Subdivision of the Occipital Lobes: an Anatomical and Functional MRI Connectivity Study

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Special issue: Research report Subdivision of the occipital lobes: An anatomical and functional MRI connectivity study

Michel Thiebaut de Schotten a,b,c,d,*, Marika Urbanski b,c,f, Romain Valabregue b,c,d,e, Dimitri J. Bayle c and Emmanuelle Volle b,c a Natbrainlab, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, King’s College London, London, UK b Universite´ Pierre et Marie Curie-Paris 6, Centre de Recherche de l’Institut du Cerveau et de la Moelle e´pinie`re (CRICM), UMRS 975, Paris, France c Inserm, U 975, Paris, France d CNRS, UMR 7225, Paris, France e Centre de Neuroimagerie de Recherche CENIR, Groupe Hospitalier Pitie´-Salpeˆtrie`re, Paris, France e Service de Me´decine et de Re´adaptation, Hoˆpitaux de Saint-Maurice, Saint-Maurice, France article info abstract

Article history: Exploring brain connectivity is fundamental to understanding the functional architecture Received 28 May 2012 of the cortex. In our study we employed tractography-based parcellation, combined with Reviewed 3 July 2012 the principal component analysis statistical framework, to divide the occipital lobes into Revised 24 September 2012 seven areas in a group of eighteen healthy participants. Tractography-based parcellation is Accepted 6 December 2012 a method based on diffusion imaging tractography, which segregates the living human Published online xxx brain into distinctive areas showing sharp differences in their anatomical connectivity. The results were compared to covarying functional networks involving distinct areas Keywords: within the occipital lobes, that we obtained using resting state functional magnetic reso- Anatomy nance imaging (fMRI), as well as to other existing subdivisions of the occipital lobes. Our Occipital subdivision results showed similarities with functional imaging data in healthy controls and cognitive Diffusion weighted imaging proﬁles in brain-damaged patients, although several differences with cytoarchitectonic, Tractography myelogenetic, myeloarchitectonic and functional maps were reported. While the similar- fMRI ities are encouraging, the potential validity and limitations of the differences observed are Functional connectivity discussed. Taken together these results suggest that tractography-based parcellation may Resting state provide a new promising anatomical subdivision of the living human brain based on its White matter anatomical connectivity, which may beneﬁt the understanding of clinical- neuroanatomical dissociations and functional neuroimaging results. ª 2012 Elsevier Ltd. All rights reserved.

1. Introduction monkey’s occipital lobe having been segregated into several sharply deﬁned subdivisions of visually responsive cortex During the course of the last 50 years, many developments in using direct electrical recording (Felleman and Van Essen, our knowledge of the human’s occipital lobe stem from the 1991; Hubel and Wiesel, 1962; Van Essen, 2003; Van Essen

* Corresponding author. CRICM-UMRS975/UMR7225, 47 Bd de l’Hoˆ pital, 75651 Paris Cedex 13, France. E-mail address: [email protected] (M. Thiebaut de Schotten). 0010-9452/$ e see front matter ª 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.cortex.2012.12.007

Please cite this article in press as: Thiebaut de Schotten M, et al., Subdivision of the occipital lobes: An anatomical and functional MRI connectivity study, Cortex (2012), http://dx.doi.org/10.1016/j.cortex.2012.12.007 2 cortex xxx (2012) 1e17

and Maunsell, 1983; Zilles and Clarke, 1997). Most of these correspond to major task-related networks as identified using subdivisions show clear differences in their anatomical standard fMRI paradigms (Smith et al., 2009). However, it is features, and given that electrical recording is very difficult in unknown whether all these functional connectivity networks the living human brain, anatomists used these anatomical reflect specific anatomical features. Some rsfMRI connectivity features as landmarks to parcellate the occipital lobes in studies showed patterns of connectivity comparable to those humans. These anatomical landmarks included, for instance, revealed with anatomical tractography (Greicius et al., 2009; the visible variation in the neuronal pattern of the six cortical Honey et al., 2009; Skudlarski et al., 2008), but functional and layers (i.e., cytoarchitectonic) (Amunts et al., 2000; Brodmann, anatomical connectivities are not necessarily similar. For 1909; Campbell, 1905; Von Economo and Koskinas, 1925; Zilles example, rsfMRI connectivity may vary according to the et al., 1986), the density of myelinated fibres (i.e., myeloarch- functional state of the measured brain (Fox et al., 2006; itectonic) (Smith, 1907; Vogt and Vogt, 1919; Zilles and Ginestet and Simmons, 2010; Hampson et al., 2006; He et al., Schleicher, 1993), the progressive myelination during matu- 2007; McAvoy et al., 2008; Seeley et al., 2007; Zhou et al., ration of the human brain (Flechsig, 1920) or how sub-regions 2011). Hence, anatomical connectivity and functional of the occipital lobe are connected with the rest of the brain connectivity are not strictly equivalent but rather comple- (Burkhalter and Bernardo, 1989; Clarke, 1993; Clarke and mentary (Deco et al., 2011; Honey et al., 2009). Miklossy, 1990). Two common factors underlying these Tractography can also be used to divide a brain area into subdivisions of the living human brain are that they employ sub-regions defined by a similar anatomical connectivity post-mortem techniques and that they are difficult to trans- pattern. Tractography-based parcellation has been employed late into in-vivo specimens due to inter-individual variability. to segregate functionally different but anatomically adjacent Alternative methods to subdivide the human living brain into areas, such as the supplementary motor area (SMA) and the distinct areas sharing close anatomical features are therefore pre-SMA (Johansen-Berg et al., 2004) or Brodmann areas (BA) required to allow us to associate variability in these subdivi- 44 and 45 (Anwander et al., 2007), because they exhibit sharp sions with healthy and pathological conditions. changes in their connectivity. These results suggest a close Exploring brain connectivity is a fundamental approach to alignment between tractography-based parcellation of the study the functional architecture of the cortex (Mesulam, living human brain and functional and cytoarchitectonic 2005). For example, although neurons from visual and audi- regions (Jbabdi and Behrens, 2012). More recently other tory cortices share similar anatomy and organisation, they authors employed tractography-based parcellation to support different functions, and show clear differences in describe a new in-vivo parcellation of the inferior parietal their anatomical connectivity with the rest of the brain. These cortex into five regions (Mars et al., 2011) that correspond to differences in connectivity, may explain their functional recent post-mortem cytoarchitectonic atlases (Caspers et al., specificity (in humans see Catani et al., 2003, 2005; ffytche 2008, 2006). However, the statistical framework used for et al., 2010; in monkeys see Petrides and Pandya, 2009; tractography-based parcellation is limited by the need to Yeterian and Pandya, 2010). This suggests that one of the best determine a priori the number of sub-regions to expect from methods to studying the functional specialisation of specific the parcellation, which relies on the experimenter’s subjec- brain regions is to examine the nature of the input and output tivity. This is a crucial issue in cases of blind parcellation of that region (Van Essen and Maunsell, 1983). approaches that aim to detect functional sub-units without The study of both anatomical and functional connectivity strong prior hypotheses (Jbabdi et al., 2009). in the living human brain has shown a considerable expan- In the present study, we employed a principal component sion in the last decade thanks to the development of diffusion- analysis, which divided the occipital lobe into several regions weighted imaging (DWI) tractography techniques (Basser that showed a different pattern of anatomical connectivity as et al., 2000; Dell’acqua and Catani, 2012; Dell’acqua et al., revealed by probabilistic tractography, without determining 2010; Jones, 2008) as well as functional magnetic resonance a priori the number of sub-regions. We report seven distinct imaging (fMRI) connectivity methods (Deco et al., 2011; Fox sub-regions in each occipital lobe, as well as their hemispheric and Raichle, 2007; Greicius et al., 2003; Mantini et al., 2007). asymmetries. Results are compared to rsfMRI covarying Tractography studies reconstruct white matter anatomical networks obtained in the same subjects, and also to standard features in the living human brain, which show similarities myeloarchitectonic (Smith, 1907), cytoarchitectonic (Caspers with those reported in post-mortem animal tracing studies et al., 2012, 2008, 2006; Kujovic et al., 2012; Rottschy et al., (Dauguet et al., 2007; Rilling et al., 2008; Thiebaut de Schotten 2007), myelogenetic (Flechsig, 1920) maps obtained from et al., 2011a, 2012) and in human brain dissections (Catani post-mortem dissections, functional maps transposed from et al., 2012; Lawes et al., 2008; Thiebaut de Schotten et al., monkey to humans (Van Essen, 2003), fMRI results and 2011b). Resting state fMRI (rsfMRI) connectivity studies iden- clinical-anatomical conclusions built from neuro- tify brain areas with similar dynamics of blood/oxygen level psychological studies. (BOLD) signal changes, i.e., covarying functional networks. Hence, rsfMRI connectivity has been employed to decompose data into a set of distinct spatial maps, each with its own time 2. Methods course (Kiviniemi et al., 2003). Recently, independent component analysis has been optimised to extract the major cova- 2.1. Participants and MRI acquisitions rying networks in the resting brain, as imaged with rsfMRI (Beckmann et al., 2005). These networks are very consistent The study was approved by the local Ethics Committee. across subjects (Damoiseaux et al., 2006) and closely Eighteen right-handed participants (10 males and eight

females) gave informed consent to participate in this study. 2001) as implemented in the FMRIB Software Library (FSL) The average age of participants was 37.33 (13.66 years). software package (http://www.fmrib.ox.ac.uk/fsl/)(Smith A total of 70 near-axial slices were acquired on a Siemens et al., 2004). White matter orientation estimation was per- 3 T VERIO TIM system equipped with a 32-channel head coil. formed with a Bayesian Estimation of Diffusion Parameters We used an acquisition sequence fully optimised for tractog- Obtained using Sampling Techniques (bedpostx, Behrens raphy of DWI, which provided isotropic (2 2 2 mm) reso- et al., 2007) modelling in our study for up to two populations lution and coverage of the whole head. The acquisition was of fibres crossing within the same voxel. peripherally-gated to the cardiac cycle (Conturo et al., 1995; A tractography algorithm based on a probabilistic approach Jones et al., 2002; Turner et al., 1990) with an echo time (Behrens et al., 2007) (20,000 samples, step length .5 mm, (TE) ¼ 85 msec. We used a repetition time (TR) equivalent to 24 curvature threshold of .2) was used to propagate streamlines RR intervals assuming that spins would have fully relaxed from ‘seed’ to ‘target’ regions of interest (ROIs) (http://brainvisa. before the repetition. At each slice location, six images were info) situated in the white matter that was the closest to the grey acquired with no diffusion gradient applied. Additionally, 60 matter. Within the white matter, water molecules diffuse more diffusion-weighted images were acquired, in which gradient freely along axons than across them (Moseley et al., 1990), directions were uniformly distributed in space. The diffusion making it possible to obtain in-vivo estimates of white matter weighting was equal to a b-value of 1500 sec mm 2. fibre orientation by measuring the diffusivity of water molecules The same participants underwent a fMRI session of resting along different directions (Basser et al., 1994, 2000; Basser and state. During the resting state session, participants were Pierpaoli, 1996). Alternatively, water molecules diffuse more instructed to relax, keep their eyes closed but to avoid falling randomly within the grey matter because of its more complex asleep. Functional images were obtained using T2-weighted organisation, which includes neuronal cell bodies, glial cells, echo-planar imaging (EPI) with blood oxygenation level- capillaries, dendrites and axons. This uncertainty often leads to dependent (BOLD) contrast using SENSE imaging. EPIs (TR/ erroneous estimates of white matter fibre orientation and may TE ¼ 3000/26 msec) comprised 32 axial slices acquired flaw the tractography. Therefore, we created thin white matter continuously in ascending order covering the entire cerebrum ROIs, which were laid just below the grey matter ribbon. These (voxel size ¼ 2 2 3mm3). ROIs were obtained using the intersection between white matter An axial three-dimensional (3D) magnetization prepared and the smoothed (2 mm full width at half maximum isotropic RApid gradient echo (MPRAGE) dataset covering the whole Gaussian kernel and using a lower threshold of .5) grey matter head was also acquired for each participant (176 slices, voxel tissue types originally segmented with Oxford Centre for func- resolution ¼ 1 1 1 mm, TE ¼ 3 msec, TR ¼ 2300 msec, flip tional MRI of the Brain (FMRIBs) Automated Segmentation Tool angle ¼ 9). (FAST) and applied to the MPRAGE dataset (Zhang et al., 2001). The result was then segmented into small ROIs of about 6 mm2 2.2. Occipital lobe mask (and 1 voxel thick) covering the entire white matter surface beneath the grey matter ribbon. In order to minimise the For the left and the right hemispheres, an occipital lobe mask amount of variables representing false positives due to complex was drawn on the Colin27 template (Holmes et al., 1998) configuration such as kissing and fanning connections, provided in MRIcron (www.mccauslandcenter.sc.edu/mricro/ additional ROIs were defined manually for the corpus callosum, mricron/) using surface anatomical landmarks. the thalamus and basal ganglia territories and used as target The temporal and occipital lobes are separated by an ROIs for the parcellation (Behrens et al., 2003; Catani and arbitrary line between the dorsal tip of the parieto-occipital Thiebaut de Schotten, 2008). sulcus and the pre-occipital notch. We overestimated the The skull was extracted from the MPRAGE dataset using lateral border of the occipital lobe including the most posterior Brain Extraction Tool (BET) provided in FMRIB Software Library. part of the angular gyrus and part of the inferior and middle The skull-stripped MPRAGE dataset and the ROIs were then temporal gyri in the mask. This choice was made in order to registered to the B0 volume of the diffusion-weighted dataset avoid artificial borders in the parcellation. For the medial using affine (FMRIB’s Linear Image Registration Tool e FLIRT) surface, the parieto-occipital sulcus was used to divide the deformation provided in FSL. parietal from the occipital lobe, and ventrally an arbitrary line From each ROI in the left and right occipital masks, we from the pre-occipital notch to the lower tip of the parieto- seeded the tractography and recorded the proportion of occipital sulcus was used to separate the occipital and streamlines that passed through each of the other ROIs as temporal lobes (Catani and Thiebaut de Schotten, 2012). a surrogate of the ‘probability of connection’ to that zone The left and the right occipital lobes were registered to the (Fig. 1aec). This ‘probability of connection’ was corrected for individual MPRAGE datasets using Advance Normalization size and the distance of the target using the following formula: Tools (ANTs, http://www.picsl.upenn.edu/ANTS/), which [(number of tracks connecting the target ROI)/(number of combine affine with diffeomorphic deformations (Avants voxel of the target ROI)] (length of the tracks). et al., 2008; Klein et al., 2009). For each participant, a ‘connectivity’ matrix between occipital ROIs (seeds) and each other ROIs of the ipsilateral 2.3. DWI tractography hemisphere (targets) was derived from the data of the probabilistic tractography (no threshold) (Johansen-Berg et al., Diffusion-weighted datasets were corrected for head motion 2004). This matrix consisted of columns that indicated each and eddy current distortions using affine registration to a non- occipital ROI, and rows that represented each ipsilateral diffusion-weighted reference volume (Jenkinson and Smith, hemisphere ROI.

Fig. 1 e Individual and group parcellation of the left occipital lobe. (a) Example of a seed ROI for the tractography. (b) Example of a target ROI for the tractography. (c) Example of the probabilistic tractography seeded from (a) to the target (b). (d) Graph of the principal components (x) according to their eigenvalue sizes ( y) for one representative subject. Original data is represented in purple and ﬁtted data in cyan. (e) Cluster extracted from the second principal component analysis in one representative subject. (f) Graph of the principal components (x) according to their eigenvalue sizes ( y) for all subjects. Different colours are used for each subject. (g) Group parcellation of the left occipital lobe obtained from averaging the result of the 18 participants.

2.4. Tractography-based parcellation principal from residual components (Fig. 1d) (Cattell, 1966). The data was fitted to a power curve and the inflexion point Statistical analysis was performed using SPSS software (SPSS, was extracted using a homemade routine written in Matlab Chicago, IL). Each value of the ‘connectivity’ matrix obtained 7.8 (http://www.matwork.com). In average, 8.2 (.56) and from the tractography was converted into z-scores and eight (.44) factors were obtained for the left and the right entered into a first principal component analysis using hemispheres respectively. a covariance matrix and quartimax rotation (with a maximum A second principal component analysis was performed of 50 iterations for convergence) in order to estimate the similarly, this time with a fixed number of eight factors to number of principal components to extract for each subject extract. The result was used to group together ROIs (registered (Fig. 1d). We plotted the components in order, according to with MPRAGE and diffusion datasets) of the occipital lobes in their eigenvalue ( y) and applied a scree test to separate the eight clusters, which corresponded to the eight factors

extracted from the second principal component analysis diffeomorphic deformations to register the left and the right (Fig. 1e). occipital lobes with the individual MRrage datasets. The For each lobe, the eight clusters registered with the average registered maps of ‘anatomical connectivity’ were MPRAGE dataset were registered to the Colin27 template using overlapped and displayed in Figs. 3 and 4 for each of the eight the inverse of the affine and diffeomorphic deformations clusters and we choose a lower threshold of 20,000 (corre- estimated with ANTs to register the left and the right occipital sponding to the 20,000 individual pathways or samples that lobes with the individual MRrage datasets. The individual are drawn through the probability distributions on principle registered clusters were overlapped into eight average clus- fibre direction). The upper threshold was set at 500,000 ters. These average clusters were gathered in a single file with subjectively in order to facilitate reading of the figure. the command find_the_biggest in FSL, which displayed clusters with the highest probability of overlap (Figs. 1g and 2a). 2.6. Asymmetries Display of axial brain slices was performed using MRIcron (http://www.mccauslandcenter.sc.edu/mricro/mricron). 3D For each lobe, the volume of the eight clusters registered with rendering of the brains were calculated using the T1 pipeline the MPRAGE dataset was extracted using FSL and a lateralisa- in BrainVISA. The overall visualisation and screenshots were tion index was calculated according to the following formula: performed using the software anatomist (http://brainvisa. ð Þ=ð þ Þ info). Volume Right Volume Left Volume Right Volume Left

Statistical signiﬁcance of the degree of lateralisation was 2.5. Pattern of ‘anatomical connectivity’ determined using a one-sample t test for each tract. Only results that survived Bonferroni correction ( p < .00625) are In this section, we challenged the validity of our method of reported in Fig. 5a. parcellation by propagating the tractography from the eight clusters revealed by individual parcellation. Our analysis 2.7. Myeloarchitectonic, cytoarchitectonic, myelogenetic, concentrated on voxels strictly within the occipital lobe, using lesion-based and functional maps subdivisions an arbitrary line between the dorsal tip of the parieto-occipital sulcus and the pre-occipital notch as lateral border. This The maps displayed in Fig. 7aed are coloured reproductions of choice was made in order to consider only the ‘anatomical the original descriptions reported: (i) in Smith (1907) for the connectivity’ emerging from the occipital lobe. For each lobe, myeloarchitectonic subdivision (Fig. 7a), (ii) in Amunts et al. the eight clusters registered with the diffusion dataset were (2000), Rottschy et al. (2007), Malikovic et al. (2007), Kujovic used as a seed to propagate the tractography and create maps et al. (2012) and Caspers et al. (2006, 2008, 2012) for the of ‘anatomical connectivity’ for each of these regions. Each of cytoarchitectonic subdivision (Fig. 7b), (iii) in Holmes (1918) for the ‘anatomical connectivity’ maps was registered to the the lesion-based subdivision (Fig. 7c), (iv) in Flechsig (1920) for Colin27 template using the inverse of the afﬁne and the myelogenetic subdivision (Fig. 7d) and (v) in Van Essen (2003) for the functional map subdivision (Fig. 7e).

2.8. rsfMRI covarying networks

For the rsfMRI covarying networks, we used an independent component analysis in order to decompose the rsfMRI of our participants into a set of distinct spatial maps, each with its own distinct time course (Kiviniemi et al., 2003; Smith, 2011). In brief, fMRI data was analysed using the standard set of tools implemented in the FSL software package (Multivariate Exploratory Linear Optimized Decomposition into Indepen- dent Components e multivariate exploratory linear optimized decomposition into independent components (MELODIC), http://www.fmrib.ox.ac.uk/fsl/)(Beckmann et al., 2005; Smith et al., 2004). fMRI datasets were corrected for head motion by rigid registration to the first volume (Jenkinson et al., 2002), capped with a high pass filter (.01 Hz, Gaussian-weighted least-squares straight line fitting, with sigma ¼ 50.0 sec) and skull-stripped (Smith, 2002). Each subject’s fMRI data was registered to that subject’s high-resolution structural image (Jenkinson et al., 2002) and then registered again, this time, with the standard Colin27 template using affine (FLIRT) and Fig. 2 e Parcellation of the left occipital lobe (a) Lateral and non-linear registration (FNIRT). In order to obtain a steady- medial views of the group result of the tractography-based state signal, the ten first volumes of each dataset were dis- parcellation for the left occipital lobe. (b) Classical sulco- carded from the analyses. All resulting datasets were gyral anatomy of the left occipital lobe. concatenated in the temporal dimension. This approach is

Fig. 3 e Anatomical connectivity for each of the clusters 1e4 identiﬁed in the left occipital lobe. The outline of the pattern of anatomical connectivity for each cluster is summarised in table. Precuneus, ipsilateral connections to the precuneus; Splenium, splenium of the corpus callosum; OR, optic radiations; Pulvinar, projections from the pulvinar; Striatum, projections from the striatum.

advantageous, as it does not assume that the associated posterior lower lip of the calcarine fissure (Fig. 2). Tractog- temporal response is consistent across subject but rather raphy from cluster 1 revealed anatomical connectivity with looks for common spatial patterns between subjects. Analysis the rest of the brain conveyed by long-range pathways such as was carried out using probabilistic independent component the inferior longitudinal fasciculus (ILF), the inferior fronto- analysis (Beckmann and Smith, 2004) as implemented in occipital fasciculus (IFOF), the splenium of the corpus cal- MELODIC Version 3.10, part of FSL package. losum, the optic radiations as well as connections to the pulvinar and the striatum (Fig. 3). As illustrated in Fig. 5a, cluster 1 was symmetrically distributed among the two 3. Results hemispheres (t ¼.676, p ¼ .509). Cluster 2 extended mainly on the medial surface of the 3.1. Tractography-based parcellation occipital lobe and included the medial portion of the superior occipital gyrus, the posterior part of the cuneus and the Overall, probabilistic tractography combined with principal posterior upper lip of the calcarine fissure (Fig. 2). This component analysis statistical framework revealed eight cluster was anatomically connected with the rest of the brain clusters sharply segregated, from which seven were in via the splenium of the corpus callosum, the optic radiations, the occipital lobe (freely downloadable at http://www. the ILF, and to the pulvinar and the striatum. Tractography natbrainlab.com or on demand [email protected]), also revealed bilateral connections of this cluster with the each showing a specific pattern of connections with the rest of precuneus and the superior parietal lobule (Fig. 3). Cluster 2 the brain (see Table 1) and for most, being symmetrically was significantly larger in the right hemisphere when distributed among the two hemispheres. compared to the left (t ¼ 3.170, p ¼ .006). This difference Cluster 1 included the occipital pole and the posterior part survived Bonferroni correction for multiple comparisons of the descending occipital, lingual, fusiform gyri and the (Fig. 5a).

Fig. 4 e Anatomical connectivity for each of the clusters 5e8 identiﬁed in the left occipital lobe. The outline of the pattern of anatomical connectivity for each cluster is summarised in Table 1. SPL, ipsilateral connections to the superior parietal lobule; Splenium, splenium of the corpus callosum; Pulvinar, projections from the pulvinar; Striatum, projections from the striatum; U shapes, local connections.

Cluster 3 was situated in the medial surface of the occipital portion of the middle occipital gyrus (Fig. 2). Tractography lobe lying behind the occipito-parietal sulcus. It included the seeded from this cluster revealed anatomical connections anterior portion of the lingual, cuneus gyri and striate cortex with the rest of the brain via the IFOF, optic radiations, sple- (Fig. 2). Cluster 3’s anatomical connectivity was mainly conveyed nium of the corpus callosum, and had connections to the via the splenium of the corpus callosum and short connection to ipsilateral superior parietal lobule, the pulvinar and the the precuneus and the superior parietal lobule of the left hemi- striatum (Fig. 4). Cluster 5 showed a rightward trend of sphere (Fig. 3). Cluster 3 was symmetrically distributed among hemispheric lateralisation, which was not signiﬁcant the two hemispheres (t ¼ .129, p ¼ .899) (Fig. 5a). (t ¼ 1.759, p ¼ .10) (Fig. 5a). Cluster 4 lied on the dorsolateral portion of the occipital Cluster 6 was located, in most of the participants, outside lobe, and included the anterior-dorsal part of the superior the occipital lobe and included the posterior portion of the occipital gyrus (Fig. 2). This cluster was anatomically con- superior and middle temporal gyri and sometimes the most nected with the rest of the brain via the splenium of the anterior portion of the middle occipital gyrus (Fig. 2). Trac- corpus callosum, the optic radiations, to the pulvinar and the tography of the occipital portion of the cluster 6 revealed striatum, and bilaterally connected to the precuneus and the anatomical connections with the rest of the brain conveyed via superior parietal lobule (Fig. 3). There was no signiﬁcant the ILF, the splenium of the corpus callosum and connections asymmetry between the left and the right clusters 4 (t ¼.470, to the pulvinar (Fig. 4). Cluster 6 was symmetrically distributed p ¼ .645) (Fig. 5a). among the two hemispheres (t ¼ .474, p ¼ .643) (Fig. 5a). Cluster 5 was visible on the lateral surface of the occipital Cluster 7 was situated halfway between the occipital, the lobe and included the descending occipital gyrus and a small parietal and the temporal lobes and included the most

Fig. 5 e Inter-individual variability of the tractography-based parcellation. (a) Lateralisation index for the volume of the eight clusters as revealed by tractography-based parcellation. *p < .05 after correction for multiple comparisons. (b) Percentage overlap of the eight reported clusters visualised on medial (left) or lateral (right) view of the occipital cortex.

posterior portion of the middle temporal gyrus, the posterior resting brain networks, each having its own distinct time part of the angular gyrus and the anterior part of the middle course. Five of these networks included parts of the occipital occipital gyrus (Fig. 2). Individual tractography of the occipital lobe (Figs. 6 and 7f). portion of the cluster 6 revealed connections with the rest Covarying network 1 included activations in the occipital of the brain via the ILF, and connections to the pulvinar and pole and, the inferior, middle and superior occipital gyri, the striatum (Fig. 4). No signiﬁcant asymmetry between the the posterior part of the fusiform gyrus and superior pari- left and the right clusters 7 was found (t ¼ .482, p ¼ .636) etal lobule. In addition, de-activation in the posterior part of (Fig. 5a). the cingulate cortex was also observed. This network Cluster 8 included most of the middle occipital and inferior matched the description of the lateral visual cortical occipital gyri (Fig. 2). Its anatomical connectivity was mainly network identiﬁed with rsfMRI by Beckmann et al. (2005) conveyed via the ILF, the IFOF, the optic radiations, the sple- and Smith et al. (2009). The peak of frequency was nium of the corpus callosum and connections to the pulvinar, w.02 Hz for this network. the striatum and the ipsilateral superior parietal lobule (Fig. 4). Covarying network 2 was observed on the medial surface Cluster 8 was symmetrically distributed among the two and included the cuneus and the lingual gyri and matched the hemispheres (t ¼.462, p ¼ .651) (Fig. 5a). original description of the medial visual cortical network The probability maps obtained for the group revealed that originally reported by Beckmann et al. (2005) and Smith et al. the tractography-based parcellation has a concentric distri- (2009) using rsfMRI. This network showed maximum resting bution with a descending gradient from the central portion oscillation between w.06 and w.07 Hz. (overlap of more than 90%) to the most peripheral zones Covarying network 3 was situated mostly in the medial (overlap above 50%) (Fig. 5b). surface including activations in the most anterior portion of the cuneus, the posterior portion of the precuneus, the supe- 3.2. rsfMRI covarying networks rior parietal lobule, the posterior and anterior cingulate gyri and was similar to the default network (Greicius et al., 2003; Overall, resting state connectivity combined with indepen- Raichle et al., 2001; Raichle and Snyder, 2007). This network dent component analysis statistical framework revealed 32 had a peak of frequency at w.02 Hz.

Fig. 6 e Covarying networks involving the occipital lobe. On the left panel red to yellow gradient represents areas covarying positively whereas dark to light blue gradient corresponds to areas covarying negatively. On the right panel red colour indicates areas covarying positively whereas blue colour corresponds to areas covarying negatively.

Covarying network 4 was situated halfway between the parcellation overlap poorly with the covarying networks in occipital, temporal and the parietal lobes and included the the resting brain. posterior part of the angular gyrus and the anterior part of the Tractography-based parcellation methods group together middle and inferior occipital gyri. More anteriorly, this voxels, which share similar patterns of connection with the network included the insula, the lateral orbital and middle rest of the brain. This approach segregates anatomical areas frontal gyri. Additionally, we noticed de-activations in the in the living human brain that have solely been identified superior frontal gyrus, the caudate and the superior parietal using post-mortem techniques. Similarly to previous studies lobule. The maximum resting oscillation was centred on (Anwander et al., 2007; Johansen-Berg et al., 2004; Mars et al., w.09 Hz for this network. 2011), our subdivision of the occipital lobe shows some simi- Covarying network 5 was mostly localised on the ventral larities with the lateral surface of previous post-mortem surface of the occipital lobe including de-activation in part of subdivisions in humans, functional subdivisions obtained the fusiform, the inferior occipital and the right middle frontal from monkey electrophysiology, functional imaging in gyri. This network had a peak of frequency at w.02 Hz. healthy participants and clinical syndromes associated with localised brain damage (Fig. 7). Cluster 4, for instance, matches the description of the 4. Discussion myelogenetic area 15, the visual area V3A and the cytoarchitectonic area hOC4d (Kujovic et al., 2012). fMRI studies In this study we used DTI probabilistic tractography combined revealed that this area is retinotopic (Gardner et al., 2008; with a principal component analysis to divide the human left Tootell et al., 1998), involved in the processing of ‘real’ motion, and right occipital lobes into several areas, which share and works independently of retinal motion. This suggests that similar anatomical connectivity. Three main findings emerge cluster 4 may play a role in perceptual stability during eye from our work. Firstly, our parcellation successfully identified movements (Braddick et al., 2001; Fischer et al., 2012; Tootell seven clusters in the occipital lobes, which showed a different et al., 1997). Surprisingly, in the rare cases reporting a stroke pattern of macroscopic anatomical connectivity. Secondly, lesion in this region (Kraft et al., this issue) this area is not most of these areas are symmetrically organised except for associated with visual motion blindness (i.e., akinetopsia), but cluster 2, which is larger in the right hemisphere when is rather linked with impaired spatial awareness of more than compared to the left. Lastly, using rsfMRI-based parcellation one object at a time (simultagnosia, see Chechlacz et al., 2012). in the same group of subjects, we showed that the clusters Akinetopsia is more likely to follow a bilateral lesion in identified in the occipital lobe with the tractography-based the area MTþ, which lies on the posterior bank of the

Fig. 7 e Subdivisions of the occipital lobe based on (a) the density of myelinated ﬁbres (i.e., myeloarchitectonic) (Smith, 1907) (b) the visible variation in the neuronal pattern of the six cortical layers (i.e., cytoarchitectonic) (Brodmann, 1909), (c) the visual impairment after brain lesion (Holmes, 1918), (d) the progressive myelination during maturation of the human forebrain (Flechsig, 1920), (e) the monkey to human geometric translation of the subdivision of visually responsive cortex based on direct electrical recording (Van Essen, 2003) and (f) covarying networks involving the occipital lobe. Please note that there is no colour correspondence between the six subdivisions.

superior temporal sulcus, corresponding to the inferior part this issue) remains to be clariﬁed in future lesion- of cluster 7 (Kraftetal.,thisissue; Rizzo et al., 1995; Zeki, symptoms studies. 1991) and area hOC5 (Malikovic et al., 2007). fMRI studies Cluster 8 overlapped with lateral occipital central/lateral report this area as being spatiotopic anteriorly (d’Avossa occipital peripheral (LOC/LOP) (V7) and V8 (Fig. 2a), FG2 and et al., 2007) and retinotopic posteriorly (Gardner et al., HOC5 (Fig. 2b; Caspers et al., 2012), and the rsfMRI covarying 2008). fMRI studies also reveal that this area is involved network 5. These areas do not precisely correspond to any in the processing of motion stimuli (Bartels et al., 2008), myeloarchitectural, or myelogenitical subdivisions known. this time, responding equally to retinal motion and ‘real’ LOC/LOP and V8 are both involved in the extraction of object motion (Fischer et al., 2012). Whether akinetopsia inter- features. LOC/LOP extracts general information about an venes after a very localised lesion in the highly speciﬁc object’s structure (Kourtzi and Kanwisher, 2000) and has been area MTþ (inferior part of cluster 7) or after a disruption of shown to participate in visual recognition (Grill-Spector et al., the tract between MTþ and V3A (cluster 4) (Vergani et al., 2000; Ungerleider and Mishkin, 1982) and body-part

(discussed further in this paper), in this section we will discuss Table 1 e Connections identified seeding tractography from the occipital portion of the eight tractography- their potential for functional interpretation. based parcellated clusters. SPL, ipsilateral connections to The cortical areas processing the lower and upper visual the superior parietal lobule; Precuneus, ipsilateral fields are retinotopic (Gardner et al., 2008; Tootell et al., 1998) connections to the precuneus; Splenium, splenium of the and have a similar organisation in terms of myeloarchitecture corpus callosum; Pulvinar, projections from the pulvinar; (striate, parastriate, peristriate) (Fig. 7a), cytoarchitecture Striatum, projections from the striatum. (BA17, BA18, BA19) (Fig. 7b), progressive myelination during Clusters maturation (areas 8 and 23) (Fig. 7d) and functional (V1, V2, V3) organisation (Fig. 7e). However, we report a subdivision 12345678 between two clusters, one below (cluster 1) and one above Association ILF (temporal) þþþþþ (cluster 2) the calcarine sulcus, each receiving input from the þþþ pathways IFOF (frontal) upper and the lower visual fields respectively (Fig. 7c). Brain þþþþþ SPL regions representing the lower and upper fields in the visual Precuneus þþþ cortex differ in regards to their neuronal input and output.

Commissural Splenium (opposite þþþþþþþ Concerning inputs, the distribution of ganglion cells across the pathway hemisphere) retina is not uniform, the density of ganglion cells is approxi- mately 60% greater in the superior retina (i.e., lower visual Projection OR (lateral geniculate þþþþþ field) than in the lower retina (Curcio et al., 1987). This asym- pathways nucleus) metry persists along the visual stream up to ventral and dorsal Pulvinar þþþþþþþ primary visual areas, where visual outputs to the rest of the Striatum þþþþþþ brain are also segregated into ventral and dorsal visual pathways, respectively (Van Essen et al., 1986). Similar projections perception (Astafiev et al., 2004). In functional imaging, the have been hypothesized in humans (Previc, 1990) and are lower portion of LOC/LOP is functionally activated during the supported by clinical observations. When damaged, cluster 1 visual recognition of objects (Cavina-Pratesi et al., 2010a, (under calcarine) is associated with upper quadrantic field 2010b; James et al., 2000; Sergent et al., 1992). When damaged defects and cluster 2 (above calcarine) is associated with by a stroke, this area is associated with an incapacity to inferior quadrantic field defects (for a review see ffytche et al., recognise objects either due to an alteration of early-level 2010; Holmes, 1918; Horton and Hoyt, 1991). Our tractography- perceptual processing (i.e., apperceptive agnosia) (Ferreira based parcellation shows, for the first time, a clear dissociation et al., 1998; Warrington and James, 1988) or the inability to in the connectivity pattern between the occipital regions rep- group component parts of an object into a coherent whole (i.e., resenting the lower and the upper visual fields, regardless of integrative agnosia) (Behrman and Kimchi, 2003; Riddoch and cytoarchitecture and myeloarchitecture. Hence, our parcella- Humphreys, 1987; Riddoch et al., 2008). Alternatively, V8, tion suggests a distinction in the anatomical connectivity which is localised in the fusiform gyrus, is more specialised in between the cortex dedicated to the superior visual field and the recognition of faces (Sergent et al., 1992), colours the cortex dedicated to the inferior visual field. One may (Hadjikhani et al., 1998) and words (Cohen et al., 2000), as putatively stipulate that the inferior visual field, located in the revealed by fMRI and positron emission tomography (PET) dorsomedial occipital cortex, would be more inclined to studies in healthy participants. When damaged, this same area connect with the visual dorsal stream ‘how/where’ (Kravitz is also associated with the incapacity to recognise faces (pro- et al., 2011; Ungerleider and Mishkin, 1982), whereas the sopagnosia) (Kleinschmidt and Cohen, 2006; Kraft et al., this superior visual field, located in the ventromedial occipital issue), colours (achromatopsia) (Bartolomeo et al., this issue; cortex, would most likely be better connected with the visual Kraft et al., this issue; Verrey, 1888) and words (pure alexia) ventral stream ‘what’(Kravitz et al., 2011; Ungerleider and (Dejerine, 1895; Epelbaum et al., 2008; Gaillard et al., 2006). Mishkin, 1982). Our anatomical data supports this hypoth- Overall, our cluster 8 appears to overlap with areas (LOC/LOP esis, as cluster 1, in the ventromedial occipital cortex which and V8) where early recognition of visual stimuli occurs. processes information from upper visual field, is highly con- Cluster 5 matches the functionally unidentified area situ- nected to the temporal cortex via the ILF, and to the orbito- ated between LOC/LOP and V2 (Van Essen, 2003), in frontal cortex via the IFOF; while the cluster 2, in the dorso- cytoarchitectonic subdivision hOC4v (Rottschy et al., 2007) medial occipital cortex, which processes information from and the myelogenetic area 23 (Flechsig, 1920). To our knowl- lower visual field, is highly connected to the precuneus and the edge, precise anatomo-functional correlates of this cluster are superior parietal lobule (see Table 1). The observed differences still poorly known and remain to be explored using fMRI in in the connectivity pattern may be an explanation to the humans and electrical recording in animals. behavioural differences in the processing of visual informa- The medial view of our tractography-based parcellation tion in the upper and lower visual fields. Several studies have shows clear differences with classical myeloarchitectural reported dominance of the lower visual field for coordinate (Smith, 1907), cytoarchitectural (Amunts et al., 2000; judgement, and in the upper visual field for categorical Brodmann, 1909; Caspers et al., 2012, 2008, 2006; Kujovic et al., judgement (Niebauer and Christman, 1998). The lower visual 2012; Malikovic et al., 2007; Rottschy et al., 2007), myeloge- field has also been reported as dominant, when compared to netical (Flechsig, 1920), functional maps (Van Essen, 2003), and the upper visual field for the processing of visual information the covarying networks in the resting brain. Although these in peripersonal space, and for the processing small movement differences may be due to the limitations of the method contrast or hue, whereas the upper visual field has been

associated with a better processing of information in extrap- anteriorly with cluster 3. This anatomical segregation is also ersonal space (Levine and McAnany, 2005; Previc, 1990). coherent with the functional properties of peripheric and Our parcellation also shows a further subdivision of the central vision. primary visual areas with cluster 3, localised in the retinotopic Cluster 2 was significantly reported as larger in the right representation of the peripheral visual field, without any hemisphere. This result is consistent with previous work further segregation between lower and upper field. In addi- reporting the right hemisphere as dominant for visuospatial tion, cluster 3 also showed weak anatomical connectivity with processing (Heilman and Van Den Abell, 1980; Thiebaut de the rest of the brain, including the absence of connection with Schotten et al., 2011a), and the lower-field advantage for the lateral geniculate nuclei via the optic radiations. Whether spatio-attentional tasks (Bayle et al., 2011; Danckert and this third cluster is a real or artificial subdivision, remains to Goodale, 2001; Kraft et al., 2007). The functional dissociation be clarified (see limitations paragraph). Alternatively, this between the upper and lower visual fields reported in the cluster may correspond to the visual brain area in charge of previous paragraph suggests that the hemispheric asymmetry the peripheral visual field processing (Fig. 7c). As for the upper of cluster 2 should also be associated with a better judgement and the lower visual fields, brain regions representing central for spatio-attentional tasks in the lower left quadrant as and peripheral fields in the visual cortex differ in regards to compared to the lower right quadrant. Line bisection test is their neuronal input and output. In the retina, distribution of a spatio-attentional task that requires estimating the centre of ganglion cells varies with eccentricity; the midget ganglion a 20-cm line usually presented in the lower visual field of the cells are mainly present in the central retina, while parasol participant. In the general population, a small left deviation in ganglion cells represent 90% of the ganglion cells in the the line bisection test is observed and referred to as the peripheral retina (Dacey, 1993; Dacey and Petersen, 1992). ‘pseudoneglect effect’ (Jewell and McCourt, 2000). This left Midget and parasol cells project, respectively, to the parvo- deviation in the line bisection is usually regarded as a mani- cellular and the magnocellular layers of the lateral geniculate festation of an asymmetrical processing of visuospatial body (Leventhal et al., 1985). Central vision receives its inputs information in the normal brain. Hence, cluster 2 and its largely from the parvocellular visual pathways and peripheral connections with the rest of the brain may induce the ‘pseu- vision the magnocellular pathways (Meissirel et al., 1997). doneglect effect’. Consequently, the antero-occipital region (cluster 3), involved No clear equivalence was found between the tractography- in peripheral vision, receives inputs mainly from magnocel- based parcellation and the rsfMRI covarying networks. Whilst lular pathways, while the posterior region (clusters 1 and 2) rsfMRI covarying networks did not seem either to respect any receives inputs principally from parvocellular visual path- of the standard myelogenetic, myelo and cytoarchitectonic ways. The distinction between central and peripheral vision is boundaries, pioneers studies demonstrate that some rsfMRI also maintained all along the visual information processing covarying networks closely match brain’s functional archi- streams. Indeed, dorsal stream areas receive relatively more tecture during activation (Fox et al., 2006; Smith et al., 2009). projections from areas processing peripheral visual field Nevertheless, it is important to insist on the fact that rsfMRI representations, whereas ventral stream areas are more covarying networks reflect correlation in the metabolism densely connected to those processing central representa- between distant brain areas. This correlation does not corre- tions (Gattass et al., 2005; Ungerleider and Desimone, 1986). spond to a direct measure of correlated neuronal activity, but The peripheral/central vision segregation can be observed up rather co-variations in the blood-oxygen level dependant to the frontal cortex. Fibres from the ventral visual pathways signal (Fox and Raichle, 2007). Further work would be required are projected preferentially to the orbito-frontal cortex, while to determine whether all metabolic correlations reported with those from the dorsal pathways are projected towards the rsfMRI measures correspond to brain’s functional architecture dorsolateral prefrontal cortex (Wilson, 1993). The central during activation and are an indirect measure of neuronal vision is also more sensitive to colour vision, and high spatial synchronisation, or if they solely reflect a residual variability frequencies. It would mainly be involved in object recognition, in blood flow. while peripheral vision is more sensitive to low spatial Another limitation in our study concerns the use of prob- frequencies or moving stimuli, and would be essential for abilistic tractography between the seeds and targets as detecting sudden changes in the environment. Other authors a surrogate for the ‘anatomical connectivity’ strength. Stricto also reported dissociation in favour of peripheral vision for the sensu probabilistic tractography does not provide an index of processing of facial expressions (Bayle et al., 2009, 2011). anatomical connectivity from one area to another in the brain, Lesion studies support the association between peripheral it offers an estimate of the mathematical confidence we can vision and the dorsal stream. Lesion to the parieto-occipital have in the reconstruction of a given pathway for a given cortex containing dorsal stream areas results in optic ataxia, dataset (Jones, 2010). Although many previous studies have which manifests in the peripheral vision (Pisella et al., 2006) used it, the estimation it provides may also not systematically and impairs perception in the periphery (Pisella et al., 2009). In hold true due to an inherent DTI tractography limitation, as other words, there is a partial structural separation between tractography may miss fibre crossing and fanning (¼false central and peripheral vision processing pathways, from negative) or reconstruct spurious connections in the case of the retina, to V1, as well as to higher-level visual areas. The kissing and intertwining configurations (¼false positive). This differences in the nature of the input and the output of limitation may account for the existence of cluster 3, which the central primary visual cortices when compared to the according to our tractography, is missing projections from the peripheral primary visual cortices may support the difference lateral geniculate nuclei via the optic radiations. Likewise, in connectivity patterns between clusters 1 and 2 and more connections through the splenium of the callosum are

identified in all the clusters with the exception of cluster 7. variations observed between the tractography-based parcel- However, the results from tracer studies on non-human lation and the independent component analysis of rsfMRI primates suggest that the callosal connections are restricted data. The same effect may also explain discrepancy between to the vertical Meridian of primary visual cortex and are very the tractography-based parcellations and other cortical sparse in the horizontal Meridian and in general in area 17 parcellations. (Cusick et al., 1984; Van Essen et al., 1982). This discrepancy is Gender differences were not estimated in our study due to likely due to the merging of the callosal fibres with the optic the small size of the sample (10 males and eight females). radiations, in other words, an artefact of the tractography Future studies will need to compare gender differences in technique. This may also explain why we did not find a larger dataset. a distinction between V1 and V2 with our tractography-based In conclusion, our study reports seven brain areas in each parcellation. occipital lobe showing different patterns of anatomical V1 and V2 show more differences in their pattern of short U connectivity. Although this subdivision of the occipital lobe is shape connections than in the way they are connected to the unique, in the sense that it does not correspond exactly to rest of the brain via long-range white matter pathways standard myeloarchitectonic, cytoarchitectonic and myelo- (Felleman and Van Essen, 1991; Zilles and Clarke, 1997). Our genetic subdivisions, it showed consistency with functional tractography may have missed these short connections properties of occipital areas, identified by functional imaging leading the principal component analysis to group these two studies in healthy controls or cognitive profiles in brain- areas together. It is interesting to note that rsfMRI did not damaged patients. Such parcellation, based on the connec- differentiate these areas either. tional anatomy, offers a new promising subdivision of the The resolution of diffusion imaging certainly modifies the living human brain, which may assist in the understanding of level of detail in reported anatomical connectivity. We suspect clinical-neuroanatomical dissociations and functional neu- the number of identified clusters as being intimately limited roimaging results. by this level of detail. Future studies employing high- resolution fibre crossing algorithms combined with high spatial resolution datasets (Dell’acqua and Catani, 2012; Acknowledgements Dell’Acqua et al., 2009; Dell’Acqua et al., 2007; Dell’Acqua et al., 2012) are required to confirm and further detail the This work was supported by the French Agence Nationale de la tractography-based subdivision of the occipital lobe. Recherche (project CAFO-RPFC, No: ANR-09-RPDOC-004-01 and We employed different statistical framework for the project HM-TC, No: ANR-09-EMER-006). We would like to tractography-based parcellation and the identification of thank the members of Forschungszentrum Ju¨ lich GmbH for rsfMRI covarying networks, which may explain some of the providing us with the cytoarchitectonic maps of the occipital differences found between these two subdivisions of the lobe, the Natbrainlab for discussions and for their help during occipital lobe. In the case of the tractography-based parcel- various stages of our project, Dominic ffytche, Jean Dauni- lation, we chose a principal component analysis rather than zeau, Marco Catani, Paolo Bartolomeo, Angela Sirigu and independent component analysis as we had no assumption Lauren Sakuma. for the non-gaussianity of the distribution of the principal components (Bugli and Lambert, 2006). 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