Functional neuronal anisotropy assessed with neuronavigated transcranial magnetic stimulation

Journal of Neuroscience Methods 256 (2015) 82–90

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Journal of Neuroscience Methods journal homepage: www.elsevier.com/locate/jneumeth

Basic Neuroscience

Functional neuronal anisotropy assessed with neuronavigated transcranial magnetic stimulation Elisa Kallioniemi a,b,∗ , Mervi Könönen a,c , Laura Säisänen a,d , Heidi Gröhn e , Petro Julkunen a,b a

Department of Clinical Neurophysiology, Kuopio University Hospital, P.O. Box 100, FI-70029 KYS, Finland Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, FI-70211 Kuopio, Finland c Department of Clinical Radiology, Kuopio University Hospital, P.O. Box 100, FI-70029 KYS, Finland d Institute of Clinical Medicine, University of Eastern Finland, P.O. Box 1627, FI-70211 Kuopio, Finland e Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, P.O. Box 100, FI-70029 KYS, Finland b

h i g h l i g h t s • A measure to study the motor cortex structure and function was developed. • Results imply that the measure detects functional and structural features. • The measure could be useful in the evaluation of motor cortex state.

a r t i c l e

i n f o

Article history: Received 10 June 2015 Received in revised form 2 August 2015 Accepted 25 August 2015 Available online 31 August 2015 Keywords: Transcranial magnetic stimulation Motor cortex Motor evoked potential Neurophysiology Neuroanatomy Diffusion tensor imaging

a b s t r a c t Background: Transcranial magnetic stimulation (TMS) can evaluate cortical excitability and integrity of motor pathways via TMS-induced responses. The responses are affected by the orientation of the stimulated neurons with respect to the direction of the TMS-induced electric field. Therefore, besides being a functional imaging tool, TMS may potentially assess the local structural properties. Yet, TMS has not been used for this purpose. New method: A novel principle to evaluate the relation between function and structure of the motor cortex is presented. This functional anisotropy is evaluated by an anisotropy index (AI), based on motor evoked potential amplitudes induced with different TMS coil orientations, i.e. different electric field directions at a cortical target. To compare the AI with anatomical anisotropy in an explorative manner, diffusion tensor imaging-derived fractional anisotropy (FA) was estimated at different depths near the stimulation site. Results: AI correlated inversely with cortical excitability through the TMS-induced electric field at motor threshold level. Further, there was a trend of negative correlation between AI and FA. Comparison with existing methods: None of the existing methods alone can detect the relationship between direct motor cortex activation and local neuronal structure. Conclusions: The AI appears to provide information on the functional neuronal anisotropy of the motor cortex by coupling neurophysiology and neuroanatomy within the stimulated cortical region. The AI could prove useful in the evaluation of neurological disorders and traumas involving concurrent structural and functional changes in the motor cortex. Further studies on patients are needed to confirm the usability of AI. © 2015 Elsevier B.V. All rights reserved.

Abbreviations: AI, anisotropy index; D, direct wave; DTI, diffusion tensor imaging; EF, electric field; EMG, electromyography; FA, fractional anisotropy; FDI, first dorsal interosseous; FWHM, full width at half maximum; I, indirect wave; MEP, motor evoked potential; MRI, magnetic resonance imaging; MT, motor threshold; nTMS, neuronavigated transcranial magnetic stimulation; rMT, resting motor threshold; rMTEF , resting motor threshold expressed with electric field; TMS, transcranial magnetic stimulation; VOI, volume of interest. ∗ Corresponding author at: Department of Clinical Neurophysiology, Kuopio University Hospital, P.O Box 100, FI-70029 KYS, Finland. Tel.: +358 50 3687356; fax: +358 17 1731 87. E-mail addresses: elisa.kallioniemi@kuh.fi, [email protected] (E. Kallioniemi). http://dx.doi.org/10.1016/j.jneumeth.2015.08.028 0165-0270/© 2015 Elsevier B.V. All rights reserved.

E. Kallioniemi et al. / Journal of Neuroscience Methods 256 (2015) 82–90

1. Introduction Neuronal excitability of the cerebral cortex can be probed noninvasively using transcranial magnetic stimulation (TMS) (Barker et al., 1985). TMS produces a short-lasting magnetic pulse, which in turn induces an electric field (EF) in the cortex (Ilmoniemi et al., 1999). If a suprathreshold stimulation intensity is employed, neurons may generate an action potential. TMS tends to activate neuronal axons as they have the highest density of ion channels within the neural structures (Huerta and Volpe, 2009). TMS is speculated to induce the excitation mainly in the axons in the cortical grey matter rather than in the subcortical white matter (Di Lazzaro et al., 2004, 2008; Edgley et al., 1997) as the grey matter is closer to the skull and has a lower electrical resistance to the TMS-induced EF (Davey et al., 1994; Thielscher et al., 2011; Toschi et al., 2009). Further, it is thought that TMS preferentially activates neuronal axons at a point where the axons bend or terminate (Abdeen and Stuchly, 1994; Ilmoniemi et al., 1999; Roth, 1994). Therefore, by slightly modulating the direction of the stimulating EF, different neuronal populations may be activated (Hamada et al., 2013; Volz et al., 2014). Hence, hypothetically, in addition to using TMS as a functional imaging tool, when combined with structural imaging through online neuronavigation, TMS could be used to study the local structural properties of muscle-specific neuronal tracts. Such an application of TMS, however, has not been introduced yet, though some studies have tried to specify the relation between cortical structure and function assessed with TMS (Herbsman et al., 2009; Hübers et al., 2012; Klöppel et al., 2008; List et al., 2013). When TMS is focused on the motor cortex, a motor evoked potential (MEP) may be produced in the contralateral peripheral muscle. Before the activation generated by the TMS reaches the target muscle, it passes through a chain of events in different cortical layers. Understanding these events is crucial for interpreting the effects of different coil orientations on MEPs. The motor cortex is divided into several layers, whereof layers II, III and V are most involved with TMS-induced responses (Di Lazzaro et al., 2004; Di Lazzaro and Ziemann, 2013). Layer I is the most superficial layer including only a few neurons, and layer IV is absent in the motor cortex (Brodmann, 1909). Layers II and III include excitatory pyramidal neurons which, due to their rather superficial location, are most excitable to TMS (Di Lazzaro and Ziemann, 2013). Furthermore, these neurons have monosynaptic inputs to fast-conducting, large pyramidal tract neurons in layer V (Anderson et al., 2010). Layer V axons first descend perpendicular to the sulcal wall, and then curve approximately in an angle of 90◦ to produce the fibres and the subcortical bundle of axons that occupy the centre of the motor cortex (Schmahmann and Pandya, 2006). It is thought that the D- and I-waves constituting the TMS-induced responses originate from the activation of layer V neurons by direct (D) stimulation of layer V axons or indirect (I) stimulation through the activation of layer II and III axons (Di Lazzaro and Ziemann, 2013). In TMS studies, the motor threshold (MT) is often used as a reference for corticospinal excitability. The MT is profoundly dependent on the direction of the TMS-induced EF with respect to the orientation of the stimulated neurons (Balslev et al., 2007; Brasil-Neto et al., 1992; Danner et al., 2012; Davey et al., 1994; Mills et al., 1992; Rösler et al., 1989). It has been reported that with a monophasic TMS focused on the motor cortex, the lowest MT can be obtained with an antero-medial current direction (Davey et al., 1994; Mills et al., 1992), and with a biphasic TMS with the first pulse directed postero-lateral and the second antero-medial (Kammer et al., 2001; Sommer et al., 2006). These studies are, however, limited by the coil orientations studied; thus, the optimal individual current direction might have gone undetected. In fact, the optimal current direction for MT is highly variable between subjects (Balslev et al., 2007).

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The anatomical microstructure is conventionally evaluated with diffusion tensor imaging (DTI) which measures the directional preference of water diffusion by determining the fractional anisotropy (FA) of the tissue (Basser et al., 1994; Le Bihan et al., 2001). The FA ranges from 0 (isotropic) to 1 (anisotropic). Anisotropy of the tissue refers to the well-organized directionality of cellular structures such as membranes, axons, myelin sheaths and neurofibrils (Beaulieu, 2002). DTI enables the estimation of 3D neuronal fibre tracts (Le Bihan et al., 2001) which can be used for evaluating the general arrangement of the neuronal microstructure. DTI is able to detect subtle microstructural changes in the brain, even when macrostructural abnormalities cannot be observed (Chiapponi et al., 2013). From the DTI perspective, white matter is highly oriented due to dense axonal bundles (Pierpaoli and Basser, 1996), whereas in grey matter there are practically no directional dependencies (Moseley et al., 1990). Brain tumours and brain traumas may alter the local structural and functional features of the cortex (Schaechter et al., 2006). Therefore, in order to predict how TMS affects the neuronal populations, the relation between neuronal structure and function needs to be understood. In this study, we aimed at evaluating the local relationship between TMS-evoked motor responses and cortical structure by developing a novel approach to assess the connection defined as functional neuronal anisotropy. This functional neuronal anisotropy was assessed with neuronavigated TMS (nTMS) utilizing a number of stimuli focused on a mapped target on the motor cortex by applying various different coil directions. Hypothetically, the relation between EF direction and induced responses is indicative of the functional neuronal anisotropy of the motor pathways of a certain muscle. The method developed can be used to determine a measure, which together with DTI-derived FA, was compared with cortical excitability parameters. 2. Materials and methods 2.1. Subjects Twelve healthy right-handed subjects (8 female, 4 male, age: 22–59 years) were recruited for this study. None of the subjects had neurological or psychiatric diseases or safety issues in participating in the magnetic resonance imaging (MRI) (Shellock and Spinazzi, 2008) or in the TMS measurement (Rossi et al., 2009). The study was accepted by the local ethics committee (8/2012), and written informed consent was obtained from all the subjects. 2.2. Magnetic resonance imaging Structural T1-weighted 3D MRI and DTI were acquired from all the subjects with a 3 T MR scanner (Philips Achieva 3.0T TX, Philips, The Netherlands). The T1-weighted images were obtained with the following parameters: TR = 8.23 ms, TE = 3.82 ms, flip angle = 9◦ , voxel size = 1.0000 mm × 0.9375 mm × 0.9375 mm. The T1-images were used for TMS neuronavigation. DTI was scanned with SENSE echo planar sequence using the following parameters: TR = 13.261 ms, TE = 56 ms, flip angle = 90◦ , voxel size = 2 mm × 2 mm × 2 mm, b-value = 800. Diffusion data was acquired using 16 directions, which has been indicated to be sufficient for FA calculation (Ni et al., 2006). Preprocessing of the data, consisting of eddy current correction, brain mask and FA map calculation, was done in FSL (Oxford, UK) (Smith et al., 2004). The DTI data was coregistered with T1-images using trilinear interpolation in SPM8 (London, UK). 2.3. Navigated transcranial magnetic stimulation First, the cortical representation areas of the first dorsal interosseous (FDI) muscle of the right and the left hand were

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Fig. 1. TMS coil was turned in tangential plane from the optimal direction (0◦ ) inducing MEPs estimated at the beginning of the measurement to ±135◦ . At each 45◦ sectors separated by a perpendicular black line on the ring, 20 pulses were given, thus, altogether 120 pulses were applied. Sectors were stimulated in random order and by keeping the angle distribution constant at each sector.

roughly mapped on the motor cortex using a biphasic wave-form with nTMS system (eXimia 3.2, Nexstim Plc, Helsinki, Finland). In the optimal FDI representation, i.e. the location of the highest amplitude MEPs, the coil was rotated in a tangential plane to find the optimal EF direction with respect to the cortex. The optimal representation was used as the stimulation target in the measurement. For both hemispheres, the resting MT (rMT) was determined with the TMS Motor Threshold Assessment Tool 2.0 (Awiszus, 2003; Awiszus and Borckardt, 2012) using 20 single pulses, based on the general minimum requirement of 17 pulses with the routine applied (Awiszus, 2011). Responses of at least 50 ␮V in peak-to-peak amplitude and without preceding muscle contraction were accepted as MEPs. To assess the influence of the induced EF direction on the MEP amplitude, the TMS coil was rotated at the stimulation target in a tangential plane within ±135◦ from the individual optimal direction with 20 pulses given in each 45◦ sector (Fig. 1). Thus, in total, 120 pulses with an inter-trial interval of 4–6 s were used in a sequence, each pulse with a different coil angle. Two sequences were applied separately at stimulation intensities of 105% and 120% of the individual rMT; an intensity of 105% of rMT can be considered highly focal whereas an intensity of 120% is speculated to activate a slightly larger area. The order of the stimulated hemispheres and the stimulated sectors was randomized. Electromyography (EMG) was measured from FDI with an integrated eXimia EMG device. 2.4. Motor evoked potential and electric field analysis The MEP amplitudes were marked online in eXimia software. Only a few MEPs needed to be rejected due to muscle contraction. To compute the AI, the MEP amplitude data was first plotted as a function of the coil angle and smoothed by taking a moving average from adjacent angles with a 20◦ window in Matlab (version: 2013b, MathWorks Inc., Natick, MA). The smoothing was required due to characteristically high inter-trial variance in MEP amplitudes (Fig. 2). The coil orientations were obtained from the eXimia software. A Gaussian distribution was optimized to the experimental curves, and a full width at half maximum (FWHM) was calculated. Considering the general shape of the smoothed MEP amplitude curve as a function of coil orientation angle, Gaussian distribution function was considered suitable. From the FWHM of the optimized

Fig. 2. The measured (grey line), smoothed (black line) and Gaussian fitted (red line) MEP amplitudes are presented at each stimulated angle. Data is shown from a singlesubject measurement. Due to characteristically high variability in the measured data, the MEP amplitudes were stabilized through smoothing. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)

distribution function the AI was obtained based on full circle normalization: AI = 1 −

 FWHM  360◦

(1)

An AI near 1 indicates that the MEP amplitudes arise within a narrow angle window, which according to our hypothesis, can be interpreted as neuronal bundles being orientated anisotropically with respect to TMS. On the contrary, an AI near 0 indicates that the underlying neuronal bundles are not optimally oriented for TMS, i.e. these neuronal bundles seem isotropic to TMS. A resembling approach to evaluate parallelism of structural features is used in polarized light microscopy (e.g. Rieppo et al., 2008). AIs determined at stimulation intensities of 105% and 120% were abbreviated as AI105 and AI120 , respectively. In addition to measuring the AI, the constructed MEP coil angle curves can be used to calculate the optimal local stimulation angle to induce the MEPs independently from the putative optimal angle in the beginning of the measurement. Additionally, eXimia software was used for calculating the TMSinduced EF. These values were taken from the cortical stimulation target as well as from the centre of the analyzed structural volume of interests (VOIs) directly under the stimulation target and in the nearest white matter structure (see Section 2.5) at rMT intensity. The EF was used as a measure of cortical excitability in addition to rMT (Danner et al., 2012, 2008; Julkunen et al., 2009) as it reduces the inter-subject variation due to varying coil-to-cortex distance (Julkunen et al., 2009) as well as due to different nTMS systems (Nieminen et al., 2015). The spatial validity of the EF distribution model in nTMS has been verified (Schmidt et al., 2014). rMT expressed as EF (rMTEF ) was defined at the depth of the grey and the white matter border by visual inspection. 2.5. Fractional anisotropy analysis The mean FA was assessed at radial columns in 6 rectangular VOIs (length = 4 mm, width = 4 mm, height = 2 mm) (Fig. 3). The VOI dimensions were chosen based on the assumption that this volume would minimize the noise within the VOI, but would also be able to evaluate the local anisotropy. VOIs were evaluated at 6 locations whereof 3 were directly under the nTMS target and the others in the nearest white matter structure. In case the stimulation target was in the centre of the gyrus, and the VOIs under the cortical stimulation target were in the white matter structure, no separate white matter VOIs were evaluated. On total 6 different VOI

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Fig. 3. Mean FAs were evaluated in six equivalent VOIs (superficial, middle, deep) under the stimulation target and in the nearest white matter structure. VOIs were situated radially to the cortical surface. Superficial VOI was at a depth of 6 mm from the cortical surface and the middle and deep VOIs were separated from each other by 5 mm in depth. The size of each VOI was 4 mm × 4 mm × 2 mm. The VOIs directly under stimulation can be either in grey or in white matter structure. If VOIs directly under stimulation were in white matter structure, no separate white matter VOIs were assessed.

locations were considered as it remains unknown which area or depth is actually activated by TMS. The VOIs were classified as superficial, middle and deep in both under the stimulation target and in the white matter structure. The superficial VOI in both locations was at a depth of 6 mm from the surface of the cortex, which is approximately twice the average thickness of the motor cortex (Butman and Floeter, 2007) to ensure that the VOIs were not in the surface of the cortex, since FA is not an optimal method to study the directionality in the cortex (Moseley et al., 1990). VOIs were separated by 5 mm from each other in depth, measured from the surface of each VOI; therefore, the centres of the VOIs were at a depth of 7, 14 and 21 mm from the cortical surface. Mean FA values were calculated in the VOIs with the freely available AMIDE software (Loening and Gambhir, 2003).

inter-hemispheric difference was observed. A significant difference was detected between the AIs induced with different stimulation intensities (105% and 120% of rMT) in both hemispheres (right: p = 0.042; left: p = 0.004).

2.6. Statistical analysis

3.2. Correlation between fractional anisotropy and cortical excitability

Pearson’s linear correlation coefficients were calculated at different VOIs between AI and EF (at different VOIs at rMT intensity), AI and rMT, AI and rMTEF as well as AI and FA. Further, the interhemispheric correlation in AI was assessed. Since the exact location of TMS-induced activation remained unknown, the correlation analyses were performed as exploratory, ‘probing’ analysis to find the location showing statistical trends. Hence, no adjustment for multiple comparisons was performed. Additionally, the influence of stimulation intensity on AI as well as inter-hemispheric differences in AI and rMT were analyzed with a paired t-test. Normality of the data distribution was verified at an acceptable level (p > 0.05) prior to the analyses using the Kolmogorov–Smirnov test. A linear mixed model was used to assess the inter-hemispheric differences and the effects of analyzed depth and location (under stimulation and in the nearest white matter structure) on FA and EF. Sidak adjustment of p-values was applied in the post hoc analyses. Statistical tests were conducted in SPSS Statistics 21.0 (IBM Corporation, Somers, NY) with a significance threshold of p = 0.05.

Correlations between mean FA and cortical excitability (EF, rMT, rMTEF ) are illustrated in Table 2. In the right hemisphere, rMT and mean FA correlated positively in the superficial VOI (under stimulation target: r = 0.691, p = 0.013; in white matter structure: r = 0.622, p = 0.031) whereas a significant negative correlation was found in the deep VOI in the white matter structure (r = −0.614, p = 0.034). Further, a significant positive correlation existed between EF and mean FA in the superficial VOI under stimulation (r = 0.576, p = 0.050) as well as between rMTEF and mean FA in the superficial VOI (under stimulation: r = 0.713, p = 0.009, white matter structure: r = 0.611, p = 0.035). At deep VOI, rMTEF and FA (r = −0.700, p = 0.011) as well as EF and mean FA correlated negatively (r = −0.751, p = 0.005). In the left hemisphere, similar trends in correlations were observed, yet these attained significance only between rMT and the deep VOI in the white matter structure (r = −0.622, p = 0.031).

3.1. AI and cortical excitability Both AIs had a trend of negative correlation with EF in the VOIs in the right hemisphere (Table 1). This correlation was significant with AI105 (r ≤ −0.637, p ≤ 0.026). Additionally, there was a significant negative correlation between AIs and rMTEF in the right hemisphere (AI105 : r = −0.756, p = 0.004, AI120 : r = −0.673, p = 0.017) but not in the left (Table 1).

3.3. Relationship between AI and fractional anisotropy 3. Results Gaussian-fitted MEP amplitude–coil angle curves are presented for right and left hemisphere and AI105 is reported for each subject in Fig. 4. Curves varied from single-peak to multi-peak curves. The multi-peak curves commonly consisted of a dominant and a secondary peak. The range of AIs in the FDI target was in the left hemisphere: AI105 = [0.6576, 0.9156], AI120 = [0.4396, 0.9973], and in the right hemisphere: AI105 = [0.7210, 0.9483], AI120 = [0.7027, 0.9066]. In the AI, no significant inter-hemispheric correlation or

A significant negative correlation was noted between the AI120 and the mean FA in the middle VOI under the stimulation target in the right hemisphere (r = −0.623, p = 0.030). This VOI was located in the exploratory analysis. Furthermore, consistently non-significant negative trends in correlations were observed in the two uppermost VOIs and a positive correlation existed in the deep VOI (Table S1, Supplementary material). Correlations between AI and mean FA were not statistically significant or did not follow a similar trend in the left hemisphere.

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Fig. 4. Smoothed MEP amplitude curves presented as a function of the stimulation coil angle for individual subjects (data organized in descending order according to the right hemisphere AI105 ). For presentation, the smoothed MEP-values were normalized with the maximum smoothed MEP amplitude. Measured data is shown for right hemisphere at stimulation intensity of 105% of rMT, and Gaussian fits for both hemispheres. The MEP curves varied from single peak curves to multi-peak curves indicating high variability in the motor cortex structure. Furthermore, in several curves, the highest amplitude peak was not exactly at the originally determined optimal angle (0◦ ). This shows the importance of evaluating the optimal current direction by testing several adjacent angles with short intervals.

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Table 1 Correlation between AI and cortical excitability (EF – electric field at difference VOIs; rMT – resting motor threshold; and rMTEF – resting motor threshold expressed as electric field). Significant correlations (p ≤ 0.05) are bolded. AIs induced with stimulation intensities of 105% and 120% are abbreviated as AI105 and AI120 . Further, VOIs at different depths and locations are indicated in the brackets (stim, under stimulation; white, in the nearest white matter structure; 1, superficial; 2, middle; 3, deep). Left hemisphere

EF (stim1) EF (stim2) EF (stim3) EF (white1) EF (white2) EF (white3) rMT rMTEF

Right hemisphere

AI105

AI120

AI105

AI120

r = −0.176, p = 0.584 r = −0.156, p = 0.627 r = −0.127, p = 0.694 r = −0.196, p = 0.542 r = −0.193, p = 0.548 r = −0.122, p = 0.706 r = 0.061, p = 0.851 r = 0.110, p = 0.733

r = 0.091, p = 0.779 r = 0.120, p = 0.709 r = 0.150, p = 0.642 r = 0.053, p = 0.870 r = 0.089, p = 0.784 r = 0.158, p = 0.624 r = 0.256, p = 0.422 r = 0.253, p = 0.427

r = −0.661, p = 0.019 r = −0.687, p = 0.014 r = −0.644, p = 0.024 r = −0.637, p = 0.026 r = −0.670, p = 0.017 r = −0.641, p = 0.025 r = −0.549, p = 0.064 r = −0.756, p = 0.004

r = −0.522, p = 0.082 r = −0.541, p = 0.069 r = −0.533, p = 0.074 r = −0.506, p = 0.094 r = −0.523, p = 0.081 r = −0.496, p = 0.101 r = −0.491, p = 0.105 r = −0.673, p = 0.017

Table 2 Correlations between fractional anisotropy (FA) and cortical excitability (EF – electric field at difference VOIs; rMT – resting motor threshold; rMTEF – resting motor threshold as electric field). Significant correlations (p ≤ 0.05) are bolded. VOIs at different depths and locations are indicated in the brackets (stim, under stimulation; white, in the nearest white matter structure; 1, superficial; 2, middle; 3, deep). Left hemisphere

FA (stim1) FA (stim2) FA (stim3) FA (white1) FA (white2) FA (white3)

Right hemisphere

EF

rMT

rMTEF

EF

rMT

rMTEF

r = 0.248, p = 0.438 r = −0.247, p = 0.438 r = −0.575, p = 0.051 r = 0.217, p = 0.498 r = 0.350, p = 0.264 r = −0.358, p = 0.253

r = −0.209, p = 0.514 r = −0.086, p = 0.791 r = −0.294, p = 0.354 r = 0.245, p = 0.443 r = 0.274, p = 0.388 r = −0.622, p = 0.031

r = −0.254, p = 0.426 r = −0.102, p = 0.751 r = −0.357, p = 0.254 r = 0.517, p = 0.085 r = 0.469, p = 0.124 r = −0.516, p = 0.086

r = 0.576, p = 0.050 r = 0.294, p = 0.353 r = −0.491, p = 0.105 r = 0.295, p = 0.353 r = 0.223, p = 0.487 r = −0.751, p = 0.005

r = 0.691, p = 0.013 r = 0.500, p = 0.098 r = −0.473, p = 0.120 r = 0.622, p = 0.031 r = 0.243, p = 0.447 r = −0.614, p = 0.034

r = 0.713, p = 0.009 r = 0.487, p = 0.109 r = −0.492, p = 0.105 r = 0.611, p = 0.035 r = 0.291, p = 0.358 r = −0.700, p = 0.011

3.4. Fractional anisotropy and electric field at different depths The mixed model analysis of mean FA revealed that there was a significant effect of hemisphere (F = 5.12, p = 0.025) and analyzed depth (F = 29.10, p < 0.001) and a significant difference in between VOIs under the stimulation target and in the nearest white matter (F = 16.80, p < 0.001). The mean FA was greater in the left hemisphere compared to the right. Further, the mean FA in the middle and deep VOI was greater than that of the superficial VOI (p < 0.001), whereas there were no differences between middle and deep VOI. In addition, the mean FA in the white matter structure was greater than under stimulation location. Analysis of EF at different VOIs indicated that only the depth had a significant effect on the EF (F = 333.04, p < 0.001) as expectedly EF decreases with increasing depth, while hemisphere or VOI location exhibited no significant effects. No significant interaction effects were observed.

4. Discussion In this study, we aimed to develop a novel approach for estimating the local structure–function relationships, and to present preliminary results based on the novel approach. The relationships found were assumed to be connected to functional anisotropy of the neurons prone to TMS activation. The method developed utilized nTMS for functional neuronal anisotropy evaluation. Our motivation was to gain more understanding on the structure–function relationship between TMS-induced motor responses and motor cortical structure, as despite its great potential, the use of nTMS as a structural tool has not yet been established. Implied by the present study, the cortical excitability (EF, rMT, rMTEF ) might be related to the anatomical anisotropy measured with DTI as well as the functional neuronal anisotropy assessed with the novel AI. Due to explorative nature of these analyses, however, more studies are needed to understand the relations. Since the AI measures the anisotropy from the perspective of TMS, and DTI the local orientation of the neuronal tracts, we did not expect these correlations to be strong as these structural measures are not the same.

Nonetheless, we demonstrated that nTMS could potentially be utilized also as a structural imaging tool to examine the local structural properties related to the motor tract of the stimulated target muscles. These local functional and structural changes might be informative in the evaluation of the cortical state in the recovery from a brain trauma, such as stroke. The AI does not possess the wide variability observed in MEP amplitudes (Kiers et al., 1993; Säisänen et al., 2008) since the MEP amplitudes are filtered with a moving average filter before calculating the AI (Fig. 2). In addition, recently the AI was demonstrated to be repeatable both in intra- and intersession evaluation when the assessment was conducted from the dominant peak (Kallioniemi et al., 2015). In contrast, the bimodal shape sometimes observed in the MEP-amplitude curve did not repeat itself consistently (Kallioniemi et al., 2015). We speculate that directional dependence of TMS-induced responses is mainly affected by the layer V neurons because they are thought to evoke the D- and I-waves constituting the responses (Di Lazzaro and Ziemann, 2013; Esser et al., 2005). With monophasic waveform the D- and I-waves are known to be consistently dependent on the direction of the TMS-induced current (Di Lazzaro et al., 2008). In the lateral-medial direction, the D-waves have the shortest latency (Di Lazzaro et al., 2008). The posterior-anterior current direction, on the contrary, evokes three different I-waves (I1 , I2 , I3 ), whereas anterior–posterior current direction generates only a later I-wave (I3 ) (Di Lazzaro et al., 2008). The influence of different current directions on D- and I-waves evoked with biphasic waveform is also evident, but less constant to that of monophasic and the effects are greatly affected by the stimulation intensity applied (Di Lazzaro et al., 2012). These potential variations depending on the current direction may potentially explain some of the bimodal shape observed in the MEP amplitude curves. Nonetheless, we have to acknowledge that the motor cortex is a complex system comprising of excitatory and inhibitory elements that have different size, location, orientation and function; to date, the effects of TMS are not fully understood (Di Lazzaro et al., 2008; Lee and Fried, 2014). Therefore, it is more probable that we measure the net orientation of the neuronal circuit comprising of several

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different cortical layer elements. Further, in addition to influence of the cortical microstructure, recent evidence also suggests that the later I-wave (I3 ) would be affected by the subcortical structures (Cirillo and Perez, 2015). Finding a relation between TMS-induced responses and neuroanatomy has also been the focus of some previous studies (Herbsman et al., 2009; Hübers et al., 2012; Klöppel et al., 2008; List et al., 2013). In what we think is the earliest study, the rMT was found to correlate negatively with FA in the pyramidal tract at various depths (Klöppel et al., 2008). These results, however, were not successfully repeated in the following studies (Herbsman et al., 2009; Hübers et al., 2012; List et al., 2013), as no correlation was found between rMT and FA. Hence, finding a relationship between TMS and DTI-derived FA is known to be challenging. Due to this, we did not anticipate to observe strong correlations, either, but focused on finding statistical trends. In the present study, interestingly, a trend towards positive correlation was observed between rMT and mean FA in the superficial VOI, whereas a tendency of negative correlation was detected in the deep VOI. Difference in our results compared to previous studies might be due to different VOI sizes and locations used in the analysis. In general, the use of rMT might not be optimal when correlating function with structural parameters, since the rMT is influenced by several factors, for example attention (Gandevia and Rothwell, 1987). We found a slightly stronger relation between cortical excitability and neuroanatomy when using local EF, which considers the coil-to-cortex distance (Julkunen et al., 2009), instead of rMT; it may be that varying coil-to-cortex distance have had a slight influence on the results in previous studies. It has been approximated that 50–60% of the inter-subject variation in rMT results from the variability in skull-to-cortex distance (Kozel et al., 2000). Also, by studying the relation between AI and diffusional kurtosis imaging-related parameters, it could be possible to gain more insight into the effects of nTMS on the motor cortex since kurtosis imaging might be able to characterize the neural microstructure more sensitively than DTI (Cheung et al., 2009). It may be though, that in addition to microstructure, the TMS-induced responses are also strongly influenced by the gyral macrostructure (Opitz et al., 2013; Richter et al., 2013). DTI measures the local orientation of the neuronal tracts, whereas TMS is only affected by the orientation of the tract at a certain point, most likely close to axon hillock (Baker et al., 1995). Thus, the function–structure relationship may be too complex to be associated with parameters from structural MRI methods, and another approach, such as the AI, would be more appropriate. In the exploratory analysis, the AI and EF were found to correlate negatively. This correlation attained significance in the right hemisphere, and similar indications were apparent also in the left. The most likely explanation for this negative correlation is that, when the AI increases, i.e. the MEPs are evoked from a narrow range of coil angles, the neurons are oriented optimally for TMS and less EF is needed to activate the neurons. It is obvious that the orientation of the induced current and thereby AI affects the depth-dependence of the induced EF, which is influenced by the regional orientation of the neurons (Laakso et al., 2014; Opitz et al., 2013). Surprisingly, the relation between AI and mean FA was only detected in the right, non-dominant hemisphere. Previous studies have found several differences in both the motor cortex macroand microstructure between the hemispheres, for instance, that the anatomical and functional hand motor area is greater in the dominant hemisphere (Hopkins and Pilcher, 2001; Triggs et al., 1999). The FA describing the microstructural features of the tissue depends on the macrostructure as it correlates with cortical curvature and sulcal depth (Kang et al., 2012). Nevertheless, in our study, the mean FA values did not differ between hemispheres within the assessed VOIs although a significant hemisphere effect

was observed in the mixed model analysis. Obviously, we cannot exclude the possibility that in the left hemisphere, the interindividual variation in AI and mean FA was too high to reveal the correlation observed in the right hemisphere. In the right hemisphere, AI increased significantly with stimulation intensity and had a significant positive correlation with the intensity. It is plausible that the cortical representation area of FDI is more sharply limited and smaller in the non-dominant motor cortex compared to the dominant motor cortex (Hopkins and Pilcher, 2001; Triggs et al., 1999), so a slight increase in stimulation intensity may activate the majority of neuronal populations. In contrast, in the dominant hemisphere, a small increase in intensity may not activate all the neuronal populations, possibly explaining why there was no increase in AI with increasing intensity. Interestingly, multiple peaks in the MEP amplitude–coil orientation curves were observed in both hemispheres (Fig. 4). These multiple peaks can be interpreted as multiple optimal stimulation directions, though commonly a dominant, highest peak can be detected along with a slightly lower peak. Therefore, in addition to combining the local structural and functional information in AI, the MEP amplitude coil orientation curve could also be used to assess the local optimal stimulation angle to induce the MEPs. Yet, multiple peaks are more challenging to model with AI and their neural origin remains unknown (Kallioniemi et al., 2015). Nevertheless, since the repeatability of the secondary peak is weak, it seems to be influenced stronger by other factors than structure. Thus, we speculate that in the evaluation of cortical structure, the dominant peak might be more relevant. Since the TMS-induced Dand I-waves are dependent on the induced current direction, it is possible that the multiple peaks origin from different I-waves or from different composition of I-waves. At this point, however, we can only speculate this and thus, the MEP-amplitude curves were modelled with a single Gaussian fit. Tissue anisotropy influences substantially the effects of nTMS, for anisotropy alters the EF distribution; different EFs have been demonstrated in grey and white matter (Opitz et al., 2011). It has been indicated that the area presumably influenced by the stimulation is larger when the EF is estimated using anisotropic models than with isotropic models (De Lucia et al., 2007); the site of maximum EF differs as well (Wagner et al., 2004). In the present study, the EF was estimated using an isotropic model which may slightly affect the results. Another limitation is the insufficient knowledge of the area that is actually activated by TMS (Laakso et al., 2014). Due to this, we evaluated the cortical microstructure at 6 different locations near the stimulation site (3 directly under the stimulation and 3 in the nearest white matter structure). Despite the efforts, the preliminary results of the present study, however, do not reveal where TMS activation occurs. In addition, the use of biphasic instead of monophasic wave-form might have influenced the results (Kallioniemi et al., 2015), since the biphasic wave-form slightly complicates the interpretation of AI and is less sensitive to the underlying cortical structure. Regardless of this, the biphasic stimulation is more powerful (Delvendahl et al., 2014) and thus, more applicable in patients with abnormal cortical excitability (Talelli et al., 2006). In future studies, the relation of multiple peaks in MEP coil angle curve to the cortical microstructure should be assessed in more detail. Potentially, more information could be achieved through the measurement of I-waves, known to be affected by coil orientations (Di Lazzaro et al., 2008), as well as through topographic MEP mapping in relation to AI. Further development on the AI in general is required to achieve a clear theory between the MEP amplitudes induced at different coil orientations. Furthermore, AI needs to be applied to different patient groups to investigate the sensitivity of the method in detecting potential abnormalities.

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5. Conclusions In the present study a novel nTMS approach was developed to assess simultaneously the relation between motor cortex structure and function, referred to as functional neuronal anisotropy. This approach produced a measure, namely the AI which, hypothetically, allows the use of nTMS as a structural method. This has not yet been established, although the ability of nTMS to detect structural characteristics follows the basic principles of electric stimulation of neurons. Our preliminary findings indicate that a weak trend in the relationship between functional and structural properties of the motor cortex could be detected with this measure, although more research is needed to fully understand the relation and to optimize the method to be able to assess subtle structural features. Once optimized, the AI introduced could potentially be applied to study local functional neuronal anisotropy in neurological disorders and traumas affecting the motor cortex as well as to study ageing-related structural and functional changes in the brain. Conflict of interest Petro Julkunen has received unrelated consulting pay from Nexstim Plc, manufacturer of the neuronavigated TMS devices. The rest of the authors declare no conflict of interest. Acknowledgements This study was funded by the Kuopio University Hospital, Kuopio, Finland (EVO, VTR 5041730); Orion-Farmos Research Foundation, Espoo, Finland; Kaute Foundation, Helsinki, Finland; The Finnish Brain Research and Rehabilitation Center Neuron, Kuopio, Finland; The Finnish Concordia Fund, Helsinki, Finland; and The Paulo Foundation, Helsinki, Finland. The funders had no further role in study design; in the collection, analysis and interpretation of data; in the writing; and in the decision to submit the paper for publication. Further, the authors thank PhD Gerald Netto for language editing the text. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.jneumeth.2015. 08.028. References Abdeen MA, Stuchly MA. Modeling of magnetic field stimulation of bent neurons. IEEE Trans Biomed Eng 1994;41:1092–5. Anderson CT, Sheets PL, Kiritani T, Shephard GM. Sublayer-specific microcircuits of corticospinal and corticostriatal neurons in the motor cortex. Nat Neurosci 2010;13:739–44. Awiszus F. TMS and threshold hunting. Suppl Clin Neurophysiol 2003;56:13–23. Awiszus F. Fast estimation of transcranial magnetic stimulation motor threshold: is it safe? Brain Stimul 2011;4:58–9. Awiszus F, Borckardt J. TMS motor threshold assessment tool 2.0; 2012 http:// clinicalresearcher.org/software.htm [accessed 25.07.15]. Baker SN, Olivier E, Lemon RN. Task-related variation in corticospinal output evoked by transcranial magnetic stimulation in the macaque monkey. J Physiol 1995;488:795–801. 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;162:309–13. Barker AT, Jalinous R, Freeston IL. Non-invasive magnetic stimulation of human motor cortex. Lancet 1985;1:1106–7. Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J 1994;66:259–67. Beaulieu C. The basis of anisotropic water diffusion in the nervous system – a technical review. NMR Biomed 2002;15:435–55. Brasil-Neto JP, Cohen LG, Panizza M, Nilsson J, Roth BJ, Hallett M. Optimal focal transcranial magnetic activation of the human motor cortex: effects of coil orientation, shape of the induced current pulse, and stimulus intensity. J Clin Neurophysiol 1992;9:132–6.

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