Clinical Neurophysiology xxx (2014) xxx–xxx

Contents lists available at ScienceDirect

Clinical Neurophysiology journal homepage: www.elsevier.com/locate/clinph

Central Motor Conduction Time and Diffusion Tensor Imaging metrics in children with complex motor disorders Daniel E. Lumsden a,b,⇑, Verity McClelland c, Jonathan Ashmore d, Geoffrey Charles-Edwards b,e, Kerry Mills c, Jean-Pierre Lin a a

Complex Motor Disorder Service, Evelina Children’s Hospital, Guy’s and St Thomas’ NHS Foundation Trust, UK Rayne Institute, King’s College London, UK Department of Clinical Neurophysiology, King’s College Hospital NHS Foundation Trust, UK d Department of Neuroradiology, King’s College Hospital NHS Foundation Trust, London e Medical Physics, Guy’s and St Thomas’ NHS Foundation Trust, UK b c

a r t i c l e

i n f o

Article history: Accepted 9 April 2014 Available online xxxx Keywords: Central Motor Conduction Time Dystonia Diffusion Tensor Imaging

h i g h l i g h t s  Both measurement of Central Motor Conduction Time (CMCT) and Diffusion Weighted Imaging (DWI)

may aid with the clinical assessment of children and young people with complex motor disorders.  Diffusion Tensor Imaging (DTI) metrics in a group of children with complex motor disorders did not

correlate with CMCT, nor were group wise differences in DTI metrics identified when children with normal and abnormal CMCT where compared.  Children and young people with acquired dystonia were frequently found to have normal CMCT values.

a b s t r a c t Objectives: To explore potential correlations between Diffusion Tensor Imaging (DTI) metrics and Central Motor Conduction Time (CMCT) in a cohort of children with complex motor disorders. Methods: For a group of 49 children undergoing assessment for potential Deep Brain Stimulation (DBS) surgery, CMCT was derived from the latency of MEPs invoked by transcranial magnetic stimulation of the contralateral motor cortex and from peripheral conduction times. Tract-Based Spatial Statistics (TBSS) was used to compare Diffusion Tensor Imaging (DTI) metrics between children with normal and abnormal CMCT. TBSS was also used to look for correlations between these metrics and CMCT across the group. Results: Median age at assessment was 9 years (range 3–19 years). For 14/49 children a diagnosis of primary dystonia had been made. No correlation could be found between DTI metrics and CMCT, with no difference in metrics found between children with normal and abnormal CMCT. Conclusions: DTI metrics did not differ between children with normal and abnormal CMCT. Tissue properties determining CMCT may not be explained by existing DTI metrics. Significance: DTI and CMCT measurements provide complementary information for the clinical assessment of children with complex motor disorders. Ó 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

1. Introduction Hypertonic motor disorders in childhood may arise from a diverse range of pathological processes, often affecting more than ⇑ Corresponding author at: Complex Motor Disorder Service, Evelina Children’s Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London, UK. Tel.: +44 20717188. E-mail address: [email protected] (D.E. Lumsden).

one motor region of the central nervous system. An important distinction to be made in clinical practise is the relative integrity of the corticospinal tract (CST) in children with hypertonic motor disorders, influencing understanding of the underlying disease process and, more importantly, the choice of clinical intervention (Lin, 2003, 2011; McClelland et al., 2011). Dystonia and spasticity are often seen coincidently in the child with pathological hypertonicity, particularly in the context of cerebral palsy (Sanger et al.,

http://dx.doi.org/10.1016/j.clinph.2014.04.005 1388-2457/Ó 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Please cite this article in press as: Lumsden DE et al. Central Motor Conduction Time and Diffusion Tensor Imaging metrics in children with complex motor disorders. Clin Neurophysiol (2014), http://dx.doi.org/10.1016/j.clinph.2014.04.005

2

D.E. Lumsden et al. / Clinical Neurophysiology xxx (2014) xxx–xxx

2003). Clinical evaluation of the child with hypertonia is challenging, and concerns exist that the relative contributions of dystonia and spasticity may be under- and over-estimated respectively (Lin, 2011). Transcranial Magnetic Brain Stimulation (TMS) is a well-established tool for probing the integrity of the CST, and has been used to demonstrate the maturation of Central Motor Conduction Time (CMCT) in children (Eyre et al., 1991; Koh and Eyre, 1988). Prolonged CMCT has been demonstrated in a number of disorders known to affect the CST, including stroke, Multiple Sclerosis (MS) and Motor Neuron Disease (MND) (Berardelli et al., 1991; Heald et al., 1993; Hess et al., 1986). We have previously reported our own experience of using CMCT as a clinical tool for assessing CST integrity in children with dystonia undergoing assessment for deep brain stimulation (DBS), demonstrating normal CMCT time in the majority of patients for whom structural Magnetic Resonance Imaging (MRI) would be suggestive of CST damage (McClelland et al., 2011). In recent years Diffusion Tensor Imaging (DTI) has become widely used in the investigation of children with movement disorders. DTI exploits the fact that the diffusion of water has different characteristics within different types of brain tissue to provide information about the microstructure of the brain, potentially providing a window into the relationship between structure and function (Le Bihan et al., 2001). Diffusion which is unrestricted and equal in any direction is termed isotropic, whereas diffusion which is restricted more in one plane than another is termed anisotropic. For example, anisotropic diffusion is seen in white matter pathways because water diffuses relatively freely along the longitudinal axis of a coherent axonal bundle, compared with relatively restricted diffusion in a direction perpendicular to this. One commonly used parameter is Fractional Anisotropy (FA), a measure of the directionality of water movement with values from 0 to 1, higher values indicating greater directionality which in turn is thought to reflect the integrity of white matter pathways. DTI has considerably advanced our understanding of the pathophysiology in cerebral palsy, and in particular the relative contributions of disruptions to motor and sensory pathways (Scheck et al., 2012). In the context of MND correlations have been demonstrated between the severity of motor disability, increasing delay in CMCT and reduction in FA (Ellis et al., 1999; Iwata et al., 2008; Mitsumoto et al., 2007; Sach et al., 2004). Taken together, these and other studies raise the possibility that FA could potentially be used as a biomarker for CST integrity. Only one reported study to date has investigated possible correlations between DTI metrics and CMCT in healthy subjects, finding no areas of correlation (Hübers et al., 2012). This study applied a voxelwise approach, utilising Tract Based Spatial Statistics (TBSS) (Smith et al., 2006) to explore possible relationships between DTI metrics and a number of TMS measures, concluding that FA alone may be a poor marker of the biophysical tissue properties underlying CMCT. We aimed to explore the relationship between CMCT and DTI metrics in a sample of children with motor disorders undergoing assessment for DBS. We utilised a TBSS approach, including FA and other DTI metrics, namely Mean Diffusivity (MD), Radial Diffusivity (RD) and Axial Diffusivity (PD).

2. Methods A retrospective analysis was performed, using data collected during the routine clinical assessment of 49 children with complex motor disorders undergoing assessment for possible DBS surgery. These children currently undergo MRI, including diffusion weighted imaging (DWI) sequences, and CMCT in order to assess the integrity of the corticospinal tract, given that evidence of

significant CST dysfunction would be considered a contraindication to DBS. Inclusion criteria for cases involved in this study were assessment at our centre between July 2008 and January 2012, inclusion of DWI sequences (see below) during routine clinical assessment and measurement of CMCT to the right upper limb performed during routine clinical assessment. All children presented with severe dystonic movement disorders, refractory to medical therapy. No child had signs suggestive of peripheral neuropathy on clinical examination. 2.1. Clinical features In all 49 children the predominant motor phenotype was dystonia, with additional clinical features of spasticity in 8 (16.3%) cases. Median age at assessment was 9 years (range 3–19 years). For 14 children dystonia was classified as primary on aetiological grounds (none of whom were found to have clinical features of spasticity). In the remaining 35 children dystonia was classified as secondary on an aetiological basis (Bressman, 2004). In the primary dystonia group 3 children had a confirmed mutation in the torsin A gene (DyT1 +ve dystonia), 1 child a confirmed mutation in the epsilon sarcogylcan gene (DyT11 +ve dystonia), with the remaining 10 children classified as having idiopathic primary dystonia. In the secondary dystonia group 19 children had a diagnosis of cerebral palsy, 4 glutaric aciduria, 1 Lesch Nyhan disease, 1 hypomyelination, 3 pantothenate kinase associated neurodegeneration, 1 methylmalonic acidaemia, 1 a genetically confirmed mitochondrial disorder and 5 children had undiagnosed presumed neurometabolic disorders. 2.2. Measurement of CMCT Measurement of CMCT was conducted according to standard neurophysiological methodology (Mills, 1999). Distal M- and F-wave latencies were measured in the ulnar nerves, bilaterally or unilaterally according to the child’s ability to cooperate with testing. TMS (MagStim 200; Magstim Company, Carmarthenshire, UK) was applied over the contralateral motor cortex using a circular coil (90 mm, maximum magnetic field strength 2.0 T). Motor Evoked Potentials (MEPs) were recorded in the activated abductor digiti minimi. Active contraction was chosen because TMS does not evoke MEPs in relaxed muscle in children below the age of 6, and consistent MEP responses are not recorded in relaxed muscle until adolescence (Eyre et al., 1991; Koh and Eyre, 1988). Magnetic stimulus intensity was progressively increased in steps of 10% maximum stimulator output until reproducible MEPs were obtained. It was not possible to measure precise resting or active corticomotor threshold because of ongoing involuntary muscle activity. The level of muscle contraction is difficult to standardise in a child with dystonia, owing to involuntary movement. However, above a level of 15% maximum voluntary contraction, the latency of MEP has been shown to stabilize (Mills, 1999). Therefore, the MEPs were recorded during activity estimated to be greater than 15% of maximum voluntary contraction. Three to eight suprathreshold MEP responses were recorded and superimposed to identify the earliest onset latency. CMCT was then calculated from the measured latencies according to the equation

CMCT ¼ MEP  ðF þ M  1Þ=2: The upper limit of normal for CMCT to the hand muscle was taken as 7.9 ms (Mills, 1999). CMCT values were classed as either normal or prolonged based on established data showing that CMCT reaches normal adult values by the age of 2–4 years for upper

Please cite this article in press as: Lumsden DE et al. Central Motor Conduction Time and Diffusion Tensor Imaging metrics in children with complex motor disorders. Clin Neurophysiol (2014), http://dx.doi.org/10.1016/j.clinph.2014.04.005

D.E. Lumsden et al. / Clinical Neurophysiology xxx (2014) xxx–xxx

limbs (Eyre et al., 1991; Koh and Eyre, 1988). Rarely, CMCT could not be calculated as an MEP could not be evoked. For the purposes of further analysis children with no recordable MEP were allocated to the ‘‘abnormal’’ CMCT group. 2.3. MRI acquisition All images were acquired on a 1.5 T MRI scanner (Achieva, Philips, Best, The Netherlands) using an 8 channel phased array head coil. Diffusion weighted single shot EPI sequences (FOV 190  190 mm, acquisition matrix 96  96, giving voxel size 1.98  1.98 mm, reconstructed to 112  112, giving a reconstructed voxel size of 1.76 mm  1.76 mm, Sense factor 2, 65  2 mm slices, slice gap 0 mm, half Fourier factor 0.62, 32 diffusion encoding directions with B values 0 and 1000, TR 9592 ms, TE 89 ms, total scan time 05 m 54 s). 2.4. Data processing for Tract-Based Spatial Statistics (TBSS) Preprocessing was performed using tools from FMRIB’s software library (FSL, http://www.fmrib.ox.ac.uk/fsl). Diffusionweighted images were registered to a nondiffusion weighted reference volume for correction of head motion and eddy currents (using the FSL eddy_correct tool). A mask was created to remove non-brain matter using BET (Smith, 2002). FA images were created by fitting a tensor model to the raw diffusion data using FSL’s FDT tool, which uses a least-squares approach. All subjects’ FA images were then aligned into a common space using the nonlinear registration tool FNIRT, which uses a b-spline representation of the registration warp field (Rueckert et al., 1999). The template image used for this registration was the FA template in International Consortium for Brain Mapping (ICBM)-152 space (Mazziotta et al., 2001). Following this registration step a mean FA image was created which was then thinned to create a mean FA skeleton which represents the centre of all FA tracts common to the group. Each subject’s aligned FA data was then projected onto this skeleton and the resulting data fed into voxel-wise cross subject statistics (see Fig. 1). The RANDOMIZE function of the FSL library was used to explore the relationship between FA and CMCT. Firstly, a group-wise comparison was performed between those children with normal and abnormal CMCT (as defined above). For those children with measurable CMCT positive and negative correlations between FA and CMCT were measured. In both cases age at scan was included as a covariate in the linear regression, and results were corrected for multiple comparisons by controlling familywise error rate after threshold-free cluster enhancement (Smith and Nichols, 2009). P < 0.05 was considered significant.

3

2.5. DTI metrics In addition to FA maps, for each child maps of Mean Diffusivity (MD), Axial Diffusivity (AD – diffusivity along the principle eigenvector) and Radial Diffusivity (RD – average of diffusivity along the second and third eigen vectors) were obtained when fitting the tensor model as described above. These maps were fed into the final step of the TBSS analysis, performing the voxelwise comparison projected on the skeleton derived from FA data. 2.6. Tract based measures An estimate for whole tract DTI metrics was obtained for the corticospinal tract arising from the left hemisphere. Values of DTI metrics for all voxels within the TBSS skeleton intersecting with the left corticospinal tract as extracted from the John Hopkins University white matter tractography atlas (Hua et al., 2008) were measured for each child (Fig. 1). The relationship between average DTI metrics and CMCT status (normal or abnormal) were compared for each tract using a general linear model with age at scan included as a covariate (SPSS 17.0, SPSS Inc, Chicago, IL, USA). 2.7. Values along tract FA values at each level in the Z-axis (head foot direction) were extracted for the left CST for each patient. The estimated CST extracted from the TBSS skeleton was divided slice by slice along the Z-axis and the average FA of voxels for each slice measured. For each individual child this gives an estimation of core tract FA values along the length of the tract, enabling a visual comparison between children with normal and abnormal CMCT measures. 3. Results 3.1. CMCT findings OF the 49 children, 28 (57%) had normal CMCT to the right upper limb. The ‘‘abnormal’’ CMCT group, n = 21, comprised 14 children (29%) with prolonged CMCT and 7 children (14%) in whom no MEP was elicited. These children were collectively considered in the ‘‘abnormal’’ CMCT group. Median age at assessment was lower in the ‘‘abnormal’’ compared to ‘‘normal’’ CMCT groups (7 years versus 10 years, Mann–Whitney U-test P = 0.006). The median age at assessment was lower in children from whom an MEP could not be recorded (7.0 compared to 9 years and 6 months in those with recordable MEP) but this difference did not reach statistical significance (Mann–Whitney U-test P = 0.146).

Fig. 1. Projection of mean FA skeleton mask (yellow) onto mean FA image from group following nonlinear registration to FMRIB58 template in (a) axial, (b) coronal. The left corticospinal tract from the John Hopkins University (JHU) Atlas White Matter probabilistic atlas (thresholded at 15% probability) is shown in (c), superimposed against the FMRIB58 template. Masking of the mean FA skeleton mask (yellow) against the probabilistic left CST extracted from the atlas (red) is shown in (d). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Please cite this article in press as: Lumsden DE et al. Central Motor Conduction Time and Diffusion Tensor Imaging metrics in children with complex motor disorders. Clin Neurophysiol (2014), http://dx.doi.org/10.1016/j.clinph.2014.04.005

4

D.E. Lumsden et al. / Clinical Neurophysiology xxx (2014) xxx–xxx

CMCT measurements were abnormal in 5 of the 8 children with overt clinical spasticity. In the children with primary dystonia CMCT measurements were abnormal in 6/14 cases. Recordings could not be evoked in 3 of these 6 cases (1 DyT1 +ve dystonia, 1 DyT11 +ve and one idiopathic dystonia). 3.2. TBSS results TBSS analysis demonstrated no voxels with a significant difference between the normal and abnormal CMCT group for FA, AD, RD or MD values. Similarly, the TBSS analysis demonstrated no voxels across the skeleton with a significant correlation between CMCT values and any of the DTI metrics. A subgroup analysis was performed excluding the 8 eldest children in the normal CMCT group, normalising the age difference between these groups (Mann–Whitney U-test, P = 0.193) and equalizing their size. No significant voxels were found for any DTI metric within this subgroup analysis. 3.3. Tract based results Results of the whole CST tract DTI metrics are shown in Fig. 2 and Table 1. As with the results of the TBSS analysis no significant differences were found between the normal and abnormal CMCT groups (Mann–Whitney U-test P > 0.05), and no correlations were found between whole tract metrics and CMCT values for the 42 children with recordable MEPs (Spearman’s correlation coefficient, P > 0.05). No significant relationships were found between CMCT and any of the DTI metrics with age accounted for within the general linear model (FA values shown in Fig. 3). 3.4. FA values along the tract Extracted FA values for the left CST for each individual child are shown in Fig. 4. There was no obvious difference in the progression of FA values along the tract comparing children with normal and abnormal CMCT.

4. Discussion We aimed to determine whether differences in DTI metrics could be identified between children with normal or abnormal CMCT, or if any regions of white matter could be found which showed correlations between CMCT and DTI metrics. We found no such relationship. These findings are consistent with those of Hübers et al. (2012), who studied DTI and CMCT in healthy adults, although the authors suggested that the narrow range of CMCT values within that healthy cohort could account for the failure to find a relationship. Our study extends this work by demonstrating no relationship across a much larger range of CMCT values. In addition, our findings contrast with those from a number of studies in patients with MND where a relationship has been found between increasing CMCT and decreasing FA (Ellis et al., 1999; Iwata et al., 2008). The finding of no relationship in a patient group with widely ranging CMCT values is both novel and important as it raises questions about how the integrity of the CST should be assessed to aid clinical decision making. There are a number of possible explanations as to why we found no relationship between DTI metrics and CMCT in this study. These include both methodological and pathophysiological factors. The diffusion tensor model is by far the most commonly reported in studies of DWI analysis, with a large number of analytical techniques applied in an increasing number of clinical scenarios. Studies utilising DTI in childhood have fundamentally improved our understanding of the pathophysiology of CP (Scheck et al., 2012) and have the potential to predict future outcome following preterm delivery or Hypoxic Ischemic Encephalopathy at birth (Counsell et al., 2008; Tusor et al., 2012; van Kooij et al., 2012). One fundamental limitation of the tensor model is that only a single fibre direction can be resolved. Reduction in FA values may arise not only as a consequence of alteration to the microstructure of a white matter pathway but also due to partial volume effects when fibres of different orientations are found within a voxel (Alexander et al., 2001). Up to 90% of white-matter voxels may include populations of fibres with mixed orientations

Fig. 2. Box and Whisker plots giving median, interquartile, minimum and maximum values for core tract wise diffusion metrics. Diffusivity is given in mm2/s. No significant differences were found between children with normal or abnormal CMCT.

Please cite this article in press as: Lumsden DE et al. Central Motor Conduction Time and Diffusion Tensor Imaging metrics in children with complex motor disorders. Clin Neurophysiol (2014), http://dx.doi.org/10.1016/j.clinph.2014.04.005

5

D.E. Lumsden et al. / Clinical Neurophysiology xxx (2014) xxx–xxx

Table 1 Diffusion Tensor Imaging (DTI) metrics for the whole Corticospinal tract (CST). Data shown included is median (25th to 75th centile). The Mann–Whitney U-test was used to measure the difference between CST tract metrics in children with normal and abnormal DTI metrics (which failed to reach statistical significance for any metric). Tract DTI metric

Normal CMCT

Abnormal CMCT

Mann–Whitney U-test

Fractional Anisotropy Mean Diffusivity (mm2/s) Radial Diffusivity (mm2/s) Axial Diffusivity (mm2/s)

0.578 (0.545–0.592) 0.000813 (0.000777–0.000852) 0.000514 (0.000479–0.000556) 0.000139 (0.000136–0.000149)

0.554 (0.542–0.581) 0.000829 (0.000778–0.000855) 0.000537 (0.000506–0.000555) 0.000142 (0.000137–0.000145)

0.130 0.189 0.176 0.241

Fig. 3. Scatterplot of CMCT values against DTI metrics. Mean Diffusivity (MD), Radial Diffusivity (RD) and Axial Diffusivity (AD) values are shown in mm2/s. No significant correlation was found between CMCT or any of the DTI metrics (smallest P-value 0.096, for Spearman correlation between CMCT and FA).

(Jeurissen et al., 2012). This is a particular problem for tractography programs, as pathway reconstruction utilising the diffusion tensor is likely to significantly underestimate tract size (Farquharson et al., 2013). However, this may be less of a limitation for our study, as for the majority of its course the relative density of descending fibres within the CST vastly outnumber any crossing fibres, thereby limiting the influence of partial volume affects. Another consideration is the TBSS analysis. Whilst this approach overcomes the intrinsic problems with inter-subject registration and spatial smoothing complicating other forms of voxelwise analysis, this is at the loss of spatial resolution. Restricting voxelwise comparison to the core of white matter tracts limits the capacity of the technique to detect changes in more peripheral white matter regions, though these more peripheral regions would be more susceptible to registration errors. The somatotopic organisation of fibres running within the corticospinal tract has been demonstrated by a number of diffusion tensor studies (Hong et al., 2010; Jang, 2011; Kwon et al., 2011), and it is unclear as to whether this would limit the sensitivity of TBSS to detect changes in fibres specifically innervating the right upper limb. In addition, group wise comparisons of patient groups are dependent upon the spatial coincidence of regions of abnormality across subject groups. In contrast, prolonged CMCT (or absent MEP) may reflect disruption of the CST anywhere along its length. Thus if the

children studied here have focal abnormalities in different anatomical locations, e.g. in the centrum semiovale in one child and in the cerebral peduncle in another, then group wise analysis would potentially fail to pick up a difference between groups. At threshold intensity TMS of the primary motor cortex is thought to result in the activation of horizontal interneurons which, in turn, activate descending corticospinal neurons. At higher intensities these corticospinal neurons are stimulated directly (Burke et al., 1993; Di Lazzaro et al., 1998; Mills, 1999). TMS activates preferentially the corticomotoneuronal component of the CST, which comprises only about 1% of the CST fibres (Day et al., 1989; Mills, 1999). These are also the largest myelinated fibres of the CST, accounting for the sensitivity of CMCT in demyelinating disorders such as MS. Thus substantial damage to the CST that spares this subset of fibres may give preserved CMCT, whereas a pathological process affecting only a small number of CST fibres could cause prolonged CMCT if those fibres are specifically the large myelinated ones. Given the average diameter of these fibres is in the order of 6–10 lm, differences affecting this population of axons may be well below the spatial resolution offered by DTI techniques. In a recent study of healthy volunteers, Hübers et al. (2012) also found no relationship between the DTI parameters FA and MD and TMS measures of CST function, including CMCT. They suggested

Please cite this article in press as: Lumsden DE et al. Central Motor Conduction Time and Diffusion Tensor Imaging metrics in children with complex motor disorders. Clin Neurophysiol (2014), http://dx.doi.org/10.1016/j.clinph.2014.04.005

6

D.E. Lumsden et al. / Clinical Neurophysiology xxx (2014) xxx–xxx

Fig. 4. Fractional Anisotropy (FA) values at different levels across the Z-axis for children with normal (blue) and abnormal (red) Central Motor Conduction Time. FA values are shown on the y-axis, with position on the Z-axis on the x-axis, running left–right inferior–superior. Illustrative axial slices from the FMRIB58 FA template are displayed, indicating the anatomical position along the Z axis. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

that factors such as fibre bundle orientation or other as yet unknown factors might account for variability of TMS measures. A further possibility, given the particular features of each methodology outlined above, is that the two techniques are providing quite different information and that a clear relationship between the two may not usually be present, except in particular circumstances such as the rather homogenous CST degeneration seen in ALS (see below). Motor neuron disease is a progressive neurodegenerative disorder affecting the upper and lower motor neuron, amongst other neuronal populations (Wijesekera and Leigh, 2009). Postmortem studies have demonstrated extensive involvement of the fibres within the CST with surrounding astrogliosis (Rafalowska and Dziewulska, 1996) with clear involvement of the largest fast conducting myelinated projections (Kohara et al., 1999). These changes evolve to include relatively large volumes of the CST, with intensity changes visible on conventional structural MRI T2- and PD-weighted sequences (Cheung et al., 1995). A number of DTI studies have demonstrated consistent reduction in CST FA, amongst a number of other fibre pathways (Li et al., 2012). The fibres activated by TMS degenerate in patients with MND as part of a more extensive degeneration of the CST involving fibre populations at a spatial resolution detectable with DTI metrics. Furthermore, progression of MND results in loss of axons in the CST, with consequent structural changes along the length of the descending pathway rather than focal lesions restricted to shorter longitudinal segments. We believe that this extensive involvement of the CST explains the finding of a relationship between increasing CMCT and decreasing FA in these patients. In contrast, the population we studied is highly heterogenous with respect to underlying pathology and, as outlined above, the location of pathology causing CMCT prolongation could be different between individuals within the group, making it less likely that group-wise TBSS comparisons would reach significance. Furthermore, the prolonged CMCT in some of our patients could reflect hypofunction or hypomyelination of CST neurons rather than axonal loss. Such changes would be less likely to be distinguished by DTI measures. As a retrospective study from a convenience sample of children undergoing clinical assessment within our service there are a

number of limitations to our current study. Firstly, no DTI control group was available. Since DTI parameters are highly dependent upon the specific scanner, it was not possible to compare directly with published data. Therefore we explored differences in imaging parameters between children with normal and abnormal CMCT. Secondly, children with dystonia are technically difficult to study, which could affect the accuracy of CMCT measurement. For seven children, no MEP could be evoked. This may reflect corticospinal tract dysfunction but could also indicate a very high threshold in a young child, or technical difficulties due to the severity of the underlying movement disorder. For the purpose of the group-wise comparison, these children were included in the ‘‘abnormal CMCT’’ group. However, for the correlation analysis, only those children with a measurable CMCT were included. Thirdly, the age range of children included was broad (3–19 years). All children were 3 years or older, by which age upper limb CMCT in typically developing children is within the normal adult range (Eyre et al., 1991). FA is known to rise during normal development of the brain, with a coincident fall in MD (Yoshida et al., 2013) necessitating inclusion of age as a covariate within our analysis. Although age was significantly different between the two groups, we would have expected this factor to enhance any difference in DTI parameters between the groups, if it were to influence the results, whereas we found no significant difference. Further, a secondary analysis of agematched subgroups also showed no significant difference. Finally, the limitations of the Diffusion Tensor model have been highlighted above. Application of newer techniques to probe the directionality of fibre pathways within a voxel, collectively described as ‘‘High Angular Resolution Diffusion Imaging’’ (HARDI), might overcome some of these limitations (Tournier et al., 2011). However, applicability of HARDI to clinically acquired DWI datasets may be limited and metrics to apply to fibre pathways identified with HARDI have only recently been described (Raffelt et al., 2012; Tournier et al., 2011). 5. Conclusions We identified no relationship between DTI metrics and CMCT in a mixed population of children with motor disorders undergoing

Please cite this article in press as: Lumsden DE et al. Central Motor Conduction Time and Diffusion Tensor Imaging metrics in children with complex motor disorders. Clin Neurophysiol (2014), http://dx.doi.org/10.1016/j.clinph.2014.04.005

D.E. Lumsden et al. / Clinical Neurophysiology xxx (2014) xxx–xxx

assessment for DBS. This finding may relate to heterogeneity within the clinical group, though it also may be due to the inability of the Diffusion Tensor model to measure the biophysical properties of the CST fibres activated by TMS. CMCT measurement appears to provide information regarding integrity of the CST within this group which may not be provided by DTI parameters alone. We therefore suggest that the two techniques provide complementary information in the assessment of patients with movement disorders. Acknowledgments The Complex Motor Disorder Team is supported a Grant from the Guy’s and St Thomas’ Charity, project number G060708. Dr. D. Lumsden and Dr. J.-P. Lin have received support for this work from Grants from Action Medical Research (Grant number GN2097) and the Dystonia Society. Dr. Verity McClelland is in receipt of a Walport Clinical Lectureship funded by NIHR. These data have been reported in brief at the Joint scientific meeting of the British Society of Clinical Neurophysiology and the Société de Neurophysiologie Clinique de Langue Française (Oxford, UK, July 2013). References Alexander AL, Hasan KM, Lazar M, Tsuruda JS, Parker DL. Analysis of partial volume effects in diffusion-tensor MRI. Magn Reson Med 2001;45:770–80. Berardelli A, Inghilleri M, Cruccu G, Mercuri B, Manfredi M. Electrical and magnetic transcranial stimulation in patients with corticospinal damage due to stroke or motor neurone disease. Electroencephalogr Clin Neurophysiol 1991;81:389–96. Bressman SB. Dystonia genotypes, phenotypes, and classification. Adv Neurol 2004;94:101–7. Burke D, Hicks R, Gandevia SC, Stephen J, Woodforth I, Crawford M. Direct comparison of corticospinal volleys in human subjects to transcranial magnetic and electrical stimulation. J Physiol 1993;470:383–93. Cheung G, Gawel MJ, Cooper PW, Farb RI, Ang LC, Gawal MJ. Amyotrophic lateral sclerosis: correlation of clinical and MR imaging findings. Radiology 1995;194:263–70. Counsell SJ, Edwards AD, Chew AT, Anjari M, Dyet LE, Srinivasan L, et al. Specific relations between neurodevelopmental abilities and white matter microstructure in children born preterm. Brain 2008;131:3201–8. Day BL, Dressler D, Maertens de Noordhout A, Marsden CD, Nakashima K, Rothwell JC, et al. Electric and magnetic stimulation of human motor cortex: surface EMG and single motor unit responses. J Physiol 1989;412:449–73. Di Lazzaro V, Oliviero A, Profice P, Saturno E, Pilato F, Insola A, et al. Comparison of descending volleys evoked by transcranial magnetic and electric stimulation in conscious humans. Electroencephalogr Clin Neurophysiol 1998;109:397–401. Ellis CM, Simmons A, Jones DK, Bland J, Dawson JM, Horsfield MA, et al. Diffusion tensor MRI assesses corticospinal tract damage in ALS. Neurology 1999;53:1051–8. Eyre JA, Miller S, Ramesh V. Constancy of central conduction delays during development in man: investigation of motor and somatosensory pathways. J Physiol 1991;434:441–52. Farquharson S, Tournier JD, Calamante F, Fabinyi G, Schneider-Kolsky M, Jackson GD, et al. White matter fiber tractography: why we need to move beyond DTI. J Neurosurg 2013;118:1367–77. Heald A, Bates D, Cartlidge NE, French JM, Miller S. Longitudinal study of central motor conduction time following stroke. 1. Natural history of central motor conduction. Brain 1993;116(Pt 6):1355–70. Hess CW, Mills KR, Murray NM. Measurement of central motor conduction in multiple sclerosis by magnetic brain stimulation. Lancet 1986;2:355–8. Hong JH, Son SM, Jang SH. Somatotopic location of corticospinal tract at pons in human brain: a diffusion tensor tractography study. Neuroimage 2010;51:952–5. Hua K, Zhang J, Wakana S, Jiang H, Li X, Reich DS, et al. Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage 2008;39:336–47. Hübers A, Kelin JC, Kang JS, Hilker R, Ziemann U. The relationship between TMS measures of functional properties and DTI measures of microstructure of the corticospinal tract. Brain Stimul 2012;5:297–304.

7

Iwata NK, Aoki S, Okabe S, Arai N, Terao Y, Kwak S, et al. Evaluation of corticospinal tracts in ALS with diffusion tensor MRI and brainstem stimulation. Neurology 2008;70:528–32. Jang SH. Somatotopic arrangement and location of the corticospinal tract in the brainstem of the human brain. Yonsei Med J 2011;52:553–7. Jeurissen B, Leemans A, Tournier JD, Jones DK, Sijbers J. Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum Brain Mapp 2012;34:2747–66. Koh TH, Eyre JA. Maturation of corticospinal tracts assessed by electromagnetic stimulation of the motor cortex. Arch Dis Child 1988;63:1347–52. Kohara N, Kaji R, Kojima Y, Kimura J. An electrophysiological study of the corticospinal projections in amyotrophic lateral sclerosis. Clin Neurophysiol 1999;110:1123–32. Kwon HG, Hong JH, Lee MY, Kwon YH, Jang SH. Somatotopic arrangement of the corticospinal tract at the medullary pyramid in the human brain. Eur Neurol 2011;65:46–9. Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, et al. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 2001;13:534–46. Li J, Pan P, Song W, Huang R, Chen K, Shang H. A meta-analysis of diffusion tensor imaging studies in amyotrophic lateral sclerosis. Neurobiol Aging 2012;33:1833–8. Lin JP. The cerebral palsies: a physiological approach. J Neurol Neurosurg Psychiatry 2003;74(Suppl. 1):i23–9. Lin JP. The contribution of spasticity to the movement disorder of cerebral palsy using pathway analysis: does spasticity matter? Dev Med Child Neurol 2011;53:7–9. Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos Trans R Soc Lond B Biol Sci 2001;356:1293–322. McClelland V, Mills K, Siddiqui A, Selway R, Lin JP. Central motor conduction studies and diagnostic magnetic resonance imaging in children with severe primary and secondary dystonia. Dev Med Child Neurol 2011;53:757–63. Mills KR. Magnetic stimulation of the human nervous system. New York: Oxford University Press; 1999 [Chapter 8]. Mitsumoto H, Ulug AM, Pullman SL, Gooch CL, Chan S, Tang MX, et al. Quantitative objective markers for upper and lower motor neuron dysfunction in ALS. Neurology 2007;68:1402–10. Rafalowska J, Dziewulska D. White matter injury in amyotrophic lateral sclerosis (ALS). Folia Neuropathol 1996;34:87–91. Raffelt D, Tournier JD, Rose S, Ridgway GR, Henderson R, Crozier S, et al. Apparent Fibre Density: a novel measure for the analysis of diffusion-weighted magnetic resonance images. Neuroimage 2012;59:3976–94. Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 1999;18:712–21. Sach M, Winkler G, Glauche V, Liepert J, Heimbach B, Koch MA, et al. Diffusion tensor MRI of early upper motor neuron involvement in amyotrophic lateral sclerosis. Brain 2004;127:340–50. Sanger TD, Delgado MR, Gaebler-Spira D, Hallett M, Mink JW. Classification and definition of disorders causing hypertonia in childhood. Pediatrics 2003;111:e89–97. Scheck SM, Boyd RN, Rose SE. New insights into the pathology of white matter tracts in cerebral palsy from diffusion magnetic resonance imaging: a systematic review. Dev Med Child Neurol 2012;54:684–96. Smith SM. Fast robust automated brain extraction. Hum Brain Mapp 2002;17:143–55. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 2006;31:1487–505. Smith SM, Nichols TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 2009;44:83–98. Tournier JD, Mori S, Leemans A. Diffusion tensor imaging and beyond. Magn Reson Med 2011;65:1532–56. Tusor N, Wusthoff C, Smee N, Merchant N, Arichi T, Allsop JM, et al. Prediction of neurodevelopmental outcome after hypoxic–ischemic encephalopathy treated with hypothermia by diffusion tensor imaging analyzed using tract-based spatial statistics. Pediatr Res 2012;72:63–9. van Kooij BJ, de Vries LS, Ball G, van Haastert IC, Benders MJ, Groenendaal F, et al. Neonatal tract-based spatial statistics findings and outcome in preterm infants. AJNR Am J Neuroradiol 2012;33:188–94. Wijesekera LC, Leigh PN. Amyotrophic lateral sclerosis. Orphanet J Rare Dis 2009;4:3. Yoshida S, Oishi K, Faria AV, Mori S. Diffusion tensor imaging of normal brain development. Pediatr Radiol 2013;43:15–27.

Please cite this article in press as: Lumsden DE et al. Central Motor Conduction Time and Diffusion Tensor Imaging metrics in children with complex motor disorders. Clin Neurophysiol (2014), http://dx.doi.org/10.1016/j.clinph.2014.04.005

Central Motor Conduction Time and diffusion tensor imaging metrics in children with complex motor disorders.

To explore potential correlations between Diffusion Tensor Imaging (DTI) metrics and Central Motor Conduction Time (CMCT) in a cohort of children with...
914KB Sizes 0 Downloads 5 Views

Recommend Documents


Bimanual motor deficits in older adults predicted by diffusion tensor imaging metrics of corpus callosum subregions.
Age-related changes in the microstructural organization of the corpus callosum (CC) may explain declines in bimanual motor performance associated with normal aging. We used diffusion tensor imaging in young (n = 33) and older (n = 33) adults to inves

Linear associations between clinically assessed upper motor neuron disease and diffusion tensor imaging metrics in amyotrophic lateral sclerosis.
To assess the relationship between clinically assessed Upper Motor Neuron (UMN) disease in Amyotrophic Lateral Sclerosis (ALS) and local diffusion alterations measured in the brain corticospinal tract (CST) by a tractography-driven template-space reg

Prediction of Motor Recovery Using Diffusion Tensor Tractography in Supratentorial Stroke Patients With Severe Motor Involvement.
To investigate whether early stage diffusion tensor tractography (DTT) values predict motor function at 3 months after onset in supratentorial stroke patients with severe motor involvement.

Corticospinal tract change during motor recovery in patients with medulla infarct: a diffusion tensor imaging study.
Diffusion tensor imaging (DTI) and tractography (DTT) provide a powerful vehicle for investigating motor recovery mechanisms. However, little is known about these mechanisms in patients with medullary lesions. We used DTI and DTT to evaluate three pa

Diffusion tensor imaging in musculoskeletal disorders.
Diffusion tensor (DT) imaging is an emerging magnetic resonance (MR) imaging technique for evaluating the microstructure of well-organized biologic tissues such as muscles and nerves. DT imaging provides information about tissue microstructure by pro

Diffusion tensor imaging correlates of cognitive-motor decline in normal aging and increased Alzheimer's disease risk.
Alzheimer's disease (AD) is typically associated with impairments in memory and other aspects of cognition, while deficits in complex movements are commonly observed later in the course of the disease. Recent studies, however, have indicated that sub

Combining diffusion tensor imaging and gray matter volumetry to investigate motor functioning in chronic stroke.
Motor impairment after stroke is related to the integrity of the corticospinal tract (CST). However, considerable variability in motor impairment remains unexplained. To increase the accuracy in evaluating long-term motor function after ischemic stro