Introduction

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., 2020).

Losses of balance are often caused by external balance perturbations, such as a trip, slip, or a poor weight shift, and require rapid responses, such as reactive stepping, to prevent a fall (Berg 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., 2016a), characterized by smaller step lengths and margin of stability (Barajas & Peterson, 2018; de Kam et al., 2014; Foreman et al., 2012; Peterson et al., 2016a; Schlenstedt et al., 2017). An inability to elicit rapid and robust reactive stepping responses has been related to falls (Mansfield et al., 2013, 2015). However, research has shown that these movements are adaptable (Jöbges et al., 2004; Monaghan et al., 2023b; Peterson et al., 2016a), and training may result in fewer falls (Monaghan et al., 2023b; Shen & Mak, 2015).

Reactive balance responses are derived from complex and sophisticated neural networks spanning the neuraxis (Boebinger et al., 2023; Jacobs & Horak, 2007; Ragothaman et al., 2022). However, the evidence describing the neural structures involved in improving reactive balance responses in PwPD remains limited. The concept of motor learning, traditionally defined as a set of processes associated with practice or experience, leading to relatively permanent changes in the capability for movement (Schmidt & Lee, 2005), is integral to understanding flexible and adaptive motor behavior. Improving performance via practice is central to rehabilitation aimed at modifying motor behavior to restore function and reduce injury risk (Seidler, 2010). Fortunately, reactive stepping can be improved through practice in PwPD (Jöbges et al., 2004; Monaghan et al., 2023b). However, responsiveness to balance rehabilitation is variable, as not all individuals benefit equally from treatments (Monaghan et al., 2023a, b). 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, 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 related to motor learning. However, these studies included populations with various neurologic diseases such as stroke (Regan et al., 2021) and multiple sclerosis (MS) (Bonzano et al., 2011, 2014; Peterson et al., 2017) and were often related to learning upper extremity tasks (Bonzano et al., 2011, 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, the primary objective was to associate changes in reactive stepping performance after a two-week training intervention, with whole-brain structural connectivity, utilizing a tract-based spatial statistical (TBSS) approach. Given its clinical relevance, the primary emphasis was retained stepping performance over time. The secondary aim was to correlate immediate changes in stepping performance post-training with whole-brain structural connectivity. Building on previous research on the neural underpinnings of short-term motor learning of reactive balance in people with MS (Peterson et al., 2017), we hypothesized that greater white matter microstructural integrity of the corpus callosum and parietal-sensorimotor fibers would be linked to more substantial immediate and retained training-related improvements in reactive stepping in PwPD. 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 the identification of PwPD most suitable for balance rehabilitation.

Methods

The data presented in this study are derived from a larger clinical trial to assess the effects and generalization of reactive balance training in PwPD and portions of these data have been published (Monaghan et al., 2023a, b). These previous reports focused on evaluating the efficacy of a two-week reactive step training intervention to enhance stepping (Monaghan et al., 2023b), as well as investigating whether cognitive factors could predict the responsiveness of individuals with Parkinson's disease (PwPD) to the training (Monaghan et al., 2023a). The current exploratory analyses, which examine the relationship between white matter structural connectivity and responsiveness to a reactive step training program, are novel.

Participants

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 five 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), a Dynamic Gait Index (DGI) of < 19 (Dibble & Lange, 2006).

Thirty-five individuals diagnosed with idiopathic PD by a neurologist were recruited, with thirty-one completing the initial baseline data collection, twenty-seven completing the immediate post-assessment, and twenty-five finishing the full protocol through the two-month follow-up. The CONSORT flow diagram is provided in Supplemental Fig. 1. Twenty-two of these twenty-five individuals also had usable MRI data. These twenty-two participants were included for analysis (see Supplemental Fig. 1 and Table 1 for participant demographics). All visits were conducted in the "ON" medication state. The Arizona State University Institutional Review Board approved study methods, and participants provided consent before participation (Table 1).

Fig. 1
figure 1

Changes in reactive stepping pre- and post-training. Individual (gray lines) and averaged (black lines) of A) AP margin of stability (AP MOS), B) step latency, and C) step length, pre-training (B2), immediately (P1) and two-months (P2) following training. Error bars represent standard deviations. B2 baseline 2; P1- post-assessment 1; P2- post-assessment 2. *** p < 0.001; ** p < 0.01; * p < 0.05 

Full size image
Table 1 Participant Demographics, reported as mean (SD) unless otherwise noted 
Full size table

Behavioral protocol and tasks

Participants completed an open-label, uncontrolled, quasi-experimental, pre-post-intervention study described in detail in (Monaghan et al., 2023b). Briefly, reactive step assessments were conducted at four-time points: two baseline assessments (B1 and B2, two weeks apart), a two-week training intervention (see below), 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. The protocol involved repetitive exposure to reactive steps on the Bertec split-belt instrumented treadmill. Each training session consisted of thirty-two reactive step trials, with eight forward, eight backward, eight leftward, and eight rightward trials presented in a pseudo-randomized order. To maximize exposure to perturbations while minimizing fatigue, these trials were divided into two blocks of four trials in each direction, allowing for the progression of perturbation intensity (see Supplemental Fig.2 ). Throughout the training program, perturbation amplitude and speed were adjusted based on individual participant performance, enabling progressive overload. In the initial training session, the first set of four perturbations in each direction matched the intensity from the assessment phase. The frequency of participants using support from the harness was monitored within each four-trial block. Harness use, defined as trials where over 10% of body weight applied to the harness, was detected through force transducers aligned with the harness system. Adjustments to perturbation acceleration were made based on assistance requirements (see Supplemental Fig. 2): if no assistance was needed in all four trials, acceleration increased by 0.2 m/s2; conversely, if assistance was required in two, three, or all four trials, acceleration decreased by 0.2 m/s2. The acceleration remained unchanged if assistance was needed in only one of the four trials.

Fig. 2
figure 2

Associations between step length retention and white matter microstructural integrity. A) Threshold-Free Cluster Enhancement (TFCE) FA maps are overlaid on the mean FA skeleton (green) and MNI 152 template. Significant associations with fractional anisotropy (FA) were localized to the left anterior thalamic radiation (ATR L), left posterior thalamic radiation (PTR L), left superior longitudinal fasciculus (SLF L), and right inferior longitudinal fasciculus (ILF R), with brighter colors (yellow, white) representing stronger associations. Results are multiple comparison-corrected, controlled for disease duration, and labeled according to peak coordinates. B) Scatterplots and Pearson correlation results representing step length retention (computed as stepping performance at the two-month follow-up relative to stepping immediately before training; positive values indicate greater retention) and FA (larger values reflect greater white matter integrity) for each significant white matter cluster

Full size image

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., 2023b) 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 and the first step onset. 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.

Statistical analysis

Reactive stepping changes were analyzed using mixed-effects models with restricted maximum likelihood estimation. Sphericity was not assumed; thus, Geisser-Greenshouse corrections were applied. Comparisons between pre-training (B2) and immediate post-training (P1), as well as the two-month follow-up (P2), were conducted to evaluate immediate and retained changes. To account for multiple comparisons, Bonferroni adjustments were applied.

Diffusion tensor imaging analysis

Magnetic resonance imaging (MRI) was collected on a 3 T 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 × 256 mm × 160 mm; b = 1500 s/mm2; scan duration = 15 min and 30 s). 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) provide 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 5000 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 immediate change in reactive stepping outcomes was computed by subtracting stepping performance immediately after (P1) and before the training intervention (B2). 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 for this study’s primary aim 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.

Table 2 Neural correlates of reactive stepping retention
Full size table

Results

Dropout and adherence to training

Refer to Supplemental Fig. 1 for the CONSORT diagram. Twenty-five participants completed the experimental protocol through P2, with MRI data available from twenty-two participants. Eighteen participants received the total training dose (six sessions), while four completed five sessions.

Stepping outcomes

As reported in a larger sample of the same cohort (Monaghan et al., 2023b), 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). Specifically, AP MOS showed an average increase of 4.75 cm (± 4.98) (p < 0.001, estimated Cohen’s d = 0.67, 95% CI of difference = [2.189, 7.312]; Fig. 1A), while step lengths increased by 4.30 cm (± 7.79) (p = 0.03, estimated Cohen’s d = 0.46, 95% CI of difference = [0.2865, 8.305]; Fig. 1C). Step latency, however, did not exhibit a statistically significant change, although latencies improved by an average of 15.83 ms (± 56.15) (p = 0.40, estimated Cohen’s d = 0.20, 95% CI of difference = [- 44.73, 13.06]; Fig. 1B).

Immediately after step training (P1), AP MOS demonstrated improvement, with an average increase of 4.31 cm (± 6.35) (p = 0.01, estimated Cohen’s d = 0.61, 95% CI of difference = [1.045, 7.579]; Fig. 1A). Step lengths and latencies were longer and faster, respectively, although not statistically significantly. Step lengths showed an average increase of 2.88 cm (± 6.37) (p = 0.09, estimated Cohen’s d = 0.34, 95% CI = [-0.4013, 6.156]; Fig. 1C) and latencies reduced by 15 ms (± 62.64) (p = 0.56, estimated Cohen’s d = 0.17, 95% CI of difference = [- 47.08, 17.39]; Fig. 1B).

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 thalamic radiation (ATR) (r = 0.66, p < 0.001), left posterior thalamic radiation (PTR) (r = 0.64, p = 0.001), the left superior longitudinal fasciculus (SLF) (r = 0.63, p = 0.002), and the right inferior longitudinal fasciculus (ILF) (r = 0.63, p = 0.0001) (Table 2, Fig. 2). Overlapping white matter pathways within the left ATR cluster included the genu of the corpus callosum (3%), cingulum (3%), left SLF (5%), and left uncinate fasciculus (8%). Overlapping white matter pathways identified within the left PTR cluster included the left ILF (29%), left inferior fronto-occipital fasciculus (IFOF) (18%), left superior longitudinal fasciculus (SLF; 3%), and left ATR (3%). Overlapping white matter pathways identified within the left SLF cluster included the cingulum (8%) and left ATR (5%), left IFOF (18%), left SLF (3%), and left ATR (3%). (Supplemental Table 2).

Retention of improvements in step latency was related to lower RD (greater WM integrity) of two white-matter clusters: left posterior corona radiata (PCR) (r = 0.61, p = 0.01) and the left PTR (r = 0.53, p = 0.01; Table 2; Fig. 3). Overlapping white matter pathways identified within the left PCR cluster included the splenium of the corpus callosum (5%), the ILF (5%), the left IFOF (5%), and left SLF (3%). Overlapping white matter pathways identified within the left PTR cluster included the left IFOF (24%), left ILF (21%), and splenium (21%) (Supplemental Table 2 ).

Fig. 3
figure 3

Associations between step latency retention and white matter. A) Threshold-Free Cluster Enhancement (TFCE) RD maps are overlaid on the mean RD skeleton (green) and MNI 152 template. Significant associations with radial diffusivity (RD) were localized to the left posterior corona radiata (PCR L) and the left posterior thalamic radiation (PTR L), with brighter colors (yellow, white) representing stronger associations. Results are multiple comparison-corrected, controlled for disease duration, and labeled according to peak coordinates. B) Scatterplots and Pearson correlation results representing step latency retention (computed as step latency at the two-month follow-up relative to the latency immediately before training; negative values indicate greater retention) and RD (lower reflects greater white matter microstructure) for each significant white matter cluster. Sensitivity analyses were performed to ensure that extreme data points were not driving the bivariate relationships (see Results for details)

Full size image

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 = 0.002) within the left PCR for one participant was deemed extreme, but removing this data point did not change the statistical inference (r = 0.44; p = 0.04). The RD values (RD = 0.001 and 0.002) within the left PTR for two participants were also deemed extreme, and removing these data points reduced the observed effect (r = 0.44; p = 0.0501).

Neural Correlates of Immediate Changes in Stepping

A comprehensive summation of the neural correlates (clusters p < 0.05, TFCE-corrected) and correlation coefficients to immediate changes in stepping following the training intervention is provided in Supplementary Table 3 and 4 and Supplementary Figs. 3 and 4. Immediate improvements in AP MOS following training were significantly related to increased FA (greater WM integrity) of three white-matter clusters: left ILF (r = 0.57, p = 0.01), right SLF (r = 0.57, p = 0.01), and right corticospinal tract (r = 0.55, p = 0.01). Immediate improvements in step length following training were significantly related to increased FA of three white-matter clusters: right (r = 0.70, p < 0.001) and left ATR (r = 0.70, p < 0.001), and the right SLF (r = 0.64, p = 0.001).

Discussion

This study investigated the relationship between white matter microstructural integrity and changes in reactive stepping 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 and immediate 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 retained and immediate improvement in reactive stepping through practice. 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). Interestingly, these findings are consistent with previous research showing that white matter integrity of the corona radiata, specifically regions along the CST, are related to retained improvements in voluntary, upper extremity tasks through practice in healthy adults (Lingo VanGilder et al., 2022; Tomassini et al., 2011; Wang et al., 2014) and people with MS (Bonzano et al., 2014). 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 one-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, 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 immediate and 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 responsiveness to training. 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 one-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-h retention in younger adults (Wang et al., 2014).

The relative consistency of findings across upper and lower extremity tasks, while encouraging, is somewhat surprising, as the current motor task was partially reactive in nature, while previously reported data from upper extremity tasks were voluntary, upper extremity reaching or tapping movements. One notable similarity across current and previously reported (Bonzano et al., 2014; Lingo VanGilder et al., 2022; Tomassini et al., 2011; Wang et al., 2014) motor tasks is that improvement through practice was partially implicit in that there was no biofeedback or explicit coaching to encourage improvements in movements. Although speculative, it is possible that this similarity drove the similar relationship between motor learning and brain connectivity across studies. Regardless of the rationale, the current study extends previous findings, indicating a potential overlap in brain regions predicting upper and lower-extremity learning across several tasks.

Finally, it is notable that while reactive step length was more robustly related to white-matter integrity than step latency (in both the number and size of clusters), there was some consistency across behaviors. Specifically, the sole structure relating to step latency retention (the left posterior thalamic radiation) was also related to step length retention. The rationale for the more robust relationship between white matter integrity and improvements in step length may be due to the fact that step length was more consistently impacted by training than step latency(Monaghan et al., 2023b) (see Fig. 1 and Supplemental Table 1). Step latency, while important to facilitate effective reactive balance, is less consistently impacted in people with PD compared to, for example, people with multiple sclerosis (Monaghan et al., 2022; Peterson et al., 2016a, 2016b). The relative lack of deficit in this aspect of reactive balance may have reduced the ability to detect and predict changes via microstructural integrity.

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 = three; voxels = ten) to isolate unique clusters, 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. Next, data were only collected ON levodopa. This choice was made to maximize the likelihood of improvement through training, as dopamine may positively affect motor learning (Beeler et al., 2012; Peterson & Horak, 2016). However, given the importance of dopamine for motor learning, additional work should be carried out to determine whether the results extend to OFF-dopamine learning. 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.

Conclusions

This study is the first to demonstrate the association between white matter microstructural integrity and the retention of reactive stepping through training in PwPD. Findings are however consistent with previous work in upper extremity motor learning and non-PD groups, highlighting the importance of association and projection white matter fibers such as the ACR, ATR, SLF, and corpus callosum with motor learning. These results provide insight into brain regions and WM tracts facilitating training-related balance improvements in PwPD.