Human Motor Corpus Callosum

Callosal motor fibers connecting the primary motor cortices of the two hemispheres map onto the posterior body and isthmus of the corpus callosum in a somatotopically organized manner, with hand fibers running significantly more anteriorly and ventrally than foot fibers, according to a study combining functional magnetic resonance imaging with diffusion tensor imaging fiber tracking. The study found that fractional anisotropy of the hand callosal motor fibers, but not the foot fibers, correlated linearly with interhemispheric inhibition between the M1 hand areas measured by paired-pulse transcranial magnetic stimulation, indicating microstructure can be directly linked to functional connectivity.

Abstract The corpus callosum CC is the principal white matter fiber bundle connecting neocortical areas of the two hemispheres. Although an object of extensive research, important details about the anatomical and functional organization of the human CC are still largely unknown. Here we focused on the callosal motor fibers CMFs that connect the primary motor cortices M1 of the two hemispheres. Topography and somatotopy of CMFs were explored by using a combined functional magnetic resonance imaging/diffusion tensor imaging fiber-tracking procedure. CMF microstructure was assessed by fractional anisotropy FA , and CMF functional connectivity between the hand areas of M1 was measured by interhemispheric inhibition using paired-pulse transcranial magnetic stimulation. CMFs mapped onto the posterior body and isthmus of the CC, with hand CMFs running significantly more anteriorly and ventrally than foot CMFs. FA of the hand CMFs but not FA of the foot CMFs correlated linearly with interhemispheric inhibition between the M1 hand areas. Findings demonstrate that CMFs connecting defined body representations of M1 map onto a circumscribed region in the CC in a somatotopically organized manner. The significant and topographically specific positive correlation between FA and interhemispheric inhibition strongly suggests that microstructure can be directly linked to functional connectivity. This provides a novel way of exploring human brain function that may allow prediction of functional connectivity from variability of microstructure in healthy individuals, and potentially, abnormality of functional connectivity in neurological or psychiatric patients. Introduction The corpus callosum CC is the principal white matter fiber bundle connecting neocortical areas of the two hemispheres Innocenti, 1986 ref-26 ; Gazzaniga, 2000 ref-21 . Although it is an object of extensive research, important details about the anatomical and functional organization of the human CC are still largely unknown. This pertains to the callosal motor fibers CMFs connecting the primary motor cortices M1 in the two hemispheres. Anatomical studies in the rhesus monkey located the CMFs into the anterior half of the body of the CC Pandya and Seltzer, 1986 ref-43 ; Schmahmann and Pandya, 2006 ref-50 , and these fibers seem to be somatotopically organized Schmahmann and Pandya, 2006 ref-50 . In contrast, evidence from transcranial magnetic stimulation TMS Meyer et al., 1998 ref-37 and two recent diffusion tensor imaging DTI studies Hofer and Frahm, 2006 ref-24 ; Zarei et al., 2006 ref-63 suggested that the CMFs in humans run in the posterior body of the CC. However, detailed data about CMFs in the human CC are still limited, and it is unknown if they are somatotopically organized. It is a long-held view that the motor CC is important for bimanual coordination and learning of bimanual motor skill. This notion was built mainly on bimanual coordination deficits observed in patients with extensive lesions of the CC or partial callosotomies Preilowski, 1972 ref-46 ; Jeeves et al., 1988 ref-28 ; Andres et al., 1999 ref-2 ; Eliassen et al., 1999 ref-16 , 2000 ref-17 ; Wiesendanger and Serrien, 2004 ref-57 ; Caille et al., 2005 ref-8 . One step toward a refined understanding of the function of the human CC would be to identify its detailed topographical organization by novel MRI techniques. DTI allows quantification of the directionality of diffusion by the fractional anisotropy FA index. FA estimates the orientational coherence of white matter fiber bundles Moseley et al., 1990 ref-38 ; Le Bihan et al., 2001 ref-35 ; Beaulieu, 2002 ref-4 ; Le Bihan, 2003 ref-34 ; Pfefferbaum et al., 2003 ref-45 . Combining DTI with functional magnetic resonance imaging fMRI allows noninvasive quantitative in vivo analysis of anatomical connections between functionally defined brain regions Conturo et al., 1999 ref-9 ; Koch et al., 2002 ref-32 ; Dougherty et al., 2005 ref-12 ; Kim and Kim, 2005 ref-30 . Regional FA of subcortical association fibers correlated topographically specifically with cognitive functions such as reading ability Klingberg et al., 2000 ref-31 , visuospatial attention Tuch et al., 2005 ref-54 , or mental object rotation Wolbers et al., 2006 ref-59 , and FA of the total CC correlated with visuomotor interhemispheric processing speed Schulte et al., 2005 ref-52 . This introduces a new window to study the brain, with regional FA serving as a microstructural surrogate marker that links directly to function. Functional connectivity between the M1 hand areas of the two hemispheres can be assessed objectively by a paired-pulse TMS protocol that measures transcallosally mediated interhemispheric inhibition IHI Ferbert et al., 1992 ref-19 ; Di Lazzaro et al., 1999 ref-11 . This study aims at reexamining by combined fMRI/DTI-fiber tracking the topography of CMFs in the human CC and at providing novel information as to whether CMFs of different body representations are somatotopically organized. Another main purpose is to clarify to which extent CMF microstructure, as measured by FA, relates to functional connectivity of these fibers, as assessed by IHI. Materials and Methods Subjects. Twelve healthy right-handed subjects mean age, 32.6 ± 9.0 years; 5 female were tested. Written informed consent to participate in the study was obtained from all subjects. The study was approved by the local ethics committee of the University Hospital of Frankfurt/Main. Image acquisition. Magnetic resonance imaging was performed on a 3 tesla Magnetom Trio Siemens Medical Solutions, Erlangen, Germany : structural T1-weighted data were acquired using a magnetization-prepared rapid-acquisition gradient echo MPRAGE sequence repetition time TR 2250 ms, echo time TE 26 ms, inversion time 900 ms, flip angle 9°, 192 sagittal slices, field of view 256 × 256 mm, voxel size 1 × 1 × 1 mm . To localize body representations lip, hand, foot in the M1 of either hemisphere, fMRI was performed in a block design experiment using a blood oxygenation level-dependent BOLD -sensitive echo planar imaging EPI sequence TR 2640 ms, TE 30 ms, flip angle 90°, 36 transverse slices, 0.75 mm gap, voxel size 3 × 3 × 3 mm, base resolution 64 × 64 . Point spread function mapping was used for online EPI distortion correction Zaitsev et al., 2004 ref-62 . Subjects performed a simple visually guided motor task. Instructions were projected on a screen mounted on the rear end of the head coil. A total of 192 scans were acquired while subjects performed four blocks of alternating rest, pursing movements of lips, flexion movements of the fingers of the left hand, fingers of the right hand, toes of the right foot, and toes of the left foot 21 s per condition . Subjects were instructed to perform the movements at a self-paced rate of ∼2 Hz. For DTI, a single shot spin-echo EPI with parallel imaging technique GRAPPA generalized auto-calibrating partially parallel acquisitions acceleration factor 2 was used. The sequence was performed with 12 gradient directions and a b-value of 700 TR 7200 ms, TE 80 ms, voxel size 1.9 × 2.0 × 1.9 mm, 60 transverse slices, base resolution 128×128 . fMRI analysis. The fMRI data were processed using Brain Voyager Brain Innovation, Maastricht, The Netherlands . Before statistical analysis, interslice scan time correction with sinc interpolation and three-dimensional 3D motion correction were used for data preprocessing. No relevant motion artifacts were detected. The realigned time series were temporally filtered with a high-pass filter of three data points. Linear drifts of the signal were removed from each pixel's time course. For alignment of fMRI and 3D anatomical data, a two-stage coregistration process was applied with an initial automatic header-based alignment, followed by fine tuning through a manual adjustment of rotational and translational parameters. At each voxel, a hemodynamic reference function was correlated with the time course of the measured BOLD signal, resulting in a correlation coefficient r . The data were thresholded at 80% of the maximum correlation coefficient in M1 for a given motor task to obtain circumscribed functional representations of comparable size in M1. The anatomical and fMRI data were exported as DICOM files. Fiber tracking. FA maps were calculated from the DTI data. T1-weighted MPRAGE, fMRI, and DTI were fused PatXfer 5.1, BrainLab, image fusion software module by a semiautomatic rigid registration procedure using intensity-based information Nimsky et al., 2006 ref-40 . Fiber tracking was performed based on a tensor deflection algorithm in which the trend of the current generated fibers is also considered Lazar et al., 2003 ref-33 ; Nimsky et al., 2006 ref-40 . The major eigenvector of each seed was calculated, and iteration was started bidirectionally until one of the following stop criteria was met: FA < 0.15 or deviation angle 40°. Tracked fibers with a length <50 mm were discarded from analysis. Retained fibers were visualized by directionally encoded color Pajevic and Pierpaoli, 1999 ref-42 Figs. 1 F1 a–d , 2 F2 a . Fiber tracking was started from a large rectangular ROI that covered the lip, hand, and foot representations defined by fMRI in the right and left M1 Fig. 1 F1 a . This first tracking step visualized corticofugal fibers originating from M1 Fig. 1 F1 a . In a second step, another rectangular ROI was placed within the area of the CC that was identified by the first tracking step, and fiber tracking was performed again Fig. 1 F1 b . Both ROIs were set to contain 30,000 seeds, regardless of their size. Because the BOLD signal is largely limited to the gray matter, tracked fibers projecting into or near to the functionally defined M1 representations were retained, including at least one more voxel beyond the gray/white matter boundary Fig. 1 F1 c–d . For the CMFs projecting into the M1 hand area, the knob of the precentral gyrus, an accepted anatomical landmark of the M1 hand area Yousry et al., 1997 ref-61 was used in addition to define the hand CMF target area Fig. 1 F1 c , d . Tracked CMFs were automatically generated as 3D objects BrainLab and color encoded lip CMFs: red, hand CMFs: green, and foot CMFs: orange Fig. 2 F2 a . Validity and specificity of this novel two-step tracking procedure were ensured by comparing tracking results with those of a one-step procedure that started from a ROI covering the entire CC as described previously by others Hofer and Frahm, 2006 ref-24 ; Zarei et al., 2006 ref-63 ; Johansen-Berg et al., 2007 ref-29 . Both procedures revealed identical anatomical sites of tracked fibers in the CC, but the two-step tracking showed superior sensitivity; i.e., more fibers were tracked. Image normalization. Images of all subjects were aligned to the anterior commissure AC /posterior commissure PC line. The aligned individual CMF data were exported in DICOM format and transformed on the basis of a proportional grid principle. Usually linear transformation on the basis of 12 parameters Fox et al., 1984 ref-20 matches individual data well on the cortical level, whereas deep internal structures of the brain, such as the CC, are not matched satisfactorily. Therefore, a modified linear transformation in-house plug-in for Brain Voyager; Steffen Volz, Brain Imaging Center, Johann Wolfgang Goethe–University Frankfurt, Frankfurt am Main, Germany was performed, where the parameters were defined by the individual CC structure: the interhemispheric plane, the plane through the genu of the CC parallel to AC–PC and planes through AC and branch of fornix instead of PC . The outer border was determined by the extent of the CC. This allowed warping individual CC data onto the CC morphology of one reference subject see Figs. 3 F3 , 4 F4 . The individual transformed CMF data were superimposed to depict interindividual variability of CMF topography see Fig. 4 F4 . DTI quantification. FA was averaged separately for hand versus foot CMFs from all voxels within the boundaries of the CC rather than from the entire CMF pathway. This restriction was applied to avoid contamination from crossing fibers, which are absent within the midbody of the CC but certainly present in other parts of the subcortical white matter. To avoid inclusion of partial volumes of surrounding CSF, a distance of one voxel to the CC/CSF boundary indicated by FA < 0.4 was respected Fig. 1 F1 e , f . IHI between the hand areas of M1. IHI between the hand areas of M1 was measured by an established paired-pulse TMS protocol Ferbert et al., 1992 ref-19 . TMS was delivered through two figure-of-eight coils diameter of each loop, 70 mm , each connected to a Magstim 200 magnetic stimulator with a monophasic current waveform Magstim, Dyfed, UK . The conditioning coil was placed tangentially over the hand area of the left M1, with the handle pointing laterally so that a lateral-to-medial current was induced in this M1. The test coil was placed over the hand area of the right M1, with the handle pointing backwards and 45° away from the midline so that the induced current in this M1 flowed from lateral/posterior to medial/anterior. For both coils, the optimal position was identified that resulted in largest motor evoked potentials MEPs in the first dorsal interosseus FDI muscle of the contralateral hand. The resting motor threshold RMT was determined for the conditioning pulse to the nearest 1% of maximum stimulator output and was defined as the minimum stimulus intensity that resulted in liminal MEPs 50 μV in at least 5 of 10 trials. IHI increases with the intensity of the conditioning stimulus Ferbert et al., 1992 ref-19 ; Hanajima et al., 2001 ref-23 . Accordingly, the intensity of the conditioning stimulus was varied from 100 to 150% RMT in 10% steps i.e., six different intensities to obtain a wide range of IHI magnitudes from threshold to maximum. The intensity of the test pulse was adjusted to produce an unconditioned MEP of on average 1 mV in peak-to-peak amplitude. The interstimulus interval between conditioning and test pulse was set to 12 ms, because previous studies showed reliable IHI at this interval Ferbert et al., 1992 ref-19 ; Hanajima et al., 2001 ref-23 . Eight trials per condition were run in randomized order, and conditional averages of the single-trial MEP amplitudes were calculated. The intertrial interval varied randomly between 4 and 6 s. IHI was expressed for each intensity of the conditioning pulse by 1 − mean conditioned MEP/mean unconditioned MEP × 100%. Accordingly, IHI = 0% indicates no inhibition, and IHI = 100% indicates complete inhibition. In addition, IHI threshold was determined for each subject by fitting a linear or second order binomial function to the individual IHI intensity curve and extrapolate those intensities of the conditioning pulse expressed as a percentage of maximum stimulator output that resulted in 25 or 50% IHI Orth et al., 2003 ref-41 . MEPs were recorded bilaterally from the FDI by surface electromyography EMG , with the active electrode placed over the muscle belly and the reference electrode on the metacarpophalangeal joint of the index finger. The EMG was bandpass filtered 20 Hz to 2 kHz and amplified Counterpoint Mk2 Electromyograph, Dantec, 2740 Skovlunde, Denmark , digitized at an analog-to-digital rate of 4 kHz per channel CED Micro 1401; Cambridge Electronic Design, Cambridge, UK , and stored on a computer for off-line analysis. Customized Spike2 software Cambridge Electronic Design was used for data acquisition and analysis. Statistics. To relate microstructure to functional connectivity, individual mean FA values of the hand CMFs were correlated to IHI interhemispheric inhibition between the hand areas of M1 and to IHI threshold, using linear regression analyses. To test for topographical specificity, FA values of the foot CMFs were also correlated to the same IHI data. Statistical significance was assumed if p < 0.05. Results CMF topography and somatotopy Figure 3 F3 shows the midsagittal topography of hand CMFs encoded in green versus foot CMFs encoded in orange of all 12 tested subjects. CMFs mapped consistently onto the posterior body and/or isthmus of the CC. CMFs of the hand and foot overlapped to some extent but showed a consistent somatotopy. In all subjects, the hand CMFs were located slightly more ventrally than the foot CMFs, and in 10 of 12 subjects the hand CMFs were located slightly more anteriorly than the foot CMFs Fig. 3 F3 . Only subjects 7 and 9 showed a partially reverse somatotopy with the hand CMFs located posteriorly to the foot CMFs Fig. 3 F3 . The lip CMFs could be reliably tracked in only one subject Fig. 2 F2 . In this individual, the lip CMFs were located most anteriorly, merging into the anterior body of the CC. Figure 4 F4 shows the interindividual variability of CMF topography by superimposing the individual normalized hand CMFs Fig. 4 F4 a and foot CMFs Fig. 4 F4 b onto one reference midsagittal CC. The arithmetic mean Talairach coordinates of the hand CMFs were y = −14.3 ± 3.2 mm and z = 17.6 ± 0.8 mm, and those of the foot CMFs were y = −17.1 ± 2.8 mm and z = 18.8 ± 0.9 mm. Differences were significant for both axes y : p = 0.049; z : p = 0.0005 , confirming a more anterior and ventral location of the hand CMFs compared with the foot CMFs Fig. 4 F4 c . Correlation between FA and IHI Linear regression analysis revealed a positive correlation between FA of the hand CMFs and the magnitude of IHI, when intensities of the conditioning pulse of 130% RMT r = 0.59; p = 0.04 , 140% RMT r = 0.66; p = 0.02 , or 150% RMT r = 0.59; p = 0.04 were used Fig. 5 F5 a . In addition, FA of the hand CMFs correlated negatively with the threshold intensities for 25% r = −0.60; p = 0.04 and 50% IHI r = −0.64; p = 0.02 Fig. 5 F5 b . In contrast, FA of the foot CMFs did not correlate with any of these IHI measures all p values 0.6 Fig. 5 F5 c , d . Discussion The main findings from this study are that CMFs map onto a circumscript region in the posterior body and isthmus of the human CC and are somatotopically organized, and microstructural information fractional anisotropy links to their function interhemispheric inhibition . Topography of CMFs Largely based on experimental work in monkeys Pandya et al., 1971 ref-44 ; Pandya and Seltzer, 1986 ref-43 ; Schmahmann and Pandya, 2006 ref-50 and a few anatomical studies in humans van Valkenburg, 1913 ref-55 ; de Lacoste et al., 1985 ref-10 , it was long thought that the CMFs connecting M1 of the two hemispheres traverse the human CC in its anterior midbody Witelson, 1989 ref-58 . However, one TMS study Meyer et al., 1998 ref-37 and two recent DTI tractography studies Hofer and Frahm, 2006 ref-24 ; Zarei et al., 2006 ref-63 strongly suggested that CMFs cross the human CC clearly more posteriorly, in the posterior body of the CC. Our study confirms this posterior topography and specifies the information from the previous DTI studies by using fMRI in addition to anatomical criteria to define M1 as the tractography target region. One explanation for the posterior shift of CMFs in humans compared with monkeys is the significant increase in prefrontal cortex volume, in particular the prefrontal white matter, in humans Eccles, 1989 ref-15 ; Schoenemann et al., 2005 ref-51 , suggesting connectional elaboration. Indeed, prefrontal cortex heavily connects between hemispheres by callosal fibers, which occupy the complete anterior half of the human CC Zarei et al., 2006 ref-63 . It should be noted that the exact topography of CMFs in the present study showed considerable interindividual variability along the anterior–posterior axis Figs. 3 F3 , 4 F4 . Coregistration of T1-weighted anatomical images, fMRI, and DTI tractography offers the opportunity to determine where exactly CMFs are localized in the CC of a given individual. The fiber-tracking results were further validated by demonstrating, according to a previous protocol Dougherty et al., 2005 ref-12 , a high degree of anatomical convergence in the CC if hand CMFs to the left and right M1 were tracked independently data not shown . In addition, the tracked hand CMFs originated largely from the hand areas of M1. A significant contribution from other premotor areas, in particular the rostrally adjacent dorsal premotor area, is unlikely because these fibers, although they may partially overlap with the CMFs, extend far into the anterior midbody of the CC Hofer and Frahm, 2006 ref-24 ; Zarei et al., 2006 ref-63 , where no fibers were tracked in the present study. The consistent findings from now three DTI tractography studies with respect to CMF localization should lead to a shift of the previously popular segmentation schemes of the CC that were based on geometrical principals de Lacoste et al., 1985 ref-10 ; Witelson, 1989 ref-58 ; Steinmetz et al., 1992 ref-53 ; Rajapakse et al., 1996 ref-47 ; Jäncke et al., 1997 ref-27 toward a CC segmentation based on individual anatomical connectivity. Somatotopy of CMFs This is, to the best of our knowledge, the first demonstration that CMFs are somatotopically organized in the human CC. Previous DTI tractography studies identified the topography of CMFs in the human CC but made no effort to subdivide the CMFs into different body representations Hofer and Frahm, 2006 ref-24 ; Zarei et al., 2006 ref-63 . We successfully tracked hand and foot CMFs in all 12 tested subjects and identified a consistent somatotopical order with the hand CMFs situated anteriorly and ventrally from the foot CMFs in 10/12 subjects Fig. 3 F3 . We failed, however, to track lip CMFs except in one subject Fig. 2 F2 . It is unlikely that these fibers do not exist, because previous anatomical tracer studies demonstrated that the M1 face area as defined by intracortical microstimulation is connected with its homolog in the other hemisphere through callosal fibers, at least in the owl monkey Gould et al., 1986 ref-22 and in the macaque monkey Rouiller et al., 1994 ref-48 . Therefore, it is more likely that technical reasons accounted for the failure to track lip CMFs, particularly the well known problem of crossing fibers Basser et al., 2000 ref-3 ; Wiegell et al., 2000 ref-56 ; Behrens et al., 2007 ref-5 , which explains the general lack of callosal projections to the lateral surface of the cortex in most DTI studies Basser et al., 2000 ref-3 ; Hofer and Frahm, 2006 ref-24 ; Johansen-Berg et al., 2007 ref-29 . Anterograde tract-tracer autoradiography experiments indicated that, similar to the present findings in humans, a somatotopical organization of CMFs also exists in the rhesus monkey with the face CMFs located most anteriorly, sequentially followed more posteriorly by hand and finally foot CMFs Schmahmann and Pandya, 2006 ref-50 . This strongly suggests that the present findings are not artificial, because they are substantiated by reliable anatomical techniques. Correlation between fractional anisotropy and interhemispheric inhibition We show for the first time that microstructural information of the hand CMFs as reflected by FA correlated significantly with functional connectivity indexed by IHI between the M1 hand areas in the two hemispheres Fig. 5 F5 a , b . The biological basis of FA is not entirely clear. This measure is influenced by the degree of myelination, axon size, and axon density in a complex way Moseley et al., 1990 ref-38 ; Beaulieu, 2002 ref-4 ; Le Bihan, 2003 ref-34 , but fiber path geometry and crossing fibers may also contribute. In the midbody of the CC, the latter two factors are not relevant because fibers run straight and crossing fibers are absent. Light- and electron-microscopic analysis of the fiber composition in the human CC revealed clear regional differences Aboitiz et al., 1992 ref-1 : thin, largely unmyelinated and densely packed fibers are present preponderantly in the genu and splenium of the CC. In contrast, larger-diameter, myelinated and less densely packed fibers are concentrated in the posterior midbody of the CC i.e., the CMF region . This regional differentiation of fiber types and densities is paralleled by a regional differentiation of FA, with higher values where thin densely packed CC fibers predominate Hofer and Frahm, 2006 ref-24 . This suggests that FA in the CC reflects primarily fiber density rather than the degree of myelination or axon diameter. Furthermore, patients with multiple sclerosis showed a direct correlation between FA and CC cross-sectional area Cader et al., 2007 ref-7 . Because, in a postmortem analysis, patients with multiple sclerosis also exhibited a direct correlation between axon density and CC cross-sectional area Evangelou et al., 2000 ref-18 , this supports further the notion that FA in the CC is significantly influenced by axon density. This is excellently compatible with the observed correlation between FA and IHI, because it would be expected that more and/or more densely packed CMFs result in stronger IHI and lower IHI threshold. IHI is thought to be mediated by excitatory transcallosal fibers that originate from the hand area of the conditioning M1 and project onto local inhibitory interneurons in the hand M1 of the other hemisphere Ferbert et al., 1992 ref-19 ; Di Lazzaro et al., 1999 ref-11 ; Hanajima et al., 2001 ref-23 . IHI plays a role in suppressing unwanted mirror movements during intended unimanual movements by inhibiting the “mirror M1” Hübers and Ziemann, 2006 ref-25 ; Duque et al., 2007 ref-14 , and in facilitating unimanual movements by disinhibiting the “active M1” Duque et al., 2007 ref-14 . Consequently, IHI at rest and its modulation by hand movement are abnormal in patients with acquired mirror movements Li et al., 2007 ref-36 , and for the paretic hand in stroke patients Murase et al., 2004 ref-39 ; Duque et al., 2005 ref-13 . These data support the notion that IHI reflects functional connectivity that is important in bimanual and unimanual motor control. One other very recent study also linked microstructure FA and a paired-pulse TMS measure of functional corticocortical connectivity, but explored the projection from dorsal premotor cortex to contralateral M1 Boorman et al., 2007 ref-6 , a network involved in externally cued action selection. It was found that individual differences in FA of white-matter interconnecting regions including dorsal premotor cortex and M1 correlated highly specifically with the TMS-indexed functional connectivity between these two regions when measured during action selection. Finally, correlations between FA and TMS measures of functional connectivity were demonstrated along another important white matter fiber bundle, the corticospinal tract. Patients with diffuse traumatic axonal brain injury showed a positive correlation of FA with MEP amplitude Yasokawa et al., 2007 ref-60 , and patients with amyotrophic lateral sclerosis, a neurodegenerative disease of the upper and lower motor neuron, exhibited a negative correlation of FA with the central motor conduction time Sach et al., 2004 ref-49 . These findings and the present results strongly suggest that FA reflects valuable microstructural information that can be linked to electrophysiological measures of functional connectivity in a meaningful way. Potentially, this linkage between structure and function will open up a new and completely noninvasive avenue to understand better the functional relevance of fiber pathways in the human brain. Finally, we would like to point out that the correlation between FA of the hand CMFs and the magnitude and threshold of IHI was topographically specific, because it was not seen if FA of the neighboring and even overlapping foot CMFs were correlated with IHI between the hand areas of M1 Fig. 5 F5 c , d . This lends further credit to the reliability of the techniques used here, and to the perspective that correlations between measures of microstructural information and functional connectivity are not global but rather specific to the system under investigation. Footnotes - This work was supported by an educational grant from TEVA Pharmaceuticals. We thank George Ogrezeanu for technical help and Ralf Deichmann for support with the revision of this manuscript. - Correspondence should be addressed to Prof. Ulf Ziemann, Motor Cortex Group, Department of Neurology, Johann Wolfgang Goethe-University of Frankfurt, Schleusenweg 2-16, D-60528 Frankfurt am Main, Germany. u.ziemann{at}em.uni-frankfurt.de