Our wearable experimental setup comprised of (i) the Gait Quantification Shoe [28] (i.e., gait characterizer), and (ii) the wireless ctDCS cap, as shown in Fig. 1. The Gait Quantification Shoe characterized one's gait parameters during overground walking. The ctDCS intervention was evaluated with the Gait Quantification Shoe to investigate the effects of ctDCS on the gait of chronic post-stroke hemiplegic patients recruited by convenience sampling from collaborating hospitals.
i. Study Participants
The hemiplegic stroke survivors, who (i) were aged between 18 and 90 years, (ii) could walk independently for at least 10 meters, (iii) could provide informed and written consent, and (iv) could understand instruction from the experimenter were contacted. Twelve post-stroke male subjects (P1-P12, Mean (SD) = 46(± 13) years) were selected for the study (see Table 1) from fourteen volunteers. Here, volunteers who underwent any recent surgery or were in the acute phase of stroke were excluded from the study. Written informed consent was obtained from each subject, and the multi-center research protocol for this study was approved by the All India Institute of Medical Sciences, New Delhi, India Institutional Review Board (IEC-129/07.04.2017), and Indian Institute of Technology Gandhinagar, India Institutional Review Board (IEC/2019-20/4/UL/046).
ii. Gait Quantification Shoe
In this study, we aimed to quantify one's gait in terms of gait-related indices by recording gait events using a pair of instrumented shoe [28]. Figure 1 shows the wearable device, namely the Gait Quantification Shoes (GaitShoe henceforth) [28], that was used in this study to record one's gait events. The GaitShoe consisted of insoles instrumented with force-sensitive resistors (FSRs) that were placed below the greater toe, lateral heel and medial heel positions of each shoe to detect one's gait events, e.g., heel-strike, toe-off, etc. These gait events were used to compute different gait-related indices, e.g., Step Length [29], Walk Ratio [30], Gait Stability Ratio [31], and Symmetry Index [32], etc. The GaitShoe was capable of transmitting the data wirelessly to a data logger computer for subsequent offline analysis.
a. Computation of Step Length
Step Length is the distance between two successive contralateral heel-strikes during gait. We wanted to explore the implication of ctDCS on one's Step Length since this can be an essential indicator of the functional gait ability of hemiplegic post-stroke patients [29]. Here, we computed the average Step Length using the average Step Time (recorded by the GaitShoe) and the average Walking Speed. The Step Time was measured by the GaitShoe from the time interval between two successive heel-strike events of contralateral legs. One's Walking Speed (during the overground walk) was computed from the time taken to walk through a pre-defined distance. Subsequently, the Step Length was calculated using Eq. (1). Finally, the Normalized Step Length was computed using the individualized height information [28] (Eq. (2)).
Step Length = Step Time * Walking Speed ……………………………………………………………………..(1)
Normalized Step Length = Step Length/Height………………………………………………………...……….(2)
b. Computation of Gait Stability Ratio
The Gait Stability Ratio (GSR) depends on one's Cadence (steps/sec) and Walking Speed (m/sec) [28]. The GSR is a good indicator of balance deficits in older adults [31]. The GSR takes into account the changes in one's Walking Speed that can influence one's Step Length. Here, a decrease in GSR might indicate increased double support time during one's walk, thereby inferring an increase in dynamic stability. The GSR was computed using Eq. (3).
Gait Stability Ratio = Cadence/Walking Speed …………………………………………………………….(3)
c. Computation of Walk Ratio
The Walk Ratio (WR) [30] can describe a relation between one's Step Length and Cadence during walking. Importantly, WR is invariant during different speeds, uneven surface conditions, but is affected by dual task-condition [33]. The Walk Ratio was computed using Eq. (4).
Walk Ratio = Cadence/ Step Length ……………………………………………………………………………(4)
d. Computation of Symmetry Index
The Symmetry Index (SI) is a measure of the extent to which one makes symmetrical use of both legs during walking [32] - smaller the value of SI, the better is the gait symmetry. One of the distinctive characteristics of post-stroke gait is the impaired gait symmetry, particularly in hemiplegic patients [34]. Here, we computed the SI using the %stance phase (of a gait cycle) measured using the GaitShoe while considering the %stance for each of the left (XL) and right legs (XR). The SI was calculated using Eq. (5).
SI= ((XL-XR) / 0.5 * (XL+XR) ) *100 …………………………………………………………………..………(5)
[Place Fig. 2 Here]
iii. Optimization of the Electrode Montage (age-specific computational modeling of ctDCS)
We used age-specific MRI templates that were obtained online at https://jerlab.sc.edu/projects/neurodevelopmental-mri-database/ with the permission of Dr. John Richards. The data comprised of average T1-weighted MRI for the head and brain and segmenting priors for gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). For this, we chose the age group that matched the age of our subjects for this study. A Realistic volumetric Approach to Simulate Transcranial Electric Stimulation (ROAST) [35] was used to create a tetrahedral volume mesh of the head. ROAST used SPM12("SPM - Statistical Parametric Mapping") to segment the head and brain. After segmentation, five tissues were identified for the tetrahedral volume mesh, namely, Scalp, Skull, Cerebrospinal Fluid (CSF), Gray Matter (GM), and White Matter (WM). These different brain tissues for the volume mesh were modeled as different volume conductors for Finite Element Analysis (FEA) in the ROAST. Here, isotropic conductivity used for the different brain tissues [36] were (in S/m): Scalp = 0.465; Skull = 0.01; CSF = 1.654; GM = 0.276; WM = 0.126. For further details on the head modeling, please refer to our prior works [21], [26].
In this study, the Electric Field (EF) distribution was modeled for two different ctDCS montages for each subject's age-specific head model created from MRI templates ( https://jerlab.sc.edu/projects/neurodevelopmental-mri-database/ ). The boundary condition was set as 2 mA injection current (Neumann boundary condition) with the following electrode configurations from our prior work where we performed ctDCS optimization [26]:
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a) Optimized configuration for dentate stimulation [26]: 3.14 cm2 disc anode was PO10h (10/5 EEG system), and 3.14 cm2 disc cathode was placed at PO9h (10/5 EEG system) for ctDCS with 2 mA direct current.
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b) Optimized configuration for leg lobules VII-IX stimulation [26]: 3.14 cm2 disc anode was Exx8 (electrodes defined by ROAST using "unambiguously illustrated (UI) 10/5 system" [37]), and 3.14 cm2 disc cathode was placed at Exx7 (defined by ROAST) for ctDCS with 2 mA direct current.
In all the simulations, the voxel size was considered as 1mm3. The contralesional anode and cathode injected the specified amount of current (source) in the volume conductor, i.e., the head model. Finite Element Analysis (FEA) was conducted on each head model to compute the ctDCS induced EF in the brain tissues. The electric field was computed at all the voxels (voxel size 1mm3) of the cerebellar lobules defined by the cerebellar electric field distribution was normalized using spatially unbiased atlas for the cerebellum and brainstem (SUIT) [38]. Subsequently, the cerebellar lobular electric field distribution was found using SUIT [38], and T1-weighted images were fitted to the SUIT template of the human cerebellum in SPM12 ("SPM - Statistical Parametric Mapping": https://www.fil.ion.ucl.ac.uk/spm/software/spm12/). The cerebellar mask was visually checked in MRIcron, and the non-linear deformation was then applied to each EF image. The volume of the cerebellar lobules, defined by the SUIT atlas [38], was used for the extraction of the lobular EF distribution. Also, we customized SUIT codes to extract the left and the right dentate EF distribution.
iv. Experimental Setup and Data Analysis
Overground gait and balance evaluation were performed based on Timed-Up-and-Go (TUG) [17] and Berg Balance Score (BBS) [39] before and after the ctDCS intervention. Figure 1 shows the experimental setup for the clinical study in a low resource setting. The ctDCS setup consisted of a wireless STARSTIM 8 stimulator (Neuroelectrics, Spain). The study required a commitment of about 30 minutes from each participant.
a. Cerebellar tDCS intervention
Based on our prior work [26], 15 minutes of 2 mA bilateral ctDCS was delivered in a repeated measure single-blind crossover design using either of the two bipolar montages with a circular (1 cm radius) contralesional anode. The electrode locations were based on the ROAST toolbox [35], and "unambiguously illustrated (UI) 10/5 system" [37]; 1. PO9h – PO10h, and 2. Exx7 – Exx8. Overground quantitative and clinical gait evaluation was performed before and after the ctDCS intervention to compute a percent normalized change measures, (POST-PRE)x100/(POST + PRE).
b. Experimental setup for overground gait analysis
The experimental setup for the overground gait analysis consisted of (i) 10 m long straight pathway (overground) marked with start and end lines, (ii) data-logger computer, and (iii) a pair of GaitShoes. In a repeated measure crossover design to compare the two ctDCS montages, we investigated the effects of ctDCS on one's gait characteristics during the overground walk. Once the participant arrived at the study hall, they were asked to sit and relax for about 5 minutes. Then, the experimenter explained to the participant what he was expected to do in the study as well as the risks. After informed consent, the baseline clinical measures, TUG, and BBS were recorded. Then, the experimenter helped the participant to wear the GaitShoe [28]. Subsequently, the experimenter prepared the participant for ctDCS by placing the neoprene cap combined with a battery-driven wireless stimulator, Starstim 8 (Neuroelectrics, Spain), and the gel-based electrodes. The participants were informed that they could discontinue from the study in case of any discomfort.
Once the participant was ready to start the study, they were asked to walk on a 10 m long straight path (overground) marked with a start and stop lines at their self-selected comfortable speed, and the participant's overground Walking Speed (SpeedOG) was computed. After this, the participant was asked to sit and relax on a chair for about 5 min. Subsequently, ctDCS was administered in a repeated measure single-blind crossover design using one of the two ctDCS montages for 15 minutes at the rest condition with a dosage of 2 mA [26]. Following this, the participant repeated the 10 m overground walk, followed by an assessment of the clinical gait and balance measures (TUG and BBS). The gait performance of the post-stroke participants was quantified using GaitShoe in terms of the gait-related indices, as described earlier. Therefore, the post-stroke participants performed two trials of the overground walk, pre, and post ctDCS intervention, at their self-selected walking speed while wearing the GaitShoe, as illustrated in Fig. 2. We also evaluated the acceptability of the ctDCS intervention in post-stroke subjects where we collected subjective feedback from the post-stroke participants prior to (PretDCS), during (ActivetDCS), and post (PosttDCS) application of ctDCS.
c) Statistical analysis and the partial least squares regression
Two-sided Wilcoxon rank-sum test was performed at the 5% significance level on the percent normalized change measures,
for the null hypothesis that the two ctDCS montages led to the same percent normalized change in the quantitative gait parameters from the same continuous distributions with equal medians. Also, Wilcoxon signed-rank test was conducted to find any change in the percent normalized change measures
due to ctDCS intervention for the null hypothesis that data comes from a distribution whose median is zero at the 5% significance level. Multivariate regression analysis was conducted to relate the changes in the balance and gait measures to the lobular electric field distribution due to ctDCS montages. Here, multicollinearity can occur when independent variables (predictors) are correlated. We have presented principal component regression analysis for multivariate linear regression of the lobular electric field distribution as the predictor with the behavioral outcomes as the response variables [
26]. The goal is to extract the relation between electric field distribution and the behavioral effects of ctDCS where Partial Least Squares (PLS) is a promising multivariate statistical technique that can combine the information about the variances of both the predictors and the responses, while also considering the correlations among them [
40]. In this study, we applied partial least squares regression approach to analyze the associations between the lobular electric field distribution as the predictor with the gait outcome measures as the response variables. Although statistical inference is the strength of PLSR approach using computational cross-validation methods (e.g., jackknife, bootstrap) [
40]; however, we will apply PLS as a correlation technique in this study. The matrix of correlations between the lobular electric field distribution as the predictor with the gait outcome measures as the response variables is subjected to the singular value decomposition that results in the singular vectors called saliences. The lobular electric field distribution as the predictor with the gait outcome measures as the response variables can be projected onto their respective saliences, which creates latent variables that are linear combinations of the original variables. Here, PLS searches for latent variables that express the largest amount of information common to both the lobular electric field distribution as the predictor and the gait outcome measures as the response variables. This is a fixed-effect model where the results can only be interpreted with respect to the current data sets from this study. In this study, PLS analysis was performed on the percent normalized change measures,
of gait indices from the Gait
shoe as the response variable, where the lobular electric field distribution for both the montages across all the subjects (found after centering the data and then singular value decomposition) was the predictor.