Sensorimotor integration in chronic stroke: Baseline differences and response to sensory training

Abstract

Background:

The integration of somatosensory information from the environment into the motor cortex to inform movement is essential for motor function. As motor deficits commonly persist into the chronic phase of stroke recovery, it is important to understand potential contributing factors to these deficits, as well as their relationship with motor function. To date the impact of chronic stroke on sensorimotor integration has not been thoroughly investigated.

Objectives:

The current study aimed to comprehensively examine the influence of chronic stroke on sensorimotor integration, and determine whether sensorimotor integration can be modified with an intervention. Further, it determined the relationship between neurophysiological measures of sensorimotor integration and motor deficits post-stroke.

Methods:

Fourteen individuals with chronic stroke and twelve older healthy controls participated. Motor impairment and function were quantified in individuals with chronic stroke. Baseline neurophysiology was assessed using nerve-based measures (short- and long-latency afferent inhibition, afferent facilitation) and vibration-based measures of sensorimotor integration, which paired vibration with single and paired-pulse TMS techniques. Neurophysiological assessment was performed before and after a vibration-based sensory training paradigm to assess changes within these circuits.

Results:

Vibration-based, but not nerve-based measures of sensorimotor integration were different in individuals with chronic stroke, as compared to older healthy controls, suggesting that stroke differentially impacts integration of specific types of somatosensory information. Sensorimotor integration was behaviourally relevant in that it related to both motor function and impairment post-stroke. Finally, sensory training modulated sensorimotor integration in individuals with chronic stroke and controls.

Conclusion:

Sensorimotor integration is differentially impacted by chronic stroke based on the type of afferent feedback. However, both nerve-based and vibration-based measures relate to motor impairment and function in individuals with chronic stroke.

Keywords

Sensorimotor integration transcranial magnetic stimulation aging stroke

1 Introduction

Motor impairment commonly persists into the chronic phase of stroke recovery, with only 25% of stroke survivors attaining completely useful function of the upper-limb, and returning to their pre-stroke activities of daily living (Miller et al., 2010). Somatosensory deficits are also common, with studies reporting up to 90% of individuals having some form of stroke-related impairment (Carey, 1995; Connell, Lincoln, & Radford, 2008; Meyer, Karttunen, Thijs, Feys, & Verheyden, 2014). Utilising behavioural evidence of motor impairment alone, it is hard to understand the underlying neurophysiological mechanisms contributing to such deficits. For example, the role of the somatosensory system is often de-emphasised; however, somatosensory deficits lead to reductions in motor control (Bolognini, Russo, & Edwards, 2016; Han, Law-Gibson, & Reding, 2002) and are important in predicting motor outcomes (Bolognini et al., 2016; Zeman & Yiannikas, 1989). In addition to stroke-induced alterations to the somatosensory and/or motor systems independently, changes in the link between the two systems, termed sensorimotor integration, may also contribute to deficits in motor impairment and function. Thus, understanding the neurophysiology of sensorimotor integration in the chronic phase of stroke recovery is important to furthering knowledge of underlying factors leading to persisting motor deficits. Additionally, interventions may then be designed to target specific underlying neurophysiological alterations induced by stroke, with the ultimate goal of reducing motor symptomology.

Sensorimotor integration is typically probed by pairing peripheral nerve stimulation with transcranial magnetic stimulation (TMS) to assess the influence of somatosensory information being relayed from primary somatosensory cortex (S1) to primary motor cortex (M1). By altering the interstimulus interval (ISI) between the peripheral nerve stimulation and cortical stimulation, distinct neuronal circuits can be assessed (Chen, Corwell, & Hallett, 1999). Short-latency afferent inhibition (SAI) is thought to relate to cholinergic and GABAergic systems, and reflect direct projections from S1 to M1 (Sailer, Molnar, Cunic, & Chen, 2002). Additional nerve-based measures of sensorimotor integration test unique neural circuitry; afferent facilitation (AF) and long-latency afferent inhibition (LAI) likely involve higher order cortical regions, though the underlying neural contributors are not fully understood (Chen et al., 1999; Devanne et al., 2009).

Sensorimotor integration neurophysiology can be more expansively investigated by examining the impact of various forms of afferent feedback on motor cortical circuitry. Recent work has broadened investigations of the neurophysiology of sensorimotor integration in young healthy individuals to include assessment of the impact of vibration on both single and paired-pulse TMS methods (Rosenkranz, Pesenti, Paulus, & Tergau, 2003; Rosenkranz & Rothwell, 2003). Using these techniques, it has been established that vibration increases corticospinal excitability, reduces GABAergic interneuronal inhibitory circuits (short-interval intracortical inhibition (SICI)), and may increase facilitatory interneuronal networks within M1 (intracortical facilitation (ICF)) (Rosenkranz & Rothwell, 2003; Rosenkranz et al., 2003).

Investigations into sensorimotor integration neurophysiology post-stroke are limited. Considering traditional nerve-based measures of sensorimotor integration, a single study has determined that SAI is decreased in the acute stage post stroke; importantly, the amount of inhibition quantified with SAI relates to motor function and impairment (Di Lazzaro et al., 2012). Preliminary work indicates that following stroke, sensorimotor integration of vibration-based afferents are also altered; while vibration still increases single-pulse motor-evoked potential (MEP) amplitudes, this response varies between the acute and sub-acute stages of recovery (Tarlaci, Turman, Uludag, & Ertekin, 2010). Given the behavioural relevance of sensorimotor integration, extending these investigations into the chronic phase of stroke recovery is essential. Further, inclusion of AF and LAI, as well as paired-pulse vibration-based measures of sensorimotor integration, will provide further insight into sensorimotor integration circuits in individuals at any stage of stroke recovery. For example, the impact of vibration on paired-pulse measures of TMS will inform sensorimotor integration into distinct motor cortical circuitry that is less dependent on spinal mechanisms than single-pulse TMS (Di Lazzaro et al., 1998; Kaneko, Kawai, Fuchigami, Shiraishi, & Ito, 1996; Nakamura, Kitagawa, Kawaguchi, & Tsuji, 1997). Investigations into sensorimotor integration post-stroke should be expanded to include the aforementioned measures, as well as extended to individuals with the chronic phase of stroke recovery. Such an assessment will extend knowledge of sensorimotor neurophysiological changes after stroke and may provide insight into underlying contributors to behavioural deficits.

If baseline deficits in sensorimotor integration are present in individuals with chronic stroke, investigation into whether these measures are modifiable with targeted intervention is warranted. Past work has shown a sensory training paradigm to be effective at modulating sensorimotor integration, quantified with vibration based-measures, in young healthy individuals (Rosenkranz & Rothwell, 2012). Importantly, this work suggests that the neurophysiological response to sensory training is specific to the type of afferent feedback used; vibration-based training alters vibration-based neurophysiological measures, whereas training focusing on cutaneous electrical stimulation does not modulate vibration-based neurophysiological measures (Rosenkranz & Rothwell, 2012). An abundance of clinical studies (Costantino, Galuppo, & Romiti, 2017; Liepert, Hamzei, & Weiller, 2000; Sim, Oh, & Chon, 2015) use various vibration-based interventions, either alone or paired with motor practice, to induce behavioural improvement, yet the underlying neurophysiology of these methods is not understood. Post-stroke plasticity has been shown in the motor system in both the acute and chronic phases of recovery (Carey & Seitz, 2007; Hodics, Cohen, & Cramer, 2006); however, investigation into the plasticity of sensorimotor integration that may contribute to behavioural improvement has not been thoroughly explored in individuals with chronic stroke.

Collectively, the current study addresses two main aims: 1) to establish baseline patterns of sensorimotor integration in individuals with chronic stroke, and the relationship between neurophysiological measures and motor deficits, and 2) to examine the effect of sensory training on neurophysiological measures sensorimotor integration in chronic stroke. We hypothesise that sensorimotor integration will be affected such that afferent feedback will have less of an impact on motor cortical measures compared to healthy populations; specifically, measures in which vibration typically induces facilitation will be less facilitated in a group of individuals with chronic stroke as compared to healthy controls. Neurophysiological measures of sensorimotor integration are expected to relate to motor function and impairment. Further, we hypothesise that sensory training will induce changes in vibration-based measures of sensorimotor integration in individuals in the chronic phase of stroke recovery, potentially shifting these measures to more age-normative patterns. This response will be specific to vibration-based measures of sensorimotor integration, with no impact on nerve-based measures of sensorimotor integration.

2 Methods

2.1 Participants

Fourteen individuals with chronic ischemic stroke (71.4±8.2 years, 8M/6F) and twelve older healthy individuals (69.2±10.0 years 7M/5F) were recruited to participate. Individuals with chronic stroke were selected based on self-reported sensorimotor deficits, which were confirmed with behavioural tests outlined below. Further, due to the neurophysiological outcome measures, all participants had to have an ipsilesional MEP. Informed consent was obtained from all participants and they were screened for contraindications to TMS using a standard screening form (Rossi, Hallett, Rossini, Pascual-Leone, & Safety of TMS Consensus Group, 2009). All experimental procedures were approved by the Clinical Research Ethics Board at the University of British Columbia.

2.2 Experimental design

Each individual participated in two sessions. In the first session, somatosensory and motor functional tests were conducted, followed by baseline neurophysiological evaluation. In the second session, sensory training was followed by the same neurophysiological investigation as was conducted in the first session. Experimental sessions were conducted at the same time of day, and occurred within 5 days of each other.

2.3 Behavioural tests

In individuals with chronic stroke, assessment of motor impairment was characterised with Fugl-Meyer (FM) Upper Extremity Scale (Fugl-Meyer, Jaasko, Leyman, Olsson, & Steglind, 1975). Additionally, arm motor function was assessed with the Wolf Motor Function Test (WMFT) rate (Hodics et al., 2012). Monofilaments and arm position matching (KINARM Endpoint, BKIN Technologies Ltd, Kingston, ON, Canada) were used to index somatosensation.

2.4 Neurophysiological assessment

Individuals were seated in an upright, comfortable position and instructed to relax as much as possible. Neurophysiological measures of somatosensory cortical excitability, motor cortical excitability, and sensorimotor integration were collected. All measures were collected from the ipsilesional (stroke) or non-dominant (older healthy) hemisphere.

2.4.1 Transcranial magnetic stimulation (TMS)

Single pulse TMS was delivered using a monophasic figure-of-eight shaped coil (Magstim 70 mm P/N 9790, Magstim Co., UK) connected to a Magstim BiStim 200² stimulator (Magstim Co., UK). Stimuli were given with a random ISI of 4-5 seconds. The coil was held in such a way to induce a posterior-anterior flow with the coil handle positioned at an angle of 45° pointing backwards. To quantify the electromyographic (EMG) response in the abductor pollicis brevis (APB) muscle, 1 cm×1 cm square surface recording electrodes were arranged with an active electrode on the muscle belly of the APB, an electrode on the interphalangeal joint, and a ground electrode on the back of the hand. EMG signals were pre-amplified (1000x), band-pass filtered at 10–1000 Hz, and sampled at 2000 Hz (LabChart 7.0, PowerLab amplification, AD Instruments, USA). Prior to electrode application, the skin was scrubbed with an exfoliating, abrasive gel (Nuprep Skin Prep Gel, Weaver and Company, USA), and then wiped with alcohol swabs to remove any excess gel. The APB ‘hot-spot’ was located using neuronavigation in concert with a standardised MRI to guide the search (Brainsight™, Rogue Resolutions Inc., Montreal, QC, Canada). The ‘hand knob’ on the precentral gyrus was initially targeted, and then a systematic search in 1 cm increments out from the centre was conducted to determine the scalp position that produced the greatest amplitude MEP. Neuronavigation was again employed to ensure consistent coil positioning on the ‘hot-spot’ throughout the experiment. Resting motor threshold (RMT) was determined by finding the lowest stimulation intensity required to evoke MEPs of at least 50μV in 5 out of 10 consecutive trials (Rossini et al., 1994).

MEP recruitment curves were collected to probe corticospinal tract excitability. A total of 100 single pulse stimulations at ten intensities, ranging in 10% increments from 80–170% RMT, were delivered. The order of intensities was randomised. MEP amplitude was determined using a custom MATLAB script for peak detection (Mathworks, USA). Data were inspected post hoc to discard trials with EMG activity in the pre-stimulus interval. Data was then averaged across the ten trials at each stimulus intensity. A linear slope was calculated across intensities for each individual (Pechmann et al., 2012; Rosenkranz, Kacar, & Rothwell, 2007).

2.4.2 Sensorimotor integration

Sensorimotor integration was tested with two forms of afferent feedback: muscle belly vibration and median nerve stimulation. Vibration-based measures were collected as previously described by Rosenkranz et al. (2003) where TMS measures collected with and without vibration were compared to assess somatosensory influences on motor circuitry (Rosenkranz et al., 2003). Briefly, vibration was applied over the muscle belly of the paretic/nondominant APB at a frequency of 80 Hz (0.2–0.5 mm amplitude) using a 0.7 cm diameter probe. The amplitude was below the threshold for perceiving movement, and EMG was monitored for confirmation. TMS was delivered 1 s into a 1.5 s vibration train (3.5 s ISI) over the contralateral APB representation in M1. In order to quantify the influence of somatosensory information on corticospinal excitability, as well as the inhibitory and facilitatory circuits within M1, multiple TMS measures were collected (MEPs, SICI, ICF) (Rosenkranz et al., 2003). As previously described, SICI was conducted using two TMS pulses (a subthreshold conditioning stimulus (CS) followed by a suprathreshold test stimulus (TS)), administered over M1 with an ISI of 2 ms, while ICF employed the same procedure with an ISI of 12 ms (Kujirai et al., 1993). In the present experiment, the conditioning stimulus was set at an intensity of 80% RMT with the test stimulus intensity set to produce consistent unconditioned MEPs of ∼1 mV amplitude. Inhibition and facilitation were then presented as a ratio of the paired pulse and unconditioned single pulse MEP amplitudes. Ten pulses of all measures (MEPs, SICI, ICF) were collected with and without vibration.

To examine the integration of somatosensory information arising from peripheral nerve stimulation, common techniques were used (SAI, AF, LAI); specifically, an electrical stimulation was delivered over the contralateral (paretic/non-dominant) median nerve prior to a TMS pulse delivered over the ipsilesional/non-dominant motor cortex while the participant was at rest. Median nerve stimulation was set at motor threshold where a twitch was visible and an m-wave was consistently produced. TMS pulse intensity was set such that an MEP of ∼1 mV was consistently produced (TS) (Sailer et al., 2002). ISIs were individualised such that the ISI for SAI was 2 ms longer than the N20 latency derived from the somatosensory evoked potential (SEP) trace, and the ISI for AF was 12 ms longer than the N20 latency (Fischer & Orth, 2011). LAI utilised an ISI of 200 ms (Chen et al., 1999; Chen, 2004; Manganotti et al., 1997; Sailer et al., 2002, 2003; Tokimura, Ridding, Tokimura, Amassian, & Rothwell, 1996). Ten pulses of each technique, as well as ten pulses of unconditioned TS were collected. Measures were then expressed as a ratio of the paired and unconditioned pulses. When collected on the second day, the m-wave amplitude was matched to that shown on the first day to ensure that potential differences in SAI, LAI, and AF were not due to changes in afferent fibre recruitment.

2.4.3 Somatosensory evoked potentials

SEPs were recorded following median nerve stimulation (pulse width 200μs, square wave pulse, cathode distal, anode proximal) with surface electrodes corresponding to CP4 or CP3 positioning in accordance with the International 10–20 System (ipsilesional/nondominant somatosensory cortex) and referenced to AFz (2000 Hz sampling rate) (NeuroPrax; Neuroconn, Ilmenau, Germany). Channel impedances were <5 kΩ. Briefly, stimulation at 2 Hz (Digitimer DS7AH, Welwyn Garden City, Hertfordshire, UK) was delivered at motor threshold, defined as the minimum intensity required to evoke a visible twitch in the target muscle. Recordings from 300 stimuli were collected. Surface EMG were recorded from the paretic/nondominant APB muscle, as described above, in order to monitor the amplitude of the m-wave. The EMG signal was amplified and analogue filtered (30 Hz to 1 kHz) with a Powerlab 4/30 EMG System (AD Instruments, Colorado Springs, CO). In order to analyse SEP data, an average trace was produced to extract the component amplitudes.

2.5 Sensory training

As previously published (Rosenkranz & Rothwell, 2004), sensory training consisted of 15 minutes of 80 Hz vibration applied over the paretic/non-dominant APB muscle belly in 2 second trains with an inter-train interval of 2 seconds. The frequency of vibration was changed with 300 ms left in 70% of trains. If a frequency change was detected, individuals were asked to respond by pressing the space bar on a keyboard in front of them. Although previous work had used smaller frequency changes, the current work presented individuals with deviations in frequency that occurred in 10 Hz increments. The baseline frequency of vibration was 80 Hz, and the change to be detected ranged from 10 Hz (i.e. from 80 to 70 Hz) to 60 Hz (i.e. from 80 Hz to 20 Hz) to account for age and/or stroke-related somatosensory decline.

2.6 Statistical tests

In order to assess baseline vibration-based measures of sensorimotor integration, two-way mixed-model ANOVAs were performed, including within-subjects factor VIBRATION (without vibration, with vibration) and between-subjects factor GROUP (healthy older, stroke). This was done for single-pulse MEP amplitude, SICI, and ICF separately, as each is known to represent different circuits within M1. Post-hoc analyses were performed using Fisher’s LSD where appropriate. To explore baseline nerve-based measures of sensorimotor integration, independent samples t-tests (GROUP) were used for each electrophysiological measure (SAI, AF, LAI).

To determine whether there was a relationship between neurophysiological measures of motor cortical excitability and sensorimotor integration with motor function, a stepwise linear regression was employed (predictors: age, recruitment curve slope, SAI, single-pulse vibration MEP amplitude; dependent variables: FM, WMFT).

Following the establishment of baseline patterns of sensorimotor integration in, vibration-based dependent variables were expressed as a percentage (i.e. (SICI with vibration/SICI without vibration)*100). In order to determine the impact of sensory training on sensorimotor integration, two-way mixed-model ANOVAs were performed, including within-subjects factor TIME (pre and post) and between-subjects factor GROUP (healthy older, stroke) for each vibration-based measure of sensorimotor integration (MEP, SICI, ICF). Further, two-way mixed-model ANOVAs, including within-subjects factor TIME (pre and post) and between-subjects factor GROUP (healthy older, stroke) examined the effect of sensory training on measures quantifying nerve-based sensorimotor integration (SAI, AF, and LAI) and corticospinal excitability (MEP recruitment curve slope). For all statistical tests, a significance level of p≤0.05 was used.

3 Results

All data were tested for normality and log transformed when non-normal (data displayed as non-log transformed), as indicated by significance in the Shapiro-Wilks test (p < 0.001) (Gamst, Meyers, & Guarino, 2008). At baseline, ICF with vibration was non-normal, while all other variables were normally distributed (ps > 0.004). Due to technical difficulties, two individuals with chronic stroke do not have nerve-based measures of sensorimotor integration. Demographic information can be seen in Table 1, and the TS amplitudes for each group and condition are shown in Table 2. TS amplitudes were not different between groups or experimental sessions, confirming that differences found were not the result of differences in TS amplitudes.

Table 1
Demographic information for all participants

Participant Age Post stroke duration FM WMFTR Monofilaments Absolute error Lesion location

S01 86 54 57 5.38 3.61, 6.65, 3.61 0.10 S (basal ganglia)

S02 68 95 53 2.43 4.31, 2.83, 4.31 0.08 M (insula/ basal ganglia)

S03 58 181 37 52.43 3.61, 2.83, 3.61 0.09 M (corona radiate)

S04 70 79 64 1.91 2.83, 2.83, 2.83 0.08 S (basal ganglia)

S05 71 123 62 1.04 4.31, 4.31, 2.83 0.06 S (internal capsule)

S06 71 49 62 1.61 4.31, 4.31, 3.61 0.11 S (anterior thalamus)

S07 77 40 54 2.88 3.61, 3.61, 3.61 0.09 S (internal capsule/corona radiata)

S08 55 85 64 1.78 3.61, 3.61, 3.61 0.03 S (internal capsule/corona radiata)

S09 75 85 58 1.66 3.61, 2.83, 3.61 0.04 S (thalamic)

S10 69 42 58 2.04 4.31, 4.31, 2.83 0.05 S (pons)

S11 78 65 62 2.49 3.61, 2.83, 2.83 0.06 S (thalamic)

S12 64 319 47 4.18 2.83, 3.61, 3.61 0.08 S (corona radiate)

S13 80 28 63 1.84 3.61, 2.83, 3.61 0.05 S (basal ganglia)

S14 78 132 56 2.25 3.61, 4.31, 4.31 0.10 S (pons)

C01 76 3.61, 3.61, 3.61 0.12

C02 73 3.61, 4.31, 3.61 0.06

C03 73 3.61, 3.61, 3.61 0.10

C04 64 3.61, 3.61, 4.31 0.06

C05 82 2.83, 3.61, 2.83 0.03

C06 50 4.31, 4.31, 2.83 0.10

C07 80 4.31, 3.61. 2.83 0.06

C08 58 3.61, 3.61, 2.83 0.03

C09 75 4.31, 4.31, 3.61 0.08

C10 74 3.61, 3.61, 3.61 0.04

C11 56 3.61, 3.61, 2.83 0.03

C12 69 2.83, 4.31, 3.61 0.07

Participant	Age	Post stroke duration	FM	WMFTR	Monofilaments	Absolute error	Lesion location
S01	86	54	57	5.38	3.61, 6.65, 3.61	0.10	S (basal ganglia)
S02	68	95	53	2.43	4.31, 2.83, 4.31	0.08	M (insula/ basal ganglia)
S03	58	181	37	52.43	3.61, 2.83, 3.61	0.09	M (corona radiate)
S04	70	79	64	1.91	2.83, 2.83, 2.83	0.08	S (basal ganglia)
S05	71	123	62	1.04	4.31, 4.31, 2.83	0.06	S (internal capsule)
S06	71	49	62	1.61	4.31, 4.31, 3.61	0.11	S (anterior thalamus)
S07	77	40	54	2.88	3.61, 3.61, 3.61	0.09	S (internal capsule/corona radiata)
S08	55	85	64	1.78	3.61, 3.61, 3.61	0.03	S (internal capsule/corona radiata)
S09	75	85	58	1.66	3.61, 2.83, 3.61	0.04	S (thalamic)
S10	69	42	58	2.04	4.31, 4.31, 2.83	0.05	S (pons)
S11	78	65	62	2.49	3.61, 2.83, 2.83	0.06	S (thalamic)
S12	64	319	47	4.18	2.83, 3.61, 3.61	0.08	S (corona radiate)
S13	80	28	63	1.84	3.61, 2.83, 3.61	0.05	S (basal ganglia)
S14	78	132	56	2.25	3.61, 4.31, 4.31	0.10	S (pons)
C01	76				3.61, 3.61, 3.61	0.12
C02	73				3.61, 4.31, 3.61	0.06
C03	73				3.61, 3.61, 3.61	0.10
C04	64				3.61, 3.61, 4.31	0.06
C05	82				2.83, 3.61, 2.83	0.03
C06	50				4.31, 4.31, 2.83	0.10
C07	80				4.31, 3.61. 2.83	0.06
C08	58				3.61, 3.61, 2.83	0.03
C09	75				4.31, 4.31, 3.61	0.08
C10	74				3.61, 3.61, 3.61	0.04
C11	56				3.61, 3.61, 2.83	0.03
C12	69				2.83, 4.31, 3.61	0.07

Age (yrs), post stroke duration (mos), motor impairment (Fugl-Meyer score), motor function (Wolf Motor Function Test rate), monofilaments (thenar, hypothenar, dorsum), limb-matching absolute error (XY, m), and lesion location (C:cortical, S:subcortical, M:mixed) are shown, where applicable.

Table 2

Test stimulus amplitudes

TS (mV)	Day 1		Day 2
	Stroke	Control	Stroke	Control
Paired pulse	0.90 (0.10)	1.10 (0.11)	1.06 (0.12)	1.02 (0.07)
Vibration-based	0.89 (0.09)	1.05 (0.08)	0.94 (0.11)	1.10 (0.08)
Nerve-based	0.89 (0.10)	1.14 (0.13)	1.06 (0.11)	1.21 (0.10)

Group average values for individuals with chronic stroke and healthy controls are shown for TS amplitudes from both experimental sessions.

3.1 Baseline sensorimotor integration

Two-way ANOVA results revealed a Group×Vibration interaction for single-pulse MEP amplitude (F_(1,25) = 4.185, p = 0.05). Post-hoc analysis revealed a reduction in the influence of vibration on single-pulse MEP amplitudes in individuals with chronic stroke compared to healthy older individuals (p = 0.016), with no difference in MEP amplitude without vibration between groups (p = 0.871). There was no effect of group or vibration on SICI, nor was there an interaction between factors (F_(1,25) = 0.016, p = 0.901). Though there was a significant interaction effect when examining ICF data, this did not reach statistical significance (F_(1,25) = 4.439, p = 0.045), post-hoc comparisons did not reach statistical significance. These results can be seen in Fig. 1.

Fig.1

Vibration-based measures of sensorimotor integration. A: Vibration increases single-pulse MEP amplitudes in older healthy controls, but not in individuals with chronic stroke. B: There is no influence of vibration on SICI in either group. C: Vibration differentially modulates vibration-induced response to ICF post-stroke compared to older healthy controls. Error bars denote standard error of the mean. Asterisks indicate statistical significance (p≤0.05).

Independent samples t-tests revealed no group differences between older healthy individuals and individuals with chronic stroke on nerve-based measures of sensorimotor integration (SAI: t_(1,22) = 0.609, p = 0.549; AF: t_(1,22) = 1.203, p = 0.242; LAI: t_(1,23) = 0.317, p = 0.754). These results can be seen in Fig. 2.

Fig.2

Nerve-based measures of sensorimotor integration. There was no difference between individuals with chronic stroke and healthy older controls in SAI (A), AF (B), or LAI (C). Error bars denote standard error of the mean. Asterisks indicate statistical significance (p≤0.05).

3.2 Association between neurophysiological and functional measures

Stepwise linear regression analyses revealed an association between sensorimotor integration and motor impairment and motor function in individuals with chronic stroke. SAI was the only significant predictor in the model for FM (R² = 0.363, F_(1,11) = 5.703, p = 0.038, β= –0.603). This relationship was oriented such that decreased SAI related to greater motor impairment. The model that explained the most variance in WMFT rate contained both nerve and vibration-based measures of sensorimotor integration. SAI and single-pulse MEP amplitude with vibration were the predictor variables that remained in the model to explain the most variance in WMFT rate (R² = 0.677, F_(1,11) = 9.421, p = 0.006, β= –0.629, –0.562). Decreases in SAI related to slower functional rates, as did an increased response to vibration. There were no significant relationships between neurophysiological measures and measures of somatosensory function.

3.3 Influence of sensory training on sensorimotor integration

Two-way mixed-model ANOVA revealed a main effect of Time (F_(1,24) = 4.966, p = 0.035), with post-hoc analysis showing that sensory training reduced vibration-induced changes in MEP amplitude in both individuals with chronic stroke and older healthy individuals (p = 0.039). Two-way mixed-model ANOVA examining vibration-induced changes in ICF showed a Time×Group interaction effect (F_(1,24) = 4.112, p = 0.05). Post-hoc analysis revealed that there was a difference in the impact of vibration at baseline between the two groups (p = 0.045), but not following sensory training (p = 0.524). Therefore, in older adults, sensory training reduced the impact of vibration on ICF, whereas this remained constant in individuals with chronic stroke. There was no effect of sensory training on the effect of vibration on SICI (F_(1,23) = 0.159, p = 0.694). These results are displayed in Fig. 3.

Fig.3

Response of vibration-based measures of sensorimotor integration to sensory training. Sensory training reduced vibration-induced facilitation of single-pulse MEP amplitudes in both individuals with stroke and controls (A). There was no influence of sensory training on SICI in either group (B). Prior to, but not following, sensory training, there was differential response of ICF to vibration (C). Error bars denote standard error of the mean. Asterisks indicate statistical significance (p≤0.05).

Sensory training did not influence any nerve-based measures of sensorimotor integration in older individuals or individuals with chronic stroke (Fig. 4).

Fig.4

Response of nerve-based measures of sensorimotor integration to sensory training. There was no influence of sensory training on SAI (A), AF (B), LAI (C) in individuals post-stroke or healthy controls.

Additionally, corticospinal excitability, quantified with recruitment curve slope, did not change following sensory training.

3.4 Performance

Performance during sensory training was assessed by evaluating the percentage of correct responses to frequency changes in vibration. Individuals with chronic stroke performed at chance (52%) with older healthy individuals performing slightly better (58%). There was not a significant difference in performance levels between the two groups. Anecdotally, though they could feel the vibration, many individuals from both groups could not detect any frequency changes, regardless of the large amplitude differences. As a result, individuals were instructed to attend to the vibration to the best of their ability for the duration of the sensory training.

4 Discussion

This is the first work to comprehensively examine multiple neurophysiological measures of sensorimotor integration in the chronic phase of stroke recovery. Our results indicate that there is differential modulation of sensorimotor integration in individuals with chronic stroke, based on the type of afferent feedback (nerve vs vibration-based). Individuals with chronic stroke and healthy older individuals had similar response to nerve-based somatosensory feedback, as seen in SAI, AF, and LAI. In contrast, vibration-based measures of sensorimotor integration were impacted by chronic stroke. More specifically, the influence of vibration on single-pulse MEP amplitude was reduced in individuals with chronic stroke, as compared to healthy older controls. Both vibration and nerve-based measures of sensorimotor integration were functionally relevant in those with chronic stroke; levels of SAI related to motor impairment (FM score), and both SAI and single-pulse response to vibration related to motor function (WMFT rate). Finally, sensory training reduced the facilitatory effect of vibration on single-pulse MEP amplitudes in individuals with chronic stroke and our sample of older healthy controls; however, the reduction in ICF with vibration shown in healthy individuals was not shown in individuals with chronic stroke. Sensory training did not impact nerve-based measures of sensorimotor integration in either group, suggesting there is differential influence on the integration of distinct forms of afferent feedback.

4.1 Baseline sensorimotor integration

Sensorimotor integration is altered in individuals with chronic stroke, compared to older healthy controls. These changes were specific to vibration-based measures of sensorimotor integration, and were not seen in nerve-based measures. The influence of vibration on single-pulse MEP amplitude was reduced in individuals with chronic stroke as compared to healthy controls, and there was a similar result suggesting less of a vibratory influence on ICF. This differential impact of stroke on various measures of sensorimotor integration may provide further evidence for the separate underlying mechanisms associated with these types of measures.

The effect of vibration on single-pulse TMS measures is primarily driven by spinal mechanisms; yet a cortical component is also likely as evidenced by a differential effect of vibration on electrical and magnetic evoked potentials (Kossev, Siggelkow, Schubert, Wohlfarth, & Dengler, 1999). While we did not measure spinal excitability to assess this claim, previous work showed that there is typically an increase in spinal reflex amplitude in the paretic limb of individuals with chronic upper limb impairment post-stroke (Phadke, Robertson, Condliffe, & Patten, 2012). However, vibration has been shown to reduce this hyperexcitability (Noma, Matsumoto, Shimodozono, Etoh, & Kawahira, 2012). In the current work, it is possible that at a spinal level, vibration induces normal, rather than increased, levels of spinal excitability via presynaptic inhibition or other spinal mechanisms that combine with a reduction in cortical receptiveness to incoming afferent information post-stroke. Although the current work cannot disentangle the spinal and cortical contributions, future investigations should include both components in experimental design to gain a more thorough picture of sensorimotor integration post-stroke.

To our knowledge, there is one previous study examining the influence of vibration on MEP amplitude in individuals with stroke. Despite slight methodological differences in vibration frequency, and stage of stroke recovery, this past work provides relevant insight into motor cortical response to vibration post-stroke. Initially, in the acute stage of stroke recovery, individuals show an increase in vibration-induced facilitation as compared to age-matched controls, but by the sub-acute stage of stroke recovery vibration-induced facilitation has returned to more age-normative values (Tarlaci et al., 2010). Taken together with the current results, it is possible that the trend towards a reduction in the influence of vibration on corticospinal activity continues into the chronic phase of stroke recovery where individuals present with less vibration-induced facilitation than older healthy individuals.

In contrast to our vibration-based measures, nerve-based measures of sensorimotor integration were not different between our group of individuals in the chronic phase of stroke recovery and older healthy controls. Although not completely understood, the neural mechanisms underpinning nerve-based measures may differ from vibration-based measures. For example, SAI and LAI have both been shown to relate to the afferent volley in area 3b of S1 (Bailey, Asmussen, & Nelson, 2016; Turco, El-Sayes, Fassett, Chen, & Nelson, 2017), whereas this has yet to be determined for vibration-based measures. Some work suggests that afferents arising from vibration may be more directly integrated into M1 through area 3a (Jones & Porter, 1980; Kaneko, Caria, & Asanuma, 1994). Therefore, while speculative, it is possible that slight differences in pathways to M1 may underlie differential modulation of these measures with chronic stroke. Reis and colleagues (2008) put forth a theoretical model for the mechanisms underpinning vibration and nerve-based measures of sensorimotor integration in addition to those driving motor cortical measures, such as SICI. In this model, they suggest that while SAI and LAI induce changes in corticospinal excitability by influencing late indirect (I)-waves, the specific I-wave recruitment resulting from vibration is not well understood. Though both vibration and SAI have been shown to interact with paired-pulse measures of TMS (Alle, Heidegger, Krivanekova, & Ziemann, 2009; Udupa, Ni, Gunraj, & Chen, 2014), the mechanism through which they do this may be different. For instance, despite potential overlapping interneuronal populations, our results suggest that nerve-based and vibration-based measures of sensorimotor integration can be modulated independently of one another, by both pathology and intervention. Furthering the understanding of neural mechanisms underlying neurophysiological measures of sensorimotor integration will aid in understanding the current findings.

Prior work investigating this neurophysiology post-stroke solely focused on the acute phase of recovery and showed disinhibition in ipsilesional SAI, relative to age-matched controls (Di Lazzaro et al., 2012). Taken together with the current result, it appears that ipsilesional SAI levels may change throughout the time course of recovery post-stroke. Acutely after cortical infarct, it has been theorised that cortical activity may shift towards disinhibition within neural networks in an attempt to promote an increased response to experience-dependent plasticity important for motor recovery. Once these new, potentially compensatory, networks are established, there is less of a dependence on reduced inhibition, and rather the focus is thought to shift towards a synaptic strengthening within these new pathways (Swayne, Rothwell, Ward, & Greenwood, 2008). This pattern has been shown post-stroke in the motor cortical measure of SICI. Mechanistically, SAI and SICI have both been linked to GABA_A receptor related inhibition (Sailer et al., 2002). Thus, it is possible that GABAergic measures, such as SAI and SICI, are reduced acutely post-stroke to optimise the neurophysiological environment for recovery, but as function is regained, this shifts back towards more normalised values. In order to test this hypothesis, future work should track these measures longitudinally through the recovery process.

The current work shows that there is a relationship between the nerve-based measure of sensorimotor integration, SAI, and motor impairment quantified with the FM. Greater reduction in SAI associated with stroke was related to higher levels of motor impairment on the FM. The current results indicate that SAI may follow a similar recovery trend to other cortical measures of inhibition where there is commonly disinhibition acutely post-stroke, which may be reduced at 6-months post-stroke. Therefore, it is possible that an absence of restoration of SAI to “normal” age values is indicative of a poorer recovery. Interestingly, while vibration-based measures of sensorimotor integration did not help to explain variance in FM scores, single-pulse response to vibration, in concert with SAI, was important in explaining variance in motor function. Potentially, the tasks used in the WMFT are more complex and demanding than those in the FM assessment, and thus a reliance on both types of sensorimotor integration is required to complete the tasks. In contrast to what was shown with SAI, increased response to vibration was related to worse motor function. Given the known spinal contributions to vibration-induced facilitation (Claus et al., 1988), this is in line with past research. Individuals in the chronic phase of stroke recovery show increased spinal excitability, indicated by H-reflex amplitudes, in the paretic compared to non-paretic limb (Phadke et al., 2012). Additionally, individuals who present with worse motor deficits also have increased spinal excitability (Pizzi, Carlucci, Falsini, Verdesca, & Grippo, 2005). Therefore, this increase in spinal excitability may contribute to the larger response to vibration and explain the relationship with motor function. Future work delineating the specific contributions of spinal and cortical mechanisms would provide important insight into the stroke-related changes in sensorimotor integration documented in the current work.

4.2 Influence of sensory training on sensorimotor integration

Sensory training altered vibration-based measures of sensorimotor integration, but had no influence on nerve-based measures. A 15-minute block of sensory training reduced vibration-induced facilitation of MEP amplitudes arising from single-pulse TMS, similar to patterns seen in older healthy controls. However, individuals with chronic stroke did not show a decrease in ICF with vibration; individuals with chronic stroke and healthy controls differed in response to vibration prior to, but not following sensory training. Cumulatively, these results suggest that sensory training may alter specific measures of sensorimotor integration in individuals with chronic stroke, but the impact of training is different between these individuals and healthy older adults.

Previously, the effect of vibration-based interventions on neurophysiology and behaviour has been tested in individuals with various movement disorders with differing degrees of success. In the current work, we utilised an intervention that, in the past, was shown to alter sensorimotor integration, indexed by SICI paired with vibration, in a population of young healthy individuals (Rosenkranz & Rothwell, 2004, 2012). Examining baseline patterns of sensorimotor integration may provide insight into the response to sensory training. Baseline methods explored the cortical influence of a single burst of vibration; logically, given that sensory training comprised of 225 applications of this vibration, it would result in repeated activation of circuits indexed at baseline. Without a baseline influence of vibration, sensory training would likely not have an effect. The influence of vibration on single-pulse MEPs showed group differences at baseline, with afferent feedback having less of an impact in the stroke group; however, on average, individuals with chronic stroke still demonstrated an increased MEP amplitude when concurrent vibration was applied. Therefore, the influence of vibration, though reduced, may still present in in individuals with chronic stroke. Repeated activation of this circuit with the sensory training paradigm then should induce a similar response. This is in line with the present results; both individuals with chronic stroke and older healthy individuals show a reduction in the vibration-induced change in MEP amplitude following sensory training.

In contrast, the response of ICF was not altered following sensory training in individuals with chronic stroke, differing from the group of healthy older controls. Again, looking at baseline trends contextualises this result. Prior to sensory training, there was a differential impact vibration on ICF in individuals with chronic stroke. Looking at raw values reveals that individuals with chronic stroke did not show any increase in ICF with concurrent vibration (89% of ICF without vibration), whereas older healthy controls showed an increased facilitation when vibration was applied simultaneously (201%). Given that there is not an influence on these circuits with a single application of vibration, it follows that sensory training would not induce an effect in individuals with chronic stroke.

The decrease in response to vibration following sensory training is in contrast to past work in younger healthy individuals. Previous work showed an increase in vibration-induced disinhibition of SICI, with no changes to the impact of vibration on single-pulse MEP amplitudes or ICF (Rosenkranz & Rothwell, 2012). The importance of attention has been determined to be essential to the induction of neurophysiological change (Rosenkranz & Rothwell, 2006; Stefan, Wycislo, & Classen, 2004); if individuals receive the same vibration pattern for 15 minutes, but are not instructed to attend to that stimulation and respond when frequency changes are detected, vibration no longer influences the response to single or paired-pulse TMS measures (Rosenkranz & Rothwell, 2006). This is similar to ex-vivo results documenting a diminishing neuronal response in the processing of unattended stimuli (Eytan, Brenner, & Marom, 2003). Despite our instructions for individuals to attend to the frequency of vibration throughout sensory training, our results follow this pattern that may arise simply from repetition. Anecdotally, individuals reported finding the task difficult to attend to and this is reflected in performance values. We cannot rule out the fact then that attention waned throughout this period, and without attention-based facilitation of this sensory information, repetition of vibration alone is not enough to induce change on a neurophysiological level. Past literature suggests differences in cortical processing based on task-relevancy (Brown, Ferris, Amanian, Staines, & Boyd, 2015) and perception for action, rather than for memorisation (Carey, 2012). It is possible that including a task-relevancy component to the sensory training paradigm would heighten attentional focus, and lead to a different result. Future work should consider this to ensure results are not due to a lack of attention on the afferents of interest.

In addition to attention, the modality of somatosensory stimulation has been shown to be important for inducing neurophysiological change. Therefore, our results showing changes in vibration-based but not nerve-based sensorimotor integration are in line with previous research. If the influence of sensory training results from repetitive stimulation of the same synaptic pathways, nerve-based measures would not be expected to change. As previously mentioned, afferent feedback arising from peripheral vibration and nerve stimulation may ascend to the cortex in slightly different ways. Strengthening synaptic connections or repetitive activation resulting from vibration, then would not lead to increased excitability or strengthened connections that would be probed with measures of indirect sensorimotor integration.

Finally, although this sensory training paradigm has not been used previously in individuals with chronic stroke, and the specific sensorimotor integration measures have not been tested, other vibration-based interventions have been shown to lead to behavioural improvements with little understanding of the underlying neurophysiology. Future work should expand the neurophysiology tested to broaden our understanding of the physiological underpinnings of functional improvement with vibration-based interventions. Additionally, capturing potential neurophysiological change occurring across multiple sessions in an attempt to quantify potential cumulative effects will be important.

4.3 Limitations

Sensory training has often been shown to change not only neurophysiological measures in the vibrated muscle, but the organisation of sensorimotor integration in the surrounding muscles. The current work only explored the response in a single muscle, and therefore cannot exclude the possibility that sensorimotor patterns for cortical representations of other muscles are not being altered with vibration. Additionally, when examining paired-pulse measures, we did not adjust the TS amplitude to be 1 mV when vibration was concurrently applied. Past work that has adjusted for this, in addition to using the approach of the current study has shown no difference between the two measures and collapsed across those conditions (Rosenkranz & Rothwell, 2003, 2004, 2012). Nevertheless, we cannot rule out the possibility that this may influence the results. Another limitation of the current work is that neurophysiological assessments were conducted on separate days. As a result, we suggest cautious interpretation of the results until future investigation into the reliability of both nerve-based and vibration-based measures of sensorimotor integration can inform the current finding. Finally, while we monitored EMG for evidence of vibration-induced movement, we cannot definitively conclude that vibration did not induce movement illusions that would not be visible in the EMG trace. Future work could address these limitations and further our understanding of the influence of chronic stroke and aging on sensorimotor integration.

5 Conclusions

In conclusion, sensorimotor integration of vibration and nerve-based afferents is differentially impacted in a group of individuals with chronic stroke. Vibration applied over the muscle belly of a focal hand muscle appears to have a reduced impact on measures of corticospinal excitability and interneuronal circuitry within M1 in individuals with chronic stroke; however, the impact of peripheral nerve stimulation on M1 excitability is not altered post-stroke. Neurophysiological measurements of sensorimotor integration are behaviourally relevant and related to the degree of motor function and impairment in chronic stroke. Additionally, a 15-minute bout of sensory training reduced vibration-induced facilitation of corticospinal excitability in both older healthy individuals and individuals with chronic stroke, though it did not alter more cortically-based circuits probed with paired pulse TMS in individuals with chronic stroke. Therefore, we suggest that vibration-based pathways of sensorimotor integration are impacted by chronic stroke, and that an intervention designed to alter sensorimotor integration differentially influences cortical neurophysiology, as compared to healthy older adults. Future investigations into stroke recovery should consider the unique role of sensorimotor integration.

Footnotes

Acknowledgments

This work was supported by the Natural Sciences and Engineering Research Council of Canada [RGPIN 401890-11]. KEB and WRS received support from the Natural Sciences and Engineering Research Council of Canada. JLN was supported by the Canadian Institute for Health Research (CIHR) and the Michael Smith Foundation for Health Research. SJF was also supported by CIHR. LAB received support from Canada Research Chairs, CIHR, NSERC, and the Michael Smith Foundation for Health Research.

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