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Journal of Neuroscience Methods 254 (2015) 46–59

Contents lists available at ScienceDirect

Journal of Neuroscience Methods

jo ur nal home p age: www.elsev ier .com/ locate / jneumeth

asic neuroscience

ICHA: An atlas of intrinsic connectivity of homotopic areas

arc Joliota,∗, Gaël Jobarda, Mikaël Naveaua, Nicolas Delcroixb, Laurent Petit a,aure Zagoa, Fabrice Crivelloa, Emmanuel Melleta, Bernard Mazoyera,athalie Tzourio-Mazoyera

GIN, UMR 5296, CNRS, CEA, Bordeaux University, Bordeaux, FranceGIP CYCERON, UMS 3408, Caen F-14000, France

i g h l i g h t s

AICHA a functional brain ROIs atlas based on resting-state fMRI data acquired in 281 individuals.AICHA include a crucial aspect of cerebral organization, namely homotopy.Each region having a unique homotopic contralateral counterpart with which it has maximal intrinsic connectivity.AICHA is ideally suited for investigating brain hemispheric specialization.

r t i c l e i n f o

rticle history:eceived 26 February 2015eceived in revised form 17 June 2015ccepted 7 July 2015vailable online 23 July 2015

eywords:tlasrain parcellationomotopy

ntrinsic connectivityMRIesting state

a b s t r a c t

Background: Atlases of brain anatomical ROIs are widely used for functional MRI data analysis. Recently,it was proposed that an atlas of ROIs derived from a functional brain parcellation could be advantageous,in particular for understanding how different regions share information. However, functional atlases sofar proposed do not account for a crucial aspect of cerebral organization, namely homotopy, i.e. that eachregion in one hemisphere has a homologue in the other hemisphere.New method: We present AICHA (for Atlas of Intrinsic Connectivity of Homotopic Areas), a functionalbrain ROIs atlas based on resting-state fMRI data acquired in 281 individuals. AICHA ROIs cover thewhole cerebrum, each having 1—homogeneity of its constituting voxels intrinsic activity, and 2—a uniquehomotopic contralateral counterpart with which it has maximal intrinsic connectivity. AICHA was built in4 steps: (1) estimation of resting-state networks (RSNs) using individual resting-state fMRI independentcomponents, (2) k-means clustering of voxel-wise group level profiles of connectivity, (3) homotopicregional grouping based on maximal inter-hemispheric functional correlation, and (4) ROI labeling.Results: AICHA includes 192 homotopic region pairs (122 gyral, 50 sulcal, and 20 gray nuclei). As an appli-cation, we report inter-hemispheric (homotopic and heterotopic) and intra-hemispheric connectivity

patterns at different sparsities.Comparison with existing method: ROI functional homogeneity was higher for AICHA than for anatomicalROI atlases, but slightly lower than for another functional ROI atlas not accounting for homotopy.Conclusion: AICHA is ideally suited for intrinsic/effective connectivity analyses, as well as for investigatingbrain hemispheric specialization.

© 2015 Elsevier B.V. All rights reserved.

. Introduction

Over the previous ten years, the use of brain parcellation atlases

Evans et al., 2012) has become more frequent with the introduc-ion of resting state functional magnetic resonance imaging (fMRI)onnectivity analysis (Biswal et al., 1995). Within this framework,

∗ Corresponding author. Tel.: +33 05 47 30 43 97.E-mail address: marc.joliot@u-bordeaux.fr (M. Joliot).

ttp://dx.doi.org/10.1016/j.jneumeth.2015.07.013165-0270/© 2015 Elsevier B.V. All rights reserved.

atlas based sets of regions of interest (ROI) are used to compute abrain graph, i.e., a model of the human brain functional connectome(Bullmore and Bassett, 2011). As shown by Craddock et al. (2012),region-based analysis has advantages compared with voxel-basedanalysis, including better sensitivity, interpretability and computa-tional time. Furthermore, the reduction of the dimensionality of the

fMRI data makes the problem statistically manageable by loweringthe required number of statistical tests (Zalesky et al., 2012).

Today, several atlases are available, and each atlas has specificproperties that should be considered with respect to the user’s

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eeds. The available atlases for functional/intrinsic connectivitynd graph analyses can be characterized by the dataset of imageshat served in their elaboration, the target space that had been cho-en for normalization, the neuro-anatomo/functional parametershat drive the parcellation, and the parcellation algorithm.

The datasets involved in atlas creation define their “specificersus generic” nature. At one end are the most “specific” atlases,hat are built from a single-subject dataset (such as in the AALase (Tzourio-Mazoyer et al., 2002); at the other end are the mostgeneric” atlases, that are built from datasets representative ofhe general population. Note that elaboration of a truly “generic”tlas is out of reach, since it would request datasets for all ages,thnicities, etc. Rather, available generic atlases are based on sam-les representative of some specific population, balancing for someiven phenotypes, such as sex (Craddock et al., 2012), or conversely,electing a specific phenotype, such right-handedness (Shen et al.,013).

Regarding the target space, two main choices are available,amely volume (Ashburner and Friston, 2005) or surface (Dale et al.,999). While the surface space has been demonstrated to be moreccurate (Jo et al., 2007), the volume space is important when ones interested in tissue other than the cortical gray matter mantle,uch as white matter or nuclei. The partial or complete parcellationroposed for a given brain tissue class should also be considered

n atlas selection. For example, network centrality graph analy-is (Bullmore and Bassett, 2011) can be critically affected in thease of partial coverage atlases because such analyses consider thetrength of the connection between all graph regions. For example,entrality analysis based on an incomplete parcellation atlas or aomplete cortex coverage atlas can lead to discrepant sets of “hub”egion identification.

For each atlas, there are some neuro-anatomo-functionalarameters that guide the parcellation scheme and constrain theefinition of landmarks that constitute the boundaries of the atlasegions. Three types of landmarks are used for gray matter par-ellation, including sulci (anatomical landmarks, Tzourio-Mazoyert al., 2002; Kennedy et al., 1998), borders of cytoarchitectonic areasCaspers et al., 2006) or limits defined from functional properties,uch as the borders of spatially coherent regions of homogeneousunctional connectivity (Craddock et al., 2012). Note that the homo-eneity in the regions of interest of a given atlas will depend on thearameters that guide its parcellation scheme.

The atlas building constraints that are explicitly or implicitlyssociated with the parcellation scheme, such as the choice of theandmarks, has a strong impact on the topology of the parcellationn terms of the shape and number of parcels and thus on the atlas’nal resolution.

The aim of the present work was to design an atlas suitableor functional/intrinsic connectivity and graph analysis that wouldonsider and benefit from a major characteristic of brain orga-ization, namely homotopy. Homotopy corresponds to the facthat the two hemispheres have comparable organization regarding

acroscopic anatomy, cytoarchitecture and large-scale functionalrganization (Fuster, 1998; Mesulam, 1990) and are for the mostart connected to each other by the white matter tracts of theorpus callosum, which originate from and terminate in the 4thortical layer. As a consequence, almost all areas from identicalortical structures and with identical hierarchical levels are con-ected through the corpus callosum in a mirroring way. However,his cortical symmetry is not perfect because of the global torsionf the brain, the Yaklovian torque, which makes difficult a point-to-oint correspondence between cortical areas that are functionally

omotopic (Toga and Thompson, 2003). In addition, the torque goeslong with asymmetries in sulcus depth and position in relation toifferences in asymmetries in neighboring cortices (Lyttelton et al.,009); the largest sulcal and cortical asymmetries in newborns and

ce Methods 254 (2015) 46–59 47

adults are located at the termination of the Sylvian fissure and atthe superior temporal sulcus (Hill et al., 2010). These gross mor-phological differences across hemispheres increase the difficultyin defining homotopic regions in the temporal and inferior parietalareas. For example, in order to calculate regional asymmetries, onemust define what is meant by homotopic regions. This definitioncan be based either on anatomical atlases, such as AAL (Tzourio-Mazoyer et al., 2002), which use a rough spatial metric based onthe position of the regions relative to the sulci, or on a cytoarchi-tectonic atlas based on both anatomical symmetry in position anddifferences in the cortical lamination patterns (Caspers et al., 2006).In functional atlases, criteria based on the functional characteristicsof the regions of the atlas areas have not yet been proposed to definehomotopic areas. However, it is common to observe homotopic-likepatterns in resting data analysis using seed based analysis (van denHeuvel and Hulshoff Pol, 2010; Jo et al., 2012) or network decom-position methods (Beckmann et al., 2005; Naveau et al., 2012;Yeo et al., 2011). Furthermore, Stark et al. (2008) have reportedthat the highest mean functional connectivity is observed betweeninter-hemispheric symmetrical areas compared with other inter-hemispheric or intra-hemispheric connections. These observationssupport that homotopic organization is a fundamental feature ofthe functional organization of the cortex and plead for the definitionof homotopic areas based on functional, rather than anatomical,criteria.

The Atlas of Intrinsic Connectivity of Homotopic Areas (AICHA)we propose here is thus a population-level, cerebrum gray matterbrain atlas of homotopic regions based on a time correlation struc-ture of the resting brain. We designed AICHA based on the followingproperties: 1—a large dataset of 281 healthy participants balancedfor handedness and gender; 2—volumetric MNI space of normal-ization to cover the gray matter of the whole cerebrum, includingthe sub-cortical gray nuclei; 3—time correlation structure of thefunctional resting state signal (intrinsic connectivity) as the basisfor landmark definition; and 4—choice of an algorithm that permitsa homotopic parcellation to maximize the weight of the functionalsignal at the final step. We describe the AICHA atlas building, itsdataset regional functional homogeneity (i.e., the intra-regionalsimilarity level of the voxel blood oxygen level dependent time-series), compare it to four existing atlases, and provide the intra-and inter- hemispheric intrinsic connectivity patterns at differentthresholds of regional connection strength.

2. Materials and methods

2.1. Participants

Two hundred eighty-one healthy adults (137 women, 144 men)aged 18–57 years (25 ± 6 years, mean ± SD) were included in thisstudy. Subject handedness was self-reported, and their manualpreference strength was measured using the Edinburgh score (ES,mean ± �, (Oldfield, 1971). 133 subjects were right-handed (66women, ES = 94 ± 10; 67 men, ES = 91 ± 13) and 148 subjects wereleft-handed (71 women, ES = −63 ± 39; 77 men, ES = −63 ± 41). Allsubjects provided informed written consent, and the local ethicscommittee (CPP de Basse-Normandie, France) approved the study.

2.2. Data acquisition and pre-processing

2.2.1. Imaging methodsImaging was performed on a 3 Tesla MRI scanner (Achieva

Philips, Best, The Netherlands). Spontaneous brain activity wasmonitored using blood oxygen level dependent (BOLD) fMRI whilethe participants performed an 8-min resting state condition (T2*-EPI, sequence parameters: 240 volumes; TR = 2 s; TE = 35 ms; flip

48 M. Joliot et al. / Journal of Neuroscience Methods 254 (2015) 46–59

Fig. 1. Flow chart of the atlas construction. The stages and steps have been described in the text. Each line lists the data computed (square box and bold); on the left, them n andI -scaleo eans

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ethod used (italic) is shown and on the right, Supplementary information is showC: Independent component; ICA: Independent component analysis; MICCA: Multif RSN; R: number of k-means repetition; k or k-level: Number of parcels of the k-m

ngle = 80◦; 31 axial slices; and 3.75 × 3.75 × 3.75 mm3 isotropicoxel size). Immediately preceding the fMRI scan, the subjectsere instructed to “keep their eyes closed, relax, refrain fromoving, stay awake and let their thoughts come and go”. Prior

o the fMRI session, high-resolution 3D T1-weighted structuralR brain images (sequence parameters: TR = 20 ms; TE = 4.6 ms;

ip angle = 10◦; inversion time = 800 ms; turbo field echo fac-or = 65; sense factor = 2; field of view = 256 × 256 × 180 mm3; and

× 1 × 1 mm3 isotropic voxel size) and T2*-weighted multi-slicemages (T2*-weighted fast field echo [T2*-FFE], sequence param-ters: TR = 3.5 ms; TE = 35 ms; flip angle = 90◦; sense factor = 2; 70xial slices; and 2 × 2 × 2 mm3 isotropic voxel size) were acquired.he subjects did not perform cognitive training or any other taskrior to image acquisition.

.2.2. Data pre-processingPre-processing was performed using Statistical Parametric Map-

ing subroutines (SPM5, Wellcome Department of Neurology,

ondon, UK; www.fil.ion.ucl.ac.uk/spm) completed by in-houseATLAB-based software. Each subject’s anatomical T1-weighted

olume was segmented into three brain tissue classes (gray matter,M; white matter, WM; and cerebrospinal fluid, CSF) and spatially

in some cases, there is a reference to the data in Fig. 2. RSN: Resting state network; clustering of individual component algorithm; ROI: Region of interest; N: numberalgorithm.

normalized using specific cerebral tissue templates built from theT1-weighted images of 80 subjects (40 men) acquired with thesame scanner and acquisition parameters (Template resolution of2 × 2 × 2 mm3 voxels; bounding box, x = −90 to 90 mm, y = −126 to91 mm, z = −72 to 109 mm) and normalized to the stereotaxic spaceof the Montreal Neurological Institute (MNI) template (Ashburnerand Friston, 2005). The fMRI data were corrected for slice timingdifferences and motions (6 parameters: 3 translations and 3 rota-tions) and registered to the T2*-FFE volume. The fMRI data werethen spatially normalized combining the T2*-FFE to T1-weightedregistration matrix and the T1-weighted stereotaxic normalizationmatrix and spatially smoothed (Gaussian 6 mm full width at halfmaximum filter). Finally, the time series for WM, CSF (average timeseries of voxels that belong to each tissue class), the six motionparameters and the temporal linear trend were regressed out ofthe fMRI data.

2.3. Atlas building methodology

The methodology is subdivided in 4 stages that are summa-rized on the flow chart in Fig. 1, with intermediate results shown in

M. Joliot et al. / Journal of Neuroscience Methods 254 (2015) 46–59 49

Fig. 2. Major steps of the atlas construction. (A) A 3-dimensional or 2-D rendering of the 34 resting state networks identified by MICCA (SM: Sensory-motor; FPT: Fronto-parieto-temporal; DMN: Default Mode Network; V–A: Visual–Auditory; Sub-Med: Subcortical and medial). (B) Illustration of the connectivity profile of one voxel. (C) Selectionof the optimal parcellation using the Rand index criteria corrected for agreement by chance. The blue shade curve shows the rand criteria and confidence interval computedbetween the 11 repetitions of the k-level. The red shade curve shows the same values between the repetitions of each successive k-level. The dashed vertical bar indicates theoptimal k-level value that precedes the intersection of the 2 confidence intervals. (D) Optimal parcellation. (E) Optimal parcellation after hemispheric separation. (F) Rulesf n (thec

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or the homotopic regional determination and grouping. (G) Homotopic parcellatioolor in this figure legend, the reader is referred to the web version of this article.)

ig. 2. The first stage covers the estimation of resting-state networksRSN’s) using an algorithm of classification of individually extractedndependent components. The second stage covers the making of

template of regions of interest (ROI’s) based on these RSN’s using k-means clustering algorithm. The rationale for choosing such aSN-ROI based (rather than voxel-level) approach was three-fold:—the need for reducing spatially-extended noise-related artifacts

n rs-fMRI correlation maps (Salimi-Khorshidi et al., 2014), 2—useSN decomposition to capture the group reproducible functionalorrelation patterns 3—the fact that most RSN’s are spatially sym-etrical, a feature we considered as a strong advantage in the

ontext of building an atlas of homotopic ROI’s. The third stageoncerns the homotopic regional grouping procedure and the par-ellation refinement, while the fourth stage deals with the atlasabeling procedure.

.3.1. Estimation of resting-state networks (Stage 1)We used a multi-scale clustering of individual components

MICCA) to estimate group-level resting-state networks (RSNs).

his algorithm has been fully described elsewhere (Naveau et al.,012); thus, we only present the key features of the procedure.irst, we computed individual independent components (ICs) forach subject using probabilistic independent component analysis

same color is used for homotopic regions). (For interpretation of the references to

provided by the FMRIB Software Library (FSL, http://www.fMRIb.ox.ac.uk/fsl) (Smith et al., 2004) under the name MELODIC (Multi-variate exploratory linear optimized decomposition into indepen-dent components, version 3.10). The number of ICs was estimatedfor each subject (i.e., model order = 48 ± 6, mean ± SD, N = 281)using Laplace approximation (Minka, 2000). Each extracted com-ponent was described as a spatial z-map (zIC) built to quantify eachvoxel membership to the specific IC. These z-values represent thecorrelation between the voxel time series and IC time series dividedby the standard error of the residual noise (Beckmann et al., 2005).In a second step, the MICCA method clustered zICs across individu-als according to their spatial similarity and provided a group of zICs(gICs) that corresponded to the same signal source. These two stepswere replicated 20 times because we used a random (stochastic)algorithm for ICA. This algorithm relies on a pseudo-randominitialization and, consequently, two decompositions of the samedata led to two different solutions. The results were aggregatedusing Icasso (Himberg et al., 2004), and we retained group-levelcomponents repeatedly identified in more than half of the repe-

titions (>10). Forty-five gICs were identified using this procedure.The gICS that demonstrated a maximal proportion of cerebrumgray matter (N = 34) were considered RSNs (Naveau et al., 2012).The 11 discarded gICS were primarily localized in the cerebrospinal

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uid (6), the white matter (3) and the cerebellum or cerebral trunk2). The 34 selected RSNs covered 98% of the cerebrum gray matter.

Using the individual zICs that belong to each RSN, we cre-ted a median z-map (zICmed) that coded how strongly the voxelselonged to the corresponding RSN. Thus, the median of the z-valueistribution across individual zIC maps included in the consideredICs was assigned to each voxel of zICmed. For display purposes,

thresholded map (gamma-Gaussian mixture model p > 0.95) ofach RSN was computed, both 3D and 3 orthogonal slice renderingsre presented in Fig. 2A and Supplementary Fig. S1, respectively.

.3.2. Construction of the optimal parcellation (Stage 2)The construction of the optimal parcellation was decomposed

n 5 steps.

.3.2.1. Step 1: Profiles of connectivity. Using the ICmed z-maps,e defined voxel-wise profiles of connectivity across the RSNs.e retained voxels of the sample average gray matter probabil-

ty map that indicated a gray matter probability above 25%. Theerebellum and brain stem were subsequently removed using aOI manually defined on the average T1-weighted volume (144,216oxels selected). For each selected voxel (v), a connectivity profileas created as an N-dimensional vector, such as Cp(v) = [zICmed,1(v),

ICmed,2(v),. . ., zICmedN(v)], where N is the number of resting-stateetworks (here N = 34). Each element of the “connectivity profile”

s thus a statistical measure of its membership to the correspond-ng network. The z-values of connectivity were normalized sohat

∑Cp(v) = 1 (the negative values were set to zero prior to the

ormalization) (Fig. 2B). Each brain voxel was thus attributed aormalized connectivity profile along the N = 34 dimensions.

.3.2.2. Step 2: k-means parcellation. The Matlab-based k-meanslgorithm (provided by the Statistical Toolbox) was chosen as thearcellation algorithm. The whole set of connectivity profiles, Cp(v)v belonging to the gray matter, see above), was submitted to mul-iple k-mean decompositions with 18 k-level initial number oflusters that ranged from 75 to 500 in steps of 25. For each k-evel of partition, 11 repetitions using random seed initialization

ere computed, which led to 11 parcellations. In the k-means clus-ering, the squared Euclidean distance was used as a measure ofissimilarity between average cluster connectivity profiles.

.3.2.3. Step 3: Definition of the optimal k-level used in the parcel-ation. To define the optimal k-level, the resulting partitions wereompared using the Rand index criteria corrected for agreementy chance (Hubert and Arabie, 1985). Two voxels are in agreement

n partition P1 and P2 if they are placed in the same cluster in P1nd in the same cluster in P2 or if they are in different clusters in1 and P2. The rand index corresponds to the proportion of voxelairs in agreement. Because the Rand index is not null in the casef random parcellation (Hubert and Arabie, 1985), we used a Randndex “corrected for chance” using a generalized hypergeometricistribution null model. Two corrected-Rand indexes were com-uted: one index between each repetition of a k-level (intra-levelgreement) and one index between repetitions of each successive-level (inter-level agreement). Confidence intervals for both intrand inter k-level corrected-Rand indexes were then computed.e observed that the intra-level agreement (Fig. 2C, blue curve)

ttained a high-level plateau, i.e., a maximal stability, in the 100nd 175 k-level range. The optimal regional parcellation was sett the highest level of partition in the maximum intra-level agree-ent range (k-level = 175). The inter-level agreement (Fig. 2C, red

urve) was below the intra-level agreement up to the k-level of75 and was at the same level as the intra-level agreement beyondhis value. These findings indicate that at higher levels, the differ-nce between two successive parcellations was in the same range

ce Methods 254 (2015) 46–59

as the difference measured by the replication. Thus, there was noclear benefit in increasing the subdivision. The use of this criterionresulted in a parcellation of the 144,216 voxels in 175 clusters.

2.3.2.4. Step 4: Construction of the optimal parcellation. Once theoptimal k-level was determined, the 11 parcellations of the 175k-level solutions were collapsed into a single parcellation. We useda two-step procedure (Fig. 2D). In the first step, we maximized thematching of the class denomination between one randomly chosenparcellation and the 10 other parcellations. For each pair, we usedthe “matchClasses” function of the “e1071” R-package (Meyer et al.,2012). For each pair, we built a contingency matrix that representedthe number of voxels that belonged to each possible pair of parcelsbetween the 2 considered parcellations. Using permutation of therows and columns of this matrix, the algorithm maximizes the traceof the matrix (i.e., the sum of the diagonal values) (Parameters of“matchClasses”: method =“exact”, maxexact = 9, iteration for greedysearch = 105). In the second step, each voxel was attributed to oneparcel based on a maximum voting criteria of the 11 repetitions.Note, that for voxels with a matching vote, they were affected tothe parcel they were connected to (edge connectivity criteria) andpseudo-randomly to one parcel for those that connected the twoparcels.

2.3.2.5. Step 5: Spatial decomposition of the parcellation. Hemi-spheres were disconnected by removing the voxels that overlappedthe inter-hemispheric fissure. The fissure geometry was defined asthe average of the anatomical T1-weighted volumes (Supplemen-tary Fig. S2). Following this virtual hemisphere disconnection, eachcluster showing on both hemispheres appears as at least 2 spatiallydisconnected regions. In fact it could be more than two regionsper cluster, as the k-means algorithm does not impose to get onlyone region because it does not consider the spatial information. Toget the parcellation we performed a connected-component analy-sis using a voxel edge-connectivity heuristic. This heuristic statesthat one voxel is connected to neighbor voxels if they share a com-mon edge, thus one voxel is connected to 18 neighbors. This stepled to a parcellation in 221 and 239 regions in the left and righthemispheres, respectively (Fig. 2E).

2.3.3. Homotopic regional grouping (Stage 3)We computed the average BOLD time-series for each region of

the atlas in each subject. For each region identified, we identifiedthe contralateral region that had the maximal average temporalPearson correlation coefficient across subjects. Thus, we consideredonly the inter-hemispheric connections. The homotopic relation-ship was defined using two steps (Fig. 2F):

2.3.3.1. Step 1. Two regions A and B were labeled as “homotopic”if they fulfilled the following three conditions: region A shows amaximal temporal correlation with region B; region B shows a max-imal temporal correlation with region A; and no other region had amaximum correlation with A or B.

Following the first step, the parcellation comprised 460 regions,including 274 homotopic regions (60% of the regions, 137 pairs) and84/102 left/right orphans regions, i.e., regions that did not belongto a homotopic pair.

2.3.3.2. Step 2. If two spatially adjacent regions B and C show amaximal correlation with the contralateral region A and if region Ashows a maximal temporal correlation with B or C, then we merged

B and C and labeled the regions (A; B U C) as “homotopic”. Note thatthe resultant regions had to fulfill the Step 1 criteria.

After this step, the parcellation comprised 386 regions, including360 homotopic regions (93% of the regions, 180 pairs) and 26 (13

M. Joliot et al. / Journal of Neuroscience Methods 254 (2015) 46–59 51

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er hemisphere) orphan regions, i.e., regions that did not belong to homotopic pair (Fig. 2G).

A visual inspection of the orphan regions revealed that theyppeared as two non-neighboring regions in one hemisphere thatad a maximal correlation with the same regions in the other hemi-phere. In such cases, the target region generally consists of twooci linked by a thin continuum of voxels located either at theray/white matter or gray/CSF interface. These continuums wereanually removed, which created the same number of regions in

ach hemisphere. The homotopic regional grouping procedure wase-applied for identifying the homotopic couples. At the end, only

regions remained orphans: two regions in the frontal lobe andwo regions in the parietal lobe (Supplementary Fig. S3). An analy-is of the correlation pattern demonstrated that if the left and rightarietal regions were maximally correlated, the right frontal regionas maximally correlated with the left parietal region, althoughot significantly more than with the left frontal region (paired t-est correlation difference, t(280) = 0.92, p = 0.36). Consequently, wee-affected both frontal regions and both parietal regions as homo-opic couples. The resulting final parcellation, which is referred tos the Atlas of Inter-Connected Homotopic Area (AICHA), is shownn Fig. 3.

.3.4. AICHA labeling (Stage 4)The labeling of the regions constituting AICHA was based

n sulcal and gyral anatomical labels. The sulci were defined

i used in the anatomical labeling.

at the intersection of the T1-weighted structural MR averageimage convex hull and the CSF compartment (Joliot and Mazoyer,1991) (Supplementary Fig. S4). The convex hull was computedusing Caret software (Van Essen et al., 2001). The sulci and gyriwere labeled using the rule used in the construction of the AALatlas (Tzourio-Mazoyer et al., 2002). AICHA parcellation encom-passes both gyral regions located in the crown of the gyri andsulcal regions located in the depth of the sulci. With the addi-tion of gray nuclei, 55 anatomical labels were defined in eachhemisphere.

The anatomical scale was not sufficiently precise to describethe fine spatial resolution of AICHA; thus, additional labeling pro-cedures were defined inside each anatomically labeled region.The different sub-regions were labeled using numbers that wereattributed according to cumulative criteria: first from anterior toposterior, then from ventral to dorsal and finally from lateral tomedial.

2.4. Homotopic ROI pair volume asymmetry

The normalized volume asymmetry (NVA) of each pair of homo-topic regions was computed as the left minus right region volume

difference divided by the mean volume of the pair of regions. Pos-itive (or negative) values indicate that the left (or right) region hasa larger volume than its contralateral region. The NVAs were sub-mitted to an outlier analysis and we report the regions that exhibit

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Table 1Gyrus names used in the anatomical labeling of AICHA and the number of functionalsubdivisions.

Gyrus anatomical name Number of subparts

G Frontal Sup 3G Frontal Mid 5G Frontal Inf Tri 1G Frontal Sup Orb 1G Frontal Mid Orb 2G Frontal Inf Orb 2G Parietal Sup 5G SupraMarginal 7G Angular 3G Parietal Inf 1G Occipital Pole 1G Occipital Lat 5G Occipital Sup 2G Occipital Mid 4G Occipital Inf 2G Insula-anterior 5G Insula-posterior 1G Rolandic Oper 2G Temporal Sup 4G Temporal Mid 4G Temporal Inf 5G Temporal Pole Sup 2G Temporal Pole Mid 3G Frontal Sup Medial 3G Frontal Med Orb 2G Subcallosal 1G Supp Motor Area 3G Cingulum Ant 2G Cingulum Mid 3G Cingulum Post 3G Paracentral Lobule 4G Precuneus 9G Cuneus 2G Lingual 6

AICHA includes 192 pairs of homotopic ROIs (Fig. 3). Of theseROIs, 122 ROIs anatomically belong to 37 gyri (Table 1), 50 ROIsoverlap 14 sulci (Table 2) and 20 ROIs are localized within the 4subcortical gray matter nuclei (Table 2). The complete labels are

Table 2Sulcus and nucleus names used in the anatomical labeling of AICHA and the numberof functional subdivisions.

Anatomical name Sulcus/nucleus Number of subparts

S Superior Frontal S 6S Inferior Frontal S 2S Orbital S 2S Olfactory S 1S Precentral S 6S Rolando S 4S Postcentral S 3S Intra-parietal S 3S Intra-occipital S 1S Superior Temporal S 5S Anterior Rostral S 1S Cingulate S 7S Parieto-occipital S 6S Calcarine S 3

2 M. Joliot et al. / Journal of Neur

VA values below the 25th and above the 75th percentiles of theistribution.

.5. Regional functional homotopic correlation

For each of the 281 participants and each region of AICHA, theean BOLD fMRI time-series was computed by averaging the sig-

als of all voxels belonging to this region. Then, for each participantnd each homotopic pair of regions, we computed the Pearson’sinear correlation coefficient (r) between the region’s BOLD fMRIime-series. Based on these individual results, the regional group-verage homotopic correlations (AHC) and variation coefficientomotopic correlation (VHC) maps were computed. Note that theHC map was computed at the basis of the z-transform of the homo-

opic correlation, taking into account the saturation effect of theorrelation measures.

.6. Comparison of atlases

We defined a regional functional homogeneity measure as theverage Pearson correlation coefficient between the BOLD timeeries of all pairs of voxels belonging to a considered region. Theegional functional homogeneity of AICHA was computed using andditional distinct resting state dataset (88 subjects), which wascquired and processed in the same manner as the AICHA gen-ration dataset. The dataset was balanced for both gender andandedness (22 subjects per combination). These measures wereompared with the measures obtained in the regions defined by 4reviously published and available atlases. Note that only the cere-rum gray matter regions were retained for this analysis. Thesetlases included the Automated Anatomical Labeling Atlas (refer-nced as AAL, 90 anatomical regions selected, Tzourio-Mazoyert al., 2002), the anatomical based Harvard-Oxford Atlas (refer-nced here as Ha-Ox, 110 anatomical regions selected, Desikant al., 2006), the Juelich cytoarchitectonic atlas (referenced heres Cyt-Jue, 90 cytoarchitectonic regions selected with a partial cov-rage of the cerebrum gray matter, Caspers et al., 2006) and onef the resting state based functional atlases provided by Craddockt al. (2012) (referenced here as Func-Cra, 394 functional regionselected).

.7. Description of the atlas’ regional functional correlations

We were interested in characterizing the between-subject pair-ise regional correlations based on AICHA’s regions. We describe

he functional inter-hemispheric (homotopic and heterotopic) andntra-hemispheric (left and right) connectivity patterns of the cre-tion atlas dataset at different thresholds of connection strength.

For each of the 281 individuals and each region of AICHA, anndividual regional BOLD fMRI time-series was computed by aver-ging signals from all voxels belonging to this region. Then, for eachndividual and each pair of regions, we computed the Pearson’s lin-ar correlation coefficient between the pair of regional BOLD fMRIime-series.

Region pair correlation values (73,536) were analyzed at a spar-ity (St) threshold going from 50 to 2% (step of 2%), i.e., only thet percentage of highest correlation values was retained for eachubject. For each threshold, the pairs that had correlation valuesbove the chosen threshold for at least 95% of the participants wereelected and were classified into the 4 categories: homotopic, het-

rotopic, intra-hemispheric left and intra-hemispheric right. Thentra-hemispheric correlations were further analyzed for the pro-ortion of bilateral (present in both hemispheres) or unilateralpresent in only one hemisphere) occurrences.

G Hippocampus 2G ParaHippocampal 5G Fusiform 7

3. Results

3.1. AICHA description

3.1.1. General description of AICHA ROIs

Amygdala N 1Caudate N 7Pallidum/Putamen N 3Thalamus N 9

M. Joliot et al. / Journal of Neuroscience Methods 254 (2015) 46–59 53

Table 3Homotopic region pair with normalized volumetric asymmetry (NVA) below the25th and above the 75th percentiles of the distribution. NVA is defined as positivewhen the left regions are larger than their right homotopic counterparts. Left andright mass center MNI stereotaxic coordinates are also shown. Regions that exhibita common border are aggregated.

Left RightRegion name NVA (%) x,y,z (mm) x,y,z (mm)

Left > RightG Frontal Sup-1 139 −16, 65, 13 13, 68, 11S Postcentral-3 118 −43, −33, 44 48, −26, 43S Superior Temporal-1 117 −50, 14, −22 52, 13, −26G Temporal Pole Mid-1 70 −45, 7, −34 48, 8, −33G SupraMarginal-7 83 −55, −52, 26 55, −46, 33S Superior Frontal-4 79 −23, 29, 47 20, 36, 48S Inferior Frontal-1 70 −44, 38, 12 46, 40, 10G Supp Motor Area-3 67 −7, 8, 64 6, 10, 65

Right > LeftG Temporal Pole Mid-2 −124 −35, 9, −33 35, 12, −34Caudate-7 −91 −18, −12, 25 17, −8, 24S Superior Frontal-1 −79 −22, 61, −8 20, 63, −6G Frontal Mid-5 −73 −43, 20, 37 42, 17, 41G Frontal Mid-3 −71 −39, 31, 35 37, 33, 35G Frontal Mid-1 −61 −40, 41, 20 41, 44, 13G Parietal Inf-1 −72 −45, −53, 49 43, −53, 48G Cingulum Post-2 −69 −4, −39, 27 8, −43, 31

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the highest variability in those regions. Indeed, the frontal lobeincluded most of those outliers: G Frontal Sup-1-L, S Sup Frontal-1, G Frontal Sup Orb-1-L, G Frontal Inf Orb-2, G Frontal Mid Orb-1, S Orbital-1, G Frontal Med Orb-1 and S Sup Frontal-2. Other

Fig. 5. Regional functional homogeneity of 5 atlases. AICHA and Craddock par-cellation are based on functional landmarks, Juelich parcellation is based on the

G Parietal Sup-1 −62 −24, −47, 59 24, −47, 62S Superior Temporal-2 −62 −55, −7, −13 54, −2, −15

rovided as Supplementary material (see Supplementary Fig. S5),ogether with the ROI volumes and mass center coordinates. Theverage volume of the AICHA ROIs is 2.1 ± 2 cm3 (mean ± S.D.) with

range from 0.16 to 10.2 cm3. A three-dimensional rendering ofach region is provided in Supplementary Fig. S5.

.1.2. Anatomical location of AICHA ROIsOverall, 74% of the ROIs (142 regions per hemisphere) were

ocalized within a gyrus or a gray matter nucleus. These regionsan thus be considered subparts of the anatomical regions basedn sulcal landmarks. This was also the case for 18 sulcal-labeledegions per hemisphere that were primarily localized in one bankf a sulcus, including the precentral, postcentral, intra-parietalnd parieto-occipital sulci ROIs (Table 2). The remaining sulcalegions (32 per hemisphere, 17% of the total) encompass both banksqually, and these ROIs represent “new” regions compared withhe anatomically defined atlases. These regions include the supe-ior and inferior frontal, orbital, anterior rostral, olfactory, Rolando,ntra occipital, superior temporal, cingulate and calcarine sulcusOIs (Table 2).

.1.3. Volume asymmetry of AICHA ROIsThe normalized volume asymmetry (NVA) distribution of

ICHA’s ROIs was centered on zero showing no asymmetry. NVAbsolute values larger than 61.8% (which corresponded to the 25thnd 75th percentiles of the distribution) were observed in 8 left-ard asymmetrical and 10 rightward asymmetrical ROIs (Table 3).

In the frontal lobe, the middle frontal regions were right-ard asymmetrical, whereas the superior frontal sulcus, superior

rontal gyrus and inferior frontal sulcus regions exhibited left-ard asymmetry. In the medial wall, the largest subpart of SMA

G Supp Motor Area-3) was leftward asymmetrical.The G Temporal Pole Mid-1 was larger in the left compared

ith right hemisphere, whereas the reverse was observed for theeighboring G Temporal Pole Mid-2 and S Superior Temporal-2egions.

In the parietal cortex, the S Postcentral-3 was larger on theeft, whereas the neighboring G Parietal Inf-1 exhibited the reversesymmetry. Note that the G SupraMarginal-7 was larger on the leftithout any neighboring ROI exhibiting the reverse asymmetry.

Fig. 4. Analysis of the AICHA homotopic correlation. (A) Regional average homotopiccorrelation (AHC) 3-Dimensional rendering map. (B) Regional variation coefficienthomotopic correlation (VHC) 3-Dimensional rendering map.

3.1.4. Regional functional homotopic correlationA 3-D rendering of regional average homotopic correlation

(AHC) values is presented on Fig. 4A. The regional AHC values rangefrom 0.26 to 0.84. The highest AHC values were observed on themedial face in regions surrounding the corpus callosum. The high-est correlations on the cortical convexity were observed in thecortex surrounding the Rolando sulcus, insula, temporal superiorgyrus, supramarginal gyrus and occipital cortices. Most of the lat-eral frontal and temporal cortices (except for the superior temporalcortex) showed the weakest correlations. Additional intermediatecorrelations values were found in the inferior frontal and parietalregions.

The coefficient of variation of the homotopic correlationstrength (VHC) is shown in Fig. 4B. Regional values look homoge-neous across the brain with some high outlier values located mostlyin the lower parts of the brain. BOLD signal susceptibility artifactsthat are important when acquiring the whole brain may explain

cytoarchitectonic borders, and Harvard-Oxford and AAL parcellations are shapedby macroscopic anatomical landmarks. Homogeneity is computed using database 2(average of 88 subjects). Each boxplot shows individual regional values, the medianand both the 25th and 75th percentiles together with the outlier limits that approx-imately correspond to ±2.7 standard deviations of the median value.

54 M. Joliot et al. / Journal of Neuroscience Methods 254 (2015) 46–59

Fig. 6. Functional connection categorical sparsity level analysis. (A) Left side: Repartition of the connections in each category in the percentage of the total number ofconnections identified at various sparsity thresholds. The 4 categories include: the intra-hemispheric left/right connections and the inter-hemispheric homotopic/heterotopicconnections. Right side: idem than left side but shows the absolute number of connections. (B) Left side: Number of intra-hemispheric symmetric connections (i.e., identifiedb motoi tric a

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etween the same 2 regions of the left and right hemispheres accordingly to the hon each hemisphere. Right side: Absolute number of connections among the symme

reas were located in (from the front to the back of the brain): Insula-anterior-1, G ParaHippocampal-3, G Temporal Pole Mid-, G Temporal Pole Mid-3, G Fusiform-2 and G Occipital Pole-1. Inhe upper parts of the brain only the G Frontal Sup-3 showed sim-lar high value.

.2. Comparison of AICHA ROI homogeneity with other atlases

For each atlas, the regional functional homogeneity was aver-ged across the subjects and presented as a boxplot in Fig. 5.

The median regional homogeneity was higher for the Func-Cratlas (0.20) followed sequentially by AICHA (0.18), Cyt-Jue (0.16),a-Ox (0.12) and AAL (0.08).

The regional homogeneity was significantly higher in Func-Craompared with AICHA (student t-test t(778) = 2.96, p = 0.003), and

hese two atlases exhibited a larger regional functional homogene-ty compared with the 2 anatomical atlases (t > 8.86, p < 10−16).

The cytoarchitectonic atlas (Cyt-Jue) homogeneity was belowhe AICHA functional atlas homogeneity (t(472) = 2.16, p = 0.03);

pic description) in the percentage of the number of intra-hemispheric connectionsnd asymmetric (identified in only one hemisphere) intra-hemispheric categories.

however, this finding must be confirmed because the former atlasprovides only a partial coverage of the gray matter.

3.3. Descriptive pattern of the regional functional correlation

The categorical distribution of functional correlation connec-tions at increasing sparsity thresholds is presented in Fig. 6A. Wefirst observe a preponderance of the homotopic categories. With asparsity threshold of 2% (noted St2), 62% of the connections werehomotopic. This proportion of homotopic connections remainedpreponderant up to a threshold at St12, where a balanced propor-tion of homotopic (27.6%), left intra-hemispheric (27.4%) and righthemispheric connections (27.9%) was observed (Fig. 6A, left panel).Note that at this threshold, 108 of the 192 potential homotopic con-nections (Fig. 6A right panel) were above the sparsity threshold inmost subjects.

The intra-hemispheric right and left proportions were compa-rable across all sparsity thresholds (Fig. 6A).

More than 50% of the intra-hemispheric connections were bilat-eral, and the proportion was stable at 80% below St10 (Fig. 6B).

M. Joliot et al. / Journal of Neuroscience Methods 254 (2015) 46–59 55

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An example of the pattern of regional connections at thresh-ld St30 for inter-hemispheric and intra-hemispheric categories isrovided in Figs. 7 and 8, respectively.

. Discussion

We will discuss the choices we made in the construction ofICHA, including the advantages and potential limitations of thesehoices.

.1. Large population dataset balanced for handedness

The issue of the dataset used to define an atlas is often over-ooked. Very popular atlases, such as the Talairach atlas (Talairachnd Tournoux, 1988), the Brodmann atlas (Brodmann and Garey,994) and AAL (Tzourio-Mazoyer et al., 2002), are based on a sin-le individual and/or a single hemisphere. The increasing abilityo obtain access to in-vivo individual data makes it possible toonsider brain variability in atlas construction by including sev-

ral brains, which raises questions regarding the selection of theample participants. The question then arises of the sample sizend whether the sample should include participants with specificemographic/phenotypic characteristics, if it should be balanced

ntified at a sparsity threshold of 30%. The category percentage and absolute number

for some of these characteristics, or if it should be randomly consti-tuted from the population. There is no consensus in the literature;the sample size ranges from one (see previous discussion) to tensof individuals (37–79, Desikan et al., 2006; Craddock et al., 2012;Shen et al., 2013), including one atlas built from 1000 subjects (Yeoet al., 2011). Most atlases are matched for gender, whereas otherphenotypes are uncontrolled. For example, of the 4 atlases pre-viously discussed, only Shen et al. (2013) reported a handednessphenotype (only right handed subjects were selected). Addition-ally, the age range of the participants is highly variable from 18years for the lower bound (in general, based on the legal majority)to approximately 50/55 years for the upper bound; however, theage distribution is typically not uniform because younger subjectrecruitment is typically more common.

We chose to build AICHA from a large set of 281 healthy vol-unteers that covered a consistent range of variability in age andcultural levels. We balanced our sample for gender and handed-ness because these two factors are known to have strong impactson anatomical and functional brain organization and asymmetries.

Sex impacts brain anatomy because of the differences in brain vol-ume (Leonard et al., 2008; Luders et al., 2009; Inano et al., 2013), aswell as brain white matter connectivity (Ingalhalikar et al., 2014)and function (for a review, see Sacher et al., 2013). Handedness

56 M. Joliot et al. / Journal of Neuroscience Methods 254 (2015) 46–59

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s also known to modify the anatomo-functional brain support ofotor functions (Amunts et al., 2000; Amunts et al., 1996; Sun et al.,

012; Herve et al., 2006), as well as other lateralized cognitive func-ions, such as language (review in Hervé et al., 2013). We believehat it was important to avoid the bias induced by the inclusion ofnly 10% of left-handers (prevalence of left handers in the generalopulation) to avoid the drawback of the under representation ofare brain organizations, such as reversed language lateralizatione.g., Mazoyer et al., 2014).

We could have utilized random sampling from the general pop-lation, which is the theoretical best solution, but it is hard toccomplish this approach in practice because this sampling wouldead to the inclusion of subjects with neurological disorders orrain lesions. Furthermore, restriction of the sampled populationo “normal” subjects would raise the unresolvable issue of definingormality.

The option that consists of ad hoc sampling, which is based onpecific criteria, for example, only right-handed men, would leado the creation of one atlas for each different combination of char-cteristics; this procedure is combinatory and can rapidly increasehe number of atlases. Furthermore, it imposes the classificationf participants according to the set of chosen properties; thus, thisay result in a difficult choice regarding which atlas to use (for

xample, with ambilateral subjects when 2 atlases are created foright and left handers, respectively). Finally, it may be difficult toompare results obtained in groups analyzed with different atlasesecause their regional homology will not be known.

.2. Volumetric normalization space

We choose to develop AICHA in the volumetric space to benefitrom the substantial amount of structured information regardinghe functional anatomy of cognitive functions that is availablen this framework. Spatial normalization of individual data in aommon space has initially been developed using a volumetric

pproach (Ashburner and Friston, 1999; see also review Evanst al., 2012), and this normalization has proven to be a success-ul means for comparing different studies in a coordinate referenceystem, such as the Talairach or Montreal Neurological Institute

ntified at a sparsity threshold of 30%. The category percentage and absolute number

(MNI) spaces. The development of this procedure has enabled thedesign of large databases (Brainmap, Fox et al., 2005b) that compileextensive functional localizations of cognitive functions (currently2466 papers) and make it available to the community (e.g., Toroet al., 2008). The methodological evolution based on gray mattersurface normalization (Dale et al., 1999) provides more sensitiveapproaches to process data (Jo et al., 2007) and offers a better hand-ling of the partial volume effect. Despite a more limited corpus ofresearch published compared with volumetric normalization, sur-face normalization has been adopted by some leading projects (e.g.,the Human connectome project, Glasser et al., 2013). In the nearfuture, we will make a surface-based version of AICHA available.

4.3. Atlas landmarks

AICHA construction was based on resting state fMRI data homo-geneity, which enabled us to set the number of regions at a desiredvalue. The limited number of sulci consistent across individualsrestricts the anatomical parcellation to a limited ROI number, suchas 45 ROIs per hemisphere for AAL (cerebrum region parcella-tion in gyri/nuclei, Tzourio-Mazoyer et al., 2002) or 74 ROIs perhemisphere in the Destrieux atlas (cerebrum region parcellationin gyri/sulci/nuclei, Destrieux et al., 2010). In addition, anatomi-cal atlases exhibit important differences in ROI size, including verylarge ROIs, such as the middle temporal in AAL, because of theabsence of a reproducible anatomical marker to enable a finer grainparcellation. Arbitrary geometric criteria are then applied, whichvary across atlases, such as the cingulum in AAL (three parts) or thefrontal pole in the Ha-Ox atlas (Desikan et al., 2006).

AICHA parcellation is based on the time correlation structureof the resting state functional signal, which is a low frequencyBOLD pseudo-oscillatory activity that subtends intrinsic brain func-tion (Doucet et al., 2011; Fox et al., 2005a; Golland et al., 2008). Ithas been demonstrated that the regional assemblies evidenced byintrinsic connectivity analyses are comparable to the networks that

subtend extrinsic or goal directed tasks (Laird et al., 2011; Smithet al., 2009). More recent work demonstrates that the brain’s func-tional network architecture during task performance is primarilyshaped by the resting state network architecture while showing

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lso some small differences (Cole et al., 2014) also reported byennes et al. (2013) in some areas. Thus, an atlas parcellation based

n intrinsic connectivity of regional assemblies is meaningful notnly for the study of intrinsic connectivity networks but also pos-ibly for the investigations of the neural substrates of cognitiveunctions.

.4. Atlas building

The algorithmic building of AICHA is based on a 4 stage pro-edure, including ICA based group analysis, k-means clustering,omotopy driven parcellation refinement and labeling.

The first stage of the analysis is rooted in the observation that theorrelation strength of the anatomically defined homotopic con-ections largely predominates over the heterotopic connectionsStark et al., 2008). Using an ICA based analysis, this organizationed to a partition in resting state networks that exhibited the grossymmetric organization characteristic of homotopy for a majorityf these networks (Beckmann et al., 2005).

ICA applied to a group of subjects (Beckmann et al., 2005;alhoun et al., 2001) has been shown to generate a brain parcella-ion in spatially resting state networks that are highly reproduciblecross many laboratories and datasets (e.g., Chen et al., 2008;amoiseaux et al., 2006; Kiviniemi et al., 2009; Varoquaux et al.,010). For this first stage, we choose the implementation of theroup ICA (MICCA, Naveau et al., 2012) based on a classification ofndividual ICAs because of the following advantages: it could handlehe massive multi-subject data involved in AICHA definition, andt has been demonstrated to lead to reproducible whole-brain par-ellation. Independent processing of the two halves of the datasetemonstrated that the extracted RSNs encompass 98% of the cere-rum gray matter (Naveau et al., 2012). The MICCA algorithm alsoas the advantage of filtering out the non-spatially reproduciblertifactual components that are commonly observed in the indi-idual ICA analysis.

The second stage, i.e., the k-means, was imposed by the out-ome of the group ICA analysis, which does not preclude a voxel toelong to two or more RSNs. The detection of a voxel that belongso 2 or more regions is a meaningful observation for a neuroscien-ist, which corresponds to the concept of the “hub” regions (Achardt al., 2006) that are believed to be essential for the flow of informa-ion between the networks (Bullmore and Bassett, 2011). However,

voxel that belongs to 2 or more regions is an undesirable featuren an atlas. For example, in the case of functional connectivity anal-sis, it will create a built-in link between the regions independentf the measured signal. To avoid this feature, we entered the RSNunctional connectivity matrices into the classification algorithm.

We selected k-means because it does not impose geometricalonstraints on the ROIs as opposed to other algorithms, such ashe Normalized-Cut, which constrains parcels to have more or lessquivalent sizes and shapes (Craddock et al., 2012; Shen et al.,013). This is a crucial point because as a result of the extremelyonvoluted shape of the brain, there is no reason to assumehat macroscopic functional units have geometrically equivalenthapes.

As previously discussed, in anatomical atlases, the number ofopulation reproducible sulci determines the number of parcels;owever, in functional atlases, the number of parcels is a userhoice. Other authors have published atlases at different resolu-ions: 7 and 17 networks per hemisphere for Yeo et al. (2011),0/100/150 regions per hemisphere for Shen et al. (2013) and 50 to000 regions (in increments of 50) for Craddock et al. (2012). For

ICHA, while a large range of k-level values was present, we used

heuristic criterion based on the robustness of the parcellationo set the optimal levels of partition. The most robust candidatesere obtained with a brain parcellation between k-level = 100 and

ce Methods 254 (2015) 46–59 57

k-level = 175 parcels. We choose the level with the highest numberof parcels to stay in the optimal range while using an aggregative(i.e., decreasing the number of parcels) procedure to uncover thehomotopic organization.

The third stage comprised the homotopy driven parcellationrefinement. Note that prior to refinement, there was an importantamount of homotopic regions (60%), i.e., regions that fulfill the max-imal correlation criteria. For the 40% remaining regions, regardingthe arbitrariness of the number of clusters, it appeared reasonableto modify the number of regions by separation or aggregation untilwe reached the desired features. We chose to implement an aggre-gation procedure because it was less arbitrary than a random basedregional separation procedure. This procedure was successful as weattained 93% regional homotopy and 99% after segmentation arti-fact corrections. Such a high level was expected because we basedour atlas construction on the existence of a predominant functionalhomotopic correlation between the two hemispheres (Stark et al.,2008). Nevertheless, it does not imply that the two hemispheres areidentical because we observed regional size differences betweenROIs of a homotopic pair that increased to 140%. Interestingly, thefrontal lobe comprises the majority of the highest volumetric asym-metries. This finding could be further studied in the framework ofthe hemispheric specialization (Hervé et al., 2013). For example,the larger frontal regions in the left hemisphere could be relatedto the leftward asymmetry observed during language processing(Vigneau et al., 2006; Vigneau et al., 2010). Finally, note that thehomotopic stage is independent of the two first stages and thuscould be applied to any existing atlas.

As previously shown by Stark et al. (2008), we observed thatthe primary cortices showed the highest homotopic correlationstrength and the heteromodal cortices the lowest. According toMesulam’s model, these areas cover the two extremes of the pro-posed hierarchical classification that are the primary areas and theheteromodal association areas, respectively (Mesulam, 2000).

4.5. Comparison of regional homogeneity on different atlases

As expected, the ROI functional homogeneity analysis (whichquantifies the similarity of the resting state signals within eachregion) demonstrates a clear cut-off between the 2 functionalbased atlases and the two anatomical based atlases, which favorsthe former atlases. This was expected because both ICA-k means(AICHA) and Normalized-CUT (Func-Cra) methodologies were cho-sen to maximize this feature. Between these 2 atlases, Func-Craexhibits a higher homogeneity. As previously discussed, this find-ing was expected as the spatial constraint of the N-Cut parcellationscheme favors more compact patches.

Note that we have restricted our analysis to the intra-regionalhomogeneity and not to a criterion taking into account both intra-and the inter-regional connectivity. Indeed, the known mixing ofhigh and low regional functional connectivity strength in the brain(see for example, Stark et al., 2008) will make the inter-regionalconnectivity complex to interpret in this framework.

4.6. AICHA as a tool to investigate brain functional asymmetries

Recent renewed interest in the hemispheric specialization(Herve et al., 2013) has emphasized the need to categorize func-tional connections as inter-hemispheric, either homotopic (Starket al., 2008; Zuo et al., 2010) or heterotopic (Gee et al., 2011; Liuet al., 2009), or intra-hemispheric (Iturria-Medina et al., 2011).AICHA provides a categorization at a regional level of sampling

that is more precise than the anatomical atlas level; however, itis less precise than the voxel level. However, at the voxel level,the dimensionality of potential connections is difficult to handle inconnectivity and graph approaches.

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The sparsity analysis completed with AICHA highlighted theajor symmetric organization of the brain. This symmetrical

rganization was present at the inter-hemispheric level betweenomotopic regions (as shown by others, Stark et al., 2008);his result was expected because of the principles that under-ie AICHA elaboration. Interestingly, however, the symmetry

as also evidenced by a similar proportion of right and leftntra-hemispheric connections. We also described a heterotopiconnection that had, per construction, lower correlation values and

distribution that favored the homoareal connections defined asallosal connections between non-homotopic contralateral siteslose to each homologue (Kaiser, 2005). An exploration of differ-nt levels of sparsity indicated that heterotopic connections werereponderant within the insula and the lower part of the occipital

obe. Whether such distribution is due to instrumental factors, suchs partial volume effect, or regional differences in organization wille the object of further investigations.

Beyond the matching level of right and left intra-hemisphericonnections, we observed that the level of symmetrical connectionseached a plateau at 80%, which was below an 8% sparsity thresh-ld. This observation reinforces the similarity feature of the twoemispheres. Interestingly, it appears that higher correlation val-es of long range connections are observed in two approximatelyrthogonal directions: the antero-posterior dimension betweenhe frontal and parietal lobes and the right-left dimension betweenhe medial frontal lobe toward the insula and lateral frontal inferiorobes. These high correlations in selective orientations fit with Zillesroposal of “unifying principles behind the structural complex-

ty of the cerebral cortex” according to the gradation hypothesis,enetic topography and cytoarchitecture (Zilles and Amunts, 2012).he 20% of asymmetric connections are interesting connections totudy in the framework of hemispheric specialization.

.7. How to obtain and use AICHA

AICHA is provided in a “nifti” image format with additional filesor use with the AAL toolbox (Tzourio-Mazoyer et al., 2002), mri-ron (http://www.mccauslandcenter.sc.edu/mricro/mricron) soft-are and FSLView atlas tools (http://fsl.fmrib.ox.ac.uk/fsl/fslview/

.For region interest analysis, we also provide an anatomical label-

ng of AICHA. This labeling is based on AAL and was adapted toonsider the specificity of AICHA. We created a sulcal regional labellabeled S-) that groups regions that encompass one or two banks ofhe cortex buried in a specific sulcus. Regions that encompass bothyral and sulcal matter were labeled, as in the original AAL defi-ition, as the gyral region. Note also that only a small part of thexisting sulci are identified in the AAL partition. This factor explainshat a majority of voxels belongs to gyral matter. Similar to AAL,he labeling provides a coarse localization to facilitate data inter-retation, but it cannot be considered as a precise measure of gyralersus sulcal gray matter; this distinction is unreachable given theesolution of functional atlases, such as AICHA.

It is important to underline that over 80% of the regions corre-ponded to a single anatomical AAL label, either a gyrus (labeled-) or a nucleus (labeled N-), although no anatomical landmarksere used in the atlas construction. This finding may represent aeuristical demonstration that the combination of the low reso-

ution BOLD EPI acquisition with the partial volume effects thatccur between the two banks of a sulcus does not lead to an arte-actual mixing of functional information. One may thus considerhat AICHA provides a functionally driven sub-partition of anatom-

cal ROIs, which is thus a tool for finer grain analyses of numerousrevious results obtained using AAL and graph analysis of res-ing state data. In this framework, using AICHA, we demonstratedhat language lateralization was associated with differences in

ce Methods 254 (2015) 46–59

inter-hemispheric connectivity during resting state by measuringtheir regional homotopic inter-hemispheric intrinsic connectivitycoefficient in 36 ROIs implicated in language processing and knownto be connected via the corpus callosum (Tzourio-Mazoyer et al.,2015).

5. Conclusion

We have developed an atlas of ROIs that covers the entirecerebral gray matter using a parcellation scheme based on theintrinsic connectivity of homotopic areas. Considering that AICHAwas developed from resting state low frequency BOLD pseudo-oscillatory activity, which is known to subtend intrinsic brainfunction and likely task performance, we believe that this atlas willbe useful for connectivity and graph analysis of both resting-stateand task-related functional imaging data. In addition, the fact thatthe hemispheric parcellation of regions is based on the strength ofhomotopic intrinsic correlations indicates it is a new and powerfultool for the investigation of regional asymmetries and thus brainhemispheric lateralization. The AICHA atlas is available on a mailrequest to the corresponding author.

Acknowledgments

We thank Guy Perchey for his essential help with data acquisi-tion.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.jneumeth.2015.07.013

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