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Combined functional MRI and tractography to demonstrate the connectivity of the human primary motor cortex in vivo
NeuroImage 19 (2003) 1349 –1360
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Combined functional MRI and tractography to demonstrate the connectivity of the human primary motor cortex in vivo Maxime Guye,a,1 Geoffrey J.M. Parker,b Mark Symms,a Philip Boulby,a Claudia A.M. Wheeler-Kingshott,c Afraim Salek-Haddadi,a Gareth J. Barker,c and John S. Duncana,* a
The National Society of Epilepsy MRI Unit, Department of Clinical and Experimental Epilepsy, University College London, London, UK b Imaging Science and Biomedical Engineering, University of Manchester, UK c Department of Neuroinflammation, Institute of Neurology, University College London, London, UK Received 9 July 2002; revised 14 February 2003; accepted 17 March 2003
Abstract In this study, we combined advanced MR techniques to explore primary motor cortex (M1) connectivity in the human brain. We matched functional and anatomical information using motor functional MRI (fMRI) and white matter tractography inferred from diffusion tensor imaging (DTI). We performed coregistered DTI and motor task fMRI in 8 right-handed healthy subjects and in 1 right-handed patient presenting with a left precentral tumour. We used the fast-marching tractography (FMT) algorithm to define 3D connectivity maps within the whole brain, from seed points selected in the white matter adjacent to the location of the maximum of fMRI activation. Connectivity maps were then anatomically normalised and analysed using statistical parametric mapping software (SPM99) allowing group comparisons (left versus right hemisphere in control subjects and patient versus control subjects). The results demonstrated, in all control subjects, strong connections from M1 to the pyramidal tracts, premotor areas, parietal cortices, thalamus, and cerebellum. M1 connectivity was asymmetric, being more extensive in the dominant hemisphere. The patient had differences in M1 connectivity from the control group. Thus, fMRI-correlated DTI-FMT is a promising tool to study the structural basis of functional networks in the human brain in vivo. © 2003 Elsevier Science (USA). All rights reserved.
Introduction Functional anatomy and connectivity of motor cerebral cortex have been widely studied in nonhuman primates. It has been demonstrated that primary motor cortex (M1) is connected not only with the spinal cord but also with other cortical areas and subcortical structures (Geyer et al., 2000; Passingham, 1997; Holsapple et al., 1991; Matelli et al., 1989; Orioli and Strick, 1989; Strick, 1985; Luppino et al., 1993; Strick and Preston, 1983; Barbas and Pandya, 1987; Petrides and Pandya, 1984). Numerous human studies, based on positron emission tomography (PET) (Honda et * Corresponding author. National Society for Epilepsy, Chalfont St Peter, Gerrards Cross, Bucks SL9 0RJ, UK. Tel: ⫹44-1494-601341. E-mail address: [email protected] (J.S. Duncan). 1 Current address: Laboratoire de Neurophysiologie et Neuropsychologie, Faculte´ de Me´decine, Universite´ de la Me´diterrane´e, Marseille, France.
al., 1998; Grafton et al., 1993; Fink et al., 1997), functional MRI (fMRI) (Rao et al., 1997), transcranial magnetic stimulation (TMS) (Munchau et al., 2002), or scalp or depth electrode electroencephalography (EEG) (Hallett and Toro, 1996; Lim et al., 1996; Chauvel et al., 1996), have suggested various similar cortico-cortical and subcortical M1 functional connections in man. To date, however, no anatomical motor network has been demonstrated in man in vivo. The most reliable technique to study axonal pathways that make up these connections used invasive procedures which are only applicable in animals or in human postmortem brain (Rye, 1999). Furthermore the exact location and variability of long fibre tracts in human adult postmortem brain have only recently been extensively studied (Burgel et al., 1997; Rademacher et al., 2001). Diffusion tensor imaging (DTI) is a noninvasive technique using quantitative three-dimensional measurements of passive tissue water diffusion to infer orientation of white
1053-8119/03/$ – see front matter © 2003 Elsevier Science (USA). All rights reserved. doi:10.1016/S1053-8119(03)00165-4
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Fig. 1. Seed points selection inferred from fMRI activation. The upper images show the seed points placement (in blue) on fractional anisotropy maps in axial (left) and coronal (right) view. The lower images show the location of the fMRI activation (in yellow) during right-hand movements. The crossing blue lines indicate the maximum of activation, from which the seed points have been deduced.
matter axonal fibres in vivo (Le Bihan et al., 2001; Melhem et al., 2002). In the brain, motion of water molecules is constrained by the structure of axons and myelin sheaths (Basser and Pierpaoli, 1996; Pierpaoli et al., 1996). In consequence, the directionality, or anisotropy, of the diffusion of water molecules depends on the orientation of brain fibre pathways. The dominant orientation and magnitude of diffusion within an imaging voxel can be quantified as a principal eigenvector and eigenvalue of diffusion obtained from the measured diffusion tensor. This information can be interrogated using colour-coding (Douek et al., 1991) or vector maps (Makris et al., 1997). The remaining information in the tensor—three eigenvectors are required to describe the anisotropy of diffusion—may be included using a display of the tensor ellipsoid (Pierpaoli and Basser, 1996). Visualisation and isolation of specific white matter pathways require an evaluation of connectivity between voxels,
which can be inferred using a range of mathematical algorithms (Mori et al., 1999, 2000; Conturo et al., 1999; Jones et al., 1999; Basser et al., 2000; Poupon et al., 2000; Parker et al., 2002a, 2002b). In this present study, we used fast marching tractography (FMT) (Parker et al., 2002a). This technique allows branching of tracts from a single seed point and provides 3D connectivity maps representing a metric (or informal probability) of connectivity between a seed point and each voxel in the brain. These connectivity maps can be displayed and analysed in an anatomically normalised framework using statistical parametric mapping (SPM99). Our aim was to study in vivo, using this technique, normal and pathological human primary motor cortex connectivity. The anatomical correlates of functional areas can vary, even in primary cortices such as M1 (Rademacher et al., 2001), in particular in the presence of structural lesion.
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Fig. 2. Example of the sensitivity of the FMT technique to seed point placement in one control subject. Maps of connectivity from three neighbouring seed voxels used to generate the “union” maps used in our experiments are shown, superimposed on the subject’s normalised FA maps. Note that while two of the three seed points maps are superficially similar (seed point 1 and seed point 2), one (seed point 3) shows markedly different distributions of connections.
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FMRI reliably localises motor functions (Lotze et al., 2000; Beisteiner et al., 2001). Thus we selected FMT seed points from the location of the signal activation during a simple fMRI motor task.
Subjects and methods Subjects Eight right-handed healthy volunteers, 7 males, aged 29 to 46 years (mean 34), without any history of neurological disorders, were included in this study. One right-handed patient, aged 30 years, presenting with a left frontal lesion, responsible for frontal lobe epilepsy, was also included. MR data acquisition All studies were performed on a 1.5 T General Electric Signa Horizon imager. Standard imaging gradients with a maximum strength of 22 mT m⫺1 and slew rate 120 Tm⫺1 s⫺1 were used. All data were acquired using a standard quadrature birdcage head coil for both RF transmission and RF reception. Diffusion tensor imaging The DTI acquisition sequence was a single-shot spinecho echo planar imaging (EPI), cardiac gated (triggering occurring on every QRS complex) (Wheeler-Kingshott et al., 2002), with TE ⫽ 95 ms, 96 ⫻ 96 acquisition (128 ⫻ 128 reconstructed) matrix, 22 cm ⫻ 22 cm field of view. Acquisitions of 60 contiguous 2.3-mm-thickness slices were obtained, covering the whole brain, with a maximum b value of 1148 mm2 s⫺1 (␦ ⫽ 34 ms, ⌬ ⫽ 40 ms, using full gradient strength), in 54 noncollinear directions. The reconstructed voxel size was 1.785 ⫻ 1.785 ⫻ 2.3 mm3. The DTI acquisition time for a total of 3600 images was approximately 25 mins (depending on the heart rate). Functional MRI The fMRI acquisition sequence was a gradient echo-EPI, with TE/TR ⫽ 40/10000 ms, 128 ⫻ 128 matrix, 22 ⫻ 22 cm field of view. Twenty seven 2.3-mm-thickness slices were obtained, with 66 repetitions. A simple alternating (every 30 s) right and left hand tapping paradigm was used as the motor task. The fMRI acquisition time was 15 mins. Coregistration Bandwidth, field of view, and matrix size were set to equalise the geometric distortions between the DTI and the fMRI acquisitions (Werring et al., 1999). The same slice prescription from a sagittal localizer was used for both acquisitions.
Data processing Functional MRI The SPM99 package [http://www.fil.ion.ucl.ac.uk/spm/ spm99.html] was used for all image preprocessing and voxelbased statistical analysis, within the context of the general linear model (Friston et al., 1995). Images were realigned, and spatially smoothed with an isotropic Gaussian kernel of 8-mm full-width-half-maximum (FWHM). Motor activity was modeled by way of a simple boxcar function convolved with a canonical hemodynamic response function which, together with a mean column, constituted the design matrix. A T contrast was specified as a unidirectional test of significance for the parameter estimate of interest. The computed SPM{T} was thresholded at the P ⬍ 0.05 level using the correction for multiple comparisons based on Gaussian random field theory. Diffusion tensor imaging The diffusion tensor eigenvalues 1, 2, 3, and eigenvectors 1, 2, 3, were calculated from DTI data, and fractional anisotropy (FA) maps were generated according to the method described by Pierpaoli and Basser (1996) and Pierpaoli et al. (1996), using locally written software. Fast-marching tractography Principles FMT was used to generate connectivity mapping and fibre tracking (Parker et al., 2002a, 2002b). This software utilises the principles of level set theory and the fast-marching algorithm which model the evolution of an interface (or front) over time from a seed point or region (Sethian, 1996). In the context of DTI, the propagation front can be controlled, using the directionality of the principal eigenvector (1). Every voxel in a brain dataset will be crossed by this front at a time depending on the arrangement of the 1 between the seed point and the voxel in question. FMT uses the arrival time map thus generated to generate a 3D connectivity map. This map represents a metric of connectivity which can be considered as an informal likelihood of connectivity between the seed point and every other voxel. This metric (or connectivity value) is designed to rank all putative connections by how well they correspond to the information provided by the DTI data. All paths may therefore be defined and their different values compared within the same subject or between different subjects. We excluded from the FMT calculation, the voxels with a FA value lower than 0.1 which excluded artifactual paths through CSF but allowed the algorithm to make cortico-cortical connections. Starting points The coregistration of fMRI and DTI images allowed us to select the seed points on the FA maps within the white matter directly adjacent to the activated precentral gray matter during fMRI. We selected a set of 3 adjacent seed points in each hemisphere, focused on the highest signifi-
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cantly activated fMRI voxel (Fig. 1). The selection of three seed voxels was based on the observation that connectivity maps resulting from neighbouring voxels often result in markedly different patterns of connectivity subsampling different set of cortical connections (as is to be expected in light of the complex nature of cerebral interregional connections) (Ciccarelli et al., 2003). The selection of three adjacent seed points therefore reduced the possibility of missing important pathways of connection due to small errors in seed point placement (Fig. 2). However, the use of the functional information guards against larger errors that could lead to accidental sampling of different (although closely related in space) sets of connections. Each seed point was processed separately using the FMT algorithm. We chose 3 adjacent voxels along the left-right axis to best follow the anatomical orientation of the precentral gyrus. Connectivity maps For each hemisphere in each subject, we generated a single connectivity map resulting from the “union” of the connectivity maps of the 3 seed points. These union maps were generated using SPM99, selecting for each voxel, the maximum value from the same coordinates in the 3 connectivity maps (Fig. 2). This option was preferred to the addition or the averaging of the 3 maps. As these 3 connectivity maps could be different, the union method gave a high connectivity value to each subsampling connections with high connectivity value. This method, in comparison with the averaging method, also decreased the effect of overlaps between the 3 connectivity maps. The resulting 3D maps of connectivity were displayed in all individual subjects as 3D colour-scaled connectivity maps. The colour scale represented the range of connectivity values without threshold (extending from 0 to a theoretical maximum of 1). The 3D connectivity maps were then spatially normalised using SPM99. This was achieved by normalising the non-diffusion-weighted data from the DTI acquisition against the EPI template provided by SPM99 (derived from the average of 13 normal controls’ echo-planar images) and then applying the transformation parameters to the connectivity maps. We used Talairach space to label all connected regions. Talairach coordinates were derived from the MNI coordinates using software called “mni2tal” (http://www.mrc-cbu. cam.ac.uk/Imaging/mnispace.html) The resulting maps of the whole brain were displayed and considered without applying any threshold. Group mapping The connectivity maps of each control subject were averaged using SPM99, to generate group maps. We generated 2 groups within the control subjects (i.e., left and right hemisphere) resulting from the averaged control maps on each side. Then we made comparisons using the unthresholded colour scales and the SPM framework which allowed us to compare connectivity values within the same regions
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in each group. The patient’s maps were compared in the same way with those of right-handed controls.
Results Controls The unthresholded averaged connectivity maps showed broad connections from the activation maxima in the hand area in all subjects (Figs. 3 and 4). Evaluation based on the connectivity values of tracts (values of voxels involved, colour-coded in figures) permitted the identification of common features in the left and right hemispheres in the control group. Different groups of tracts could be described according to the range of their connectivity values, suggesting a hierarchy in the “likelihood” of connection: Connectivity values ⬎ 0.30 —One group of tracts followed the course of the pyramidal tracts, traversing the corona radiata, the internal capsule, the lateral and anterior part of the basal midbrain, and then the medulla, and crossing at the pyramidal decussation. —Three groups of tracts involved the premotor areas. One involved the medial wall of the frontal lobe within the white matter of the superior frontal gyrus (SFG) to reach the supplementary motor area (SMA). This group of connections extended, inferiorly, to the cingulate sulcus corresponding to the caudal part of the cingulate motor areas. This group also extended, anteriorly, slightly beyond the VCA line (Talairach and Tournoux, 1988) (i.e., vertical line passing through the anterior commissure at 90° to the line joining the anterior and posterior commissure). The two other groups of tracts involved the lateral part of the frontal lobe. The first involved the white matter of the dorso-lateral part of the precentral gyrus (PCG) to reach the dorsal premotor cortex and also the posterior part of the middle frontal gyrus (MFG). The second involved the white matter of the ventro-lateral part of the PCG and the posterior opercular part of the inferior frontal gyrus (IFG). —Two groups of tracts involved the parietal lobe. One involved the postcentral gyrus, and the other involved the white matter of the parietal lobe to reach mostly the medial wall, around the intraparietal sulcus. —One group followed the course of the corticospinal tracts and then involved the lateral and inferior part of the thalamus. The resolution of the technique did not permit determination of the exact nuclei involved. —One group left the cortico-pyramidal tracts at the level of the pons and involved the ipsilateral middle cerebellar peduncle (MCP). —One group of tracts involved the middle and posterior part of the corpus callosum. A part of this group reached the contralateral fornix.
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Fig. 3. Averaged connectivity maps from the left primary motor cortex in eight right-handed controls, superimposed on the normalised single-T1 images from the MNI provided by SPM99. Note the strong connections with the corticospinal tract, the supplementary motor area, the lateral premotor areas, parts of the parietal cortices, the thalamus, and the middle cerebellar peduncle. The left of the brain is on the left of the images. Note that these maps are displayed as raw connectivity maps without applying any threshold to the connectivity values. CST, corticospinal tracts; SMA, supplementary motor area; PMd, dorso-lateral premotor cortex; PMv, ventro-lateral premotor cortex; PPL, posterior parietal lobule; Th, thalamus; MTG, middle temporal gyrus; OL, occipital lobe; MCP, middle cerebellar peduncle; SFG, superior frontal gyrus; MFG, middle frontal gyrus; IFG, inferior frontal gyrus; PCG, precentral gyrus; PostCG, postcentral gyrus; BG, basal ganglia; CC, corpus callosum; Fx, fornix; CR, corona radiata.
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els with connectivity values between 0.30 and 0.20 which were located surrounding the voxels with connectivity values ⬎0.30. —One group involved the posterior and lateral part of the basal ganglia including putamen and striatum. These tracts followed corticospinal tracts through the internal capsule and the external capsule. Connectivity values ⬍ 0.20 These connections were widespread all over the brain and were totally nonspecific. Differences between left and right hemispheres in right handers In general, the left hemisphere showed more extensive M1 connectivity than the right hemisphere (Fig. 4). Two groups of tracts were identified in the left but not in the right hemisphere (Figs. 3 and 4). One group involved the white matter of the superior part of the left occipital lobe (connectivity values were between 0.15 and 0.35). The other involved the white matter of the posterior part of the left superior temporal gyrus (STG) and the white matter of the left occipito-temporal junction at the level of the middle temporal gyrus (MTG) (connectivity values were between 0.15 and 0.25). The connections were broader and generally had higher average connectivity values in the left hemisphere. There were higher connectivity values in the right hemisphere in only two areas: one involving the white matter below the precentral gyrus in the corona radiata (maximum of connectivity value of voxels 0.48 in the right versus a maximum 0.42 in the left); the other involving the white matter of the lateral premotor cortices (maximum 0.42 in the right versus 0.40 in the left). Patient data The patient with the left medial precentral lesion had modified M1 connections (Fig. 5).
Fig. 4. Comparison of connectivity of left and right primary motor cortices (M1) in eight right-handed controls, superimposed on the normalised single-T1 images from the MNI provided by SPM99, showing more extensive connectivity from the left M1. The left of the brain is on the left of the images. As in Fig. 2, these maps display all the connections (unthresholded maps). CST, corticospinal tracts; SMA, supplementary motor area; PMd, dorso-lateral premotor cortex; PMv, ventro-lateral premotor cortex; PPL, posterior parietal lobule; Th, thalamus; MCP, middle cerebellar peduncle; CC, corpus callosum; CR, corona radiata.
Connectivity values between 0.30 and 0.20 —The groups of connections involving the corticospinal tracts, the premotor and parietal cortices, the thalamus, the corpus callosum, and the cerebellar peduncle included vox-
Left hemisphere Connections with the SMA appeared displaced laterally, surrounding the tumour. A group of tracts branched in the inferior part of this last connection and followed a course to the medial inferior part of the prefrontal lobe. The inferior and superior lateral premotor connections extended also to the prefrontal cortex. There was a group of tracts to the lateral part of the posterior parietal lobe. Contralateral connections passed through the posterior half of the corpus callosum, mostly to the precentral area. All premotor connections had higher connectivity values than the averaged controls’ connectivity maps (connectivity values between 0.35 and 0.6). Higher connectivity values were also found in the posterior parietal lobe (connectivity values between 0.3 and 0.45). In contrast, the corticospinal tracts had lower
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connectivity values (connectivity values between 0.15 and 0.30). Right hemisphere Right M1 connectivity appeared broader than in controls. It involved parts of the prefrontal lobe, lateral part of the posterior parietal lobe, and occipital lobe. Widespread contralateral connections also passed through the corpus callosum. Lateral premotor areas and parietal connections had higher connectivity values (between 0.30 and 0.55, and between 0.40 and 0.50, respectively) than the averaged controls’ connectivity maps. Connections through the corpus callosum had high connectivity values (composed between 0.25 and 0.45) and connected to several contralateral areas including premotor, parietal, and prefrontal regions.
Discussion Validity and limitations of the FMT method In this study, we used the FMT method, to demonstrate for the first time, the connectivity of the primary motor cortex in man. Validity and reliability of FMT as a tractography method has been assessed in a previous study demonstrating accurate connectivity maps of corticospinal tracts and optic radiations in macaque and human brain (Parker et al., 2002b). The two advantages of FMT over other tractography algorithms are its capability to estimate a metric of connections which rank the different possible white matter pathways and to allow the branching of pathways. Here we demonstrate the potential usefulness of this technique in the study of human brain connections in vivo. There are limitations inherent in the tractography methods. First, the poor resolution of DTI (mm) compared with the size of the fibre tracts (m), as well as the influence of the signal to noise ratio, can lead to false definition of a specific tract direction in a voxel. A given DTI voxel may be crossed by several crossing fibres or may be the site of unrelated tracts running parallel to the tract of interest. This may be responsible for false positives and negatives in our study (Parker et al., 2002b). In addition, this implies that only large connections can be identified, whereas small connections which could be functionally important, are potentially not determined (leading to false negatives). Therefore, FMT has to be considered as a “macropicture” of brain connections, giving an overview from a distance. Second, the tractography methods cannot differentiate between afferent and efferent fibre tracts (Rye, 1999). Third, pathological modifications may lead to partial volume effects between the principal eigenvectors of different neighbouring voxels and result in false quantitative modifications of connectivity values using FMT. This last issue could have been responsible for the unlikely decrease of the connectivity values in the corticospinal tracts in the patient, considering the absence of any motor deficit. Fourth, difficulties
exist in the interpretation of the FMT results. We have no perfect indication of whether connections identified using the tracing methods are genuine or whether they represent false positives or negatives. Three sources of validation are used. One is with reference to the knowledge of monkey anatomical connections. This knowledge may be extrapolated to the human brain to give us an idea of which brain regions we may expect to be connected. A second possible validation is through known anatomy and functional connectivity in the human brain, and a third is from lesion studies. However, none of these approaches are perfect: the exact degree of similarity between monkey and human connectivity is unknown, extensive anatomy of associated fibres in man is not well known to date, functional connectivity between regions does not imply direct white matter connectivity, and lesion studies are imprecise. A second difficulty in interpreting our results is the lack of precise knowledge of cortical parcellation in our subjects. Thus we defined M1 connections on the basis of gross neuroanatomical definitions using the Talairach and Tournoux atlas. For example, while we have identified parts of the SMA as being connected to the primary motor area, we could not define clear-cut limits of these connections in term of cytoarchitectonic delineations. Therefore, our findings need to be treated with caution at this stage. Nonetheless, our results are encouraging with regard to the usefulness of this technique to study human cortical areas connections in vivo. The only way to validate findings obtained using MR diffusion tracing methods appears to be to perform direct comparative studies of the methods in the monkey brain. Such studies will define the confidence with which we may apply diffusion tracing techniques. Cortical and subcortical motor network demonstration in control subjects: interpretation and validity Our results showed a hierarchy of strength of connections. In this study, connectivity values ⬎0.3 gave the most expected results in control group, demonstrating a reproducible pattern of connections from the primary motor cortex to the corticospinal tracts, parts of premotor and parietal cortices, the thalamus, the corpus callosum, and the ipsilateral cerebellar peduncle. As concerns the premotor cortices, three areas that appeared strongly connected to M1 in our results may correspond to those described to be connected to M1 in monkey (i.e., the SMA, the dorsolateral premotor cortex or PMd and the ventrolateral premotor cortex or PMv) (Rizzolatti et al., 1998; Luppino et al., 1993, 1994; Picard and Strick, 1996, 2001; Barbas and Pandya, 1987). All these frontal motor areas also form part of the cortical origin of the corticospinal tracts (Luppino et al., 1994; He et al., 1993, 1995). Monkey studies have found similar connections to the parietal connections between M1 and the postcentral gyrus as well as the posterior parietal lobule found in our study
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(Strick and Kim, 1978; Petrides and Pandya, 1984; Wise et al., 1997; Matelli and Luppino, 2001; Rizzolatti et al., 1998). Regarding the thalamic connections, direct connections between the ventrolateral thalamic nuclei (ventralis lateralis pars oralis (VLo) and ventralis posterior lateralis pars oralis (VPLo)) and M1 have been noted in macaque brain (Matelli et al., 1989; Holsapple et al., 1991). No clear thalamic nuclei were visualized in our results, but likely tracts were localised in the lateral and inferior part of the thalamus. As concerns the corticospinal tracts, our findings concur with the current knowledge of the route of these tracts from motor cortices to the spinal cord (Rademacher et al., 2001). In our results, however, these tracts had a slightly more posterior position than expected in the mid brain. This may be due to a partial volume effect of numerous crossing fibres in this region, intersubject variability, and a visual effect induced by the addition of fibre tracts connected with the ventro-posterior part of the thalamus. However, the global location of corticospinal tracts in the brainstem is similar to a previous tractography study (Stieltjes et al., 2001). In the corpus callosum, the strongest connections were present in the posterior one-third of it whereas in monkey corpus callosum connections have been described in the middle one-third (Pandya et al., 1971). We also found connections with the cerebellar peduncles, but no direct connections with M1 have been demonstrated in the monkey, in whom the cerebellum is connected via the VPLo nucleus of the thalamus and the red nucleus (Matelli et al., 1995; Orioli and Strick, 1989; Holsapple et al., 1991; Strick, 1985). These cerebellar connections could have been considered as likely by FMT despite the existence of a synaptic relay in the thalamus. If 0.30 is regarded as the most relevant threshold for determining true connectivity, we also found less expected connections with occipital cortex and posterior superior and middle temporal gyri in the left hemisphere. No direct connections have been reported between either occipital or temporal lobe and M1 in monkeys. These connections were less strong than the premotor and parietal connections with connectivity values between 0.25 and 0.35. Thus, they may represent false positives due to the branching of occipitofrontal and temporo-frontal tracts. Several functional imaging and electrophysiological studies have highlighted the possible involvement in motor processes, of the cortical and subcortical areas, that were found to be connected to M1 in our results (Honda et al., 1998; Grafton et al., 1993; Fink et al., 1997; Rao et al., 1997; Munchau et al., 2002; Hallett and Toro, 1996; Lim et al., 1996; Chauvel et al., 1996). All these regions participate in the motor network responsible for preparation, perception, imagination of action, or in cognitive functions related to movements (Georgopoulos, 2000; Sanes, 2000; Toni et al., 2001; Jeannerod, 2001). This network implicates cortico-cortical (above all fronto-parietal) and cortico-subcortical connections. Those studies, however, have not demon-
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strated any direct anatomical connections between these different areas. Usefulness of combined fMRI and DTI tractography Our study illustrates the combination of fMRI and DTIFMT to link brain anatomy and function. Conturo et al. were the first to use fMRI activation (visual task) as the location of tractography seed points, to define the optic radiation (Conturo et al., 1999). However, the authors employed a restrictive method, tracking fibres between two regions of interest. Two previous studies have combined motor fMRI and DTI with the aim of imaging M1 and the corticospinal tracts in patients presenting with tumours potentially affecting these structures (Holodny et al., 2001; Krings et al., 2001). However, none of them attempted to define other M1 connections. Moreover, Holodny et al. used restrictions in the same manner as in the Conturo et al. study, to consider only the pyramidal tracts and avoid other connections. In this study we defined the seed region using fMRI to ensure meaningful loci in all subjects. This enabled comparison between normal controls and cases where there had been significant anatomical deformation, for instance, the mass effect of tumours. In addition, in the subcortical regions where we selected our seed points, adjacent voxels often showed larger variations (FA values and orientations of the principal eigenvector) than in the middle of well organised major white matter tracts (cf. Fig. 1). Therefore, the seed points placement based on anatomical landmarks in the diffusion data may be prone to bias and random error. Crucially, fMRI activations offer the possibility of operatorindependent placement. Hemisphere asymmetry Our results suggested broader connections from the primary motor cortex in the dominant hemisphere. This may be due, at least in part, to the hemisphere asymmetry concerning the primary motor cortex (Amunts et al., 1996). Amunts et al. have recently demonstrated a significant macrostructural (deeper left central sulcus) and microstructural asymmetry suggesting an increased connectivity of the left M1 in right handers. This finding gives a possible insight into the structural basis of differences in cerebral hemisphere functions. Patient assessment The patient study demonstrated the capability of FMT to assess modified connectivity. The patient’s data suggested modification of M1 connectivity mainly in the left hemisphere, but also some modifications in the right hemisphere with a broader connectivity than in controls. One speculation is that M1 connectivity in the right hemisphere could have been modified by neuronal plasticity resulting from dominant hemisphere impairment. However, the results
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Fig. 5. Connectivity of left and right primary motor cortices (M1) in patient with medial left precentral tumour, superimposed on the normalised patient b0 images. The left of the brain is on the left of the images. As in Figs. 2 and 3, the maps are unthresholded. CST, corticospinal tracts; SMA, supplementary motor area; PMd, dorso-lateral premotor cortex; PMv, ventro-lateral premotor cortex; PPL, posterior parietal lobe; Th, thalamus; CC, corpus callosum; MCP, middle cerebellar peduncle; PFC, prefrontal cortex.
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must be interpreted with great caution. First, compaction of tracts might result in an artificial increase of connectivity values in affected areas. In the present case, an increase of the connectivity values was found in the premotor areas probably distorted by the tumour. Second, displacement of structures might modify the shape and angles of the tracts. Then, the algorithm used might define some paths as less or more likely because of the modification of the shape of the paths. For instance, in our patient, although there was no motor deficit, right corticospinal tracts were found with lower connectivity values than in controls. In contrast, right prefrontal connections were found as “likely paths” that were not identified in the averaged controls’ maps. Third, we used an averaged control map for comparisons. Therefore, some modifications of the connectivity values may be linked to normal interindividual differences. Fourth, the presence of a tumour may damage pathways that do not contribute to motor connectivity, but affect measures of connectivity via partial volume effects. Degeneration of the tracts may lead to an “unmasking” of some of the motor fibres, thus allowing connections to be identified that are not apparent in the control group (Pierpaoli et al., 2001). To accurately assess impairment of connectivity in patients it will be necessary to compare the patient’s maps with a larger number of controls, using statistical voxel-based analysis. Conclusion The tractography methods, despite technical limitations at this stage in comparison with invasive anatomical techniques, have the advantage of being feasible in vivo. This study opens several perspectives for further investigations. The combination of fMRI activation tasks and DTI-FMT may be helpful in defining the structural basis of functional connectivity in the normal brain, and the derangement of these networks in disease states. Acknowledgments This work was funded by National Society for Epilepsy. M. Guye was supported by the French League Against Epilepsy (Pfizer grant). References Amunts, K., Schlaug, G., Schleicher, A., Steinmetz, H., Dabringhaus, A., Roland, P.E., Zilles, K., 1996. Asymmetry in the human motor cortex and handedness. NeuroImage 4, 216 –222. Barbas, H., Pandya, D.N., 1987. Architecture and frontal cortical connections of the premotor cortex (area 6) in the rhesus monkey 2. J. Comp. Neurol. 256, 211–228. Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A., 2000. In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44, 625– 632.
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Basser, P.J., Pierpaoli, C., 1996. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. B 111, 209 –219. Beisteiner, R., Windischberger, C., Lanzenberger, R., Edward, V., Cunnington, R., Erdler, M., Gartus, A., Streibl, B., Moser, E., Deecke, L., 2001. Finger somatotopy in human motor cortex. NeuroImage 13, 1016 –1026. Burgel, U., Mecklenburg, I., Blohm, U., Zilles, K., 1997. Histological visualization of long fiber tracts in the white matter of adult human brains. J. Hirnforsch. 38, 397– 404. Ciccarelli, O., Parker, G.J.M., Toosy A.T., Wheeler-Kingshott, C.A.M., Barker, G.J., Boulby, P.A., Miller D.H., Thompson, A.J., 2003. From diffusion tractography to quantitative white matter tract measures: a reproducibility study. NeuroImage, 18, 348 –359. Chauvel, P.Y., Rey, M., Buser, P., Bancaud, J., 1996. What stimulation of the supplementary motor area in humans tells about its functional organization. Adv. Neurol. 70, 199 –209. Conturo, T.E., Lori, N.F., Cull, T.S., Akbudak, E., Snyder, A.Z., Shimony, J.S., McKinstry, R.C., Burton, H., Raichle, M.E., 1999. Tracking neuronal fiber pathways in the living human brain. Proc. Natl. Acad. Sci. USA 96, 10422–10427. Douek, P., Turner, R., Pekar, J., Patronas, N., Le Bihan, D., 1991. MR color mapping of myelin fiber orientation. J. Comput. Assist. Tomogr. 15, 923–929. Fink, G.R., Frackowiak, R.S., Pietrzyk, U., Passingham, R.E., 1997. Multiple nonprimary motor areas in the human cortex. J. Neurophysiol. 77, 2164 –2174. Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.-P., Frith, C.D., Frackowiack, R.S.J., 1995. Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2, 189 –210. Georgopoulos, A.P., 2000. Neural aspects of cognitive motor control. Curr. Opin. Neurobiol. 10, 238 –241. Geyer, S., Matelli, M., Luppino, G., Zilles, K., 2000. Functional neuroanatomy of the primate isocortical motor system. Anat. Embryol. (Berl) 202, 443– 474. Grafton, S.T., Woods, R.P., Mazziotta, J.C., 1993. Within-arm somatotopy in human motor areas determined by positron emission tomography imaging of cerebral blood flow 4. Exp. Brain. Res. 95, 172–176. Hallett, M., Toro, C., 1996. Generation of movement-related potentials in the supplementary sensorimotor area. Adv. Neurol. 70, 147–152. He, S.Q., Dum, R.P., Strick, P.L., 1993. Topographic organization of corticospinal projections from the frontal lobe: motor areas on the lateral surface of the hemisphere 4. J. Neurosci. 13, 952–980. Holodny, A.I., Ollenschleger, M.D., Liu, W.C., Schulder, M., Kalnin, A.J., 2001. Identification of the corticospinal tracts achieved using bloodoxygen-level-dependent and diffusion functional MR imaging in patients with brain tumors. AJNR Am. J. Neuroradiol. 22, 83– 88. Holsapple, J.W., Preston, J.B., Strick, P.L., 1991. The origin of thalamic inputs to the “hand” representation in the primary motor cortex. J. Neurosci. 11, 2644 –2654. Honda, M., Wise, S.P., Weeks, R.A., Deiber, M.P., Hallett, M., 1998. Cortical areas with enhanced activation during object-centred spatial information processing. A PET study. Brain 121, 2145–2158. Jeannerod, M., 2001. Neural simulation of action: a unifying mechanism for motor cognition. NeuroImage 14, S103–S109. Jones, D.K., Simmons, A., Williams, S.C., Horsfield, M.A., 1999. Noninvasive assessment of axonal fiber connectivity in the human brain via diffusion tensor MRI. Magn. Reson. Med. 42, 37– 41. Krings, T., Reinges, M.H., Thiex, R., Gilsbach, J.M., Thron, A., 2001. Functional and diffusion-weighted magnetic resonance images of space-occupying lesions affecting the motor system: imaging the motor cortex and pyramidal tracts. J. Neurosurg. 95, 816 – 824. Le Bihan, D., Mangin, J.F., Poupon, C., Clark, C.A., Pappata, S., Molko, N., Chabriat, H., 2001. Diffusion tensor imaging: concepts and applications. J. Magn. Reson. Imaging 13, 534 –546. Lim, S.H., Dinner, D.S., Luders, H.O., 1996. Cortical stimulation of the supplementary sensorimotor area. Adv. Neurol. 70, 187–197.
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M. Guye et al. / NeuroImage 19 (2003) 1349 –1360
Lotze, M., Erb, M., Flor, H., Huelsmann, E., Godde, B., Grodd, W., 2000. fMRI evaluation of somatotopic representation in human primary motor cortex. NeuroImage 11, 473– 481. Luppino, G., Matelli, M., Camarda, R., Rizzolatti, G., 1993. Corticocortical connections of area F3 (SMA-proper) and area F6 (pre-SMA) in the macaque monkey. J. Comp. Neurol. 338, 114 –140. Luppino, G., Matelli, M., Camarda, R., Rizzolatti, G., 1994. Corticospinal projections from mesial frontal and cingulate areas in the monkey. NeuroReport 5, 2545–2548. Makris, N., Worth, A.J., Sorensen, A.G., Papadimitriou, G.M., Wu, O., Reese, T.G., Wedeen, V.J., Davis, T.L., Stakes, J.W., Caviness, V.S., Kaplan, E., Rosen, B.R., Pandya, D.N., Kennedy, D.N., 1997. Morphometry of in vivo human white matter association pathways with diffusion-weighted magnetic resonance imaging. Ann. Neurol. 42, 951–962. Matelli, M., Luppino, G., 2001. Parietofrontal circuits for action and space perception in the macaque monkey. NeuroImage 14, S27–S32. Matelli, M., Luppino, G., Fogassi, L., Rizzolatti, G., 1989. Thalamic input to inferior area 6 and area 4 in the macaque monkey. J. Comp. Neurol. 280, 468 – 488. Matelli, M., Luppino, G., Rizzolatti, G., 1995. Convergence of pallidal and cerebellar outputs on the frontal motor areas. Acta Biomed. Ateneo. Parmense. 66, 83–92. Melhem, E.R., Mori, S., Mukundan, G., Kraut, M.A., Pomper, M.G., van Zijl, P.C., 2002. Diffusion tensor MR imaging of the brain and white matter tractography. AJR Am. J. Roentgenol. 178, 3–16. Mori, S., Crain, B.J., Chacko, V.P., van Zijl, P.C., 1999. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45, 265–269. Mori, S., Kaufmann, W.E., Pearlson, G.D., Crain, B.J., Stieltjes, B., Solaiyappan, M., van Zijl, P.C., 2000. In vivo visualization of human neural pathways by magnetic resonance imaging. Ann. Neurol. 47, 412– 414. Munchau, A., Bloem, B.R., Irlbacher, K., Trimble, M.R., Rothwell, J.C., 2002. Functional connectivity of human premotor and motor cortex explored with repetitive transcranial magnetic stimulation. J. Neurosci. 22, 554 –561. Orioli, P.J., Strick, P.L., 1989. Cerebellar connections with the motor cortex and the arcuate premotor area: an analysis employing retrograde transneuronal transport of WGA-HRP. J. Comp. Neurol. 288, 612– 626. Pandya, D.N., Karol, E.A., Heilbronn, D., 1971. The topographical distribution of interhemispheric projections in the corpus callosum of the rhesus monkey. Brain Res. 32, 31– 43. Parker, G.J., Wheeler-Kingshott, C.A., Barker, G.J., 2002a. Estimating distributed anatomical connectivity using fast marching methods and diffusion tensor imaging. IEEE Trans. Med. Imaging 21, 505–12. Parker, G.J., Stephan, K.E., Barker, G.J., Rowe, J.B., MacManus, D.G., Wheeler-Kingshott, C.A., Ciccarelli, O., Passingham, R.E., Spinks, R.L., Lemon, R.N., Turner, R., 2002b. Initial demonstration of in vivo tracing of axonal projections in the macaque brain and comparison with the human brain using diffusion tensor imaging and fast marching tractography. NeuroImage 15, 797– 809. Passingham, R., 1997. Functional organisation of the motor system, in: Frackowiak, R.S.J., Friston, K.J., Frith, C.D., Dolan, R.J., Mazziotta, J.C. (Eds.), Human Brain Function. Academic Press, San Diego, pp. 243–274. Petrides, M., Pandya, D.N., 1984. Projections to the frontal cortex from the posterior parietal region in the rhesus monkey. J. Comp. Neurol. 228, 105–116.
Picard, N., Strick, P.L., 1996. Motor areas of the medial wall: a review of their location and functional activation. Cereb. Cortex 6, 342–353. Picard, N., Strick, P.L., 2001. Imaging the premotor areas. Curr. Opin. Neurobiol. 11, 663– 672. Pierpaoli, C., Basser, P.J., 1996. Toward a quantitative assessment of diffusion anisotropy. Magn. Reson. Med. 36, 893–906. Pierpaoli, C., Jezzard, P., Basser, P.J., Barnett, A., Di Chiro, G., 1996. Diffusion tensor MR imaging of the human brain. Radiology 201, 637– 648. Pierpaoli, C., Barnett, A., Pajevic, S., Chen, R., Penix, L., Virta, A., Basser, P., 2001. Water diffusion changes in Wallerian degeneration and their dependence on white matter architecture. NeuroImage 13, 1174 –1185. Poupon, C., Clark, C.A., Frouin, V., Regis, J., Bloch, I., Le Bihan, D., Mangin, J., 2000. Regularization of diffusion-based direction maps for the tracking of brain white matter fascicles. NeuroImage 12, 184 –195. Rademacher, J., Burgel, U., Geyer, S., Schormann, T., Schleicher, A., Freund, H.J., Zilles, K., 2001. Variability and asymmetry in the human precentral motor system. A cytoarchitectonic and myeloarchitectonic brain mapping study. Brain 124, 2232–2258. Rao, S.M., Harrington, D.L., Haaland, K.Y., Bobholz, J.A., Cox, R.W., Binder, J.R., 1997. Distributed neural systems underlying the timing of movements. J. Neurosci. 17, 5528 –5535. Rizzolatti, G., Luppino, G., Matelli, M., 1998. The organization of the cortical motor system: new concepts. Electroencephalogr. Clin. Neurophysiol. 106, 283–296. Rye, D.B., 1999. Tracking neural pathways with MRI. Trends Neurosci. 22, 373–374. Sanes, J.N., 2000. Motor cortex rules for learning and memory. Curr. Biol. 10, R495–R497. Sanes, J.N., Donoghue, J.P., 2000. Plasticity and primary motor cortex. Annu. Rev. Neurosci. 23, 393– 415. Sethian, J.A., 1996. A fast marching level set method for monotonically advancing fronts. Proc. Natl. Acad. Sci. USA 93, 1591–1595. Stieltjes, B., Kaufmann, W.E., van Zijl, P.C., Fredericksen, K., Pearlson, G.D., Solaiyappan, M., Mori, S., 2001. Diffusion tensor imaging and axonal tracking in the human brainstem. NeuroImage 14, 723–735. Strick, P.L., 1985. How do the basal ganglia and cerebellum gain access to the cortical motor areas? Behav. Brain Res. 18, 107–123. Strick, P.L., Kim, C.C., 1978. Input to primate motor cortex from posterior parietal cortex (area 5). I. Demonstration by retrograde transport. Brain Res. 157, 325–330. Strick, P.L., Preston, J.B., 1983. Input-output organization of the primate motor cortex. Adv. Neurol. 39, 321–327. Talairach, J., Tournoux, P., 1988. Coplanar Stereotaxic Atlas of the Human Brain. Thieme, Stuttgart. Toni, I., Thoenissen, D., Zilles, K., 2001. Movement preparation and motor intention. NeuroImage 14, S110 –S117. Werring, D.J., Clark, C.A., Parker, G.J.M., Miller, D.H., Thompson, A.J., Barker, G.J., 1999. A direct demonstration of both structure and function in the visual system: combining diffusion tensor imaging with functional magnetic resonance imaging. NeuroImage 9, 352–361. Wheeler-Kingshott, C.A., Boulby, P.A., Symms, M.R., Barker, G.J., 2002. Optimised cardiac gating for high-resolution whole-brain DTI on a standard scanner. Proc. Intl. Soc. Magn. Reson. Med. 10, 1118. Wise, S.P., Boussaoud, D., Johnson, P.B., Caminiti, R., 1997. Premotor and parietal cortex: corticocortical connectivity and combinatorial computations. Annu. Rev. Neurosci. 20, 25– 42.
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Combined functional MRI and tractography to demonstrate the connectivity of the human primary motor cortex in vivo Maxime Guye,a,1 Geoffrey J.M. Parker,b Mark Symms,a Philip Boulby,a Claudia A.M. Wheeler-Kingshott,c Afraim Salek-Haddadi,a Gareth J. Barker,c and John S. Duncana,* a
The National Society of Epilepsy MRI Unit, Department of Clinical and Experimental Epilepsy, University College London, London, UK b Imaging Science and Biomedical Engineering, University of Manchester, UK c Department of Neuroinflammation, Institute of Neurology, University College London, London, UK Received 9 July 2002; revised 14 February 2003; accepted 17 March 2003
Abstract In this study, we combined advanced MR techniques to explore primary motor cortex (M1) connectivity in the human brain. We matched functional and anatomical information using motor functional MRI (fMRI) and white matter tractography inferred from diffusion tensor imaging (DTI). We performed coregistered DTI and motor task fMRI in 8 right-handed healthy subjects and in 1 right-handed patient presenting with a left precentral tumour. We used the fast-marching tractography (FMT) algorithm to define 3D connectivity maps within the whole brain, from seed points selected in the white matter adjacent to the location of the maximum of fMRI activation. Connectivity maps were then anatomically normalised and analysed using statistical parametric mapping software (SPM99) allowing group comparisons (left versus right hemisphere in control subjects and patient versus control subjects). The results demonstrated, in all control subjects, strong connections from M1 to the pyramidal tracts, premotor areas, parietal cortices, thalamus, and cerebellum. M1 connectivity was asymmetric, being more extensive in the dominant hemisphere. The patient had differences in M1 connectivity from the control group. Thus, fMRI-correlated DTI-FMT is a promising tool to study the structural basis of functional networks in the human brain in vivo. © 2003 Elsevier Science (USA). All rights reserved.
Introduction Functional anatomy and connectivity of motor cerebral cortex have been widely studied in nonhuman primates. It has been demonstrated that primary motor cortex (M1) is connected not only with the spinal cord but also with other cortical areas and subcortical structures (Geyer et al., 2000; Passingham, 1997; Holsapple et al., 1991; Matelli et al., 1989; Orioli and Strick, 1989; Strick, 1985; Luppino et al., 1993; Strick and Preston, 1983; Barbas and Pandya, 1987; Petrides and Pandya, 1984). Numerous human studies, based on positron emission tomography (PET) (Honda et * Corresponding author. National Society for Epilepsy, Chalfont St Peter, Gerrards Cross, Bucks SL9 0RJ, UK. Tel: ⫹44-1494-601341. E-mail address: [email protected] (J.S. Duncan). 1 Current address: Laboratoire de Neurophysiologie et Neuropsychologie, Faculte´ de Me´decine, Universite´ de la Me´diterrane´e, Marseille, France.
al., 1998; Grafton et al., 1993; Fink et al., 1997), functional MRI (fMRI) (Rao et al., 1997), transcranial magnetic stimulation (TMS) (Munchau et al., 2002), or scalp or depth electrode electroencephalography (EEG) (Hallett and Toro, 1996; Lim et al., 1996; Chauvel et al., 1996), have suggested various similar cortico-cortical and subcortical M1 functional connections in man. To date, however, no anatomical motor network has been demonstrated in man in vivo. The most reliable technique to study axonal pathways that make up these connections used invasive procedures which are only applicable in animals or in human postmortem brain (Rye, 1999). Furthermore the exact location and variability of long fibre tracts in human adult postmortem brain have only recently been extensively studied (Burgel et al., 1997; Rademacher et al., 2001). Diffusion tensor imaging (DTI) is a noninvasive technique using quantitative three-dimensional measurements of passive tissue water diffusion to infer orientation of white
1053-8119/03/$ – see front matter © 2003 Elsevier Science (USA). All rights reserved. doi:10.1016/S1053-8119(03)00165-4
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Fig. 1. Seed points selection inferred from fMRI activation. The upper images show the seed points placement (in blue) on fractional anisotropy maps in axial (left) and coronal (right) view. The lower images show the location of the fMRI activation (in yellow) during right-hand movements. The crossing blue lines indicate the maximum of activation, from which the seed points have been deduced.
matter axonal fibres in vivo (Le Bihan et al., 2001; Melhem et al., 2002). In the brain, motion of water molecules is constrained by the structure of axons and myelin sheaths (Basser and Pierpaoli, 1996; Pierpaoli et al., 1996). In consequence, the directionality, or anisotropy, of the diffusion of water molecules depends on the orientation of brain fibre pathways. The dominant orientation and magnitude of diffusion within an imaging voxel can be quantified as a principal eigenvector and eigenvalue of diffusion obtained from the measured diffusion tensor. This information can be interrogated using colour-coding (Douek et al., 1991) or vector maps (Makris et al., 1997). The remaining information in the tensor—three eigenvectors are required to describe the anisotropy of diffusion—may be included using a display of the tensor ellipsoid (Pierpaoli and Basser, 1996). Visualisation and isolation of specific white matter pathways require an evaluation of connectivity between voxels,
which can be inferred using a range of mathematical algorithms (Mori et al., 1999, 2000; Conturo et al., 1999; Jones et al., 1999; Basser et al., 2000; Poupon et al., 2000; Parker et al., 2002a, 2002b). In this present study, we used fast marching tractography (FMT) (Parker et al., 2002a). This technique allows branching of tracts from a single seed point and provides 3D connectivity maps representing a metric (or informal probability) of connectivity between a seed point and each voxel in the brain. These connectivity maps can be displayed and analysed in an anatomically normalised framework using statistical parametric mapping (SPM99). Our aim was to study in vivo, using this technique, normal and pathological human primary motor cortex connectivity. The anatomical correlates of functional areas can vary, even in primary cortices such as M1 (Rademacher et al., 2001), in particular in the presence of structural lesion.
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Fig. 2. Example of the sensitivity of the FMT technique to seed point placement in one control subject. Maps of connectivity from three neighbouring seed voxels used to generate the “union” maps used in our experiments are shown, superimposed on the subject’s normalised FA maps. Note that while two of the three seed points maps are superficially similar (seed point 1 and seed point 2), one (seed point 3) shows markedly different distributions of connections.
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FMRI reliably localises motor functions (Lotze et al., 2000; Beisteiner et al., 2001). Thus we selected FMT seed points from the location of the signal activation during a simple fMRI motor task.
Subjects and methods Subjects Eight right-handed healthy volunteers, 7 males, aged 29 to 46 years (mean 34), without any history of neurological disorders, were included in this study. One right-handed patient, aged 30 years, presenting with a left frontal lesion, responsible for frontal lobe epilepsy, was also included. MR data acquisition All studies were performed on a 1.5 T General Electric Signa Horizon imager. Standard imaging gradients with a maximum strength of 22 mT m⫺1 and slew rate 120 Tm⫺1 s⫺1 were used. All data were acquired using a standard quadrature birdcage head coil for both RF transmission and RF reception. Diffusion tensor imaging The DTI acquisition sequence was a single-shot spinecho echo planar imaging (EPI), cardiac gated (triggering occurring on every QRS complex) (Wheeler-Kingshott et al., 2002), with TE ⫽ 95 ms, 96 ⫻ 96 acquisition (128 ⫻ 128 reconstructed) matrix, 22 cm ⫻ 22 cm field of view. Acquisitions of 60 contiguous 2.3-mm-thickness slices were obtained, covering the whole brain, with a maximum b value of 1148 mm2 s⫺1 (␦ ⫽ 34 ms, ⌬ ⫽ 40 ms, using full gradient strength), in 54 noncollinear directions. The reconstructed voxel size was 1.785 ⫻ 1.785 ⫻ 2.3 mm3. The DTI acquisition time for a total of 3600 images was approximately 25 mins (depending on the heart rate). Functional MRI The fMRI acquisition sequence was a gradient echo-EPI, with TE/TR ⫽ 40/10000 ms, 128 ⫻ 128 matrix, 22 ⫻ 22 cm field of view. Twenty seven 2.3-mm-thickness slices were obtained, with 66 repetitions. A simple alternating (every 30 s) right and left hand tapping paradigm was used as the motor task. The fMRI acquisition time was 15 mins. Coregistration Bandwidth, field of view, and matrix size were set to equalise the geometric distortions between the DTI and the fMRI acquisitions (Werring et al., 1999). The same slice prescription from a sagittal localizer was used for both acquisitions.
Data processing Functional MRI The SPM99 package [http://www.fil.ion.ucl.ac.uk/spm/ spm99.html] was used for all image preprocessing and voxelbased statistical analysis, within the context of the general linear model (Friston et al., 1995). Images were realigned, and spatially smoothed with an isotropic Gaussian kernel of 8-mm full-width-half-maximum (FWHM). Motor activity was modeled by way of a simple boxcar function convolved with a canonical hemodynamic response function which, together with a mean column, constituted the design matrix. A T contrast was specified as a unidirectional test of significance for the parameter estimate of interest. The computed SPM{T} was thresholded at the P ⬍ 0.05 level using the correction for multiple comparisons based on Gaussian random field theory. Diffusion tensor imaging The diffusion tensor eigenvalues 1, 2, 3, and eigenvectors 1, 2, 3, were calculated from DTI data, and fractional anisotropy (FA) maps were generated according to the method described by Pierpaoli and Basser (1996) and Pierpaoli et al. (1996), using locally written software. Fast-marching tractography Principles FMT was used to generate connectivity mapping and fibre tracking (Parker et al., 2002a, 2002b). This software utilises the principles of level set theory and the fast-marching algorithm which model the evolution of an interface (or front) over time from a seed point or region (Sethian, 1996). In the context of DTI, the propagation front can be controlled, using the directionality of the principal eigenvector (1). Every voxel in a brain dataset will be crossed by this front at a time depending on the arrangement of the 1 between the seed point and the voxel in question. FMT uses the arrival time map thus generated to generate a 3D connectivity map. This map represents a metric of connectivity which can be considered as an informal likelihood of connectivity between the seed point and every other voxel. This metric (or connectivity value) is designed to rank all putative connections by how well they correspond to the information provided by the DTI data. All paths may therefore be defined and their different values compared within the same subject or between different subjects. We excluded from the FMT calculation, the voxels with a FA value lower than 0.1 which excluded artifactual paths through CSF but allowed the algorithm to make cortico-cortical connections. Starting points The coregistration of fMRI and DTI images allowed us to select the seed points on the FA maps within the white matter directly adjacent to the activated precentral gray matter during fMRI. We selected a set of 3 adjacent seed points in each hemisphere, focused on the highest signifi-
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cantly activated fMRI voxel (Fig. 1). The selection of three seed voxels was based on the observation that connectivity maps resulting from neighbouring voxels often result in markedly different patterns of connectivity subsampling different set of cortical connections (as is to be expected in light of the complex nature of cerebral interregional connections) (Ciccarelli et al., 2003). The selection of three adjacent seed points therefore reduced the possibility of missing important pathways of connection due to small errors in seed point placement (Fig. 2). However, the use of the functional information guards against larger errors that could lead to accidental sampling of different (although closely related in space) sets of connections. Each seed point was processed separately using the FMT algorithm. We chose 3 adjacent voxels along the left-right axis to best follow the anatomical orientation of the precentral gyrus. Connectivity maps For each hemisphere in each subject, we generated a single connectivity map resulting from the “union” of the connectivity maps of the 3 seed points. These union maps were generated using SPM99, selecting for each voxel, the maximum value from the same coordinates in the 3 connectivity maps (Fig. 2). This option was preferred to the addition or the averaging of the 3 maps. As these 3 connectivity maps could be different, the union method gave a high connectivity value to each subsampling connections with high connectivity value. This method, in comparison with the averaging method, also decreased the effect of overlaps between the 3 connectivity maps. The resulting 3D maps of connectivity were displayed in all individual subjects as 3D colour-scaled connectivity maps. The colour scale represented the range of connectivity values without threshold (extending from 0 to a theoretical maximum of 1). The 3D connectivity maps were then spatially normalised using SPM99. This was achieved by normalising the non-diffusion-weighted data from the DTI acquisition against the EPI template provided by SPM99 (derived from the average of 13 normal controls’ echo-planar images) and then applying the transformation parameters to the connectivity maps. We used Talairach space to label all connected regions. Talairach coordinates were derived from the MNI coordinates using software called “mni2tal” (http://www.mrc-cbu. cam.ac.uk/Imaging/mnispace.html) The resulting maps of the whole brain were displayed and considered without applying any threshold. Group mapping The connectivity maps of each control subject were averaged using SPM99, to generate group maps. We generated 2 groups within the control subjects (i.e., left and right hemisphere) resulting from the averaged control maps on each side. Then we made comparisons using the unthresholded colour scales and the SPM framework which allowed us to compare connectivity values within the same regions
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in each group. The patient’s maps were compared in the same way with those of right-handed controls.
Results Controls The unthresholded averaged connectivity maps showed broad connections from the activation maxima in the hand area in all subjects (Figs. 3 and 4). Evaluation based on the connectivity values of tracts (values of voxels involved, colour-coded in figures) permitted the identification of common features in the left and right hemispheres in the control group. Different groups of tracts could be described according to the range of their connectivity values, suggesting a hierarchy in the “likelihood” of connection: Connectivity values ⬎ 0.30 —One group of tracts followed the course of the pyramidal tracts, traversing the corona radiata, the internal capsule, the lateral and anterior part of the basal midbrain, and then the medulla, and crossing at the pyramidal decussation. —Three groups of tracts involved the premotor areas. One involved the medial wall of the frontal lobe within the white matter of the superior frontal gyrus (SFG) to reach the supplementary motor area (SMA). This group of connections extended, inferiorly, to the cingulate sulcus corresponding to the caudal part of the cingulate motor areas. This group also extended, anteriorly, slightly beyond the VCA line (Talairach and Tournoux, 1988) (i.e., vertical line passing through the anterior commissure at 90° to the line joining the anterior and posterior commissure). The two other groups of tracts involved the lateral part of the frontal lobe. The first involved the white matter of the dorso-lateral part of the precentral gyrus (PCG) to reach the dorsal premotor cortex and also the posterior part of the middle frontal gyrus (MFG). The second involved the white matter of the ventro-lateral part of the PCG and the posterior opercular part of the inferior frontal gyrus (IFG). —Two groups of tracts involved the parietal lobe. One involved the postcentral gyrus, and the other involved the white matter of the parietal lobe to reach mostly the medial wall, around the intraparietal sulcus. —One group followed the course of the corticospinal tracts and then involved the lateral and inferior part of the thalamus. The resolution of the technique did not permit determination of the exact nuclei involved. —One group left the cortico-pyramidal tracts at the level of the pons and involved the ipsilateral middle cerebellar peduncle (MCP). —One group of tracts involved the middle and posterior part of the corpus callosum. A part of this group reached the contralateral fornix.
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Fig. 3. Averaged connectivity maps from the left primary motor cortex in eight right-handed controls, superimposed on the normalised single-T1 images from the MNI provided by SPM99. Note the strong connections with the corticospinal tract, the supplementary motor area, the lateral premotor areas, parts of the parietal cortices, the thalamus, and the middle cerebellar peduncle. The left of the brain is on the left of the images. Note that these maps are displayed as raw connectivity maps without applying any threshold to the connectivity values. CST, corticospinal tracts; SMA, supplementary motor area; PMd, dorso-lateral premotor cortex; PMv, ventro-lateral premotor cortex; PPL, posterior parietal lobule; Th, thalamus; MTG, middle temporal gyrus; OL, occipital lobe; MCP, middle cerebellar peduncle; SFG, superior frontal gyrus; MFG, middle frontal gyrus; IFG, inferior frontal gyrus; PCG, precentral gyrus; PostCG, postcentral gyrus; BG, basal ganglia; CC, corpus callosum; Fx, fornix; CR, corona radiata.
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els with connectivity values between 0.30 and 0.20 which were located surrounding the voxels with connectivity values ⬎0.30. —One group involved the posterior and lateral part of the basal ganglia including putamen and striatum. These tracts followed corticospinal tracts through the internal capsule and the external capsule. Connectivity values ⬍ 0.20 These connections were widespread all over the brain and were totally nonspecific. Differences between left and right hemispheres in right handers In general, the left hemisphere showed more extensive M1 connectivity than the right hemisphere (Fig. 4). Two groups of tracts were identified in the left but not in the right hemisphere (Figs. 3 and 4). One group involved the white matter of the superior part of the left occipital lobe (connectivity values were between 0.15 and 0.35). The other involved the white matter of the posterior part of the left superior temporal gyrus (STG) and the white matter of the left occipito-temporal junction at the level of the middle temporal gyrus (MTG) (connectivity values were between 0.15 and 0.25). The connections were broader and generally had higher average connectivity values in the left hemisphere. There were higher connectivity values in the right hemisphere in only two areas: one involving the white matter below the precentral gyrus in the corona radiata (maximum of connectivity value of voxels 0.48 in the right versus a maximum 0.42 in the left); the other involving the white matter of the lateral premotor cortices (maximum 0.42 in the right versus 0.40 in the left). Patient data The patient with the left medial precentral lesion had modified M1 connections (Fig. 5).
Fig. 4. Comparison of connectivity of left and right primary motor cortices (M1) in eight right-handed controls, superimposed on the normalised single-T1 images from the MNI provided by SPM99, showing more extensive connectivity from the left M1. The left of the brain is on the left of the images. As in Fig. 2, these maps display all the connections (unthresholded maps). CST, corticospinal tracts; SMA, supplementary motor area; PMd, dorso-lateral premotor cortex; PMv, ventro-lateral premotor cortex; PPL, posterior parietal lobule; Th, thalamus; MCP, middle cerebellar peduncle; CC, corpus callosum; CR, corona radiata.
Connectivity values between 0.30 and 0.20 —The groups of connections involving the corticospinal tracts, the premotor and parietal cortices, the thalamus, the corpus callosum, and the cerebellar peduncle included vox-
Left hemisphere Connections with the SMA appeared displaced laterally, surrounding the tumour. A group of tracts branched in the inferior part of this last connection and followed a course to the medial inferior part of the prefrontal lobe. The inferior and superior lateral premotor connections extended also to the prefrontal cortex. There was a group of tracts to the lateral part of the posterior parietal lobe. Contralateral connections passed through the posterior half of the corpus callosum, mostly to the precentral area. All premotor connections had higher connectivity values than the averaged controls’ connectivity maps (connectivity values between 0.35 and 0.6). Higher connectivity values were also found in the posterior parietal lobe (connectivity values between 0.3 and 0.45). In contrast, the corticospinal tracts had lower
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connectivity values (connectivity values between 0.15 and 0.30). Right hemisphere Right M1 connectivity appeared broader than in controls. It involved parts of the prefrontal lobe, lateral part of the posterior parietal lobe, and occipital lobe. Widespread contralateral connections also passed through the corpus callosum. Lateral premotor areas and parietal connections had higher connectivity values (between 0.30 and 0.55, and between 0.40 and 0.50, respectively) than the averaged controls’ connectivity maps. Connections through the corpus callosum had high connectivity values (composed between 0.25 and 0.45) and connected to several contralateral areas including premotor, parietal, and prefrontal regions.
Discussion Validity and limitations of the FMT method In this study, we used the FMT method, to demonstrate for the first time, the connectivity of the primary motor cortex in man. Validity and reliability of FMT as a tractography method has been assessed in a previous study demonstrating accurate connectivity maps of corticospinal tracts and optic radiations in macaque and human brain (Parker et al., 2002b). The two advantages of FMT over other tractography algorithms are its capability to estimate a metric of connections which rank the different possible white matter pathways and to allow the branching of pathways. Here we demonstrate the potential usefulness of this technique in the study of human brain connections in vivo. There are limitations inherent in the tractography methods. First, the poor resolution of DTI (mm) compared with the size of the fibre tracts (m), as well as the influence of the signal to noise ratio, can lead to false definition of a specific tract direction in a voxel. A given DTI voxel may be crossed by several crossing fibres or may be the site of unrelated tracts running parallel to the tract of interest. This may be responsible for false positives and negatives in our study (Parker et al., 2002b). In addition, this implies that only large connections can be identified, whereas small connections which could be functionally important, are potentially not determined (leading to false negatives). Therefore, FMT has to be considered as a “macropicture” of brain connections, giving an overview from a distance. Second, the tractography methods cannot differentiate between afferent and efferent fibre tracts (Rye, 1999). Third, pathological modifications may lead to partial volume effects between the principal eigenvectors of different neighbouring voxels and result in false quantitative modifications of connectivity values using FMT. This last issue could have been responsible for the unlikely decrease of the connectivity values in the corticospinal tracts in the patient, considering the absence of any motor deficit. Fourth, difficulties
exist in the interpretation of the FMT results. We have no perfect indication of whether connections identified using the tracing methods are genuine or whether they represent false positives or negatives. Three sources of validation are used. One is with reference to the knowledge of monkey anatomical connections. This knowledge may be extrapolated to the human brain to give us an idea of which brain regions we may expect to be connected. A second possible validation is through known anatomy and functional connectivity in the human brain, and a third is from lesion studies. However, none of these approaches are perfect: the exact degree of similarity between monkey and human connectivity is unknown, extensive anatomy of associated fibres in man is not well known to date, functional connectivity between regions does not imply direct white matter connectivity, and lesion studies are imprecise. A second difficulty in interpreting our results is the lack of precise knowledge of cortical parcellation in our subjects. Thus we defined M1 connections on the basis of gross neuroanatomical definitions using the Talairach and Tournoux atlas. For example, while we have identified parts of the SMA as being connected to the primary motor area, we could not define clear-cut limits of these connections in term of cytoarchitectonic delineations. Therefore, our findings need to be treated with caution at this stage. Nonetheless, our results are encouraging with regard to the usefulness of this technique to study human cortical areas connections in vivo. The only way to validate findings obtained using MR diffusion tracing methods appears to be to perform direct comparative studies of the methods in the monkey brain. Such studies will define the confidence with which we may apply diffusion tracing techniques. Cortical and subcortical motor network demonstration in control subjects: interpretation and validity Our results showed a hierarchy of strength of connections. In this study, connectivity values ⬎0.3 gave the most expected results in control group, demonstrating a reproducible pattern of connections from the primary motor cortex to the corticospinal tracts, parts of premotor and parietal cortices, the thalamus, the corpus callosum, and the ipsilateral cerebellar peduncle. As concerns the premotor cortices, three areas that appeared strongly connected to M1 in our results may correspond to those described to be connected to M1 in monkey (i.e., the SMA, the dorsolateral premotor cortex or PMd and the ventrolateral premotor cortex or PMv) (Rizzolatti et al., 1998; Luppino et al., 1993, 1994; Picard and Strick, 1996, 2001; Barbas and Pandya, 1987). All these frontal motor areas also form part of the cortical origin of the corticospinal tracts (Luppino et al., 1994; He et al., 1993, 1995). Monkey studies have found similar connections to the parietal connections between M1 and the postcentral gyrus as well as the posterior parietal lobule found in our study
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(Strick and Kim, 1978; Petrides and Pandya, 1984; Wise et al., 1997; Matelli and Luppino, 2001; Rizzolatti et al., 1998). Regarding the thalamic connections, direct connections between the ventrolateral thalamic nuclei (ventralis lateralis pars oralis (VLo) and ventralis posterior lateralis pars oralis (VPLo)) and M1 have been noted in macaque brain (Matelli et al., 1989; Holsapple et al., 1991). No clear thalamic nuclei were visualized in our results, but likely tracts were localised in the lateral and inferior part of the thalamus. As concerns the corticospinal tracts, our findings concur with the current knowledge of the route of these tracts from motor cortices to the spinal cord (Rademacher et al., 2001). In our results, however, these tracts had a slightly more posterior position than expected in the mid brain. This may be due to a partial volume effect of numerous crossing fibres in this region, intersubject variability, and a visual effect induced by the addition of fibre tracts connected with the ventro-posterior part of the thalamus. However, the global location of corticospinal tracts in the brainstem is similar to a previous tractography study (Stieltjes et al., 2001). In the corpus callosum, the strongest connections were present in the posterior one-third of it whereas in monkey corpus callosum connections have been described in the middle one-third (Pandya et al., 1971). We also found connections with the cerebellar peduncles, but no direct connections with M1 have been demonstrated in the monkey, in whom the cerebellum is connected via the VPLo nucleus of the thalamus and the red nucleus (Matelli et al., 1995; Orioli and Strick, 1989; Holsapple et al., 1991; Strick, 1985). These cerebellar connections could have been considered as likely by FMT despite the existence of a synaptic relay in the thalamus. If 0.30 is regarded as the most relevant threshold for determining true connectivity, we also found less expected connections with occipital cortex and posterior superior and middle temporal gyri in the left hemisphere. No direct connections have been reported between either occipital or temporal lobe and M1 in monkeys. These connections were less strong than the premotor and parietal connections with connectivity values between 0.25 and 0.35. Thus, they may represent false positives due to the branching of occipitofrontal and temporo-frontal tracts. Several functional imaging and electrophysiological studies have highlighted the possible involvement in motor processes, of the cortical and subcortical areas, that were found to be connected to M1 in our results (Honda et al., 1998; Grafton et al., 1993; Fink et al., 1997; Rao et al., 1997; Munchau et al., 2002; Hallett and Toro, 1996; Lim et al., 1996; Chauvel et al., 1996). All these regions participate in the motor network responsible for preparation, perception, imagination of action, or in cognitive functions related to movements (Georgopoulos, 2000; Sanes, 2000; Toni et al., 2001; Jeannerod, 2001). This network implicates cortico-cortical (above all fronto-parietal) and cortico-subcortical connections. Those studies, however, have not demon-
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strated any direct anatomical connections between these different areas. Usefulness of combined fMRI and DTI tractography Our study illustrates the combination of fMRI and DTIFMT to link brain anatomy and function. Conturo et al. were the first to use fMRI activation (visual task) as the location of tractography seed points, to define the optic radiation (Conturo et al., 1999). However, the authors employed a restrictive method, tracking fibres between two regions of interest. Two previous studies have combined motor fMRI and DTI with the aim of imaging M1 and the corticospinal tracts in patients presenting with tumours potentially affecting these structures (Holodny et al., 2001; Krings et al., 2001). However, none of them attempted to define other M1 connections. Moreover, Holodny et al. used restrictions in the same manner as in the Conturo et al. study, to consider only the pyramidal tracts and avoid other connections. In this study we defined the seed region using fMRI to ensure meaningful loci in all subjects. This enabled comparison between normal controls and cases where there had been significant anatomical deformation, for instance, the mass effect of tumours. In addition, in the subcortical regions where we selected our seed points, adjacent voxels often showed larger variations (FA values and orientations of the principal eigenvector) than in the middle of well organised major white matter tracts (cf. Fig. 1). Therefore, the seed points placement based on anatomical landmarks in the diffusion data may be prone to bias and random error. Crucially, fMRI activations offer the possibility of operatorindependent placement. Hemisphere asymmetry Our results suggested broader connections from the primary motor cortex in the dominant hemisphere. This may be due, at least in part, to the hemisphere asymmetry concerning the primary motor cortex (Amunts et al., 1996). Amunts et al. have recently demonstrated a significant macrostructural (deeper left central sulcus) and microstructural asymmetry suggesting an increased connectivity of the left M1 in right handers. This finding gives a possible insight into the structural basis of differences in cerebral hemisphere functions. Patient assessment The patient study demonstrated the capability of FMT to assess modified connectivity. The patient’s data suggested modification of M1 connectivity mainly in the left hemisphere, but also some modifications in the right hemisphere with a broader connectivity than in controls. One speculation is that M1 connectivity in the right hemisphere could have been modified by neuronal plasticity resulting from dominant hemisphere impairment. However, the results
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Fig. 5. Connectivity of left and right primary motor cortices (M1) in patient with medial left precentral tumour, superimposed on the normalised patient b0 images. The left of the brain is on the left of the images. As in Figs. 2 and 3, the maps are unthresholded. CST, corticospinal tracts; SMA, supplementary motor area; PMd, dorso-lateral premotor cortex; PMv, ventro-lateral premotor cortex; PPL, posterior parietal lobe; Th, thalamus; CC, corpus callosum; MCP, middle cerebellar peduncle; PFC, prefrontal cortex.
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must be interpreted with great caution. First, compaction of tracts might result in an artificial increase of connectivity values in affected areas. In the present case, an increase of the connectivity values was found in the premotor areas probably distorted by the tumour. Second, displacement of structures might modify the shape and angles of the tracts. Then, the algorithm used might define some paths as less or more likely because of the modification of the shape of the paths. For instance, in our patient, although there was no motor deficit, right corticospinal tracts were found with lower connectivity values than in controls. In contrast, right prefrontal connections were found as “likely paths” that were not identified in the averaged controls’ maps. Third, we used an averaged control map for comparisons. Therefore, some modifications of the connectivity values may be linked to normal interindividual differences. Fourth, the presence of a tumour may damage pathways that do not contribute to motor connectivity, but affect measures of connectivity via partial volume effects. Degeneration of the tracts may lead to an “unmasking” of some of the motor fibres, thus allowing connections to be identified that are not apparent in the control group (Pierpaoli et al., 2001). To accurately assess impairment of connectivity in patients it will be necessary to compare the patient’s maps with a larger number of controls, using statistical voxel-based analysis. Conclusion The tractography methods, despite technical limitations at this stage in comparison with invasive anatomical techniques, have the advantage of being feasible in vivo. This study opens several perspectives for further investigations. The combination of fMRI activation tasks and DTI-FMT may be helpful in defining the structural basis of functional connectivity in the normal brain, and the derangement of these networks in disease states. Acknowledgments This work was funded by National Society for Epilepsy. M. Guye was supported by the French League Against Epilepsy (Pfizer grant). References Amunts, K., Schlaug, G., Schleicher, A., Steinmetz, H., Dabringhaus, A., Roland, P.E., Zilles, K., 1996. Asymmetry in the human motor cortex and handedness. NeuroImage 4, 216 –222. Barbas, H., Pandya, D.N., 1987. Architecture and frontal cortical connections of the premotor cortex (area 6) in the rhesus monkey 2. J. Comp. Neurol. 256, 211–228. Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A., 2000. In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44, 625– 632.
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Basser, P.J., Pierpaoli, C., 1996. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. B 111, 209 –219. Beisteiner, R., Windischberger, C., Lanzenberger, R., Edward, V., Cunnington, R., Erdler, M., Gartus, A., Streibl, B., Moser, E., Deecke, L., 2001. Finger somatotopy in human motor cortex. NeuroImage 13, 1016 –1026. Burgel, U., Mecklenburg, I., Blohm, U., Zilles, K., 1997. Histological visualization of long fiber tracts in the white matter of adult human brains. J. Hirnforsch. 38, 397– 404. Ciccarelli, O., Parker, G.J.M., Toosy A.T., Wheeler-Kingshott, C.A.M., Barker, G.J., Boulby, P.A., Miller D.H., Thompson, A.J., 2003. From diffusion tractography to quantitative white matter tract measures: a reproducibility study. NeuroImage, 18, 348 –359. Chauvel, P.Y., Rey, M., Buser, P., Bancaud, J., 1996. What stimulation of the supplementary motor area in humans tells about its functional organization. Adv. Neurol. 70, 199 –209. Conturo, T.E., Lori, N.F., Cull, T.S., Akbudak, E., Snyder, A.Z., Shimony, J.S., McKinstry, R.C., Burton, H., Raichle, M.E., 1999. Tracking neuronal fiber pathways in the living human brain. Proc. Natl. Acad. Sci. USA 96, 10422–10427. Douek, P., Turner, R., Pekar, J., Patronas, N., Le Bihan, D., 1991. MR color mapping of myelin fiber orientation. J. Comput. Assist. Tomogr. 15, 923–929. Fink, G.R., Frackowiak, R.S., Pietrzyk, U., Passingham, R.E., 1997. Multiple nonprimary motor areas in the human cortex. J. Neurophysiol. 77, 2164 –2174. Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.-P., Frith, C.D., Frackowiack, R.S.J., 1995. Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2, 189 –210. Georgopoulos, A.P., 2000. Neural aspects of cognitive motor control. Curr. Opin. Neurobiol. 10, 238 –241. Geyer, S., Matelli, M., Luppino, G., Zilles, K., 2000. Functional neuroanatomy of the primate isocortical motor system. Anat. Embryol. (Berl) 202, 443– 474. Grafton, S.T., Woods, R.P., Mazziotta, J.C., 1993. Within-arm somatotopy in human motor areas determined by positron emission tomography imaging of cerebral blood flow 4. Exp. Brain. Res. 95, 172–176. Hallett, M., Toro, C., 1996. Generation of movement-related potentials in the supplementary sensorimotor area. Adv. Neurol. 70, 147–152. He, S.Q., Dum, R.P., Strick, P.L., 1993. Topographic organization of corticospinal projections from the frontal lobe: motor areas on the lateral surface of the hemisphere 4. J. Neurosci. 13, 952–980. Holodny, A.I., Ollenschleger, M.D., Liu, W.C., Schulder, M., Kalnin, A.J., 2001. Identification of the corticospinal tracts achieved using bloodoxygen-level-dependent and diffusion functional MR imaging in patients with brain tumors. AJNR Am. J. Neuroradiol. 22, 83– 88. Holsapple, J.W., Preston, J.B., Strick, P.L., 1991. The origin of thalamic inputs to the “hand” representation in the primary motor cortex. J. Neurosci. 11, 2644 –2654. Honda, M., Wise, S.P., Weeks, R.A., Deiber, M.P., Hallett, M., 1998. Cortical areas with enhanced activation during object-centred spatial information processing. A PET study. Brain 121, 2145–2158. Jeannerod, M., 2001. Neural simulation of action: a unifying mechanism for motor cognition. NeuroImage 14, S103–S109. Jones, D.K., Simmons, A., Williams, S.C., Horsfield, M.A., 1999. Noninvasive assessment of axonal fiber connectivity in the human brain via diffusion tensor MRI. Magn. Reson. Med. 42, 37– 41. Krings, T., Reinges, M.H., Thiex, R., Gilsbach, J.M., Thron, A., 2001. Functional and diffusion-weighted magnetic resonance images of space-occupying lesions affecting the motor system: imaging the motor cortex and pyramidal tracts. J. Neurosurg. 95, 816 – 824. Le Bihan, D., Mangin, J.F., Poupon, C., Clark, C.A., Pappata, S., Molko, N., Chabriat, H., 2001. Diffusion tensor imaging: concepts and applications. J. Magn. Reson. Imaging 13, 534 –546. Lim, S.H., Dinner, D.S., Luders, H.O., 1996. Cortical stimulation of the supplementary sensorimotor area. Adv. Neurol. 70, 187–197.
1360
M. Guye et al. / NeuroImage 19 (2003) 1349 –1360
Lotze, M., Erb, M., Flor, H., Huelsmann, E., Godde, B., Grodd, W., 2000. fMRI evaluation of somatotopic representation in human primary motor cortex. NeuroImage 11, 473– 481. Luppino, G., Matelli, M., Camarda, R., Rizzolatti, G., 1993. Corticocortical connections of area F3 (SMA-proper) and area F6 (pre-SMA) in the macaque monkey. J. Comp. Neurol. 338, 114 –140. Luppino, G., Matelli, M., Camarda, R., Rizzolatti, G., 1994. Corticospinal projections from mesial frontal and cingulate areas in the monkey. NeuroReport 5, 2545–2548. Makris, N., Worth, A.J., Sorensen, A.G., Papadimitriou, G.M., Wu, O., Reese, T.G., Wedeen, V.J., Davis, T.L., Stakes, J.W., Caviness, V.S., Kaplan, E., Rosen, B.R., Pandya, D.N., Kennedy, D.N., 1997. Morphometry of in vivo human white matter association pathways with diffusion-weighted magnetic resonance imaging. Ann. Neurol. 42, 951–962. Matelli, M., Luppino, G., 2001. Parietofrontal circuits for action and space perception in the macaque monkey. NeuroImage 14, S27–S32. Matelli, M., Luppino, G., Fogassi, L., Rizzolatti, G., 1989. Thalamic input to inferior area 6 and area 4 in the macaque monkey. J. Comp. Neurol. 280, 468 – 488. Matelli, M., Luppino, G., Rizzolatti, G., 1995. Convergence of pallidal and cerebellar outputs on the frontal motor areas. Acta Biomed. Ateneo. Parmense. 66, 83–92. Melhem, E.R., Mori, S., Mukundan, G., Kraut, M.A., Pomper, M.G., van Zijl, P.C., 2002. Diffusion tensor MR imaging of the brain and white matter tractography. AJR Am. J. Roentgenol. 178, 3–16. Mori, S., Crain, B.J., Chacko, V.P., van Zijl, P.C., 1999. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45, 265–269. Mori, S., Kaufmann, W.E., Pearlson, G.D., Crain, B.J., Stieltjes, B., Solaiyappan, M., van Zijl, P.C., 2000. In vivo visualization of human neural pathways by magnetic resonance imaging. Ann. Neurol. 47, 412– 414. Munchau, A., Bloem, B.R., Irlbacher, K., Trimble, M.R., Rothwell, J.C., 2002. Functional connectivity of human premotor and motor cortex explored with repetitive transcranial magnetic stimulation. J. Neurosci. 22, 554 –561. Orioli, P.J., Strick, P.L., 1989. Cerebellar connections with the motor cortex and the arcuate premotor area: an analysis employing retrograde transneuronal transport of WGA-HRP. J. Comp. Neurol. 288, 612– 626. Pandya, D.N., Karol, E.A., Heilbronn, D., 1971. The topographical distribution of interhemispheric projections in the corpus callosum of the rhesus monkey. Brain Res. 32, 31– 43. Parker, G.J., Wheeler-Kingshott, C.A., Barker, G.J., 2002a. Estimating distributed anatomical connectivity using fast marching methods and diffusion tensor imaging. IEEE Trans. Med. Imaging 21, 505–12. Parker, G.J., Stephan, K.E., Barker, G.J., Rowe, J.B., MacManus, D.G., Wheeler-Kingshott, C.A., Ciccarelli, O., Passingham, R.E., Spinks, R.L., Lemon, R.N., Turner, R., 2002b. Initial demonstration of in vivo tracing of axonal projections in the macaque brain and comparison with the human brain using diffusion tensor imaging and fast marching tractography. NeuroImage 15, 797– 809. Passingham, R., 1997. Functional organisation of the motor system, in: Frackowiak, R.S.J., Friston, K.J., Frith, C.D., Dolan, R.J., Mazziotta, J.C. (Eds.), Human Brain Function. Academic Press, San Diego, pp. 243–274. Petrides, M., Pandya, D.N., 1984. Projections to the frontal cortex from the posterior parietal region in the rhesus monkey. J. Comp. Neurol. 228, 105–116.
Picard, N., Strick, P.L., 1996. Motor areas of the medial wall: a review of their location and functional activation. Cereb. Cortex 6, 342–353. Picard, N., Strick, P.L., 2001. Imaging the premotor areas. Curr. Opin. Neurobiol. 11, 663– 672. Pierpaoli, C., Basser, P.J., 1996. Toward a quantitative assessment of diffusion anisotropy. Magn. Reson. Med. 36, 893–906. Pierpaoli, C., Jezzard, P., Basser, P.J., Barnett, A., Di Chiro, G., 1996. Diffusion tensor MR imaging of the human brain. Radiology 201, 637– 648. Pierpaoli, C., Barnett, A., Pajevic, S., Chen, R., Penix, L., Virta, A., Basser, P., 2001. Water diffusion changes in Wallerian degeneration and their dependence on white matter architecture. NeuroImage 13, 1174 –1185. Poupon, C., Clark, C.A., Frouin, V., Regis, J., Bloch, I., Le Bihan, D., Mangin, J., 2000. Regularization of diffusion-based direction maps for the tracking of brain white matter fascicles. NeuroImage 12, 184 –195. Rademacher, J., Burgel, U., Geyer, S., Schormann, T., Schleicher, A., Freund, H.J., Zilles, K., 2001. Variability and asymmetry in the human precentral motor system. A cytoarchitectonic and myeloarchitectonic brain mapping study. Brain 124, 2232–2258. Rao, S.M., Harrington, D.L., Haaland, K.Y., Bobholz, J.A., Cox, R.W., Binder, J.R., 1997. Distributed neural systems underlying the timing of movements. J. Neurosci. 17, 5528 –5535. Rizzolatti, G., Luppino, G., Matelli, M., 1998. The organization of the cortical motor system: new concepts. Electroencephalogr. Clin. Neurophysiol. 106, 283–296. Rye, D.B., 1999. Tracking neural pathways with MRI. Trends Neurosci. 22, 373–374. Sanes, J.N., 2000. Motor cortex rules for learning and memory. Curr. Biol. 10, R495–R497. Sanes, J.N., Donoghue, J.P., 2000. Plasticity and primary motor cortex. Annu. Rev. Neurosci. 23, 393– 415. Sethian, J.A., 1996. A fast marching level set method for monotonically advancing fronts. Proc. Natl. Acad. Sci. USA 93, 1591–1595. Stieltjes, B., Kaufmann, W.E., van Zijl, P.C., Fredericksen, K., Pearlson, G.D., Solaiyappan, M., Mori, S., 2001. Diffusion tensor imaging and axonal tracking in the human brainstem. NeuroImage 14, 723–735. Strick, P.L., 1985. How do the basal ganglia and cerebellum gain access to the cortical motor areas? Behav. Brain Res. 18, 107–123. Strick, P.L., Kim, C.C., 1978. Input to primate motor cortex from posterior parietal cortex (area 5). I. Demonstration by retrograde transport. Brain Res. 157, 325–330. Strick, P.L., Preston, J.B., 1983. Input-output organization of the primate motor cortex. Adv. Neurol. 39, 321–327. Talairach, J., Tournoux, P., 1988. Coplanar Stereotaxic Atlas of the Human Brain. Thieme, Stuttgart. Toni, I., Thoenissen, D., Zilles, K., 2001. Movement preparation and motor intention. NeuroImage 14, S110 –S117. Werring, D.J., Clark, C.A., Parker, G.J.M., Miller, D.H., Thompson, A.J., Barker, G.J., 1999. A direct demonstration of both structure and function in the visual system: combining diffusion tensor imaging with functional magnetic resonance imaging. NeuroImage 9, 352–361. Wheeler-Kingshott, C.A., Boulby, P.A., Symms, M.R., Barker, G.J., 2002. Optimised cardiac gating for high-resolution whole-brain DTI on a standard scanner. Proc. Intl. Soc. Magn. Reson. Med. 10, 1118. Wise, S.P., Boussaoud, D., Johnson, P.B., Caminiti, R., 1997. Premotor and parietal cortex: corticocortical connectivity and combinatorial computations. Annu. Rev. Neurosci. 20, 25– 42.