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Repeatability of functional anisotropy in navigated transcranial magnetic stimulation – coil-orientation versus response Elisa Kallioniemia,c, Mervi Könönena,b and Petro Julkunena,c Transcranial magnetic stimulation (TMS) can be used for evaluating the function of motor pathways. According to the principles of electromagnetism and electrophysiology, TMS activates those neurons that are suitably oriented with respect to the TMS-induced electric field. We hypothesized that TMS could potentially be able to evaluate the neuronal structure, although until now, this putative application has not been exploited. We have developed a TMS-based method to evaluate the function and structure of the motor cortex concurrently in a quantitative manner. This method produced a measure, the anisotropy index (AI), which is based on the motor-evoked potentials induced at different coil orientations. The AI was demonstrated to exhibit an association with both motor cortex excitability and neuronal structure. In the present study, we evaluated the repeatability (intrasession and intersession) of AI in three consecutive measurements. In addition, we studied the repeatability of the optimal coil angle in inducing motorevoked potentials. Two of the measurements were conducted on the same stimulation target and the third on a remapped target. The coefficient of repeatability of the AI was 0.022 for intrasession and 0.040 for intersession

Introduction Several neurological disorders and brain traumas, such as stroke and focal dysplasia, are associated with the presence of both functional and structural changes in the affected brain area [1,2]. The structural changes have been conventionally evaluated with anatomical MRI [3] or diffusion tensor imaging [4], whereas function has been assessed using different techniques, for example, electroencephalography [5], transcranial magnetic stimulation (TMS) [6], or functional MRI [7]. Unfortunately, none of these methods alone can detect the relationship between function and neuronal structure. TMS is a method in which a short-lasting magnetic pulse generated by a coil induces an electric field (EF) in the cortex. If axons located within the induced EF have bends or endings in the field, the gradient of the EF may become large enough to activate the neuron [8]. When targeting the motor cortex, this activation can be measured through the motor-evoked potential (MEP) of the contralateral hand. Hence, the induction of a TMS response is dependent on both the functional excitability of the motor cortex and the underlying neuronal structure. We have exploited this dependence to develop a novel approach to measure the relationship between MEPs and the underlying structure using navigated TMS. Using this 0959-4965 Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.

assessments. For the optimal stimulation angle, the coefficients of repeatability were 3.7° and 5.1°, respectively. Both the AI and the optimal stimulation angle demonstrated good repeatability (Cronbach’s α > 0.760). In conclusion, the results indicate that the AI can provide a reliable estimation of local functional anisotropy changes under conditions affecting the cortex, such as during stroke or focal dysplasia. NeuroReport 26:515–521 Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved. NeuroReport 2015, 26:515–521 Keywords: motor cortex, motor-evoked potential, navigated brain stimulation, repeatability, transcranial magnetic stimulation Departments of aClinical Neurophysiology, bClinical Radiology, Kuopio University Hospital and cDepartment of Applied Physics, University of Eastern Finland, Kuopio, Finland Correspondence to Elisa Kallioniemi, MSc (Tech), Department of Clinical Neurophysiology, Kuopio University Hospital, P.O Box 100, Kuopio FI-70029, Finland Tel: + 358 503 687 356; fax: + 358 171 731 87; e-mails: [email protected]; [email protected] Received 12 March 2015 accepted 21 April 2015

approach, one can determine a value that has been termed as the anisotropy index (AI); this is considered to describe functional anisotropy of the cortex at the stimulation location, namely the extent to which the TMS-induced response is dependent on the direction of the induced EF. Previously, the AI was shown to be associated with both the cortical structure and the excitability [9]. Therefore, it was decided to test whether the tool would possess sufficient test–retest repeatability in healthy individuals and thus, whether a single measurement could provide information on local structural–functional changes such as those occurring in neurological disorders. The aim of this study was to assess the intrasession and intersession repeatability of AI measurements in healthy individuals. It was hypothesized that, should good repeatability be achieved, determination of the AI could be a promising tool for measuring functional anisotropy of the motor cortex.

Methods Participants and measurements

We recruited 10 healthy right-handed individuals (six male, age range: 23–30 years) without any history of neurological disorders or brain traumas. The study was approved by the DOI: 10.1097/WNR.0000000000000380

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local ethical committee (8/2012), and a written informed consent was collected from all the participants. A navigated TMS system (eXimia 3.2.2; Nexstim Plc., Helsinki, Finland) was used, with a biphasic figure-of-eight coil to provide the stimulation. Electromyography (EMG) was conducted using an integrated EMG device. To use the neuronavigation system with TMS, the participants underwent a three-dimensional T1-weighted structural MRI scan. Measurement was started by first mapping the cortical motor representation area of the left-hand first dorsal interosseous muscle (FDI) with a suprathreshold stimulation intensity. The coil location with the greatest associated MEP – that is, the optimal representation site for FDI – was selected as the stimulation target. At the target, the orientation of the stimulation coil was optimized [10]. Thereafter, the resting motor threshold (rMT) was determined using the TMS Motor Threshold Assessment Tool 2.0 [11], with an intertrial interval of at least 5 s. During measurement of the AI, the stimulating coil was turned around the optimal representation site in a tangential plane within ± 135° from the optimal coil angle. At each 45° sector (Fig. 1a), 20 single pulses were applied, each with a different, randomly selected coil angle using a stimulation intensity of 120% of the rMT. Thus, altogether, 120 different coil angles were used. This routine was performed twice at the same cortical target when studying the intrasession repeatability. Thereafter, the EMG electrodes were removed and the participant stood up from the chair for a few minutes, and then the preparations and measurements were repeated including EMG electrode placement, FDI representation site mapping, rMT calculation, and AI measurement to conduct the evaluation of intersession repeatability. Data analysis

The data were analyzed using eXimia workstation (Nexstim Plc.). First, those trials with prestimulus muscle tension were rejected. The software automatically detected the minimum and maximum of the MEP, and the markings were visually verified. The minimum peakto-peak amplitude considered for MEPs was 50 μV. Other responses were marked as nonresponses. The software also logged the site and coil orientation, from which the coil angles used were calculated. After preprocessing, the data were imported to Matlab (version 7.5; The Mathworks Inc., Natick, Massachusetts, USA) in which the MEP amplitudes were plotted as a function of the coil angles (by moving-average smoothing with a 20° window to reduce MEP amplitude variation), and a Gaussianfitted full-width at half maximum (FWHM) was calculated from the amplitude curve (Fig. 1a and b). This was used to compute the AI [9]: AI ¼ 1

FWHM : 3601

ð1Þ

The AI ranges from 0 to 1, where 0 corresponds to a situation in which MEPs arise from all angles, making the MEP curve presented in Fig. 1 to have a broadened appearance with no clear optimal angle. In principle, this would represent the situation in which the neuronal axons were isotropically oriented with respect to the TMS-induced EF vector, meaning that the coil orientation angle was not exerting any effect on the resulting response. In contrast, a value of AI near 1 indicated a situation in which MEPs were induced only from a narrow angle range, corresponding to the situation in which the neurons were arranged anisotropically with respect to TMS. In addition to the AI, the peak of the MEP curve constructed for AI calculation provides information about the angle at which one obtains MEPs with the greatest amplitudes in response to TMS. This angle represents the optimal stimulation angle between cortical microstructure and macrostructure and TMS. The calculated angle was the angle between the induced electric field and the parasagittal line in the transaxial plane. To account for the bimodal shape in the MEP amplitude–coil orientation curve (Fig. 1b), two Gaussian curves were fitted to perform a deconvolution function of the curve. The higher of the two peaks – that is, the fit of the dominant peak – was used to assess the repeatability of the AI and the optimal stimulation angle in the case of bimodal response curves.

Statistics

The intrasession and intersession repeatability of the AI and the optimal stimulation angle were evaluated using Matlab (version 2013b; The Mathworks Inc.), and Bland–Altman plots were constructed [12]. The differences in the AIs, as well as in the optimal stimulation angles, were normally distributed according to the Kolmogorov–Smirnov test (P > 0.05). The Bland–Altman plot assesses repeatability by determining the mean difference in the evaluated parameters and the limits of agreements, which show the mean difference ± 1.96 SD of the differences. As the differences were confirmed to be normally distributed, if the results are within the limits of agreement, one can estimate that 95% of the participants lie within these limits. The smaller the width of the limits of agreement, the better is the agreement between the two measurements. To assess the goodness of repeatability, Cronbach’s α-values were assessed for all intrasession and intersession pairs, and values above 0.7 were considered as evidence of good internal consistency. Furthermore, the Bland–Altman statistics determine the coefficient of repeatability (CR) as 1.96 SD of the differences between the evaluated measurements. The CR expresses the minimal detectable relevant change that can be observed by the method.

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Fig. 1

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(a) The TMS coil was turned in a tangential plane within ± 135° from the mapped target. At each 45° sector, 20 single pulses were applied. The order of the sectors was randomized. The stimulation produced a curve of induced MEP amplitudes as a function of the stimulation angle. In this figure, the angles are normalized to the optimal stimulation angle. Normalized MEP amplitudes (normalized to the maximum MEP amplitude) are shown from data obtained from one individual. (b) In the majority of participants, a bimodal response curve was also observed. In these cases, the response curve was deconvoluted with two (unimodal) Gaussian functions, and the AI was estimated separately for each peak. AI, anisotropy index; MEP, motor-evoked potential; TMS, transcranial magnetic stimulation.

Results and discussion Results

The AIs ranged from 0.832 to 0.965 and the optimal stimulation angles from 23.3° to 62.4° with respect to the parasagittal line. Bland–Altman plots revealed an outlier in the intrasession and intersession evaluation of the optimal angle. Removal of the outlier was considered necessary because the analysis method being used was very sensitive to outliers. Approximately one-third of the MEP amplitude–coil orientation curves were found to have a bimodal shape (Fig. 1b). In all of the bimodal curves, a dominant peak was observed, namely a higher peak, along with a lower, secondary peak. The differences in coil orientations of the peaks in the bimodal curves varied from 17° to 87°. The bimodal shape did not appear consistently between repetitions; a bimodal shape was observed in both intrasession repetitions in 10% of the participants and in both intersession repetitions in 20% of the participants. Repeatability of the anisotropy index

The limits of agreement between the AIs determined in the same session were − 0.044 to 0.046 (Fig. 2a), whereas the limits of agreement for the AIs of separate sessions were − 0.079 to 0.087 (Fig. 2b). The CR for the AIs in the same session was 0.022 and that for the AIs of separate sessions was 0.040. Cronbach’s α-values for the intrasession and intersession AI pairs were 0.914 and 0.761 respectively, both representing evidence of good repeatability. Not surprisingly, the intersession repeatability in

AI was slightly less impressive than the intrasession repeatability. Repeatability of the optimal stimulation angle

The limits of agreement for the intrasession assessment were − 6.2° to 8.3° (Fig. 3a) and for the intersession assessment − 9.5° to 11.4° (Fig. 3b). The CRs for the angles were 3.7° and 5.1°, respectively. Cronbach’s α-values for the intrasession and intersession stimulation angle pairs were 0.960 and 0.931, both indicating good repeatability.

Discussion Our study demonstrated that these straightforward concurrent function–structure measurements show good repeatability according to Cronbach’s α-values for both intrasession and intersession evaluations. The repeatability of the AI was not markedly affected by methodological issues such as electrode placement or TMS coil direction. In addition, the optimal stimulation angle demonstrated good repeatability according to Cronbach’s α-value. As a result of the good internal consistency and the rather low interindividual variation indicated by the limits of agreement, we propose that AI may be capable of estimating the function–structure relationships within the motor cortex. However, before it becomes a useful clinical tool, the sensitivity of the tool will need to be increased to distinguish normal intraindividual variability from the small variability attributable to neurological disorders. On the basis of the present data, it was estimated that abnormal changes of about 0.040 in the AI

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Fig. 2

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could potentially be detected with our tool. At this stage, we cannot predict whether this would be adequate, as no patient studies have been conducted. Furthermore, it is known that stroke inflicts widely variable effects on the structural and functional properties of the motor cortex at an individual level, complicating the situation further. Nevertheless, this study has shown that the AI does provide reliable information about the local function–structure relationship, as its repeatability in intrasession and intersession settings was good. Surprisingly, the MEP amplitude–coil orientation curves often showed a bimodal shape instead of the expected unimodal form. It is possible that in the bimodal curves,

two separate neuronal populations are activated, whereas in unimodal curves, only a single population contributes to the responses. Alternatively, the bimodal shape might originate from a neuronal anatomy. However, as the bimodal curves did not repeat themselves consistently, it seems more plausible that they originate from neurophysiology rather than from the neuronal anatomy at the stimulation target; at this stage we can only speculate on their origin. In addition, the stimulation intensity utilized may be able to influence the appearance of the bimodal shape. It is anticipated that TMS studies would be subjected to several sources of methodological and neurophysiological

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fluctuations, leading to some intraindividual and interindividual variability. The MT used to express the excitability of the cortex has been observed to possess high intraindividual repeatability [10], although there are differences in different MT measurement methods, whereas MEPs have been shown to exhibit wide intraindividual variability [13]. The variability in MEP amplitudes may be due to continuous fluctuations in corticospinal and motoneuronal excitabilities [14], reflecting the variation in the numbers of activated motor units. These fluctuations in cortical excitability cannot be fully controlled during the measurement, but methodological stability can already be achieved as quickly as after determining 10–15 MEPs [15]. Further, the application of neuronavigation with TMS has been reported to reduce the variability in the MEP amplitudes [16]. Other studies have claimed that the stability of MEP amplitudes can only be achieved after 20 [17] or 30 pulses [18]. Despite these conflicting results in previous studies, in this study, before the determination of the AI, more than 30 single pulses were applied to the AI target during the estimation of the optimal FDI representation and the calculation of the rMT. In this respect, we fulfilled all the putative requirements for minimum numbers of pulses to be applied to achieve MEP stability. Nonetheless, although it is known that the MEP variability can be decreased with increased stimulation intensity [19] and with a moderate prestimulus muscle contraction [20], these methodological issues are not applicable for the AI, as increased stimulation intensity would also activate a larger cortical area. Furthermore, with this type of arrangement, it would be difficult to maintain moderate muscle contraction during the measurement, especially for patients. The variability in diffusion tensor imaging-derived fractional anisotropy measuring microstructural properties has been claimed to be rather small in healthy individuals [21], and therefore it can be assumed that the variability in AI is mainly attributable to functional variability.

measuring some degree of technical variability, which cannot be fully controlled. The AI is induced by turning the coil in a tangential plane within ± 135° from the optimal coil angle determined during motor mapping. The AI is not sensitive to the originally chosen optimal coil angle, as the peak of the curve is calculated from the resulting MEP amplitudes. However, the stimulation intensity used to measure the AI is dependent on the coil orientation angle, which also exerts a minor effect on the AI [9]. In the present study, the AI value was determined twice from the same target using the same coil angle. Although the intrasession repeatability of the optimal coil angle was good, the limits of agreement of accepted angle variability was 14.5° in total. Consequently, the optimal coil angle is not only affected by the underlying neuronal structure, but it is also modified by functional variations. In a previous study, the optimal coil angle correlated linearly with the angle of the precentral gyrus in relation to the parasagittal line [29], which supports our finding that the optimal coil angle is relatively stable.

The variability in TMS measures is also influenced by the coil orientation used to induce the MEPs [22]. The optimal stimulation angle in the motor cortex has been postulated to be ~ 45° from the parasagittal line [23,24]; however, it has also been stated that the interindividual variability is wide and no single angle can be considered as being optimal [22]. This extensive interindividual variability was also observed in the present study (Fig. 4). When different coil angles are applied, different neuronal populations are activated [25,26]. Furthermore, different coil angles also induce different EFs in the stimulated tissue, because the EF distribution is affected by the tissue type and anisotropy, as well as by the macrostructure of the stimulated area [27,28]. This means that at different coil angles, different neuronal populations will be stimulated with various EFs, and this effect cannot be excluded from the AI. In other words, in addition to functional–structural relationships, we are also

The ability to conduct concurrent function–structure measurements would be of major clinical value in longitudinal studies of brain traumas affecting the motor cortex, such as stroke, which is characterized by both functional and structural changes [1]. However, the AI is intended to estimate only the local cortical changes and not the extensive interhemispheric and intrahemispheric changes commonly associated with stroke. Nonetheless, local changes might be affected by these large-scale changes. As the AI is determined using TMS-induced MEPs, which are characterized by the complete motor system, it is challenging to determine all the possible factors that might exert an impact on the AI value. It is worthwhile noting that stroke would not alter the electrical and physiological principles governing the relationship between coil orientation and induction of MEPs, implying that determination of the AI value would also be valid in stroke patients.

There are some limitations to the present study; the stimulation was performed with a biphasic waveform, although this is considered to be directionally less specific than the monophasic waveform [30]. The biphasic waveform, however, is more powerful than its monophasic counterpart. In neurological disorders and brain traumas, such as stroke, abnormal cortical excitabilities have been observed [31], and under conditions in which cortical excitability has substantially decreased, a monophasic waveform may not be sufficiently powerful. In our pilot study (data not shown), we compared AIs measured with biphasic and monophasic waveforms, and the monophasic waveform was observed to be 0.1 greater than the biphasic one, which implies that the monophasic waveform is more sensitive with respect to the orientation. The shape of the MEP curves, however, was similar between the two waveforms.

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Fig. 4

= First target = Second target

= First optimal coil direction = Second optimal coil direction = Third optimal coil direction

Individual optimal coil directions estimated from the Gaussian-fitted MEP coil angle curve overlaid on right hemisphere structural MRIs. The location of the close-up image is indicated in the whole-head image with posterior on the left and anterior on the right. Coil directions are color-coded; the first two coil directions from the same stimulation target are shown as yellow and pink arrows, and the third as a light blue arrow from a remapped target. Coil directions are the closest actual coil directions corresponding to the optimal coil angle from the Gaussian fit. As the MEP data have been smoothed, the actual coil directions may slightly vary from the estimated optimal angles. As can be seen from the figure, the coil directions are not always perpendicular to the nearest sulcus. MEP, motor-evoked potential.

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To develop more individualized rehabilitation routines and prognostic measures, it is essential to understand the relationship between local function and structure. At present, there is a lack of a reliable and quick method with which to evaluate the local changes taking place during the course of a brain trauma; commonly, several methods need to be combined, complicating their interpretation by the physician. Accordingly, our primary aim was to develop a useful and repeatable tool for clinical evaluations, as well as for scientific research, that could fill the gap mentioned above. Despite the encouraging results obtained in the present study, the AI still needs to be further developed to decrease the intraindividual and interindividual variation, so as to be able to differentiate a small intrinsic variability from variations attributable to changes linked to neurological disorders. Furthermore, the origin of the bimodality in the MEP amplitude–coil orientation curves needs to be clarified.

Acknowledgements Dr Ewen MacDonald is acknowledged for language editing. The authors acknowledge the Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding (project 5041730, Kuopio, Finland). This study was also funded by The Finnish Concordia Fund, Helsinki, Finland, The Finnish Brain Research and Rehabilitation Center Neuron, Kuopio, and The Paulo Foundation, Helsinki, Finland. Conflicts of interest

Petro Julkunen has received consulting fees from Nexstim Plc., the manufacturer of navigated TMS systems, unrelated to this study. For the remaining authors there are no conflicts of interest.

References 1 Schaechter JD, Moore CI, Connell BD, Rosen BR, Dijkhuizen RM. Structural and functional plasticity in the somatosensory cortex of chronic stroke patients. Brain 2006; 129 (Pt 10):2722–2733. 2 Kim DW, Lee SK, Chu K, Park KI, Lee SY, Lee CH, et al. Predictors of surgical outcome and pathologic considerations in focal cortical dysplasia. Neurology 2009; 72:211–216. 3 Thulborn KR, Carpenter PA, Just MA. Plasticity of language-related brain function during recovery from stroke. Stroke 1999; 30:749–754. 4 Møller M, Frandsen J, Andersen G, Gjedde A, Vestergaard-Poulsen P, Østergaard L. Dynamic changes in corticospinal tracts after stroke detected by fibretracking. J Neurol Neurosurg Psychiatry 2007; 78:587–592. 5 Finnigan SP, Walsh M, Rose SE, Chalk JB. Quantitative EEG indices of subacute ischaemic stroke correlate with clinical outcomes. Clin Neurophysiol 2007; 118:2525–2532. 6 Adeyemo BO, Simis M, Macea DD, Fregni F. Systematic review of parameters of stimulation, clinical trial design characteristics, and motor outcomes in non-invasive brain stimulation in stroke. Front Psychiatry 2012; 3:88. 7 Könönen M, Tarkka IM, Niskanen E, Pihlajamäki M, Mervaala E, Pitkänen K, Vanninen R. Functional MRI and motor behavioral changes obtained with constraint-induced movement therapy in chronic stroke. Eur J Neurol 2012; 19:578–586. 8 Abdeen MA, Stuchly MA. Modeling of magnetic field stimulation of bent neurons. IEEE Trans Biomed Eng 1994; 41:1092–1095.

9

10

11

12 13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

Kallioniemi E, Könönen M, Säisänen L, Gröhn H, Julkunen P. Cortical excitability and neuronal anisotropy are related: TMS-DTI study (poster ID: 3055); 2014. Hamburg, Germany: Organization for Human Brain Mapping Annual Meeting. Julkunen P, Säisänen L, Danner N, Niskanen E, Hukkanen T, Mervaala E, Könönen M. Comparison of navigated and non-navigated transcranial magnetic stimulation for motor cortex mapping, motor threshold and motor evoked potentials. Neuroimage 2009; 44:790–795. Awiszus F, Borckardt J. TMS Motor Threshold Assessment Tool 2.0. 2012. Available at: http://clinicalresearcher.org/software.htm. [Accessed 27 October 2013]. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; 1:307–310. Livingston SC, Ingersoll CD. Intra-rater reliability of a transcranial magnetic stimulation technique to obtain motor evoked potentials. Int J Neurosci 2008; 118:239–256. Kiers L, Cros D, Chiappa KH, Fang J. Variability of motor potentials evoked by transcranial magnetic stimulation. Electroencephalogr Clin Neurophysiol 1993; 89:415–423. Julkunen P, Säisänen L, Hukkanen T, Danner N, Könönen M. Does secondscale intertrial interval affect motor evoked potentials induced by single-pulse transcranial magnetic stimulation? Brain Stimul 2012; 5:526–532. Julkunen P, Säisänen L, Danner N, Niskanen E, Hukkanen T, Mervaala E, Könönen M. Comparison of navigated and non-navigated transcranial magnetic stimulation for motor cortex mapping, motor threshold and motor evoked potentials. Neuroimage 2009; 44:790–795. Schmidt S, Cichy RM, Kraft A, Brocke J, Irlbacher K, Brandt SA. An initial transient-state and reliable measures of corticospinal excitability in TMS studies. Clin Neurophysiol 2009; 120:987–993. Cuypers K, Thijs H, Meesen RL. Optimization of the transcranial magnetic stimulation protocol by defining a reliable estimate for corticospinal excitability. PLoS One 2014; 9:e86380. van der Kamp W, Zwinderman AH, Ferrari MD, van Dijk JG. Cortical excitability and response variability of transcranial magnetic stimulation. J Clin Neurophysiol 1996; 13:164–171. Caramia MD, Cicinelli P, Paradiso C, Mariorenzi R, Zarola F, Bernardi G, Rossini PM. 'Excitability changes of muscular responses to magnetic brain stimulation in patients with central motor disorders. Electroencephalogr Clin Neurophysiol 1991; 81:243–250. Pfefferbaum A, Adalsteinsson E, Sullivan EV. Replicability of diffusion tensor imaging measurements of fractional anisotropy and trace in brain. J Magn Reson Imaging 2003; 18:427–433. Balslev D, Braet W, McAllister C, Miall RC. Inter-individual variability in optimal current direction for transcranial magnetic stimulation of the motor cortex. J Neurosci Methods 2007; 162:309–313. Davey NJ, Romaiguère P, Maskill DW, Ellaway PH. Suppression of voluntary motor activity revealed using transcranial magnetic stimulation of the motor cortex in man. J Physiol 1994; 477 (Pt 2):223–235. Sommer M, Alfaro A, Rummel M, Speck S, Lang N, Tings T, Paulus W. Half sine, monophasic and biphasic transcranial magnetic stimulation of the human motor cortex. Clin Neurophysiol 2006; 117:838–844. Hamada M, Murase N, Hasan A, Balaratnam M, Rothwell JC. The role of interneuron networks in driving human motor cortical plasticity. Cereb Cortex 2013; 23:1593–1605. Volz LJ, Hamada M, Rothwell JC, Grefkes C. What makes the muscle twitch: motor system connectivity and TMS-induced activity. Cereb Cortex 2014. http://dx.doi.org/10.1093/cercor/bhu032. [Epub ahead of print]. Thielscher A, Opitz A, Windhoff M. Impact of the gyral geometry on the electric field induced by transcranial magnetic stimulation. Neuroimage 2011; 54:234–243. Opitz A, Legon W, Rowlands A, Bickel WK, Paulus W, Tyler WJ. Physiological observations validate finite element models for estimating subject-specific electric field distributions induced by transcranial magnetic stimulation of the human motor cortex. Neuroimage 2013; 81:253–264. Richter L, Neumann G, Oung S, Schweikard A, Trillenberg P. Optimal coil orientation for transcranial magnetic stimulation. PLoS One 2013; 8: e60358. Di Lazzaro V, Oliviero A, Pilato F, Saturno E, Dileone M, Mazzone P, et al. The physiological basis of transcranial motor cortex stimulation in conscious humans. Clin Neurophysiol 2004; 115:255–266. Di Lazzaro V, Pilato F, Dileone M, Profice P, Capone F, Ranieri F, et al. Modulating cortical excitability in acute stroke: a repetitive TMS study. Clin Neurophysiol 2008; 119:715–723.

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Repeatability of functional anisotropy in navigated transcranial magnetic stimulation--coil-orientation versus response.

Transcranial magnetic stimulation (TMS) can be used for evaluating the function of motor pathways. According to the principles of electromagnetism and...
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