n THFRs n sngle task on gyr/sulc. (a): Segmented gyr and sulc of one example subject. (b)-(c): Spatal patterns of THFRs (red) on gyr (b) and sulc (c) n emoton task data of the example subject, respectvely. (d) Percentages of nvolved grayordnates n THFRs on gyr/sulc n the example subject n all seven tasks. (e) Mean percentages of nvolved grayordnates n THFRs on gyr/sulc across all subjects n all seven tasks (E: emoton; G: gamblng; L: language; M: motor; R: relatonal; S: socal; W: workng memory).

30 30 Fg. 7. Percentages of nvolved grayordnates n THFRs n sngle task on gyr/sulc of another sx subjects ndexed by (a)-(f), respectvely. (E: emoton; G: gamblng; L: language; M: motor; R: relatonal; S: socal; W: workng memory) Table 5. Mean rato of percentage of nvolved grayordnates n THFRs on gyr vs that on sulc across all subjects n seven tasks. The rato s represented as mean±standard devaton. Bold values ndcate p-values smaller than Emoton Gamblng Language Motor Ratonal Socal WM Rato 2.22± ± ± ± ± ± ±1.59

31 31 p-value 9.48E E E E E E E-22 Moreover, we assessed the spatal patterns of dentfed THFRs across multple tasks (Fg. 4) on cortcal gyr and sulc. Fg. 8a shows the segmented gyr and sulc of the example subject whch s provded n HCP grayordnate data (Glasser et al., 2013). Fgs. 8b-8c show the spatal patterns of THFRs on gyr/sulc of one example subject across at least four tasks. More examples are shown n Supplemental Fg. 9. Fg. 8d shows the percentage of nvolved grayordnates n THFRs on gyr and sulc across at least three to seven tasks n the example subject. More examples are shown n Fg. 9. Fg. 8e shows the mean percentage of nvolved grayordnates n THFRs across multple tasks on gyr and sulc across all subjects. It can be seen that the percentage of nvolved grayordnates n THFRs across multple tasks on gyr s consstently larger than that on sulc. We further calculated the rato of percentage of nvolved grayordnates n THFRs on gyr vs that on sulc for all subjects. Table 6 provdes the mean rato of percentage of nvolved grayordnates n THFRs on gyr vs that on sulc across all subjects. We can see that the percentage of nvolved grayordnates n THFRs across multple tasks on gyr s statstcally sgnfcantly larger than that on sulc (p < 0.05) across all subjects by usng pared t-test. Moreover, the more tasks are nvolved, the larger the mean rato of percentage of nvolved grayordnates n THFRs on gyr vs that on sulc s (except for 6 tasks and 7 tasks). Supplemental Table 8 shows the rato of percentage of nvolved grayordnates n THFRs across multple tasks on gyr vs that on sulc n ndvdual subjects.

32 32 Fg. 8. Spatal patterns of THFRs on gyr/sulc n multple tasks and the percentages of nvolved grayordnates n THFRs n multple tasks on gyr/sulc. (a): Segmented gyr and sulc of one example subject. (b)-(c): Spatal patterns of THFRs (red) on gyr (b) and sulc (c) across at least four tasks of the example subject, respectvely. (d) Percentages of nvolved grayordnates n THFRs on gyr/sulc n the example subject across at least three tasks. (e) Mean percentages of nvolved grayordnates n THFRs on gyr/sulc across at least three tasks across all subjects.

33 33 Fg. 9. Percentages of nvolved grayordnates n THFRs across multple tasks on gyr/sulc of another sx subjects ndexed by (a)-(f), respectvely. Table 6. Mean rato of percentage of nvolved grayordnates n THFRs across multple tasks on gyr vs that on sulc across all subjects. The rato s represented as mean±standard devaton. Bold values ndcate p- values smaller than tasks 4 tasks 5 tasks 6 tasks 7 tasks Rato 1.31± ± ± ± ±1.05 p-value 2.68E E E E E-27

34 34 THFRs Dstrbuton Dfference between Cortcal Gyr and Sulc s Not Due to Sgnal-to-nose Dfference n Hgh-resoluton FMRI When acqurng fmri data at hgh resoluton (2mm sotropc voxels n ths paper or below), there can be a tme seres sgnal to nose rato (tsnr) dfference between dfferent portons of cortex (Kruger and Glover, 2001; Wener and Grll-Spector, 2010). To examne whether the dentfed THFRs dstrbuton dfference between cortcal gyr and sulc s merely due to the possble tsnr dfference between cortcal gyral and sulcal regons n hgh-resoluton fmri data, we measured the tsnr of each nvolved grayordnate n the dentfed THFRs. Specfcally, for each grayordnate nvolved n the THFRs as the center, we obtaned ts 3-rng (about 5 mm radus, Wener and Grll-Spector, 2010) neghborhood grayordnates on the cortcal mesh surface. We then extracted the fmri tme seres of all grayordnates wthn the 3-rng and calculated the tsnr of the centered grayordnate as follows (Wener and Grll-Spector, 2010): mean( tmeseres) tsnr (6) std( tmeseres) Fg. 10b shows the tsnr map of the THFRs on gyr and sulc n emoton task data of the same example subject n Fgs. 6b-6c, respectvely. We further calculated the mean tsnr of nvolved grayordnates n THFRs on gyr/sulc across all subjects n all seven tasks. As shown n Fg. 10c, we see that the tsnr of THFRs on sulc has larger mean value whle also larger standard devaton compared wth that on gyr. We further examned f there s statstcal tsnr dfference of THFRs between gyr and sulc va unpared two-sample t-test (p<0.01). The results showed that the mean values of tsnr of gyr and sulc are statstcally equal across all subjects n all seven tasks. In concluson, the dentfed THFRs dstrbuton dfference between cortcal gyr and sulc s not a result of the possble tsnr dfference n hgh-resoluton fmri data, and mght truly reveal novel functonal archtecture of cortcal gyr and sulc.

35 35 Fg. 10. tsnr measurement: THFRs dstrbuton dfference between gyr and sulc s not due to SNR dfference. (a): Segmented gyr and sulc of the same example subject n Fg. 6a. (b): tsnr map of the THFRs on gyr and sulc n emoton task data of the same example subject n Fgs. 6b-6c, respectvely. (c) Mean tsnr of nvolved grayordnates n THFRs on gyr/sulc across all subjects n all seven tasks (E: emoton; G: gamblng; L: language; M: motor; R: relatonal; S: socal; W: workng memory). Dscusson and Concluson We proposed a data-drven sparse representaton framework on HCP grayordnate-based wholebran tfmri sgnals to systematcally dentfy and characterze the task-based heterogeneous functonal regons n specfc task performances and to assess ther spatal pattern dstrbuton dfference on cortcal gyr and sulc. Our results have shown that both consstent meanngful taskevoked networks and ICNs were effectvely and robustly reconstructed smultaneously across all subjects and seven tasks n HCP grayordnate tfmri datasets va our proposed computatonal framework. Our results have also shown that the dentfed THFRs (nvolvng the dentfed taskevoked networks and ICNs) relatvely consstently locate at the blateral paretal lobe, frontal lobe, and vsual assocaton cortces across all subjects n both sngle task and across multple tasks.