Stroke Lesion Volume and Injury to Motor Cortex Output Determines Extent of Contralesional Motor Cortex Reorganization

Abstract

Background

After stroke, increases in contralesional primary motor cortex (M1_CL) activity and excitability have been reported. In pre-clinical studies, M1_CL reorganization is related to the extent of ipsilesional M1 (M1_IL) injury, but this has yet to be tested clinically.

Objectives

We tested the hypothesis that the extent of damage to the ipsilesional M1 and/or its corticospinal tract (CST) determines the magnitude of M1_CL reorganization and its relationship to affected hand function in humans recovering from stroke.

Methods

Thirty-five participants with a single subacute ischemic stroke affecting M1 or CST and hand paresis underwent MRI scans of the brain to measure lesion volume and CST lesion load. Transcranial magnetic stimulation (TMS) of M1_IL was used to determine the presence of an electromyographic response (motor evoked potential (MEP+ and MEP−)). M1_CL reorganization was determined by TMS applied to M1_CL at increasing intensities. Hand function was quantified with the Jebsen Taylor Hand Function Test.

Results

The extent of M1_CL reorganization was related to greater lesion volume in the MEP− group, but not in the MEP+ group. Greater M1_CL reorganization was associated with more impaired hand function in MEP− but not MEP+ participants. Absence of an MEP (MEP−), larger lesion volumes and higher lesion loads in CST, particularly in CST fibers originating in M1 were associated with greater impairment of hand function.

Conclusions

In the subacute post-stroke period, stroke volume and M1_IL output determine the extent of M1_CL reorganization and its relationship to affected hand function, consistent with pre-clinical evidence.

ClinicalTrials.gov Identifier: NCT02544503

Keywords

stroke motor cortex upper extremity paresis reorganization TMS MRI

Introduction

Compromised skilled hand function is one of the most common long-term deficits after stroke¹ due to the dependence of skilled hand function on the primary motor cortex (M1) and its corticospinal projections (CST)² which are commonly affected by ischemic stroke.³ In non-human primates, lesions to M1 and/or lesions to the CST detrimentally affect hand function contralateral to the lesion^4,5 and the ability to move the fingers independently does not recover.^6,7 The neuronal substrate supporting affected hand function post-stroke recovery likely involves M1 and CST of the lesioned hemisphere, referred to as ipsilesional M1 and CST (M1_IL and CST_IL).^8,9 In patients with chronic stroke involving CST or M1, impaired skilled hand function is related to abnormally low output from M1_IL¹⁰ and extent of CST and M1 damage.¹¹ Interventions targeting M1_IL hand area result in improved hand function.¹²

While these data provide evidence that M1_IL and CST_IL serve as the primary neural substrates for supporting hand function, reorganization of M1 in the hemisphere contralateral to the stroke (M1_CL) may, however, serve as an additional substrate to support hand function^13,14 for review see Refs.^9,15 The extent and outcome of such reorganization depends on many factors, including lesion size and time since stroke.¹⁶ In the chronic phase post-stroke, M1_CL seems to interfere with motor function of the paretic hand in a subset of patients, possibly through abnormally increased inhibition of lesioned M1 by M1_CL. In such patients, decreasing M1_CL excitability results in improved performance of the paretic hand (for review see Ref.⁹). However, emerging evidence, suggests a potentially supportive role of M1_CL because abnormally increased excitatory neural activity and activation in M1_CL was also seen in patients with good hand function.^9,17-23 Decreasing M1_CL excitability in these patients may result in deterioration of dexterity of hand performance.²² Furthermore, in non-human primates and rats the extent of M1 injury is related to motor area reorganization in the contralesional hemisphere, which includes long-term changes in neurotransmitter systems, dendritic growth, and synapse formation (for review see Ref.²⁴). Inhibiting the contralesional hemisphere in rats that recovered from large ischemic infarcts generates behavioral deficits of the impaired forelimb²⁵ and excitatory stimulation can promote reorganization and recovery.²⁶

Taken together, these findings support the view that M1_CL reorganization is involved in supporting hand function in a subgroup of patients with stroke but the factors that determine its extent are not well understood. Here, we determined key factors that are associated with the reorganization of M1_CL during the subacute phase of stroke. Our working hypothesis is that the extent of M1_IL and/or corticospinal tract (CST_IL) damage, as measured by the electromyographic response to transcranial magnetic stimulation (TMS) of M1_IL (MEP presence or absence) and structural MRI (stroke lesion volume, lesion load of the entire CST_IL, and lesion load of the CST_IL originating in M1 (CST_IL-M1)²⁷), will determine the extent of M1_CL reorganization as measured by the area under the TMS stimulus response curve (M1_CL SRC_AUC), impairment of hand function as measured by the Jebsen Taylor Hand Function Test (JTT),²⁸ and the relationship between M1_CL reorganization and affected hand function (Figure 1).

Figure 1.

Relationship between injury of stroke and contralesional M1 reorganization: (A) lesion masks from the 35 participants with stroke projected into standard space. Lesions from the right hemisphere were flipped across the sagittal axis for illustration purposes only. Hot colors indicate a voxel is lesioned in more participants’ lesion masks and (B) our working hypothesis is that the extent of M1_IL and/or corticospinal tract (CST_IL) damage, as measured by the electromyographic response to TMS of M1_IL (MEP presence or absence) and structural MRI (stroke lesion volume, lesion load of CST_IL or CST_IL originating in M1(CST_IL-M1)²⁷ will determine the extent of M1_CL reorganization (M1_CL-SRC_AUC), impairment of hand function (JTT) and how M1_CL reorganization is related to affected hand function (JTT). Primary outcome measures: M1_CL reorganization (M1_CL-SRC_ACU), lesion volume, lesion load of CST_IL and CST_IL-M1, presence of MEP in response to M1_IL stimulation (MEP +/−), and JTT score.

Methods

Study Design and Overview of Experiments

We conducted a prospective longitudinal study of patients with stroke who were included into the study based on our inclusion criteria (see below) to determine the role of M1_CL reorganization in the subacute (1 month) and chronic (6 months) phase of stroke. In the present paper we report the results of the structural MRI, TMS, and behavioral measures obtained during the subacute phase of stroke only. Primary outcome measures were M1_CL area under the SRC (M1_CL SRC_ACU), presence of MEP in response to M1_IL stimulation (MEP+/−), CST_IL lesion load, CST_IL lesion load originating from M1 (CST_IL-M1), stroke volume, and JTT score (Figure 1). All patients with stroke admitted to medical centers of the Georgia-NIH StrokeNet of all sexes, races, and ethnicities, age 40 to 80 years were considered for inclusion into study. The study was approved by the Emory University Institutional Review Board. Imaging data analysis was performed by KPR and MWH who were blinded to the results of the TMS and behavioral measures. TMS data analysis was performed by persons blinded to the recording side, the time of recording, the results of the imaging, and the behavioral measures.

Inclusion Criteria

Single lesion cerebral ischemic infarction <1 month affecting the M1 output system as defined by MRI of the brain, paresis of the UE (as assessed in their NIH stroke scale and chart review), no other neurological disorders, no aphasia that prevented participants from communicating effectively with the study team, no or only mild cognitive impairment based on RBANS²⁹ and clinical assessment, no or only mild depression (Hamilton Depression score of <19³⁰) no contraindication to TMS or MRI, no intake of CNS active drugs that block plasticity, ability to provide informed consent.

Measurement of Hand Function

Hand function was assessed with the 7 time-based subtests of the JTT.²⁸ Two subtests (writing and simulated feeding) were subsequently omitted due to low test-retest reliability.³¹ The raw score was normalized to age- and sex-matched standard scores that accounted for hand dominance.^28,32 A normalized score >0 indicated impaired hand function.

Brain Imaging

MRI Data Acquisition

High-resolution structural scans of all participants were collected on a Siemens Prisma 3T MRI system with a 64-channel head coil. T1-MPRAGE and T2-weighted structural images were collected according to the HCP Lifespan Project protocol.³³ T1_MPRAGE: TR = 2400 ms, TE = 2.24 ms, flip angle = 8°, FOV = 256 mm × 240 mm × 208 mm, voxel size 0.8 mm³. T2w_SPC: TR = 3200 ms, TE = 564 ms, FOV = 256 mm × 240 mm × 208 mm, voxel size 0.8 mm³.

M1_CL Reorganization Post-Stroke

TMS was used to measure M1 excitability. The experimental design, as detailed below, is similar to the approach used successfully in previous studies.^10,17,19 Briefly, TMS was applied through a figure of 8-coil (7 cm wing diameter) using 2 Magstim 200² stimulators connected via a Bistim module (Magstim Company, UK). Electromyographic (EMG; bandpass 1 Hz-1 kHz) activity was recorded from the extensor carpi ulnaris muscle (ECU), using surface electrodes (11 mm diameter) in a belly-tendon montage and a data acquisition system (LabVIEW, NI, CA, USA). The optimal site (hot spot) for each participant was identified and marked on the structural MRI of their brain using a neuronavigation system (BrainSight, Rogue Research, Montreal, Canada). Raw EMG was digitized at 5 kHz and stored on a PC for off-line analysis. The following measures were obtained in a random order with respect to the hemisphere.

Motor Threshold (MT)

The coil was placed tangentially to the scalp and rotated 45° away from the midline. The MT was determined to the nearest 1% of the maximum stimulator output (MSO) using adaptive parameter estimation by sequential testing (PEST)³⁴ where the probabilistic relationship between TMS intensity and evoked MEP amplitude is modeled. This threshold- hunting method is faster when compared to the relative-frequency approach without sacrificing accuracy.³⁵ In participants with no measurable response to TMS of the lesioned M1 at 100% MSO, MT was defined as >100% MSO, and MEP response was noted as absent (MEP−).

Stimulus Response Curve

Single TMS pulses were applied to the ECU hotspot at increasing intensities in increments of 5% of MSO up to 80% of MSO.^10,19 At each intensity, 10 stimuli were given.

Data Analysis

Analysis of Brain Imaging Data

Stroke Lesion Volume

Lesion masks were hand-drawn on each participant’s T1 image using MRIcron.³⁶ Participants’ structural images and lesion masks were then transformed to standard space. Transformation between an individual’s structural image and a template in atlas space can be challenging when lesions are present as standard affine or non-linear normalization techniques may result in poor template matches or overnormalization.³⁷ Enantiomorphic normalization³⁸ was used to reduce the effects of varying lesion size that may occur when normalization is restricted in certain brain areas by lesion masks. “Healed” T1 images were nonlinearly warped to the MNI152 2009 template in MNI space, with the resulting warp applied to the (lesioned) original image and the corresponding lesion mask to bring each participant’s brain and lesion mask into MNI space. Normalized lesion mask volumes (mm³) were extracted from MRIcron.

CST Lesion Load and CST Subpathway Originating in M1

CST lesion load was quantified using the SMATT template³⁹ because individual CST tract construction from DTI data can be challenging when large lesions are present. Subjects’ normalized lesion masks were projected onto the SMATT template. The number of overlapping voxels was divided by the total number of voxels in the template tract to quantify the percent overlap for each participant’s lesion (CST_IL). In addition to the entire CST, we also determined lesion load for the component of the SMATT template probabilistically beginning in M1 (CST_IL-M1).

Analysis of TMS Data

Trials were excluded from further data analysis because of coil mispositioning, participant’s eye closure, or increased EMG background activity defined as mean amplitude exceeding 0.05 mV in the 50 ms preceding the TMS pulse. This resulted in the exclusion of 3.18% of the data. In all other trials, the peak-to-peak MEP amplitudes were measured off-line in LabVIEW. A minimum of 5 trials per intensity were required for the data to reach criterion for mean calculation.^10,40 Intensities with fewer than 5 trials were considered “missing data.” For each SRC the area under the curve (SRC_AUC) was calculated in the range from 25% to 80% MSO. In the current study, M1_CL-SRC_AUC serves as a measure of M1_CL reorganization, while M1_IL-SRC_AUC serves as an estimate of the stroke injury although reorganizational processes are likely also ongoing.⁴¹ To account for these different processes, we will refer to M1_IL-SRC_AUC as M1 output measure.

Statistical Analysis

The primary statistical analysis was done to address our pre-stated hypothesis. Lesion volume, CST_IL lesion load, and CST_IL-M1 lesion load are continuous predictors, while MEP presence is categorical and binary. Prior to implementation of linear regression analysis, all assumptions were assessed. The appropriateness of the assumptions of normality and homogeneity of variance were examined through graphical analysis of the residuals (the difference between the observed value and fitted value). Log10 transforms of lesion volume and M1_CL-SRC_AUC were performed. Linear regression analyses were performed to analyze how each continuous predictor, MEP presence, and the interaction between the continuous predictor and MEP presence affected the 2 primary study outcomes: hand function (JTT score) and M1_CL-SRC_AUC. When the interaction term was not significant, a common slope model or simple linear regression model was used to determine the relationship between a predictor and the outcome measures. Because of the small sample size and concern with model overfitting, multiple linear regression analyses were limited to the covariates age,^42,43 race,^44,45 and sex.^42-44 Covariate selection was driven by available knowledge and biological plausibility of potential confounders taking into consideration the research hypothesis.

Linear regression analyses were implemented with SAS PROC REG (version 9.4, SAS Institute Inc). Statistical significance was defined as 2-sided P value of less than .05. Mean and SE are reported if not otherwise indicated. For regression analysis the slope and SE are reported if not otherwise indicated.

In a secondary analysis, we explored the relationship between affected hand function and M1_CL-SRC_AUC and M1_IL-SRC_AUC. Potential multicollinearity between M1_CL-SRC_AUC and M1_IL-SRC_AUC was evaluated by the variance inflation factor (VIF).^46,47 The VIF identifies correlation between independent variables and the strength of that correlation. VIFs between 1 and 5 suggest that there is a moderate correlation, but it is not severe enough to warrant corrective measures.

Results

Study Population

Sixty-six participants with reported ischemic stroke were consented and enrolled into the study. Thirty participants were subsequently excluded because of screening failure (n = 25) or because they declined to participate in all aspects of the experiments (n = 5). Thirty-six participants met all inclusion criteria indicated above and participated in all experiments. Data from 1 participant was subsequently excluded because of excessive head motion artifacts in the structural MRI. Data from the remaining 35 participants (19 female, age 59.49 ± 9.63 years) were included in the group analysis (see Supplemental Table 1 and Figure 1 for a detailed description of study population).

Covariates That Could Potentially Modify the Tested Relationship Between Our Predictors and Outcome Measures

Univariate analyses did not reveal any relationships between age, sex or race, and the measure of M1_CL reorganization (M1_CL-SRC_AUC, Supplemental Table 2). Similarly, there were no significant relationships between age, sex or race, and hand function (Table 1).

Table 1.

Simple linear Regression of JTT Score With Baseline Predictors and Covariates (n = 35).

Predictor/covariate	AUC slope/mean estimate	SE	Lower 95% CI	Upper 95% CI	P
Age
Per 5 year increase	–0.0033	0.00551	–0.0145	0.00792	.554
Sex
F	0.4675	0.07237	0.3203	0.6147	.81
M	0.4928	0.07886	0.3324	0.6533
Race^a
Black	0.5149	0.06995	0.3726	0.6572	.44
white	0.4313	0.08077	0.2670	0.5956
Lesion volume_log10
Per 1 log increase	0.1225	0.05253	0.01563	0.2294	.026
CST_IL lesion load
Per 0.1 unit increase	0.1861	0.07314	0.03728	0.3349	.015
CST_IL-M1 lesion load
Per 0.1 unit increase	0.2201	0.07422	0.06911	0.3711	.0056
MEP presence
0	0.7968	0.06782	0.6589	0.9348	<.0001
1	0.3334	0.04592	0.24	0.4269
M1_CL-SRC_AUCLog10
Per 0.1 unit increase	0.009419	0.007415	–0.0057	0.02450	.21

Abbreviations: IL, ipsilesional; CL, contralesional; AUC, area under the stimulus response curve (SRC); M1, primary motor cortex; MEP, motor evoked potential; CST, corticospinal tract.

Measures are reported as mean ± SE.

2 Asians participants were included in the white participant group due to small number.

Relationship Between Stoke Volume, CST Lesion Load, and M1_CL Reorganization

No single measure of CST_IL functional or structural damage (MEP presence, lesion volume, CST lesion load) was significantly related to M1_CL reorganization in univariate analyses (Figure 2, Supplemental Table 2). Adding MEP status to the regression analysis (Table 2) showed that the relationship between stroke lesion volume (Lesion Vol _Log10) and M1_CL reorganization (M1_CL-SRC_AUCLog10) changed depending on MEP status (P = .055, Figure 2A). Greater M1_CL reorganization was seen as lesion volume increased in MEP− participants (MEP− slope 0.603, P = .015). In contrast, M1_CL reorganization was not related to lesion volume in MEP+ participants (MEP+ slope 0.065, P = .63). The relationship between CST lesion load and M1_CL reorganization was similar irrespective of MEP status as indicated by the lack of an interaction between MEP status and CST lesion load (MEP × CST_IL lesion load: P = .78, MEP × CST_IL-M1 lesion load: P = .84, Table 2, Figure 2B and C).

Figure 2.

Relationship between M1_CL reorganization and stroke lesion volume and/or lesion load of CST and hand function in MEP− and MEP+ participants with stroke. (A) Lesion volume (Lesion Vol_log10), (B, C) lesion load of CST_IL and CST_IL-M1, and (D) affected hand function (JTT normalized score) is plotted against the extent of M1_CL reorganization (M1_CL-SRC_AUClog10) depending on presence or absence of an MEP (MEP+/−). There is a significant relationship between extent of M1_CL reorganization and lesion volume in the MEP− participants with stroke. In this group of MEP− participants with stroke, greater impairment of hand function was associated with greater M1_CL reorganization.

Table 2.

Linear Regression for the Outcome Measure M1_CL-SRC_AUClog10 on Each Continuous Predictor Plus the Presence or Absence of MEP and the Interaction Between M1_CL-SRC_AUClog10 and MEP.

Predictor	MEP presence	Sample size	Slope ± SE	P for slope = 0	Adjusted mean Log₁₀ and 95% CI	Log 10 mean difference	P values
Predictor	MEP presence	Sample size	Slope ± SE	P for slope = 0	Adjusted mean Log₁₀ and 95% CI	Log 10 mean difference	MEP	Predictor	Predictor by MEP^‡
Lesion volume Log₁₀ (mm³)	No	11	0.603 ± 0.234	0.015	–0.772 (–1.184, –0.361)	–0.288 (–0.780, 0.203)	.041	.019	.055
Lesion volume Log₁₀ (mm³)	Yes	24	0.065 ± 0.1350	0.63	–0.484 (–0.752, –0.216)
CST Lesion Load (%)	No	11	0.344 ± 0.242	0.17	–0.716 (–1.152, –0.280)	–0.255 (–0.778, 0.267)	.3369	.10	.78
CST Lesion Load (%)	Yes	24	0.243 ± 0.262	0.36	–0.461 (–0.749, –0.172)
CST M1 Lesion Load (%)	No	11	0.402 ± 0.253	0.12	–0.785 (–1.247, –0.324)	–0.354 (–0.905, 0.197)	.204	.07	.84
	Yes	24	0.320 ± 0.309	0.31	–0.431 (–0.733, –0.130)

Abbreviations: IL, ipsilesional; CL, contralesional; AUC, area under the stimulus response curve (SRC); M1, primary motor cortex; MEP, motor evoked potential; CST, corticospinal tract.

Measures are reported as mean ± SE (n).

‡

P value testing whether the slope is different for participants with or without measurable MEP.

MEP status was not related to stroke volume (MEP+ = 16.0 ± 5.2 cm³, MEP− = 27.9 ± 13.4 cm³, P = .31) but was significantly related to CST lesion load, particularly the lesion load of CST fibers originating in M1 (CST _IL lesion load: MEP+ = 4.4% ± 1.1%, MEP− = 8.5% ± 2.7%, P = .10; CST _IL-M1 lesion load: MEP+ = 3.2% ± 0.9%, MEP− = 9.2% ± 2.5%, P = .009).

Extent of Damage to M1 and/or CST Relates to Hand Function

All structural (stroke volume, CST lesion load (CST_IL and CST_IL-M1)) and functional (MEP+/MEP−) measures of stroke injury were significantly related to hand function in univariate analyses (Table 1). Greater lesion volume (Figure 3A, Lesion volume_log10 slope: 0.123 ± 0.05, P = .026) and greater CST lesion load (Figures 3 B and C, CST_IL slope = 0.186 ± 0.073, P = .015; CST_IL-M1 slope = 0.220 ± 0.074, P = .006) were significantly related to greater impairment of hand function. MEP+ participants had significantly better hand function compared to MEP− participants (MEP+ = 0.333 ± 0.04; MEP− = 0.797 ± 0.068; P < .0001). When JTT score was expressed as a linear function of lesion load or stroke volume, the interaction between the continuous predictors and MEP status was not statistically significant (all P > .1, Table 3).

Figure 3.

Extent of damage to M1 (M1_IL) and/or CST (CST_IL) and M1_CL reorganization relates to hand function. The extent of stroke injury as measured by (A) lesion volume (Lesion Vol_log10), (B, C) lesion load of CST_IL and CST_IL-M1, and (D) M1_CL reorganization (M1_CL-SRC_AUClog10) is plotted against hand function (JTT, normalized score) depending on the presence or absence of an MEP (MEP+/−). There was a positive relationship between greater lesion volume, lesion load of CST_IL and CST_IL-M1 and greater impairment of hand function.

Table 3.

Linear Regression for the Outcome Measure Hand Function (JTT Score) on Each Continuous Predictor Plus the Presence or Absence of MEP and the Interaction Between JTT Score and MEP (n = 35).

Predictor	MEP presence	Slope ± SE	P value	Adjusted means and 95% CI	Mean difference	P values
Predictor	MEP presence	Slope ± SE	P value	Adjusted means and 95% CI	Mean difference	MEP	Predictor	Predictor by MEP
Lesion volume_log10	No	0.0803 ± 0.0747	0.29	0.777 (0.645, 0.908)	0.433 (0.277, 0.590)	<.0001	.03	.96
Lesion volume_log10	Yes	0.0851 ± 0.0430	0.057	0.343 (0.258, 0.429)
CST_M1 lesion load	No	0.0623 ± 0.0741	0.41	0.780 (0.646, 0.913)	0.427 (0.267, 0.587)	<.001	.07	.42
CST_M1 lesion load	Yes	0.1519 ± 0.0803	0.068	0.353 (0.264, 0.441)
CST_IL-M1 lesion load	No	0.0596 ± 0.0802	0.46	0.772 (0.626, 0.918)	0.413 (0.239, 0.587)	<.001	.15	.54
CST_IL-M1 lesion load	Yes	0.1377 ± 0.0976	0.17	0.359 (0.264, 0.455)
M1_CL-SRC_AUClog10	No	0.0215 ± 0.0072	0.005	0.816 (0.696, 0.935)	0.485 (0.340, 0.629)	<.001	.02	.10
	Yes	0.0053 ± 0.0061	0.40	0.331 (0.251, 0.412)

Abbreviations: IL, ipsilesional; CL, contralesional; AUC, area under the stimulus response curve (SRC); M1, primary motor cortex; MEP, motor evoked potential; CST, corticospinal tract.

Measures are reported as mean ± SE. Predictor adjusted JTT score means by presence or absence of MEP.

‡

P value testing whether the slope is different for participants with or without measurable MEP.

Relationship Between M1_CL Reorganization and M1_IL Output

In a secondary analysis, we explored the relationship between M1_CL reorganization and M1_IL output in more detail. In 12 participants, TMS of the M1_IL did not elicit a measurable MEP. In these MEP− individuals the SRC areas were 0. Linear regression analysis showed a significant positive correlation between ipsilesional M1 output (M1_IL-SRC_AUC) and contralesional M1 reorganization (M1_CL-SRC_AUClog10; slope: 0.678 ± 0.24, P = .007). This relationship was still present when only the 23 MEP+ participants were included (slope: 0.344 ± 0.11, P = .004).

Relationship Between M1_CL Reorganization, M1_IL Functional Injury, and Affected Hand Function

We also tested the relationship between M1_CL reorganization (M1_CL-SRC_AUClog10), M1_IL injury as measured by MEP status, and hand function (Figure 1B). A model including M1_CL reorganization, MEP status, and the interaction did not show a significant interaction between M1_IL injury and M1_CL reorganization (Table 3, P = .1, Figure 3). However, planned comparisons indicated a significant relationship between greater M1_CL reorganization and more impaired hand function in MEP− participants (slope = 0.022 ± 0.007, P = .005). This relationship was not seen in the MEP+ participants (slope = 0.005 ± 0.006, P = .4).

We also explored the relationship between hand function and the measures of M1_CL reorganization and M1_IL output as measured with M1-SRC_AUC. Specifically, we asked whether the magnitude of M1_IL output was related to affected hand function and whether variability in affected hand function was better explained by including measures of both M1_CL reorganization and M1_IL output. Univariate analyses did not show a statistically significant relationship between hand function and either M1_CL reorganization or M1_IL output alone (M1_CL-SRC_AUClog10 slope: 0.094 ± 0.07, P = .2; M1_IL-SRC_AUC slope: −0.19 ± 0.11, P = .09, Supplemental Figure 1). A multiple linear regression analysis with hand function as the dependent variable and M1_CL reorganization and M1_IL output as independent variables was calculated. The VIF of 1.24 indicates that M1_CL reorganization and M1_IL output are not problematically correlated. Including both measures into the model explained more variance of hand function (r² = .22) than either univariate model (ipsilesional r² = 0.08; contralesional r² = .05). In this model, holding measure of M1_CL reorganization constant, hand function is better as M1_IL output increases. When holding M1_IL output constant, hand function is more impaired as M1_CL reorganization increases.

Finally, we explored whether stroke lesion volume explains variability in both M1_CL reorganization and impaired hand function. We compared models where stroke lesion volume and M1_CL reorganization are independent variables and hand function is the dependent variable. As described above, M1_CL reorganization was not significantly related to hand function. Adding lesion volume to the model significantly improved the fit of the model (χ²(1) = 4.35, P = .04), though adding an additional interaction between M1_CL reorganization and lesion volume did not (χ²(1) = 0.03, P = .86). Adding M1_CL reorganization to a model already containing a significant relationship between lesion volume and hand function did not significantly improve model fit (χ²(1) = 0.68, P = .41).

Discussion

In this study of participants with subacute stroke, we tested the hypothesis that the extent of M1_IL and/or CST_IL damage determines the extent of M1_CL reorganization, the degree of impairment of hand function, and how M1_CL reorganization is related to hand function. While M1_CL reorganization was not significantly related to stroke volume or measures of CST lesion load across our entire sample, we found that larger stroke volume was significantly related to greater M1_CL reorganization in the subset of participants with no measurable (MEP−). We also found that participants’ hand function declined as extent of M1_IL and/or CST damage increased. The variability in hand function was best explained by a model that included both M1_IL output and M1_CL reorganization.

We used structural MRI measures to determine the extent of damage to the hemisphere (lesion volume) and CST. At the subacute time point of about 4 weeks the stroke lesion approaches final infarct volume.⁴⁸ While structural reorganizational changes are likely ongoing⁴⁹ and may affect these measures, the small magnitude of these changes and the relatively short time since stroke would suggest that the selected measures are indeed a good estimate of the lesion extent. We did not separately calculate injury to anatomically defined M1 as only 3 participants had purely cortical lesions. Both lesion load of the entire CST and CST fractional anisotropy measures suffer from a lack of specificity for the tract anatomy,¹⁰ where 2 similar sized lesions can impact different pathways and therefore may impact the motor output of the hand very differently.⁵⁰ In contrast, lesion load of CST_IL-M1 more specifically measures injury to the fibers mediating output from M1.

We also used a functional measure to estimate the extent of functional impact of the stroke on the M1_IL and its corticospinal projections. Specifically, MEP status (MEP+/−) and SRC were determined. Both measures depend on excitability of multiple levels including the spinal cord.^9,17,19 The close relationship between CST_IL-M1 lesion load and MEP status point to a supraspinal level of functional disruption, specifically, M1 cortiocospinal projections located in the corona radiata to the level of the cerebral peduncle. Extensive functional reorganization of M1_IL has been described in humans and animal stroke models.^9,24 MEP status is therefore a binary measure that reflects both the injury of the stroke (i.e disruption of projections due to the lesion) but also functional reorganizational processes.⁴¹ The close relationship between the structural measure of CST_IL-M1 lesion load and MEP presence in our study would support the notion that this measure captures functional injury to the CST_IL-M1.

In the current study, M1_CL reorganization is determined from SRC. A greater area under the SRC indicates either greater excitability of the stimulated neurons or greater number of neurons being stimulated.⁵¹ Increased M1_CL excitability following stroke has been described in studies of both humans (for detailed review see Ref.⁹) and animals where the balance between excitatory and inhibitory activity was shifted toward an increase of excitatory activity(for detailed review^24,41). These remote changes are considered part of the neuronal network undergoing plastic changes in response to stroke injury and subsequent behavioral modification. Our results are in line with results from non-human primate and rodent experiments, where a relationship between M1 lesion size and the reorganization of motor representations in M1_CL has been reported (for review see Ref.²⁴). However, a direct comparison between the results of these pre-clinical data and our participants with stroke is hampered by the fact that, in our participants, stroke injury was not restricted to M1 or even to cortex, which is a shortcoming of these animal stroke models (for review see Ref.⁵²). The findings of a relationship between greater stroke volume and greater M1_CL reorganization in MEP− participants indicates that the extent of the lesion is important but that there are other factors such as the functional damage to M1_IL/CST_IL (MEP−) that determine the extent of the M1_CL reorganization. This is topic of future research but it is conceivable that greater stroke volume is more likely to affect critical structures such as premotor areas that have abundant interhemispheric connections to homotopic areas and project to M1 that impact M1_CL reorganization²⁴ and that these processes are facilitated in the setting of severely impaired M1_IL/CST_IL function.

We found a positive association between measures of M1_CL reorganization (M1_CL-SRC_AUC) and M1_IL output (M1_IL-SRC_AUC). M1_IL output depends on the structural integrity of M1 and CST but also on the neuronal excitability at the cortical, subcortical, and spinal levels.^9,17,19 In the current study, the origin of M1_IL output variability likely localizes to the supraspinal level because of the occurrence of greater MEP amplitude with smaller lesion load. The positive relationship between M1_IL output and M1_CL reorganization would argue against excessive inhibition from M1_CL on M1_IL (for review see Ref.⁹). Instead, our results are in line with earlier cross-sectional studies of participants with subacute stroke^17,19 and longitudinal studies of stroke recovery^53,54 who did not find any evidence for M1_CL related inhibition of M1_IL output.

We found that hand function is related to structural injury of CST_IL, particularly the lesion load of CST_IL-M1, which is consistent with the established relationship between CST anatomy and hand function in non-human primates. CST_IL functional damage, as measured by MEP status, is similarly related to hand function where MEP+ participants had significantly better hand function than MEP− participants. In non-human primates, anatomical and electrophysiological evidence supports the direct projections of pyramidal tract neurons located in the caudal subdivision of M1projecting to the alpha motor neurons innervating hand- and finger muscles (for review see Ref.²⁴). Because the MEP evoked by TMS of M1 is mediated by these monosynaptic connections,⁹ our results support the notion that dexterity of the fingers and hand depends on this pathway. There are few reports of a close relationship between MEP amplitude and hand function in the subacute^55,56 and chronic phase of stroke.^10,56 Whether gross hand functions such as power grasp demonstrate a similarly strong relationship with M1_IL output needs to be further investigated. A different M1_IL output for distal and proximal arm muscles in relation to recovery was reported⁵⁶ and would support a differential approach to different aspects of hand and upper extremity function. Results from non-human primate studies demonstrate that muscles of the upper extremity are innervated by multiple motor tracts such as the CST,⁴ the reticulospinal tract,⁵⁷ and the vestibulospinal tract.⁵⁸ Loss of more gross aspects of motor control was more transient after CST transection compared the dexterity of hand function.⁶ In contrast to other studies,⁵⁹ we report a correlation between stroke volume and hand function which could be explained by our inclusion criterion requiring injuries to M1 or CST.

In the current study, hand function was better explained by the combination of M1_CL reorganization and M1_IL functional output than by either measure alone which is in line with evidence from Lotze et al²² where transient disruption of M1_CL had a detrimental effect on affected hand function in patients with stroke. While the evidence from non-human primates and humans supports a causal relationship between compromised M1_IL output and impaired hand function,^8,10,12 the nature of the relationship between M1_CL reorganization and hand function is less clear. In the present study, there is shared variability between stroke volume and impaired hand function and M1_CL reorganization. Whether there is a causal relationship is not evident from this analysis. While adding M1_CL reorganization to the model of stroke volume and hand function does not explain additional variability in hand function, adding M1_CL reorganization to the model of M1_IL output and hand function does. This lack of relationship in 1 case but presence in another would support the notion of a weak relationship between M1_CL reorganization and hand function. While comparison is limited, our findings differ from results by Stinear et al⁵³ where improvement of UE function was unrelated to M1_CLexcitability. Our results would suggest that the extent of stroke injury should be considered when studying functional measures of M1_CL reorganization, M1 M1_IL output and hand function (for review see Ref.⁶⁰).

In conclusion, we provide evidence for stroke volume and M1_IL output determining the extent of M1_CL reorganization and its relationship to affected hand function in the subacute post-stroke period which confirms our stated hypothesis. Consistent with the pre-clinical data we also found that participants’ hand function declined as stroke volume or lesion load to CST, particularly CST_M1, increased. In extending previous findings, the variability in hand function was best explained by a model that included both M1_IL output and M1_CL reorganization.

Supplemental Material

sj-docx-1-nnr-10.1177_15459683231152816 – Supplemental material for Stroke Lesion Volume and Injury to Motor Cortex Output Determines Extent of Contralesional Motor Cortex Reorganization

Supplemental material, sj-docx-1-nnr-10.1177_15459683231152816 for Stroke Lesion Volume and Injury to Motor Cortex Output Determines Extent of Contralesional Motor Cortex Reorganization by Cathrin M. Buetefisch, Marc W. Haut, Kate P. Revill, Scott Shaeffer, Lauren Edwards, Deborah A. Barany, Samir R. Belagaje, Fadi Nahab, Neeta Shenvi and Kirk Easley in Neurorehabilitation and Neural Repair

Supplemental Material

sj-docx-2-nnr-10.1177_15459683231152816 – Supplemental material for Stroke Lesion Volume and Injury to Motor Cortex Output Determines Extent of Contralesional Motor Cortex Reorganization

Supplemental material, sj-docx-2-nnr-10.1177_15459683231152816 for Stroke Lesion Volume and Injury to Motor Cortex Output Determines Extent of Contralesional Motor Cortex Reorganization by Cathrin M. Buetefisch, Marc W. Haut, Kate P. Revill, Scott Shaeffer, Lauren Edwards, Deborah A. Barany, Samir R. Belagaje, Fadi Nahab, Neeta Shenvi and Kirk Easley in Neurorehabilitation and Neural Repair

Footnotes

Acknowledgements

We thank the participants for their contribution to this study, S. Buetefisch and J. Hudson for technical support, E. Sperin for assistance with regulatory work, and G. Kowalski, A. Caliban, A. Mangin, I. Vernon, and I. Chung for assistance with the data collection.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute of Neurological Diseases and Stroke, National Institute of Child Development and Health, National Institutes of Health (grant numbers R56NS070879, R01NS090677, 3R01NS090677-04S1). DAB received support from the Emory University NIH/NINDS T32 training and translational research in neurology program (T32NS007480), Georgia StrokeNet (1U10NS086607), and the American Heart Association (18POST34060183). DAB is currently at the Department of Kinesiology, University of Georgia, and Augusta University/University of Georgia Medical Partnership, Athens, Georgia. LE received support from the Emory University NIH/NINDST32 training and translational research in neurology program (T32NS007480), and the American Heart Association (18POST34060183).

ORCID iDs

Cathrin M. Buetefisch

Marc W. Haut

Deborah A. Barany

Kirk Easley

Supplementary material for this article is available on the Neurorehabilitation & Neural Repair website along with the online version of this article.

References

Dromerick

Lang

Birkenmeier

Hahn

Sahrmann

Edwards

DF.

Relationships between upper-limb functional limitation and self-reported disability 3 months after stroke. J Rehabil Res Dev. 2006;43(3):401-408.

Porter

The corticomotoneuronal component of the pyramidal tract: corticomotoneuronal connections and functions in primates. Brain Res. 1985;357(1):1-26.

Corbetta

Ramsey

Callejas

, et al. Common behavioral clusters and subcortical anatomy in stroke. Neuron. 2015;85(5):927-941.

Lemon

Mechanisms of cortical control of hand function. Neuroscientist. 1997;3(6):389-398.

Lang

Schieber

MH.

Differential impairment of individuated finger movements in humans after damage to the motor cortex or the corticospinal tract. J Neurophysiol. 2003;90(2):1160-1170. doi:10.1152/jn.00130.2003

Lawrence DG, Kuypers HG. The functional organization of the motor system in the monkey. I. The effects of bilateral pyramidal lesions. Brain. 1968;91(1):1-14.

Zaaimi

Edgley

Soteropoulos

Baker

SN.

Changes in descending motor pathway connectivity after corticospinal tract lesion in macaque monkey. Brain. 2012;135(Pt 7):2277-2289.

Nudo

Wise

SiFuentes

Milliken

GW.

Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Science. 1996;272(5269):1791-1794.

Guggisberg

Koch

Hummel

Buetefisch

CM.

Brain networks and their relevance for stroke rehabilitation. Clin Neurophysiol. 2019;130(7):1098-1124. doi:10.1016/j.clinph.2019.04.004

10.

Buetefisch

Pirog Revill

Haut

, et al. Abnormally reduced primary motor cortex output is related to impaired hand function in chronic stroke. J Neurophysiol. 2018;120:1680-1694. doi:10.1152/jn.00715.2017

11.

Ejaz

Hertler

, et al. Separable systems for recovery of finger strength and control after stroke. J Neurophysiol. 2017;118(2):1151-1163. doi:10.1152/jn.00123.2017

12.

Revill

Haut

Belagaje

Nahab

Drake

Buetefisch

CM.

Hebbian-type primary motor cortex stimulation: a potential treatment of impaired hand function in chronic stroke patients. Neurorehabil Neural Repair. 2020;34(2):159-171. doi:10.1177/1545968319899911

13.

Weiller

Chollet

Friston

Wise

Frackowiak

RS.

Functional reorganization of the brain in recovery from striatocapsular infarction in man. Ann Neurol. 1992;31(5):463-472.

14.

Cramer

Nelles

Benson

, et al. A functional MRI study of subjects recovered from hemiparetic stroke. Stroke. 1997;28(12):2518-2527.

15.

Dancause

Nudo

RJ.

Shaping plasticity to enhance recovery after injury. Prog Brain Res. 2011;192:273-95. doi:B978-0-444-53355-5.00015-4

16.

Ward

Brown

Thompson

Frackowiak

RS.

Neural correlates of motor recovery after stroke: a longitudinal fMRI study. Brain. 2003;126(Pt 11):2476-2496.

17.

Butefisch

Netz

Wessling

Seitz

Homberg

Remote changes in cortical excitability after stroke. Brain. 2003;126(Pt 2):470-481.

18.

Butefisch

Kleiser

Korber

, et al. Recruitment of contralesional motor cortex in stroke patients with recovery of hand function. Neurology. 2005;64(6):1067-1069.

19.

Butefisch

Wessling

Netz

Seitz

Homberg

Relationship between interhemispheric inhibition and motor cortex excitability in subacute stroke patients. Neurorehabil Neural Repair. 2008;22(1):4-21.

20.

Nair

Hutchinson

Fregni

Alexander

Pascual-Leone

Schlaug

Imaging correlates of motor recovery from cerebral infarction and their physiological significance in well-recovered patients. Neuroimage. 2007;34(1):253-263.

21.

Gerloff

Bushara

Sailer

, et al. Multimodal imaging of brain reorganization in motor areas of the contralesional hemisphere of well recovered patients after capsular stroke. Brain. 2006;129(Pt 3):791-808.

22.

Lotze

Markert

Sauseng

Hoppe

Plewnia

Gerloff

The role of multiple contralesional motor areas for complex hand movements after internal capsular lesion. J Neurosci. 2006;26(22):6096-6102.

23.

Revill

Barany

Vernon

, et al. Evaluating the abnormality of bilateral motor cortex activity in subacute stroke patients executing a unimanual motor task with increasing demand on precision. Front Neurol. 2022;13:836716. doi:10.3389/fneur.2022.836716

24.

Dancause

Touvykine

Mansoori

BK.

Inhibition of the contralesional hemisphere after stroke: reviewing a few of the building blocks with a focus on animal models. Prog Brain Res. 2015;218:361-87. doi:10.1016/bs.pbr.2015.01.002

25.

Biernaskie

Szymanska

Windle

Corbett

Bi-hemispheric contribution to functional motor recovery of the affected forelimb following focal ischemic brain injury in rats. Eur J Neurosci. 2005;21(4):989-999. doi:10.1111/j.1460-9568.2005.03899.x

26.

Carmel

Kimura

Martin

JH.

Electrical stimulation of motor cortex in the uninjured hemisphere after chronic unilateral injury promotes recovery of skilled locomotion through ipsilateral control. J Neurosci. 2014;34(2):462-466. doi:10.1523/JNEUROSCI.3315-13.2014

27.

Riley

Der-Yeghiaian

, et al. Anatomy of stroke injury predicts gains from therapy. Stroke. 2011;42(2):421-426. doi:10.1161/STROKEAHA.110.599340

28.

Jebsen

Taylor

Trieschmann

Trotter

Howard

LA.

An objective and standardized test of hand function. Arch Phys Med Rehabil. 1969;50(6):311-319.

29.

Randolph

Tierney

Mohr

Chase

TN.

The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): preliminary clinical validity. J Clin Exp Neuropsychol. 1998;20(3):310-319.

30.

Hamilton

Development of a rating scale for primary depressive illness. Br J Soc Clin Psychol. 1967;6(4):278-296.

31.

Stern

EB.

Stability of the Jebsen-Taylor Hand Function Test across three test sessions. Am J Occup Ther. 1992;46(7):647-649.

32.

Hackel

Wolfe

Bang

Canfield

JS.

Changes in hand function in the aging adult as determined by the Jebsen Test of Hand Function. Phys Ther. 1992;72(5):373-377.

33.

Harms

Somerville

Ances

, et al. Extending the human connectome project across ages: imaging protocols for the lifespan development and aging projects. Neuroimage. 2018;183:972-984. doi:10.1016/j.neuroimage.2018.09.060

34.

Awiszus

TMS and threshold hunting. Suppl Clin Neurophysiol. 2003;56:13-23. doi:10.1016/s1567-424x(09)70205-3

35.

Silbert

Patterson

Pevcic

Windnagel

Thickbroom

GW.

A comparison of relative-frequency and threshold-hunting methods to determine stimulus intensity in transcranial magnetic stimulation. Clin Neurophysiol. 2013;124(4):708-712. doi:10.1016/j.clinph.2012.09.018

36.

Rorden

Brett

Stereotaxic display of brain lesions. Behav Neurol. 2000;12(4):191-200.

37.

Crinion

Ashburner

Leff

Brett

Price

Friston

Spatial normalization of lesioned brains: performance evaluation and impact on fMRI analyses. Neuroimage. 2007;37(3):866-875. doi:10.1016/j.neuroimage.2007.04.065

38.

Nachev

Coulthard

Jager

Kennard

Husain

Enantiomorphic normalization of focally lesioned brains. Neuroimage. 2008;39(3):1215-1226. doi:10.1016/j.neuroimage.2007.10.002

39.

Archer DB, Vaillancourt DE, Coombes SA. A template and probabilistic atlas of the human sensorimotor tracts using diffusion MRI. Cereb Cortex. 2018;28(5):1685-1699. doi:10.1093/cercor/bhx066

40.

Brasil-Neto

Cohen

Panizza

Nilsson

Roth

Hallett

Optimal focal transcranial magnetic activation of the human motor cortex: effects of coil orientation, shape of the induced current pulse, and stimulus intensity. J Clin Neurophysiol. 1992;9(1):132-136.

41.

Jones

Allred

Jefferson

, et al. Motor system plasticity in stroke models: intrinsically use-dependent, unreliably useful. Stroke. 2013;44(6 Suppl 1):S104-S106. doi:10.1161/STROKEAHA.111.000037

42.

Phan

Gall

Blizzard

, et al. Sex differences in quality of life after stroke were explained by patient factors, not clinical care: evidence from the Australian Stroke Clinical Registry. Eur J Neurol. 2021;28(2):469-478. doi:10.1111/ene.14531

43.

Reeves

Bushnell

Howard

, et al. Sex differences in stroke: epidemiology, clinical presentation, medical care, and outcomes. Lancet Neurol. 2008;7(10):915-926. doi:10.1016/S1474-4422(08)70193-5

44.

Boehme

Siegler

Mullen

, et al. Racial and gender differences in stroke severity, outcomes, and treatment in patients with acute ischemic stroke. J Stroke Cerebrovasc Dis. 2014;23(4):e255-e261. doi:10.1016/j.jstrokecerebrovasdis.2013.11.003

45.

Qian

Fonarow

Smith

, et al. Racial and ethnic differences in outcomes in older patients with acute ischemic stroke. Circ Cardiovasc Qual Outcomes. 2013;6(3):284-292. doi:10.1161/CIRCOUTCOMES.113.000211

46.

Vatcheva

Lee

McCormick

Rahbar

MH.

Multicollinearity in regression analyses conducted in epidemiologic studies. Epidemiology. 2016;6(2):277. doi:10.4172/2161-1165.1000227

47.

Kutner

Nachtsheim

Neter

Applied Linear Statistical Models. 4 ed. McGraw-Hill; 2004.

48.

Gaudinski

Henning

Miracle

Luby

Warach

Latour

LL.

Establishing final infarct volume: stroke lesion evolution past 30 days is insignificant. Stroke. 2008;39(10):2765-2768.

49.

Carmichael

ST.

Emergent properties of neural repair: elemental biology to therapeutic concepts. Ann Neurol. 2016;79(6):895-906. doi:10.1002/ana.24653

50.

Greene

Revill

Cieslak

Rose

Buetefisch

Grafton

ST.

Shortest-path disconnection mapping after stroke. Ann Neurol. 2018; 84:S159-S159.

51.

Ridding

Rothwell

JC.

Stimulus/response curves as a method of measuring motor cortical excitability in man. Electroencephalogr Clin Neurophysiol. 1997;105(5):340-344.

52.

Corbett

Carmichael

Murphy

, et al. Enhancing the alignment of the preclinical and clinical stroke recovery research pipeline: consensus-based core recommendations from the stroke recovery and rehabilitation roundtable translational working group. Neurorehabil Neural Repair. 2017;31(8):699-707. doi:10.1177/1545968317724285

53.

Stinear

Petoe

Byblow

WD.

Primary motor cortex excitability during recovery after stroke: implications for neuromodulation. Brain Stimul. 2015;8(6):1183-1190. doi:10.1016/j.brs.2015.06.015

54.

Branscheidt

Schambra

, et al. Rethinking interhemispheric imbalance as a target for stroke neurorehabilitation. Ann Neurol. 2019;85:502-513. doi:10.1002/ana.25452

55.

Binkofski

Seitz

Arnold

Classen

Benecke

Freund

HJ.

Thalamic metbolism and corticospinal tract integrity determine motor recovery in stroke. Ann Neurol. 1996;39(4):460-470.

56.

Schambra

Branscheidt

, et al. Differential poststroke motor recovery in an arm versus hand muscle in the absence of motor evoked potentials. Neurorehabil Neural Repair. 2019;33(7):568-580. doi:10.1177/1545968319850138

57.

Baker

Zaaimi

Fisher

Edgley

Soteropoulos

DS.

Pathways mediating functional recovery. Prog Brain Res. 2015;218:389-412. doi:10.1016/bs.pbr.2014.12.010

58.

Markham

CH.

Vestibular control of muscular tone and posture. Can J Neurol Sci. 1987;14(Suppl. 3):493-496.

59.

Feng

Wang

Chhatbar

, et al. Corticospinal tract lesion load: An imaging biomarker for stroke motor outcomes. Ann Neurol. 2015;78(6):860-870. doi:10.1002/ana.24510

60.

Veldema

Nowak

Gharabaghi

Resting motor threshold in the course of hand motor recovery after stroke: a systematic review. J Neuroeng Rehabil. 2021;18(1):158. doi:10.1186/s12984-021-00947-8

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.15 MB

0.02 MB

Stroke Lesion Volume and Injury to Motor Cortex Output Determines Extent of Contralesional Motor Cortex Reorganization

Abstract

Background

Objectives

Methods

Results

Conclusions

Keywords

Introduction

Methods

Study Design and Overview of Experiments

Inclusion Criteria

Measurement of Hand Function

Brain Imaging

MRI Data Acquisition

M1CL Reorganization Post-Stroke

Motor Threshold (MT)

Stimulus Response Curve

Data Analysis

Analysis of Brain Imaging Data

Stroke Lesion Volume

CST Lesion Load and CST Subpathway Originating in M1

Analysis of TMS Data

Statistical Analysis

Results

Study Population

Covariates That Could Potentially Modify the Tested Relationship Between Our Predictors and Outcome Measures

Relationship Between Stoke Volume, CST Lesion Load, and M1CL Reorganization

Extent of Damage to M1 and/or CST Relates to Hand Function

Relationship Between M1CL Reorganization and M1IL Output

Relationship Between M1CL Reorganization, M1IL Functional Injury, and Affected Hand Function

Discussion

Supplemental Material

sj-docx-1-nnr-10.1177_15459683231152816 – Supplemental material for Stroke Lesion Volume and Injury to Motor Cortex Output Determines Extent of Contralesional Motor Cortex Reorganization

Supplemental Material

sj-docx-2-nnr-10.1177_15459683231152816 – Supplemental material for Stroke Lesion Volume and Injury to Motor Cortex Output Determines Extent of Contralesional Motor Cortex Reorganization

Footnotes

Acknowledgements

Declaration of Conflicting Interests

Funding

ORCID iDs

References

Supplementary Material

M1_CL Reorganization Post-Stroke

Relationship Between Stoke Volume, CST Lesion Load, and M1_CL Reorganization

Relationship Between M1_CL Reorganization and M1_IL Output

Relationship Between M1_CL Reorganization, M1_IL Functional Injury, and Affected Hand Function