Associating White Matter Microstructural Integrity and Improvements in Reactive Stepping in People with Parkinson’s Disease

doi:10.21203/rs.3.rs-3117246/v1

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Associating White Matter Microstructural Integrity and Improvements in Reactive Stepping in People with Parkinson’s Disease

https://doi.org/10.21203/rs.3.rs-3117246/v1

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published 26 Mar, 2024

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Reactive steps are rapid responses after balance challenges. People with Parkinson's Disease demonstrate impaired reactive stepping, increasing fall risk. Although PwPD can improve steps through practice, the neural mechanisms contributing to improved reactive stepping in people with PD are poorly understood. This study investigated white-matter correlates of responsiveness to reactive step training in people with PD. Participants completed an eighteen-week multiple-baseline study consisting of two baseline assessments (B1 and B2) before training, a two-week, six-session training protocol, and two post-training assessments (immediate; P1) and two months after training (P2). Each assessment consisted of 3 backward reactive step trials. Outcomes included the anterior-posterior margin of stability, step length, and step latency. Tract-Based Spatial Statistics were performed to correlate white-matter microstructural integrity (fractional anisotropy and radial diffusivity) with retained improvements in reactive stepping at the two-month follow-up (P2–B2). Complete datasets were available from 22 participants. Greater retention of step length was associated with increased fractional anisotropy (better white-matter integrity) within the left anterior corona radiata (r = 0.54, p < 0.01), left posterior thalamic radiation (r = 0.54, p < 0.01), and right (r = 0.43, p = 0.04) and left (r = 0.0.40, p = 0.06) superior longitudinal fasciculi. Greater retention of step latency improvements was associated with lower radial diffusivity (greater white-matter integrity) within the left posterior (r = 0.60, p < 0.01) and anterior corona radiata (r = 0.61, p < 0.01). These findings highlight the importance of white-matter microstructural integrity in motor learning and retention processes in PD and may inform the development of targeted interventions to improve balance in people with PD.

Parkinson Disease

Diffusion Tensor Imaging

White Matter

Reactive stepping

Neuroimaging

Learning

Parkinson's Disease (PD) is a progressive neurological disorder affecting over one million people across North America (Marras et al., 2018). Hallmark motor symptoms of people with PD (PwPD) include tremor, rigidity, bradykinesia/akinesia, and postural instability (Balestrino & Schapira, 2020). Significantly, such motor deficits predispose people with PD to more falls than their neurotypical peers (Allen et al., 2013; Marras et al., 2018), resulting in serious physical, mental, social, and financial repercussions (Fasano et al., 2017; Genever et al., 2005; Rudzińska et al., 2013; Yang et al., 2019).

Many falls occur following external perturbations to balance, such as a trip, slip, or a poor weight shift requiring rapid compensatory balance responses, such as reactive stepping, to maintain balance and prevent a fall (Berg W. et al., 1997; Luukinen et al., 2000; Robinovitch et al., 2013). Notably, reactive stepping responses are impaired in PD (Barajas & Peterson, 2018; de Kam et al., 2014; Peterson et al., 2016), characterized by smaller step lengths and margin of stability (Barajas & Peterson, 2018; de Kam et al., 2014; Foreman et al., 2012; Peterson et al., 2016; Schlenstedt et al., 2017). An inability to elicit rapid and robust reactive stepping responses has been related to falls (Mansfield et al., 2015; Mansfield et al., 2013). However, research has shown that these movements are adaptable (Jöbges et al., 2004; Monaghan et al., 2023; Peterson et al., 2016), and training can result in fewer falls (Monaghan et al., 2023; Shen & Mak, 2015).

Reactive balance responses are composed of three phases mediated by a distributed and complex neural network: 1) short-latency (SL) < 80 ms, 2) medium-latency (ML) 80–120 ms, and 3) long-latency (LL) > 120 ms (Jacobs & Horak, 2007). The earliest phases of the responses involve spinal (Fung & Macpherson, 1999; Macpherson et al., 1997) and subcortical structures (Honeycutt & Nichols, 2010; Hsu et al., 2017; Stapley & Drew, 2009), with cortical regions exerting more significant influence as the balance responses progress (Adkin et al., 2006; Dietz et al., 1985; Dimitrov et al., 1996; Ghosn et al., 2020; Marlin et al., 2014; Palmer et al., 2021; Payne & Ting, 2020). Although little work has assessed the neural underpinnings of reactive balance in PwPD, a recent connectivity study associated the subcortical and cerebellar networks with reactive balance in PwPD (Ragothaman et al., 2022). In addition to understanding the neural control of reactive balance responses, it is prudent to understand how different brain regions may contribute to the adaptability of such responses. Fortunately, reactive stepping is adaptable with practice in people with PD (Jöbges et al., 2004; Monaghan et al., 2023). However, responsiveness to reactive balance interventions in PD is variable. Recent evidence suggests that brain connectivity predicts responsiveness to motor training in some populations. For example, the white matter within the superior longitudinal fasciculus (SLF) (Bonzano et al., 2011; Lingo VanGilder et al., 2022; Peterson et al., 2017; Regan et al., 2021; Steele et al., 2012; Tomassini et al., 2011), the corpus callosum (Bonzano et al., 2011; Bonzano et al., 2014; Peterson et al., 2017; Sisti et al., 2012), thalamic radiations (Lingo VanGilder et al., 2022), the corticospinal tract (CST) (Bonzano et al., 2014; Lingo VanGilder et al., 2022; Tomassini et al., 2011), and frontostriatal tracts (Bennett et al., 2011) have all been implicated in responsiveness to practicing a motor task. However, these studies included populations with various neurologic diseases such as stroke (Regan et al., 2021) and multiple sclerosis (MS) (Bonzano et al., 2011; Bonzano et al., 2014; Peterson et al., 2017) and were often related to learning upper extremity tasks (Bonzano et al., 2011; Bonzano et al., 2014; Lingo VanGilder et al., 2022; Regan et al., 2021; Sisti et al., 2012; Steele et al., 2012; Tomassini et al., 2011). The extent to which white matter microstructure predicts learning of a lower-extremity, fall-relevant task in PwPD remains unknown.

Therefore, this study aimed to improve our understanding of the neural underpinnings of training-related changes in reactive stepping in PwPD. Specifically, we associated changes in reactive stepping performance following a two-week training program with whole-brain structural connectivity using a tract-based spatial statistical (TBSS) approach. Identifying neural correlates of responsiveness to reactive step training will help further our understanding of the neurophysiology of reactive balance responses in PD and may facilitate identification of PwPD most suitable for balance rehabilitation.

Data presented here are part of a larger clinical trial, and elements of these data have been published previously (Monaghan et al., 2023). However, the current analyses assessing the relationship between white matter structural connectivity and responsiveness to a reactive step training program are novel.

Participants

Thirty-one people with neurologist-confirmed idiopathic PD were recruited. Twenty-two participants completed training through follow-up and had usable magnetic resonance imaging (MRI) data. Participants were recruited from the Phoenix metro area in collaboration with Mayo Clinic and local support groups. Participants were between 50 and 90 years old, could stand for 5 minutes without aid, and did not exhibit non-PD neurological pathology, orthopedic impairments affecting balance, or contraindications for MRI. Participants were also “at risk of falls,” defined as experiencing one or more falls in the past year, an Activities Balance Confidence (ABC) Scale score of < 80 (Mak & Pang, 2009), or 3), or a Dynamic Gait Index (DGI) of < 19 (Dibble & Lange, 2006). Participants' demographics are shown in Table 1. The Arizona State University Institutional Review Board approved study methods, and participants provided consent before participation.

Table 1

Participant Demographics, reported as mean (SD) unless otherwise noted
Age (years)	70.55 (6.40)
Sex (Female/Male)	9/13
MDS UPDRS-III	31.14 (12.28)
Hoehn and Yahr Scale
1	1
2	13
3	8
Disease Duration (years)	5.29 (3.89)
LEDD (mg)	481.63 (209.92)
NFOG	4.23 (6.21)
MoCA	26.23 (2.67)
SCOPA-COG	27.86 (4.84)
Falls in the Previous 6 Months^a	1 (0:20)
Mini-BESTest	21.37 (3.56)
ABC	71.97 (11.50)
DGI	18.95 (2.59)
Abbreviations: MSD UPDRS III, Movement Disorders Society Unified Parkinson Disease Rating Scale; NFGOG, New Freezing of Gait Questionnaire; MoCA, Montreal Cognitive Assessment; SCOPA-COG, Scales for Outcomes in Parkinson Disease-Cognition; ABC, The Activities-specific Balance Confidence (ABC) Scale; DGI, Dynamic Gait Index; LEDD, Levodopa Equivalent Daily Dosage. ^aValues are reported as median (min:max).

Behavioral Protocol and Tasks

Participants completed an open-label, uncontrolled, quasi-experimental, pre-post-intervention study described in detail in (Monaghan et al., 2023). Briefly, reactive step assessments were conducted at four time points: two baseline assessments (B1 and B2, two weeks apart) and two post-assessments (P1 and P2) to assess the intervention's immediate (P1) and retained (2-month; P2) effects. Reactive steps were elicited using support-surface translations via a Bertec split-belt instrumented treadmill (Bertec Corporation, Columbus, Ohio). Participants completed approximately six reactive step training sessions over two weeks, each consisting of eight forward and eight backward reactive step trials, followed by eight leftward and eight rightward trials. Perturbation direction was randomized within forward/backward and left/right blocks, and the intensity was progressively increased throughout training (Jöbges et al., 2004).

Behavioral Data Analysis and Outcomes

A 14-camera 3D motion capture system (100 Hz; Motion Analysis Corporation in Santa Rosa, California) assessed stepping kinematics. A lower body and trunk marker cluster model was used, with marker clusters placed on the sacrum and T-12 vertebrae and bilaterally on the feet, shank, and thigh. Marker data were low-pass filtered at 20 Hz. Force plate data were collected from a Bertec split-belt instrumented treadmill with a sampling frequency of 2000 Hz and was low-pass filtered at 10 Hz.

Stepping outcomes were analyzed using custom software in The MotionMonitor xGen, including anterior-posterior (AP) margin of stability (AP MOS), first step length, and step latency. AP MOS was computed as the position of the extrapolated center of mass (xCOM) with respect to the base of support at first foot contact (Hof et al., 2005), with xCOM derived from the position and velocity of the COM (see (Hof et al., 2005; Monaghan et al., 2023) for details). Step length was calculated as the AP distance between the left and right foot, and step contact times were determined using ground reaction forces. Step latency was calculated as the time between the perturbation onset to first step start. We focus on backward reactive stepping characteristics due to the difficulty with backward postural control in PwPD (Carpenter et al., 2004). Stepping outcomes were averaged across three backward-stepping trials at each visit.

Diffusion Tensor Imaging Analysis

Magnetic resonance imaging (MRI) was collected on a 3T Philips Achieva MRI scanner equipped with an eight-channel receiver head coil (Philips Medical Systems, Andover, Massachusetts). A whole-brain echo-planar imaging sequence was also acquired (TR = 11921 ms; TE = 110 ms: field of view = 256 mm x 256 mm x 160 mm; b = 1500 s/mm^2; scan duration = 15 minutes and 30 seconds). The images were sensitized for diffusion along 64 different directions with one non-diffusion weighted image (b = 0 s/mm²) at the beginning of the scan. FSL and the FMRIB diffusion toolbox (http://www.fmrib.ox.ac.uk/fsl) were used to process the diffusion data. First, data were corrected for eddy current distortions and motion artifacts, then averaged to improve the signal-to-noise ratio (Eickhoff et al., 2010), and skull-stripped using FSL's brain extraction tool (Smith, 2002). FSL uses Bayes' rules to estimate the primary diffusion direction for each voxel, accounting for noise and uncertainty in the data. FMRIB's linear registration tool then normalized each individual's fractional anisotropy (FA) images into MNI space using a linear (affine) registration and Fourier interpolation. The diffusion tensor was calculated from the averaged diffusion-weighted (b = 1500 s/mm²) and non-diffusion-weighted image (b = 0 s/mm²). Following the diagonalization of the diffusion tensor, the eigenvalues λ1, λ2, and λ3 were produced along with the eigenvectors that define the predominant diffusion direction. Fractional anisotropy (FA) and radial diffusivity (RD) were the primary diffusion outcome measures as these indices reflect the integrity of the primary and orthogonal directions, or tensors, respectively, and have been associated with motor learning (Lingo VanGilder et al., 2022). FA reflects the directional dependency (i.e., anisotropy) of molecular motion in tissue and is a normalized index ranging from 0 (totally isotropic) to 1 (totally anisotropic), where higher values reflect the increased alignment of cellular structures within fiber tracts and better microstructural integrity (Basser & Pierpaoli, 1996; Maffei et al., 2015). RD (computed as (λ2 + λ3) / λ2)) reflects diffusivity perpendicular to axonal fibers and is correlated with myelin abnormalities, either dysmyelination or demyelination (Caeyenberghs & Swinnen, 2015). Lower RD values are indicative of greater white matter integrity.

Tract-Based Spatial Statistics

Tract-based spatial statistics (Smith et al., 2006) (TBSS) provides white matter microstructural integrity analyses restricted to the core of the brain's connectional architecture (Smith et al., 2006). TBSS was performed within FSL (Smith et al., 2004) using each participant's FA image registered to a common space (FMRIB_58 FA MNI template) via a nonlinear transform followed by an affine transform to the MNI152 space. A group average FA map was created, skeletonized, and used for a voxel-wise statistical analysis with 1000 permutations. A threshold of 0.2 was used to generate a mean skeleton. A whole-brain TBSS skeleton analysis identified associations between white matter microstructural integrity (FA and RD) and the retention of training improvements at the two-month follow-up. Retention was assessed by subtracting stepping performance at the two-month follow-up (P2) from the performance immediately before training (B2). This outcome was chosen as it reflects the retained improvement over time and, thus, the most clinically relevant outcome for prediction. The stepping data was demeaned across the entire sample and then regressed against the imaging outcomes, controlling for disease duration, using randomise within the FSL environment. The statistical threshold was established as p < 0.05, with multiple comparison corrections using threshold-free cluster enhancement (TFCE) (Smith & Nichols, 2009).

Analysis of Functional NeuroImages (AFNI) software was used to identify and isolate clusters of significant associations identified in the TFCE maps and extract FA and RD values within clusters. The clusterize function was used to group contiguous voxels that exceeded the statistical threshold of p < 0.05 (TFCE corrected). To identify clusters representing true effects, the nearest neighbor (NN) was set to three and voxel size to ten within the clusterize environment. The John Hopkins University (JHU) ICBM-DTI-81 White-Matter Labels labeled clusters showing significant association with stepping outcomes. Cluster locations are reported as peak coordinates representing the voxel with the highest intensity (i.e., lowest p-value) within a cluster. Overlapping white matter pathways located within a cluster are described below and provided in Supplemental Table 2 as a percentage that provides the (average) probability of a voxel or mask being a member of the different labeled regions within the atlas. Pearson correlations demonstrated the association between the diffusion indices (FA and RD) values in significant clusters and stepping outcomes.

Stepping Outcomes

As reported in a larger sample of the same cohort (Monaghan et al., 2023), on average, AP MOS and step length, but not step latency, improved at the two-month follow-up (P2) compared to pre-training (B2) performance (Supplemental Table 1; Fig. 1). AP MOS was on average 4.75 cm (± 4.98) larger (p < 0.001, estimated Cohen’s d = 0.95, 95% CI=[0.44, 1.45]; Fig. 1A) while step lengths were 4.40 cm (± 4.98) larger (p = 0.02, estimated Cohen’s d = 0.55, 95% CI=[0.10, 0.99]; Fig. 1B). Step onsets were quicker by an average of 15.83 ms (± 56.15), but this change was not statistically significant (p = 0.20, estimated Cohen's d = 0.28, 95% CI=[− 0.15, 0.71]; Fig. 1C).

Neural Correlates of Stepping Retention

A comprehensive summation of the neural correlates (clusters p < 0.05, TFCE-corrected) and correlation coefficients to retention is provided in Table 2. Retention of improvements in reactive step length was significantly related to increased FA (greater WM integrity) of four white-matter clusters: left anterior corona radiata (ACR) (r = 0.54, p < 0.01), left posterior thalamic radiation (PTR) (r = 0.54, p < 0.01), and the right (r = 0.43, p = 0.04) and left (r = 0.0.40, p = 0.06) SLF (Table 2, Fig. 2). Overlapping white matter pathways within the left ACR cluster included the genu of the corpus callosum (13%), left inferior frontal-occipital fasciculus (IFOF; 8%), and left uncinate fasciculus (UF; 8%). Overlapping white matter pathways identified within the left PTR cluster included the left inferior longitudinal fasciculus (ILF; 29%), left IFOF (18%), left superior longitudinal fasciculus (SLF; 3%), and left ATR (3%) (Supplemental Table 2).

Table 2

Neural correlates of reactive stepping retention
Step Length	Fractional Anisotropy
Step Length	r	Peak X (mm)	Peak Y (mm)	Peak Z (mm)	COG X (mm)	COG Y (mm)	COG Z (mm)
Cluster 1 (11893 voxels)
Left anterior corona radiata	0.54	-21	36	3	18.4	-23.8	20.3
Cluster 2 (3662 voxels)
Left posterior thalamic radiation	0.54	-34	-61	9	-24.7	-54.7	25.1
Cluster 3 (500 voxels)
Right superior longitudinal fasciculus	0.43	38	-59	34	37.3	-60.9	31.3
Cluster 4 (275 voxels)
Left superior longitudinal fasciculus	0.40	-45	10	8	-46.4	1.8	19.2
Step Latency	Radial Diffusivity
Step Latency	r	Peak X (mm)	Peak Y (mm)	Peak Z (mm)	COG X (mm)	COG Y (mm)	COG Z (mm)
Cluster 1 (8192 voxels)
Left posterior corona radiata	0.60	-26	-59	19	-8.6	-18.7	27.1
Cluster 2 (156 voxels)
Left anterior corona radiata	0.61	-25	31	4	-25.9	29.4	11.1
Peak and Center of gravity (COG) coordinates are in MNI space. White matter pathways were identified with (JHU) ICBM-DTI-81 White-Matter Labels and peak coordinates. r represents the sample correlation coefficient between retention in stepping outcomes and the white matter microstructural integrity measure within the identified cluster. Retention was computed as stepping performance at the two-month follow-up (P2) minus performance before training (B2). Positive values for step latency retention reflect less retention (i.e., step onsets were slower).

Retention of improvements in step latency improvements was related to lower RD (greater WM integrity) of 2 white-matter clusters: left posterior corona radiata (PCR) (r = 0.60, p < 0.01) and the left anterior corona radiata (ACR) (r = 0.61, p < 0.01; Table 2; Fig. 3B). Overlapping white matter pathways identified within the left PCR cluster included the splenium of the corpus callosum (5%) and left ATR (3%). Overlapping white matter pathways identified within the left ACR cluster included the left IFOF (29%), left UF (3%), and left ATR (Supplemental Table 2).

Sensitivity analyses were performed to ensure that extreme data points were not leveraging relationships between RD and step latency retention. The Robust regression and Outlier removal (ROUT) method with a Q (false discovery rate) of 1% was used to detect extreme data points. The RD value (RD = 0.001) within the left PCR for one participant was deemed extreme, but removing this data point did not change the statistical inference (r = 0.45; p = 0.04). The RD values (RD = 0.001; 0.002; 0.002) within the left PCR for three participants were also deemed extreme, but removing these data points did not change the statistical inference (r = 0.55; p = 0.01).

This study investigated the relationship between white matter microstructural integrity and the retention of reactive stepping improvements following a two-week training program in PwPD. Greater white matter integrity within a broad group of long association, commissural, and projection fibers were associated with retained improvements in reactive stepping, specifically the corona radiata, thalamic radiations, superior longitudinal fasciculi, and corpus callosum. These results are consistent with previous work outlining neural structures related to motor learning in neurotypical and MS groups and extend findings to PwPD. The current findings contribute to our understanding of the relationship between white matter integrity and motor learning in PD and may help inform the development of more targeted and effective rehabilitation.

Several commissural, association and projection white matter fibers were related to learning. Within the first cluster labeled as the corona radiata, step length retention was associated with the left ATR and the genu of the corpus callosum. The corona radiata comprises a sheet of ascending and descending axons carrying most of the neural traffic to and from the cerebral cortex and is associated with the corticopontine tract, the corticobulbar tract, and the corticospinal tract (Brackmann & Fetterman, 2007). These findings link the anterior corona radiata with retained improvements in motor performance through practice, consistent with previous research showing that white matter integrity of the corona radiata, specifically regions along the CST, are related to learning in healthy adults (Lingo VanGilder et al., 2022; Tomassini et al., 2011; Wang et al., 2014) and people with MS (Bonzano et al., 2014). However, it should be noted that these studies involved upper extremity motor sequence learning. The current study extends these findings, indicating a potential overlap in brain regions predicting upper and lower-extremity learning. We also observed a relationship between reactive balance learning and ATR structural connectivity, which relays motor signals via the thalamocortical pathway through the anterior limb of the internal capsule (Niida et al., 2018). This finding is also consistent with previous work, as this pathway has been implicated in motor learning of upper extremity tasks in older adults (Lingo VanGilder et al., 2022), reinforcing the link between striatal-thalamocortical connectivity and learning (Bennett et al., 2011; King et al., 2013; Lingo VanGilder et al., 2022).

White matter integrity of the genu of the corpus callosum was predictive of retention of improved stepping performance. This aspect of the corpus callosum connects frontal hemispheres and has been previously related to learning in people with MS. For example, corpus callosum integrity was related to 1-day retention of reactive stepping improvements in MS patients (Peterson et al., 2017). More globally, the corpus callosum has been linked to learning upper extremity motor tasks in healthy adults (Sisti et al., 2012; Song et al., 2012; Wang et al., 2014) and people with MS (Bonzano et al., 2011; Bonzano et al., 2014). The exact mechanism by which the corpus callosum influences motor learning is unclear. However, the corpus callosum plays a significant role in mediating interhemispheric inhibition (Takeuchi et al., 2012), and intracortical inhibition has been related to motor learning (Stagg et al., 2011). Further, people with PD have poor contralateral communication and reduced intracortical inhibition (Ridding et al., 1995), potentially implicating this mechanism in altered learning in PwPD. However, the influence of interhemispheric communication in reactive stepping is limited, so future research is required to confirm the role of the corpus callosum and/or intracortical inhibition in learning reactive balance tasks.

Association fibers such as the SLF were also associated with sustained improvements in step length. These white matter fiber bundles connect posterior parietal cortical areas with frontal cortical and motor regions. The posterior parietal cortex (PPC) is crucial in movement planning and execution. It integrates sensory information to reflect body position and environmental and exteroceptive cues and incorporates this information to form internal representations of desired movements (Buneo & Andersen, 2006). Therefore, it is perhaps unsurprising that participants with greater SLF white matter integrity would exhibit greater stepping retention. This assertion is supported by previous research implicating the SLF in motor learning in healthy adults (Lingo VanGilder et al., 2022; Steele et al., 2012; Tomassini et al., 2011), people with MS (Bonzano et al., 2011; Peterson et al., 2017), and with stroke (Regan et al., 2021). For example, greater white matter integrity of the SLF was associated with 1-day retention of improvements in stepping in people with MS (Peterson et al., 2017). Similarly, the SLF was related to motor skill retention one week after practice (Lingo VanGilder et al., 2022) in older adults and 48-hour retention in younger adults (Wang et al., 2014).

Limitations

There are several limitations to note. First, as this report was exploratory, disease duration was the only covariate included in the TBSS analysis. Second, despite thresholding p-values and selecting conservative clusterization parameters (nearest neighbor = 3; voxels = 10) to isolate unique pathways, the TBSS analyses and clusterization resulted in reasonably large clusters incorporating overlapping white matter pathways. The white matter tracts identified in each cluster using the JHU White-Matter Tractography Atlas are provided in Supplemental Table 2. Finally, for consistency, the clusters were labeled according to their peak coordinates using the JHU ICBM-DTI-81 White-Matter Labels (Table 2). Therefore, the cluster's name may not entirely represent the total white matter tracts in the cluster. Supplemental Table 2 identifies all the tracts identified within clusters.

This study is the first to demonstrate the association between white matter microstructural integrity and the retention of reactive stepping through training in PwPD. Specifically, findings highlight the importance of association and project white matter fibers such as the ACR, ATR, SLF, and corpus callosum with learning reactive stepping. These results provide insight into brain regions and WM tracts facilitating training-related balance improvements in PwPD.

Acknowledgments: We would like to thank the participants for donating their time to facilitate this study. Also, we would like to thank Jessica L. Trevino and Jordan S. Barajas for their assistance with data collection.

Funding Sources: This study was supported by a Michael J. Fox Foundation, Grant #008373

Conflict of Interest: The author(s) declare no competing interests.

Ethics Approval: The study was conducted according to declaration of Helsinki. Approval was provided by the Institutional Review Board at Arizona State University.

Consent to Participate: Signed consent was collected from all participants after being informed of the study description.

Consent for Publication: All authors have approved of publishing this manuscript which has been read thoroughly by all of them.

Availablility of Data and Material: The data that support the findings of this study are available from the corresponding author on reasonable request. available from the corresponding author upon request.”

Code Availability: Not applicable.

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Associating White Matter Microstructural Integrity and Improvements in Reactive Stepping in People with Parkinson’s Disease

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Abstract

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Introduction

Methods

Participants

Behavioral Protocol and Tasks

Behavioral Data Analysis and Outcomes

Diffusion Tensor Imaging Analysis

Tract-Based Spatial Statistics

Results

Stepping Outcomes

Neural Correlates of Stepping Retention

Discussion

Limitations

Conclusions

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References

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