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Intracortical inhibition abnormality during the remission phase of multiple sclerosis is related to upper limb dexterity and lesions

Clinical Neurophysiology, In Press, Corrected Proof, Available online 31 August 2015, Available online 31 August 2015

Highlights

Abstract

Objective

The impact of inhibitory cortical activity on motor impairment of people with relapsing-remitting multiple sclerosis (RRMS) has not been fully elucidated despite its relevance to neurorehabilitation. The present study assessed the extent to which transcranial magnetic stimulation (TMS)-based metrics of intracortical inhibition are related to motor disability and brain damage.

Methods

Participants included forty-three persons with RRMS in the remitting phase and twenty-nine healthy controls. We stimulated the dominant hemisphere and recorded from the dominant hand to assess short-interval intracortical inhibition (SICI) and cortical silent period (CSP) duration. Disability was evaluated with the Multiple Sclerosis Functional Composite (MSFC). Regional cortical thickness and lesion volume were measured.

Results

RRMS participants with dominant upper limb dexterity impairments had prolonged CSP, but equivalent SICI, compared to participants with preserved function. CSP was not related to walking or cognitive performance. Higher normalized lesion volume correlated with longer CSP duration. When adjusting for normalized lesion volume, longer CSP significantly predicted worse dominant upper extremity impairment.

Conclusions

High intracortical inhibition possibly contributes to (or prevents remission from) motor impairment. Lesions may be associated with intracortical inhibition shifts.

Significance

CSP duration and lesion burden should be considered when developing interventions aiming to mitigate motor impairment.

Abbreviations: RRMS - relapsing-remitting multiple sclerosis, RRMS-P - RRMS participant with preserved motor function, RRMS-I - RRMS participant with impaired motor function, HC - healthy control, GABA - γ-aminobutyric acid, TMS - transcranial magnetic stimulation, M1 - primary motor cortex, SICI - short-interval intracortical inhibition, CSP - cortical silent period, EDSS - Expanded Disability Status Scale, CSTi - intracortical cortico-spinal tract, MSFC - Multiple Sclerosis Functional Composite, T25FW - Timed 25-foot walk, 9HPT - 9-hole peg test, PASAT - the Paced Auditory Serial Addition Test, RMT - resting motor threshold, MEP - motor-evoked potential.

Keywords: Multiple sclerosis, Motor control, Brain damage, Transcranial magnetic stimulation, Intracortical inhibition, Cortical silent period.

1. Introduction

Multiple sclerosis is a chronic neuroinflammatory disease that can cause motor impairments, among other debilitating symptoms ( Compston and Coles, 2008 ). For people with the relapsing-remitting course of multiple sclerosis (RRMS), symptoms associated with transient neuroinflammatory events greatly improve during spontaneous clinical remission phases despite the persistence of structural brain damage (Hauser and Oksenberg, 2006 and Steinman, 2014). Physiological mechanisms contributing to the severity of residual disability present during the remission phases of RRMS have not been fully elucidated.

Symptom recovery after neurological damage may be influenced by the brain’s main inhibitory neurotransmitter, γ-aminobutyric acid (GABA), which has a key role in synaptic plasticity and motor learning (Sanes and Donoghue, 2000, Stagg et al, 2011, Kim et al, 2014, Sampaio-Baptista et al, 2014, and Blicher et al, 2015). In humans, intracortical inhibitory activity can be studied non-invasively through a variety of protocols that involve analyzing peripheral electromyographic signals associated with transcranial magnetic stimulation (TMS) of the primary motor cortex (M1). Pharmacological evidence supports that the short-interval intracortical inhibition (SICI) metric is linked to inhibitory activity at ionotropic GABAA receptors, while the cortical silent period (CSP) primarily reflects metabotropic GABAB receptor activity ( Ziemann, 2013 ).

The relationship between these TMS-based metrics and clinical manifestations of multiple sclerosis is not fully known. While CSP prolongation (indicating higher intracortical inhibition) has been reported to occur during the clinical remission phase of RRMS ( Caramia et al., 2004 ), it is not clear if this alteration is related to the preservation or impairment of function. As divergent areas of research support that plasticity and motor learning favor a low inhibitory state (Levy et al, 2002, Floyer-Lea et al, 2006, and Stagg et al, 2011), it could be hypothesized that higher intracortical inhibition during remission is linked to more severe persisting impairment. However, the reverse could also be true, as intracortical inhibition deficits are common during relapses ( Caramia et al., 2004 ) and among individuals with the later, secondary progressive form of multiple sclerosis (Conte et al, 2009 and Vucic et al, 2012). Moreover, it has not been directly assessed if intracortical inhibition is related specifically to impairment of the limb contralateral to the TMS stimulation site, as opposed to other neurological symptoms of this complex disease.

Of further consideration is the impact of brain damage on TMS-based outcomes. Conte et al. (2009) reported that SICI of people with multiple sclerosis is not correlated with lesion load within the whole brain nor within the intracranial cortico-spinal tract (CSTi). While not previously assessed among people with multiple sclerosis, it is possible that neuroimaging analysis techniques that estimate atrophy and lesion impact near the stimulated cortex may provide metrics of damage relevant to intracortical inhibition abnormities. Understanding how inhibitory cortical activity may interact with brain damage to produce (or prevent) motor disability may support the development of optimal tools to assess disease burden and to treat symptoms of people with neurological conditions such as RRMS.

The primary objective of this study was to assess the extent to which TMS markers of intracortical inhibition (SICI, CSP) are abnormal among people with RRMS in remission that have upper limb dexterity impairments when compared to those with preserved upper limb function. Secondly, we investigated the specificity of the identified neurophysiological abnormalities in predicting poor performance of the limb contralateral to the TMS stimulation site compared to other types of disability. The relationship between intracortical inhibition and structural brain damage (as measured throughout the whole brain and near the stimulation site) was also investigated. We predicted that among RRMS participants, intracortical inhibition would be related to upper limb disability, as well as to damage around the cortical region stimulated. Our additional multimodal analysis explored whether intracortical inhibition is related to disability independently of brain damage measured with MRI.

2. Methods

2.1. Participants

A random selection process was used to recruit people with RRMS from a clinical research database at the Montreal Neurological Institute and Hospital in Canada. Age- and sex-matched healthy control (HC) participants were recruited through advertising posters in the community. Age, sex, time since diagnosis, date of most recent relapse, medications, and Expanded Disability Status Scale (EDSS) score were extracted from the clinical database for RRMS participants, and applicable variables for HC participants were self-reported. People were not invited to participate if they: (1) had risk factors for undergoing TMS or MRI (e.g. medications lowering seizure threshold, history of seizure, pregnancy, ferromagnetic metal in body), (2) were taking medications known to affect intracortical inhibition (e.g. baclofen), (3) had a pre-existing health condition not attributed to MS (e.g. bipolar disorder, limb amputation), (4) had experienced a clinically-significant relapse within the three months prior to participation, or (5) were left-handed. Of people who took part in the study, two HC participants were excluded for abnormally poor motor performance (>2 standard deviations worse than published norms ( Oxford Grice et al., 2003 )). Two participants (1 RRMS, 1 HC) did not complete the study because their resting motor threshold was too high to assess SICI or CSP with our protocol. All participants provided informed consent. The Research Ethics Board at the Montreal Neurological Institute and Hospital in Canada approved this study. The final sample included 29 HC and 43 RRMS participants.

2.2. Multiple Sclerosis Functional Composite

The Multiple Sclerosis Functional Composite (MSFC) ( Fischer et al., 1999 ), an assessment of performance across functional domains that has been validated among people with MS ( Cutter et al., 1999 ), was used to measure disability. The MSFC consists of three subscales: Timed 25-foot walk (T25FW), 9-hole peg test (9HPT), and the 3 second version of the Paced Auditory Serial Addition Test (PASAT), which measure leg function/ambulation, hand/arm dexterity, and cognitive function, respectively.

2.3. Neurophysiological assessments

TMS pulses were delivered with a Magstim 2002 stimulator and figure-of-8 coil (outer wing diameter = 9.5 cm) held against the head (left hemisphere) at a 45-degree angle to the sagittal plane (handle oriented posteriorly). Electromyographic data was collected with surface electrodes placed in a belly-tendon montage on the dominant hand (contralateral to the TMS stimulation site), with the recording electrode over the first dorsal interosseus (FDI) muscle. Data was amplified and filtered (bandwidth = 10–3000 Hz, Grass P511 AC Amplifiers) and collected at a sampling rate of 6 kHz. Using BrainSight 2 stereotaxic navigation software (Rogue Research Inc), the optimal target site to elicit an MEP from the target FDI was identified ( Thielscher and Kammer, 2002 ), marked, and referenced for all further stimulations. Resting motor threshold (RMT) was defined as the lowest intensity of stimulation required to induce a motor evoked potential (MEP) of at least 50 μV in 5 of 10 trials in the target FDI ( Rossini et al., 1994 ). TMS data were analyzed semi-automatically with a MATLAB (Mathworks, MA, USA) analysis tool (dataWizard, version 0.7.7, A.D. Wu, UCLA) and manually edited by a researcher blinded to group membership.

The paired-pulse method (used to determine SICI) ( Kujirai et al., 1993 ) required a second stimulator connected through a Bistim module. A conditioning stimulus (80% of RMT) was delivered, followed by the test stimulus (120% of RMT) at randomly ordered inter-stimulus intervals of 1, 2, and 3 ms. Single-pulse test stimuli at 120% RMT (with no conditioning stimulus) were interspersed throughout the paired-pulse procedure. For each interstimulus interval and for the single-pulse condition, eight MEPs were obtained and peak-to-peak amplitude was measured. For each paired-pulse interstimulus interval, SICI was expressed as (1 − (mean paired-pulse MEP amplitude/mean single-pulse MEP amplitude) × 100%) ( Ayache et al., 2014 ), such that higher SICI values would reflect a greater percentage of inhibition relative to the single-pulse MEP.

For the contralateral cortical silent period (CSP) technique ( Cantello et al., 1992 ) a single suprathreshold stimulus (120% of RMT) was delivered while the FDI muscle was voluntarily contracted at 40% of maximum voluntary pinch strength (determined by feedback from a Preston pinch gauge (Sammons Preston, Illinois, USA)), over 10 trials. Trials deemed invalid due to error in muscle contraction (monitored by a second researcher who was not conducting the stimulations) were repeated to ensure accuracy. For visual assessment of the CSP, the electromyographic traces of all valid trials were rectified, averaged, and highly magnified. The minimal absolute CSP duration was measured from the end of the MEP until the earliest onset of the contracted muscle EMG activity return (Caramia et al, 2004 and Kallioniemi et al, 2014) (see Fig. 1 ). To confirm that our main result did not rely solely on the conservative definition of the CSP chosen a priori, we performed additional analyses using alternative definitions of CSP duration, including: (1) CSP end point defined as the EMG potential returning to 50 μV on the average rectified trace, and (2) MEP onset used as the CSP start point.

gr1

Fig. 1 Example of CSP data from a participant with RRMS. The asterisk indicates the time point the TMS pulse was delivered, and the double dagger indicates the MEP onset location. The un-rectified average trace with raw data superimposed (upper panel), was used to assist identification of the MEP end point at the cross point of the EMG potential baseline. The rectified average trace, viewed at a high level of magnification (lower panel) was used to identify the earliest time point where contracted muscle EMG activity began to return to baseline. Upward and downward pointing arrows indicate the start and end points of the absolute CSP, respectively.

MEPs obtained during single pulse stimulations were measured for peak-to-peak amplitude and onset time to measure average MEP amplitude and latency, respectively. This was assessed separately for data collected from relaxed muscles and from active muscles (40% of maximum voluntary contraction).

2.4. Neuroimaging data collection and analysis

A subset of participants (15 HC, 38 RRMS) took part in the additional magnetic resonance imaging (MRI) protocol within 45 days of TMS and MSFC assessment. Neuroimaging was performed at the Montreal Neurological Institute and Hospital (Siemens TIM Trio scanner, 3 Tesla) using a 32-channel head coil. The protocol included acquisition of: (1) T1-weighted 3D fast low-angle shot sequence (repetition time (TR) = 20 ms, echo time (TE) = 5 ms, field of view (FOV) = 256 mm, number of slices = 192, slice thickness = 1 mm), (2) T2-weighted 3D fluid-attenuated inversion recovery (FLAIR) images turbo echo spin sequence (TR = 6000 ms, TE = 355 ms, FOV = 256 mm, slices number = 192, slice thickness = 1 mm), (3) proton density/T2-weighted dual spin echo sequence (TR = 2100 ms, TE = 17/76 ms, FOV = 256 mm, slices number = 60, slice thickness = 3 mm). Scans were co-registered during the preprocessing steps and coded to ensure blinding for imaging analysis steps.

T1-weighted images were processed in FreeSurfer (version 5.1.0, http://surfer.nmr.mgh.harvard.edu/ ), a validated semi-automated segmentation system allowing for the study of specific neuroanatomical regions (Fischl et al, 2002 and Derakhshan et al, 2010). M1 of the left and right hemispheres were defined based on the precentral gyrus labels created with FreeSurfer. Based on FreeSurfer segmentation, cortical thickness (within the whole brain and within each M1) was measured.

T2-weighted lesions within the white matter were detected by a semi-automated lesion-detection software system ( Francis, 2004 ) and manually corrected by a researcher who had been trained by a neuroradiologist. The subsequent analysis was designed to assess the amount of lesioned white matter in the region juxtacortical to M1 of the stimulated and non-stimulated hemispheres. Using the image dilation function in FSL ( Jenkinson et al., 2012 ), M1 masks were dilated by one voxel in each direction to define the region within the white matter that bordered the M1 cortical regions. The volume of the white matter lesions that overlapped with the dilated M1 labels was measured (see Fig. 2 ). Lesion volume within the CSTi of the left and right hemispheres (defined using the John Hopkins University diffusion tensor imaging-based white-matter atlas ( Hua et al., 2008 ), which had been transformed into each participant’s native space using ANTs ( Avants et al., 2011 )) was also measured. To produce inter-subject-comparable measures, the scaling factor generated from SienaX ( Smith, 2002 ) was applied to all lesion volumes.

gr2

Fig. 2 Example of a participant with RRMS who had a T2-weighted white matter lesion that bordered M1 of the left hemisphere. (A) A T2-weighted image of a RRMS participant with a white matter lesion identified near M1 of the left hemisphere is shown. (B, C) T1-weighted images with overlaying anatomical labels are shown. Grey matter labels (excluding M1) are shown in light grey. Light blue and light green indicate M1 of the right and left hemispheres, respectively. The dilated masks for M1 are shown in darker blue and darker green. A zoomed-in section from (A) is shown in (B). An example of the placement of the TMS coil relative to the participant’s brain is also shown in (C).

2.5. Statistical analysis

9HPT completion times for the dominant upper limb were converted into z-scores based on a comparison to the performance of healthy participants. RRMS participants who performed within two standard deviations (SD) of the healthy participants’ mean time were placed in the preserved function (RRMS-P) group, and those performing slower than two SD of the healthy participants’ time were placed into the impaired function (RRMS-I) group.

For all statistical tests, data were analyzed with SPSS and considered statistically significant at p < 0.05.

2.5.1. Group analyses

Descriptive analyses were conducted to characterize the sample on all demographic, clinical, neurophysiological, and MRI-based variables. Chi-squared tests were used to compare groups on categorical variables including sex and use of immunomodulatory medication. To compare RRMS-P and RRMS-I groups on disease duration and EDSS score, Mann–Whitney U tests were used. The Shapiro–Wilk test of normality was conducted for each continuous variable. One-way Analysis of Variance (ANOVA) was used to compare HC, RRMS-P and RRMS-I participants for differences on normally distributed variables, followed by post hoc Tukey t-tests tests. Non-normally distributed variables were compared with a Kruskall-–Wallis H test, followed by a post hoc Mann–Whitney U tests as appropriate. A Bonferroni correction was used to account for the number of group comparisons for each post hoc test.

2.5.2. Correlational analyses

As the goal of the study was to assess the extent to which abnormalities in TMS-based intracortical inhibition metrics are related to clinical disability and brain damage, only intracortical inhibition variables found to differ from HC participants in the group analyses were included in the subsequent correlational analyses. Spearman rank correlations were used to evaluate the relationships between: (1) TMS-based intracortical inhibition metrics and clinical outcomes, and (2) TMS-based intracortical inhibition metrics and MRI outcomes. We also assessed if EDSS score, disease duration, and MEP latency were similarly related to the neurophysiological and neuroimaging outcomes of interest.

2.5.3. Regression

We performed a series of multiple linear regression analyses to assess if TMS-based intracortical inhibition outcomes predicted disability independently of potentially confounding MRI-based or neurophysiological variables. Negative reciprocal transformations were applied to the 9HPT, EDSS and lesion volume data to ensure that assumptions of linear regression analysis were not violated due to non-normally distributed residuals.

3. Results

3.1. Clinical and demographic outcomes

Table 1 outlines the demographic and clinical characteristics of participants. Differences in age (F2,71 = 1.01, p = 0.37) and sex (χ2(2) = 3.51, p = 0.17) were not statistically significant. RRMS-P and RRMS-I groups did not differ in disease duration (U = 138, p = 0.87) or in the proportion of individuals taking immunomodulatory medications to treat MS (χ2(1) = 1.59, p = 0.21).

Table 1 Demographic characteristics of participants.

  HC RRMS-P RRMS-I
Full sample
 Total number of subjects 29 30 13
 Women (n (%)) 21 (72) 22 (73) 6 (46)
 Taking immunomodulatory medication for MS (n (%)) 0 (0) 17 (57) 10 (77)
 Age, years (mean ± SD) 45.0 ± 13.0 42.9 ± 11.0 48.6 ± 12.6
 Disease duration , years (median (Q1, Q3)) 8.9 (3.2, 12.4) 8.3 (5.7, 11.6)
 EDSS, score (median (Q1, Q3)) 1.8 (1.0, 2.4) 3.0 (2.0, 3.5)
 MSFC, score (mean ± SD) 0.65 ± 0.28 0.46 ± 0.41 −0.38 ± 0.59
 
MRI subgroup
 Total number of subjects 15 26 12
 Women (n (%)) 12 (80) 19 (73) 6 (50)
 Taking immunomodulatory medication for MS (n (%)) 0 (0) 16 (62) 10 (83)
 Age, years (mean ± SD) 45.9 ± 15.3 41.5 ± 10.5 48.9 ± 13.2
 Disease duration , years (median (Q1, Q3)) 7.1 (2.8, 11.3) 8.0 (4.9, 10.4)
 EDSS, score (median (Q1, Q3)) 1.5 (1.0, 2.4) 2.5 (2.0, 3.5)
 MSFC, score (mean ± SD) 0.72 ± 0.31 0.49 ± 0.40 −0.38 ± 0.62

a Missing data from three RRMS-P and two RRMS-I participants.

EDSS = Expanded Disability Status Scale; MSFC = Multiple Sclerosis Functional Composite.

Outcomes of MSFC subscale scores, including significance of post hoc assessments, are shown in Table 2 . The T25FW test was not measured for one HC and two RRMS-P participants, because they did not return within 3 weeks for the subsequent session. Groups differed in 9HPT score (for both the dominant (F2,71 = 73.6, p < 0.001) and non-dominant hands (χ2(2) = 23.1, p < 0.001), T25FW time (χ2(2) = 13.3, p = 0.001), PASAT score (χ2(2) = 11.8, p = 0.003), and MSFC score (χ2(2) = 25.1, p < 0.001). The basis by which the groups were defined was confirmed, as dominant hand 9HPT time was longer for RRMS-I participants than RRMS-P participants, while HC and RRMS-P groups did not differ significantly. RRMS-I participants also performed worse on the non-dominant hand 9HPT and PASAT compared to RRMS-P and HC participants. For the T25FW, RRMS-P and RRMS-I groups did not differ, although both performed worse than controls. EDSS score was higher for RRMS-I participants compared to RRMS-P participants (U = 119, p = 0.04).

Table 2 Comparisons between HC participants and RRMS participants with preserved and impaired motor function.

  HC RRMS-P RRMS-I HC vs. RRMS-P HC vs. RRMS-I RRMS-P vs. RRMS-I
Performance Median (Q1, Q3) Median (Q1, Q3) Median (Q1, Q3)      
 9-HPT dominant hand time (s) 17.6 (17.0, 18.4) 19.0 (18.1, 19.9) 25.0 (22.9, 27.3) ‡‡‡ ∗∗∗
 9-HPT non-dominant hand time (s) 18.6 (17.4, 19.7) 19.9 (19.1, 21.3) 23.1 (22.3, 28.9) ‡‡‡ ∗∗
 T25FW time (s) 3.6 (3.3, 4.3) 4.5 (3.7, 4.8) 4.8 (4.1, 6.8)
 PASAT score 52 (45, 55) 51 (39, 55) 35 (24, 44) ‡‡ ∗∗
 Maximal pinch strength (kg) 5.4 (4.5, 6.1) 6.1 (4.1, 6.8) 5.9 (3.6, 6.8)
 
TMS – resting muscle conditions Median (Q1, Q3) Median (Q1, Q3) Median (Q1, Q3)      
 RMT (%) 42 (39, 47) 46 (39, 49) 51 (46, 53)
 Single pulse MEP amplitude (mV) 0.71 (0.47, 1.54) 0.67 (0.38, 1.22) 0.33 (0.20, 0.42) ‡‡
 MEP latency (ms) 22.0 (21.6, 23.2) 22.9 (22.3, 23.6) 24.9 (24.8, 26.0) ‡‡‡ ∗∗∗
 SICI – 1 ms ISI (%) 76.8 (59.3, 88.1) 68.1 (56.2, 78.7) 68.1 (57.4, 74.3)
 SICI – 2 ms ISI (%) 67.2 (45.8, 79.5) 66.2 (46.3, 77.7) 65.0 (44.2, 77.6)
 SICI – 3 ms ISI (%) 71.0 (55.7, 79.6) 63.7 (42.0, 74.5) 65.5 (43.4, 73.6)
 
Normalized T2w white matter lesion volumes (mm3) Median (Q1, Q3) Median (Q1, Q3) Median (Q1, Q3)      
 Whole brain 0 (0, 0) 5341 (2157, 10058) 20357 (5656, 25115) ††† ‡‡‡
 CSTi
  Left hemisphere 0 (0, 0) 119 (0, 429) 500 (95, 1135) ††† ‡‡‡
  Right hemisphere 0 (0, 0) 145 (43, 248) 180 (55, 590) ††† ‡‡‡
 Bordering M1
  Left hemisphere 0 (0, 0) 0 (0, 24) 30 (16, 144) †† ‡‡‡
  Right hemisphere 0 (0, 0) 6 (0, 45) 34 (7, 198) †† ‡‡‡
 
Cortical thickness (mm) Mean (SD) Mean (SD) Mean (SD)      
 Total cortex 2.58 (0.08) 2.51 (0.11) 2.41 (0.16) ‡‡
 M1
  Left hemisphere 2.68 (0.18) 2.70 (0.16) 2.52 (0.33)
  Right hemisphere 2.75 (0.15) 2.69 (0.16) 2.50 (0.43)

Symbols indicate a significant differences identified from the post hoc assessment comparing: HC and RRMS-P (p < 0.05, ††p < 0.01, †††p < 0.001), HC and RRMS-I (p < 0.05, ‡‡p < 0.01, ‡‡‡p < 0.001), and RRMS-P and RRMS-I (*p < 0.05, **p < 0.01, ***p < 0.001). Dashes (–) indicate non-significant outcomes (p > 0.05). SD, Q1, Q3 indicate standard deviation, lower quartile, and upper quartile, respectively.

3.2. Neurophysiological outcomes

Outcomes of TMS-based measures assessed during muscle relaxation are shown in Table 2 . The groups did not differ significantly in RMT (χ2(2) = 5.2, p = 0.08), nor SICI at any of the inter-stimulus intervals (all χ2(2) < 2.3, ps > 0.05). Group differences were however found for MEP amplitude (χ2(2) = 11.6, p = 0.003) and MEP latency (F2,71 = 13.2, p < 0.001) of resting muscles, with RRMS-I participants having lower amplitudes and longer latencies than both HC and RRMS-P participants. While no between-group differences were found for maximal pinch strength (F2,71 = 0.50, p > 0.05) nor average MEP amplitude of a contracted muscle (F2,71 = 0.84, p > 0.05), between-group differences were found in MEP latency during muscle contraction (F2,71 = 8.9, p < 0.001) and CSP duration (χ2(2) = 9.02, p = 0.01), with the RRMS-I group having significantly longer latencies and CSP duration than the other groups ( Fig. 3 ). These between-group differences in CSP duration remained significant even after removing the youngest females from the HC (n = 2) and RRMS-P (n = 3) groups, which minimized the trending differences in age and sex between groups. Confirming this result was not unique to the conservative definition of the CSP duration chosen a priori for the present study, we found that the observed between-group differences in CSP duration remained significant when measuring the absolute CSP duration with the alternative, 50 μV threshold-based definition of the CSP endpoint (HC: 68 ± 28 ms, RRMS-P: 69 ± 26 ms, RRMS-I: 101 ± 43 ms), as well as when measuring the relative CSP beginning at the MEP onset (HC: 94 ± 29 ms, RRMS-P: 96 ± 28 ms, RRMS-I: 130 ± 43 ms) (both χ2(2) > 8.5, ps < 0.05).

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Fig. 3 Outcomes of TMS-evoked electromyographic data collected during muscle contraction. When TMS was performed during muscle contraction, MEP amplitude (A) did not differ significantly between groups, although MEP latency (B) and CSP duration (C) were longer among RRMS-I participants compared to both other groups. Significance of non-parametric two-tailed post hoc tests (corrected for multiple comparisons) is shown for the MEP latency and CSP duration data.

The full sample of RRMS participants (RRMS-P and RRMS-I groups combined) did not differ from HC participants in age (t(70) = 0.12, p = 0.91) or sex (χ2 = 0.42, p = 0.52). CSP duration of RRMS participants was positively correlated with 9HPT performance of the dominant upper limb (rs(42) = 0.37, p = 0.01), but not the non-dominant upper limb (rs(42) = 0.18, p > 0.05). Furthermore, CSP duration of MS participants was not correlated with walking speed, PASAT score, total MSFC score, or disease duration (ps > 0.05), but was modestly correlated with EDSS (rs(42) = 0.36, p = 0.02). MEP latency during active muscle contraction correlated with longer 9HPT performance with either hand, as well as MSFC total score (rs(42) > 0.32, ps < 0.05), but not other variables (ps > 0.05). None of the performance-based variables were significantly correlated with CSP duration or MEP latency among HC participants (ps > 0.05). A model with both CSP and MEP latency as predictors of dominant hand 9HPT performance was significant (R2 = 0.22, F2,42 = 5.65, p = 0.007), with CSP significantly predicting performance (β = 0.36, p = 0.013) independently of MEP latency (β = 0.27, p = 0.063).

3.3. Neuroimaging outcomes

Demographic characteristics of participants who took part in the MRI component of the study are shown in Table 1 , and group comparisons of neuroimaging outcomes are summarized in Table 2 . Group differences in age (F2,52 = 1.54, p = 0.23), sex (χ2(2) = 3.11, p = 0.21), disease duration (U = 105, p = 0.70), and prevalence of participants taking immunomodulatory medications for MS (χ2(1) = 1.81, p = 0.18) remained non-significant when comparing participants of the subgroup. Group differences in cortical thickness occurred within the whole brain (F2,52 = 7.19, p = 0.002), left hemisphere (F2,52 = 3.23, p = 0.048), and right hemisphere M1 (F2,52 = 3.68, p = 0.032). Differences were also found for normalized volume of T2-weighted white matter lesions within the whole brain, within the CSTi and in the regions bordering M1 of the left and right hemispheres (all χ2(2) > 19, ps < 0.001).

Cortical thickness was lower among RRMS-I participants compared to RRMS-P participants within the whole brain and within M1 of the left hemisphere, but not within M1 of the right hemisphere. Compared to RRMS-P participants, RRMS-I participants had significantly higher normalized volumes for T2-weighted white matter lesions within the whole brain and region bordering M1 of the left hemisphere, but not other regions. EDSS score correlated with whole brain normalized lesion volume (rs(37) = 0.40, ps = 0.013), but not with any of the other MRI-based outcomes (ps > 0.05).

In Table 3 , the relationships between the neuroimaging outcomes and CSP duration are shown. Among the RRMS participants, higher normalized lesion volume (both within the whole brain and in the region bordering the left M1) was positively correlated with longer CSP after correcting for multiple comparisons, while no other significant relationships were found. By contrast, MEP latency was not significantly correlated with any of the MRI-based outcomes (ps > 0.05).

Table 3 Relationships between MRI outcomes and CSP duration.

Predictor HC RRMS
Cortical thickness (mm)
 Whole brain −0.42 −0.22
 M1
  Left hemisphere −0.14 −0.32
  Right hemisphere −0.24 −0.24
 
T2w white matter lesion volume (mm3)
 Whole brain 0.51∗∗
 CSTi
  Left hemisphere 0.43
  Right hemisphere 0.12
 Bordering M1
  Left hemisphere 0.44
  Right hemisphere 0.12

The correlation coefficient from Spearman’s rank analysis is shown.

Asterisks indicate 2-tailed significance after correcting for multiple comparisons (*p < 0.05, **p < 0.01).

Multiple linear regression models including both CSP and normalized lesion volume as predictors of dominant hand 9HPT performance were significant when assessing lesion volume within the entire brain (R2 = 0.17, F2,37 = 3.55, p = 0.039), as well as well as within the region bordering the left hemisphere M1 (R2 = 0.18, F2,37 = 3.94, p = 0.029). In both models, CSP was found to be a significant predictor of 9HPT performance (βs > 0.38, ps < 0.05) independently of lesion volume (βs < 0.14, ps > 0.05). However, CSP did not predict EDSS independently of lesion volume in either region (ps > 0.05).

4. Discussion

The results of this study implicate CSP lengthening in specific manifestations of motor impairment present during remission phases of RRMS, and suggest that this neurophysiological irregularity is partially associated with lesion burden.

4.1. Intracortical inhibition and disability

The present study investigated the clinical implications of intracortical inhibition abnormalities in people with RRMS during the relatively stable remission phase. To obtain biomarkers of intracortical inhibition, we stimulated the dominant hemisphere and analyzed recordings from an electrode over a muscle on the contralateral (dominant) hand. On average, RRMS participants with impaired dominant upper extremity motor function had normal SICI, but abnormally long CSP duration. By contrast, RRMS participants with preserved motor abilities did not differ from healthy controls in either outcome. We further assessed the clinical specificity of this neurophysiological abnormality, finding that longer CSP was correlated with 9HPT performance of the dominant upper limb, while CSP did not correlate with non-dominant 9HPT completion time, walking speed, nor cognitive performance of RRMS participants.

Similar to the present study, others have reported that, outside of relapse, SICI is normal among people with RRMS (Conte et al, 2009 and Vucic et al, 2012), while CSP prolongation has been reported among people with RRMS in remission ( Caramia et al., 2004 ) and among those with clinically isolated syndrome who later develop multiple sclerosis ( Pallix-Guyot et al., 2011 ). Our results further demonstrate that among remitting RRMS individuals, longer CSP is related to more severe upper extremity disability. This finding is consistent with studies of people in remission from a stroke, which support that higher GABA impedes motor recovery (Lazar et al, 2010, Bachtiar and Stagg, 2014, and Blicher et al, 2015), potentially by interfering with synaptic plasticity and motor learning (Levy et al, 2002, Floyer-Lea et al, 2006, Krakauer, 2006, and Stagg et al, 2011). Nonetheless, the present study’s results may be surprising considering that low intracortical inhibition has been reported during relapses ( Caramia et al., 2004 ) as well as during the later, secondary progressive stage of multiple sclerosis (Conte et al, 2009 and Vucic et al, 2012). Potentially, disability associated with CSP prolongation during remission could be related to a physiological mechanism that is largely distinct from intracortical inhibition deficits occurring in other disease phases.

Considering the previously described responses of SICI and CSP to specific neurotransmitter receptor agonists/antagonists and the temporal characteristics of these signals ( Ziemann, 2013 ), a possible interpretation of the present result is a circumscribed increase in the long-lasting activity of GABAB receptors, as opposed to the faster-acting, and shorter-lived GABAA receptor activity, among individuals with dominant limb dexterity impairment. Alternatively, CSP prolongation in the absence of SICI change may be associated with neurophysiological alterations related to voluntary motor drive ( Tergau et al., 1999 ), motor attention (Hoshiyama and Kakigi, 1999 and Ziemann, 2013), or spinal mechanisms (Inghilleri et al, 1993 and Ziemann et al, 1993), which could involve GABAergic activity directly or indirectly. While it is possible that factors other than inhibitory neurotransmission may contribute to CSP duration alterations, the present results are consistent with findings from a magnetic resonance spectroscopy study on people with RRMS by Bhattacharyya et al. (2013) , which found higher GABA concentration within a sensorimotor brain volume of the dominant hemisphere to predict worse performance on the 9HPT, but not to predict walking speed nor PASAT performance. Together with the present study, this work implicates a role for GABA associated with the dominant hemisphere motor region of people with RRMS in potentially mediating (or precluding recovery from) dominant upper limb dexterity impairment. Further investigation of this mechanism is warranted, including assessing other forms of inhibition that can be measured with TMS (e.g. long-interval intracortical inhibition, ipsilateral cortical silent period).

4.2. Impact of lesion burden

Our subsequent neuroimaging analysis investigated possible physical origins of CSP prolongation observed among the motor-impaired individuals. To our knowledge, this is the first study to identify a significant relationship between MRI-based outcomes and TMS measures of intracortical inhibition among people with multiple sclerosis. Higher normalized volume of lesioned white matter within the whole brain and within the region bordering M1 of the stimulated hemisphere (but not the non-stimulated hemisphere) correlated with longer CSP duration.

Volume of white matter lesions in the region near the stimulated cortex was found to be more strongly related to CSP duration compared to lesions in the parallel region of the opposite hemisphere. One caveat, however, is that juxtacortical lesions are often located close to lesioned grey matter tissue, to which conventional structural MRI techniques are mostly blind ( Vural et al., 2013 ). Thus, we cannot be certain if the relationship between lesions bordering M1 of the stimulated cortex with CSP was due to structural abnormalities within the white matter pathways leading to the cortex, or instead a direct consequence of cortical damage. Non-conventional imaging sequences (e.g. magnetization transfer, double-inversion recovery) are more sensitive to cortical damage than conventional MRI techniques (Vural et al, 2013 and Filippi et al, 2014), and could thus help to elucidate this in future work. However, even the most sophisticated in vivo neuroimaging techniques cannot detect a large proportion of grey matter lesions known to exist based on postmortem histology (Geurts et al, 2005 and Chen et al, 2013).

Interestingly, it has been proposed in the stroke literature that CSP prolongation may be the result of the deafferentation of M1 due to lesions in various remote brain regions connected with the targeted cortical area, rather than to damage within the motor cortical region stimulated (von Giesen et al, 1994 and Classen et al, 1997). As the present study also found higher total lesion volume to be correlated with longer CSP, it is possible that this holds true in multiple sclerosis. Since CSP was not significantly associated with lesions within the CSTi after correcting for multiple comparisons, it is possible that such deafferentation was more greatly influenced by damage within cortico-cortical, rather than cortico-spinal, pathways within the brain.

The interpretation that CSP prolongation is a direct consequence of structural brain damage does not, however, explain why CSP duration was not related to cortical thickness. While one possibility is that lesion volume is a more sensitive metric of structural damage relevant to CSP duration compared to cortical thickness, it should be noted that T2-weighted lesions are non-specific indicators of brain tissue that has been affected by demyelination, cell loss, inflammation, ischemia, edema or gliosis (Ferguson et al, 1997 and Fox et al, 2011). Therefore, increases of any, or a combination, of these factors may have contributed to CSP prolongation. Interestingly, molecular immunology studies and research with animal models of neuroinflammatory disease have shown GABA to have immunosuppressant and anti-inflammatory properties (Tian et al, 2004, Bhat et al, 2010, Jin et al, 2013, and Paul et al, 2014). Another possibility is that intracortical inhibition could be increased in response to glutamate ( Tremblay et al., 2013 ), which has been found to be abnormally high in normal-appearing white matter and acute lesions of people with multiple sclerosis (Srinivasan et al, 2005 and Tisell et al, 2013). Further research combining the current protocol with biomarkers of other disease-mediating mechanisms will be required to fully uncover whether CSP prolongation, and the associated motor impairment, is direct consequence of structural damage, or rather linked to a neural mechanism responding to neuroinflammation or glutamate excitotoxicity.

4.3. Multimodal analysis

We further investigated the combined influence of lesion burden and neurophysiological alterations on disability. Longer CSP was found to be a significant predictor of 9HPT performance when adjusting for lesion volume, demonstrating that the link between CSP prolongation and motor impairment is not simply secondary to the impact of lesions. This result further highlights the need to investigate if other disease-related factors cause CSP prolongation in future work.

In contrast to dominant upper extremity motor performance, EDSS score was not related to CSP duration after adjusting for lesion volume. Alongside our finding that CSP duration is not correlated with total MSFC score, this outcome demonstrates the need for caution when interpreting TMS-based data in clinical populations, as certain metrics may be associated with specific symptoms rather than to general clinical decline. Nonetheless, biomarkers of neurophysiological changes related to specific symptoms may be useful in the development of more targeted and individualized therapies.

4.4. Conclusions/implications

The novel findings of this study provide insight into the pathophysiology of upper extremity motor disability, and highlight factors to consider when interpreting TMS data in the context of complex brain diseases such as multiple sclerosis. Our results warrant further investigation regarding clinical applications of using CSP as a biomarker of disease burden in neuromotor conditions and/or as a target for neuromodulatory therapies aiming to mitigate disability.

Acknowledgments

The authors thank Serge Gallant, Elena Lebedeva, Afiqah Yusef, Rebecca Taylor-Sussex, and Stanley Hum for assistance with data collection. We also thank Dr. David Araujo and other collaborators at the Montreal Neurological Institute for training and assistance with the neuroimaging components.

Conflict of interest: This study was supported by the Canadian Institutes of Health Research (Grant Nos.: MOP97847, MOP-119428) and The Research Institute of the McGill University Health Centre (fund numbers: 9967, 4857). Work of the first author (JCN) was supported by a Vanier Canada Graduate Scholarship. We declare no conflicts of interest.

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Footnotes

a Integrated Program in Neuroscience, McGill University, 3801 University Street, Room 141, Montreal, Que. H3A 2B4, Canada

b Research Institute of the McGill University Health Centre, 2155 Guy Street, 5th Floor, Montreal, Que. H3H 2R9, Canada

c Department of Neurology and Neurosurgery, McGill University, 845 Rue Sherbrooke Ouest, Montréal, Que., Canada

d Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Que. H3A 2B4, Canada

Corresponding author at: 835 Pine Avenue West, Montréal, Que. H3A 1A1, Canada. Tel.: +1 514 226 5104.


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