Long Term Aerobic Exercise to Slow Progression in Parkinson's Disease
Phase: NA
Conditions: Parkinson's Disease
Sponsor: VA Office of Research and Development

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01. Introduction to Revised Application 
We thank the reviewers for their excellent guidance. We addressed their concerns below and made changes to 
the Research Plan that improved our proposal. Due to the extent of the revisions, we did not mark the specific 
changes in the Research Plan individually. Briefly, we made the following changes in our proposal to make it 
more feasible and to provide a crisper contrast between experimental groups, resulting in larger effect sizes, 
thus, smaller sample size and related costs: 
x 
We will use “usual care with PD specific health education” as control group instead of “light exercise”.  
x 
We dropped Specific Aim 3 (comparison to “natural history” using historic cohorts) due to inherent 
issues with such analyses and the lack of a clear contribution to the primary research questions. 
However, we used our past PD driving study cohort as a historic “usual care” control group to estimate 
sample sizes for the current proposal.  
x 
We shortened the duration from 24 to 12 months, and removed interim assessments for outcomes.  
x 
We reduced outcome measures and brought our primary outcome measures more in line with our own 
preliminary data, which helped with providing more detailed sample size estimates. 
Control group: We agree with the concerns of the reviewer panel that light intensity walking can have 
confounding effect and changed our control intervention to “usual care with PD specific health education” as 
detailed in C.2.ii. Both groups will be allowed extra-study physical activities. We will match social attention 
between both groups by contacting them biweekly about their physical activity, general health, adverse events, 
medication changes, and other concerns that come up.   
Duration of the study: We reduced the duration of the interventions from 24 to 12 months based on the 
review of our preliminary data1 and the recent literature,2 which suggest that significant contrasts can be 
observed between a usual care group and aerobic exercise group in motor function and DTI values in patients 
with PD over 6-12 months. We will collect primary outcome measures only at baseline and 12 months, which 
will reduce learning effects, attrition, and sample size requirements due to repeated measurements. 
Primary outcome measures: We agree with Reviewer 2’s concern of “lack of continuity between preliminary 
data and a number of proposed outcome measures". Therefore, we changed our primary outcome measures 
as follows: We will use flanker task outcome (PIS) for cognition3 and the DTI nodes where we observed robust 
rD change with significant baseline clinical correlations (putamen, cingulum, and the SLF, see Table).4-6   
 Region 
VO2max 
PIS 
PegT
Table. Spearman correlations of regional radial diffusivity 
(rD) with clinical features at baseline (***p<0.001, **p,0.01, 
*p<0.05, ‡p<0.1). PIS (Percent Increase Score) is the 
outcome measure of flanker task,  PegT is Peg Board 
completion time. The rD regions in the table except 
R_putamen represent those regions which showed 
significant improvement after 6 months aerobic training. For 
rD, PIS, and PegT lower values are better; whereas for 
VO2max higher values are better. Higher aerobic fitness is 
associated with better tissue integrity (lower rD) values. 
Better performance on the flanker task (PIS) and Peg 
Board time is associated with better rD at baseline. Higher 
bilateral putamen rD also correlated with higher levodopa 
equivalent daily dose (p<0.05), a marker of PD severity.
L_Putamen 
-.56*** 
.38* 
.29‡ 
R_Putamen 
-.50** 
.52** 
.34* 
L_Cingulum 
-.48** 
.23 
.42** 
R_Cingulum 
-.43** 
.24 
.39* 
L_Sup_Long_Fasc 
-.39* 
.33* 
.38* 
R_Sup_Long_Fasc 
-.33* 
.27‡ 
.42** 
L_Ant_Int_Cap 
-.36* 
.32* 
.41** 
L_Post_Cor_Rad 
-.13 
.31‡ 
.35* 
L_Sup_Cor_Rad 
-.36* 
.31‡ 
.29‡ 
Correlations of DTI metrics and behavior or CRF. Reviewer 2 states "…no correlations were seen with any 
of their DTI metrics and behavior or CRF". This is true for correlation of changes in these metrics; however, we 
saw correlation of DTI metrics with CRF and behavior at baseline, especially in regions that showed a 
significant improvement after the aerobic exercise program (Table).  These correlations were in the expected 
direction, namely, better performances on tests or aerobic fitness correlated with higher tissue integrity (i.e., 
lower diffusivity).Of the 48 white matter regions tested, 7 regions showed improvement in rD with aerobic 
training, and 6 of these 7 areas correlated with VO2max at baseline, whereas only 2 of 41 areas that did not 
improve correlated with aerobic fitness at baseline. Thus, significant correlation with aerobic fitness at baseline 
was robustly associated with improvement of rD in that region after aerobic training (OR for regional rD 
improvement after aerobic exercise if associated with aerobic fitness at baseline was 4.8 (2.2, 7.3), p<0.001, 
Fisher’s Exact test). This suggests that some areas in the brain might be more likely to benefit from increased 

 
 
 
Fig. Effect of aerobic exercise (blue) vs. usual 
care (red) on motor UPDRS (mean±SEM). 
p=0.002, adjusted for age, education, sex, 
baseline LEDD, and change in LEDD. 
aerobic fitness. We did not detect any significant associations with change in regional DTI and change in 
aerobic fitness or clinical variables, which could have been due to limited spread of data as all subjects were in 
the intervention group.  However, having a “usual care” control group in the current proposal is expected to 
generate a dataset with strong variability in fitness gains, clinical and DTI changes, potentially enabling us to 
find correlations between changes in DTI, CRF, and clinical features. 
Selection of DTI nodes. We agree with Reviewer 2 that our primary outcome nodes were not completely 
based on our own data. Therefore, we decided to change them to putamen, cingulum, and SLF (as defined by 
Mori et al, 20087) as we have observed significant changes in these nodes in our preliminary study along with 
significant correlations with baseline aerobic fitness and several clinical features (Table).4-6 These regions have 
been shown to have decreased FA and increased diffusivity in PD in cross-sectional and longitudinal studies.8-
12 Mean diffusivity changes in the white matter in PD were found to be predominantly related to an increase in 
rD,13 which increases importance of our finding that we mainly found rD improvements rather than aD.6  The 
putamen is the rostral terminus of the nigrostriatal pathway, but also pathology in cingulum and SLF play an 
important role in clinical manifestations of PD:  The severity of postural instability and gait disorder correlates 
with increased diffusivity (especially rD) in the SLF;14-16 DTI abnormalities in cingulum and SLF are associated 
with cognitive dysfunction.14,17,18,18-24 Furthermore, white matter microstructure mediates the relationship 
between CRF and cognition in older adults.25 Also, SLF integrity is associated with flanker task performance26 
as in our preliminary study (Table). The primary DTI outcome measure will be the change in rD in putamen, 
cingulum, and SLF. The proposed tracts in the prior submission will be used for secondary outcomes. 
Management of participants who require absences or breaks for travel or illnesses. We added a section 
to Human Subjects titled “Protocol for Managing Illness/Injury and Other Health Problems or Travel” (section 
04.2.b.), similar to the one used in the LIFE study.27 However, with shorter duration of the study (1-year instead 
of 2-years) in this submission, potential absences or breaks are expected to be of a lesser concern.  
Subject burden. We reduced the duration of the study, number of visits, and number of outcome measures. 
We will spread detailed baseline and post-intervention assessments over 2 days if needed. We will only do 
motor MDS-UPDRS scoring and MRI for DTI during the OFF stage, which do not have significant physical 
demands. With the new design, the number of OFF period measurements has been reduced from 5 to 2 
across the study. We will wait for dopaminergic medications to kick in before we do any testing. 
Outcomes to evaluate the feasibility, compliance, and adherence to the aerobic exercise. Electronic 
heart monitor data and exercise diaries will be reviewed with the subject during biweekly phone calls. We will 
assist the subject selecting walking paths. A similar approach worked well in our preliminary study. 
Monitoring concurrent rehabilitation therapies, other exercise activities, and medications. We agree 
that strict monitoring of extra-study physical activities and various medication categories (e.g., antidepressant, 
antihypertensives) is needed in addition to randomization. We will use diaries and biweekly phone calls with 
subjects to monitor for these. We will include these factors in analyses to determine their effect on outcomes. 
Sample size. Sample size estimates were “vague” 
and were “not provided” for some outcome measures 
in the original submission, as commented upon by 
Reviewer 2. The following explains our approach in 
detail. We are planning to recruit N=100 (50/group) 
that would provide 80% power to detect an effect size 
of Cohen’s d’=0.7 (equivalent to f=0.35 or a correlation 
of r=0.33 or R2=0.11) at a 2-sided Į=.05 after taking 
into consideration that 10% of subjects may be lost to 
follow up and 25% might discontinue the intervention. 
We used our uncontrolled preliminary study on aerobic 
exercise in PD to estimate effect sizes in the aerobic 
exercise group (same exercise regimen as in the 
current proposal).3 To estimate effect sizes in usual 
care group, we used our observational longitudinal 
cohort study on driving in PD for motor and driving 
outcomes,1 the usual care control group of an aerobic 
exercise study for aerobic fitness gain (VO2max) 
outcome,2 and a longitudinal observational study on DTI changes.11  

 
 
In our prior longitudinal driving study (PD vs. normal aging),1 subjects underwent motor and cognitive testing at 
baseline and then annually for 2 years (road test was only administered at baseline and 2 years later, please 
see B.1. Preliminary Studies). We matched 49 patients from the PD driving cohort to 49 patients from our PD 
aerobic exercise study3 based on baseline PD  severity (as indexed by the UPDRS scores) and age. Here, we 
will present data from baseline to Year 1. Please note that to compensate for the different follow up periods in 
the exercise study (6 months) and driving study (12-24 months), the values of second measurements in the 
driving study were extrapolated assuming a linear change. For example, if the motor UPDRS score of a driving 
study subject was 20 at baseline and 26 at Year 1 assessment, then the extrapolated value of 23 (= 20 + [(26-
20)/2]) was assigned as the 6-month follow up value. The Figure shows trajectory of motor UPDRS in aerobic 
exercise and usual care groups: Adjusted estimates of change in motor UPDRS were -3.1±4.8 (mean±SD) for 
exercise group and 1.2±4.8 for the usual care group, resulting in effect size of Cohen’s d’=0.9.  
The estimates of VO2max change were +1.65±2.90 ml/kg/min after aerobic exercise (our study) and          -
1.3±2.5 after usual care (Schenkman et al, 2017),2 resulting in Cohen’s d’=1.09. For cognition, we observed a  
-3.5±8.2% change in flanker cost (PIS) in our uncontrolled aerobic exercise study. As we did not have a control 
group, we compared the change in PIS between the 72 % subset who had VO2max gain ('PIS= -5.7±7.0%) 
vs. the 28% subset who did not ('PIS= -0.2±7.9%), resulting in an effect size of Hedges g’ =0.76 (used for 
unequal group sample sizes) for those with aerobic improvement.  For driving, the annualized change in road 
error counts based on our cohort1 were an increase of 7.3±8.4 in the PD group and 0.6±6.0 in the control 
group. If the proposed PD exercise group were to do midway between the PD group and the control group in 
our past driving study, and if the proposed usual care group were to do similar as in the PD group in the past 
study, then the effect size would be Cohen’s d’~0.7. 
We did not have our own historic usual care group for DTI changes. However, we used the data by Zhang et al 
(2016)11 who scanned 122 recently diagnosed PD patients one year apart.11 In almost all regions, PD patients 
showed an increase in rD and aD, accompanied by a decrease in FA. Within the PD group, the rD increased 
up to 4% over 1 year across different regions, averaging 1.7±1.2% over the whole brain. From this annual 
change, we estimated the change for 6 months for the whole brain (increase of 0.86±0.58%) and used in 
sample size calculations to represent the changes in the usual care group in comparison to our own study. 
During our 6-months aerobic exercise study, rD decreased 1.4±2.5% in the SLF, 1.5±3.1%  and 1.3±3.2% in 
the putamen. Estimated Hedges’ g’ for SLF was 1.24, for Cingulum 1.46, and for putamen 1.09.  
In summary, the potential effect size estimates for various outcomes in a 6-12 months aerobic exercise 
intervention vs. usual care appear to range between Cohen’s d’  (or Hedges g’) of 0.7 and 1.46.  It is possible 
that effect size of aerobic exercise may increase with prolonged intervention to 1 year.28 Based on these 
considerations, we decided to use Cohen’s d’=0.7 as a basis of our sample size calculations. 
Do observed DTI changes manifest restoration of brain integrity versus compensatory mechanisms in  
response to exercise? Addressing this fundamental question by Reviewer 3 may require animal models, 
which suggest a restorative role for exercise in PD.29 We will attempt to approach this question by performing 
volumetric analyses of the putamen30,31 to determine if the hypothesized improvement of the putaminal rD is 
accompanied by parallel changes in putaminal volume, which may then suggest brain tissue preservation 
beyond compensatory changes.  
Gain in executive function vs. cognition. We apologize for our unclear expression in A.4.i. as pointed out by 
Reviewer 3. We recognize that executive functions are one of the several domains of cognition. A more 
accurate statement would have been that some studies found benefits primarily in executive functions, but not 
in “overall cognition” or “other domains of cognition”. We removed confusing statements from A.4.i. and added 
that overall studies in animals and humans suggest that aerobic exercise in aging improves cognition and its 
imaging and electrophysiological biomarkers.32,33    
TBI/PTSD. The subjects with history of clinically significant TBI/PTSD will be excluded. Please note that the 
study is also open for recruitment to non-veterans from the community and the University of Iowa Hospital. 
Low effort and salary support for MRI specialist and biostatistician: With the change in study design and 
sample size, the fiscal pressure on the study emanating from the funding cap $275K/year diminished, which 
helped to increase salary support (both at 10% now) and effort allocation to experts important for the study. 
Ambitious time line. Reducing the study duration from 24 to 12 months and the sample size from 120 to 100 
will result in a more feasible timeline, allowing us to complete recruitment in the first 3 years at a pace of 3-4 
subjects/month. 

 
 
Long Term AEROBIC EXERCISE 
È
Ç CardioRespiratory Fitness (VO2max)
È
ÇNeuroplasticity 
Ç BRAIN TISSUE INTEGRITY (DTI) 
È
  ÇMotor ÇCognition ÇNon-motor
È                        È
    ÇIADL (Driving)
ÇQoL 
  Fig 1. Hypothesized effects of LTAE    
  on the brain & clinical features in PD. 
02. Specific Aims 
Parkinson’s disease (PD) culminates in dementia, immobility, and death at a huge societal cost. Even early in 
the course, motor and cognitive dysfunction impairs instrumental 
activities of daily living (IADL). Non-motor symptoms due to fatigue, 
mood, sleep, and autonomic disorders further reduce quality of life 
(QoL). DTI shows progressive decline in brain tissue integrity. Usual 
care of PD centers on medical and surgical treatments relieve 
motor symptoms, but cause side effects and lose efficacy over time. 
Usual treatment for non-motor manifestations using 
pharmaceuticals (e.g., antidepressants) is symptomatic and not 
specific for PD. Acetylcholine esterase inhibitors exert modest 
symptomatic benefits on dementia, but there is no approved 
treatment for mild cognitive impairment. Physical Therapy is usually 
prescribed in later stages when mobility impairment ensues. There 
is no approved standard exercise regimen for PD. There is no cure 
or disease modifying treatment. Thus, there is a critical need for 
treatments that provide broad spectrum of benefits and slow PD.  
Preliminary research suggests that aerobic exercise has potential to meet this need. However, aerobic 
exercise is demanding and carries some risks.  It is unknown if aerobic exercise is more beneficial than usual 
care in PD in long term due to gaps in our knowledge about the effects of cardiorespiratory fitness (CRF) on 
brain tissue integrity, motor function, cognition, IADL, QoL, and disease progression. Limitations of current 
studies include short duration, small sample size, lack or inadequacy of controls, lack of outcome measures for 
cognition and IADL, and lack of biological markers to measure progression. Our objective in this application is 
to fill the translational gap by determining the biological, clinical, and functional effects of long term aerobic 
exercise (LTAE) in PD.                                                                                                                               
Our overall hypothesis is that LTAE improves brain tissue integrity and slows down PD (Fig 1).                          
Specific Aim 1: Determine the effects of LTAE on clinical features and functional abilities in PD.  
Our 6-month, uncontrolled preliminary trial showed that aerobic exercise improves aspects of motor function, 
cognition, and QoL in PD, but long term clinical and functional outcomes are unknown. We hypothesize that 
LTAE will improve motor function, cognition, and non-motor symptoms with translation of benefits to QoL and 
IADL (driving). Driving is key for independence and depends on integrity of cognitive and motor functions. We 
will test our hypotheses with a 1-year randomized controlled trial (RCT) that compares the effects of moderate 
(i.e., aerobic) vs usual care, as in Fig 2.  
Specific Aim 2: Determine the mechanism of LTAE effects in PD.  
CRF reflects complex improvements in vascular, cardiac, and metabolic health from AE. There is preliminary 
evidence in aging that higher CRF is associated with better brain health and motor/cognitive function, and that 
aerobic exercise improves these outcomes. We showed improved clinical function and brain tissue integrity in 
the striatum and white matter on DTI in PD in our 6-months, uncontrolled, study, but it is unclear how these 
changes counteract PD progression over long term. Our hypotheses are: 1) LTAE will improve brain tissue 
integrity as indexed by DTI, 2) LTAE effects on motor and cognitive function are mediated by changes in tissue 
integrity of critical neural structures on DTI, and 3) physiological processes leading to improved CRF from 
LTAE are critical to the benefits on the brain tissue integrity and motor/cognitive function.  We will test these 
hypotheses by determining the effects of LTAE on CRF and DTI, and the association between individual 
differences in training-related changes in motor and cognitive function, DTI, and CRF.  
Impact. This proposal leverages our diverse interdisciplinary team, strong preliminary data and past work, and 
unique infrastructure to determine if LTAE slows down neurodegeneration and clinical disability in PD.  
Compliance 
Adverse Events 
Fig 2.   
Study design 
A=Aerobic 
C=Control 
Monitoring/2 weeks 
Motor, Driving 
Cognitive, DTI 
VO2max, QoL 
Motor, Driving 
Cognitive, DTI 
VO2max, QoL 
          0                                              6                                      12 months 
A
A
C
C

 
 
 
  
      Fig 1. Course of PD.34 
 
Fig 3.  Impaired information processing in drivers with PD.41 
The driver has to attend to the hazard, judge its severity, plan 
response using memory of past experiences,, and execute the 
selected action (e.g., braking), while monitoring the process for 
corrective actions. Words in red show dysfunction of key domains. 
02a. Research Plan 
A.  Background and Significance 
A.1. 
SIGNIFICANCE 
PD is a progressive devastating neurodegenerative condition without a cure or disease modifying treatment.34 
The prevalence of PD is 1% of the population over the age of 60, projected to increase by ~60% within the 
next two decades.35 The estimated annual economic cost PD is $23 billion in the US, with projected increase to 
$50 billion in the next 25 years.35    
A.2. 
PROGRESSION OF PD 
General course. PD involves dopaminergic 
and cholinergic pathways leading to motor and 
cognitive dysfunction. Involvement of other 
neuronal systems contributes to depression, 
anxiety, dysautonomia, fatigue, and sleep 
disorders resulting in a poor quality of life. 
Non-motor symptoms usually start before 
diagnosis and become a major source of 
disability in advanced phases of PD when non-
dopaminergic symptoms take the center stage 
(Fig 1). Relentless progression leads  to 
immobility and dementia, culminating in 
nursing home placement and death.34  
Motor function. Postural instability and gait 
disorder (PIGD) is the most potent indicator of 
evolving disability.36 About 50% of patients 
develop postural instability within 5 years of diagnosis,37 followed by falls and immobility over time.38  
Cognition. Mild cognitive impairment is present in ~20% of patients at diagnosis, or develops ~1/3 of patients 
within 3 years of diagnosis.39  Cognitive impairment starts with executive dysfunction, accompanied by deficits 
in attention, information processing, working memory, and visuospatial abilities, followed by memory 
impairments. Dementia is almost inevitable 
after 15-20 years, especially in older patients.38 
IADLs and driving. IADLs,40 including driving,  
are impaired even in early PD, 1,41-48 leading to  
unemployment and social withdrawal.49 Driving 
is a key IADL that allows a person to be more 
independent for work, leisure, travel and 
socialization.50  Cognitive, visual, and motor 
deficits all contribute to driving problems in PD  
(Fig 3).41,51  PD patients fail the road test in 30-
56% of cases compared to 0-24% among 
healthy aging controls.45  Eventually, PD 
patients cease driving much  earlier than their 
peers without PD.44 
Effects on brain tissue integrity as measured by DTI. The main correlates of neurodegenerative diseases 
on DTI are reduced FA, indicating loss of integrity of white matter due to the breakdown of directionally 
oriented axonal membranes, and increased MD, reflecting a reduction in density of cellular membranes that 
hinder diffusion.11 Mean diffusivity consists of radial diffusivity (rD) and axial diffusivity (aD).  In general, an 
increase in rD has been associated with demyelination whereas an increase in aD is thought to primarily 
indicate cell degradation and axonal loss.11 
Cross-sectional changes: Fig 4 shows regions of abnormal DTI in PD across 39 studies.8 DTI abnormalities 
are present in PD in frontal and parietal white matter,52 cingulum,9 striatum,10 substantia nigra (SN),52,53 and 
corpus callosum,52  and correlate with cognitive and motor function.9,54 Interestingly, diffusivity changes in the 
white matter in PD were found to be predominantly related to an increase in rD.13  

 
 
 
Fig 4. Cross-sectional DTI abnormalities in PD vs. controls. Meta-analysis of 39 studies showing 
significantly increased MD and/or decreased FA in PD (n=1087) vs. controls (n=768).8  
 
Fig 5. Longitudinal progression of DTI in PD vs. controls. Differences in regional progression rates 
between PD and controls (PFDR<0.05). Color scales indicate annual % change form baseline, separately 
for decline in fractional anisotropy (FA), increase in radial (aD) and axial diffusivity (aD).11 

 
 
Longitudinal changes: Fig 5 shows deterioration of regional DTI in SN, midbrain, internal and external capsule, 
corpus callosum, and various subcortical white matter regions over one year in early PD, while controls stayed  
stable.11 Longitudinal deterioration of DTI markers in the SN53,55 and putamen12 along with clinical worsening 
demonstrated usefulness of DTI as a biomarker of progression in PD. 
In summary, DTI is abnormal in PD (FAÈ, MDÇ, aDÇ, rDÇ) compared to controls in cross-sectional 
studies and deteriorates faster in PD than controls in longitudinal studies. 
Treatment of PD. Current treatment of PD is aimed at relief of motor symptoms using pharmacological 
replacement of dopamine and deep brain stimulation, which have side effects and lose efficacy over time. 
Pharmaceuticals exert modest and transient symptomatic benefits on dementia and there is no approved 
treatment for earlier stages of cognitive impairment. There is no cure or disease modifying treatment for PD.34
A.3. 
INNOVATION 
Our study will fill the knowledge gaps on aerobic exercise in PD by leveraging our diverse interdisciplinary 
team, strong preliminary data, and unique infrastructure. Although moderate aerobic exercise is recommended 
by the American Heart Association for 18-64 year old healthy adults, guidelines for aging and PD do not exist. 
Despite widespread belief, it is unproven that aerobic exercise improves cognition or brain health in aging or 
PD due to inconsistent results of the current studies with many methodological limitations.56  
The main innovation of the proposed study is in the comprehensive assessment of motor, cognitive, functional, 
physiologic, and neural measures in PD, and their utility to assess the impact of aerobic exercise on brain-
behavioral correlations in PD subjects, as is related to symptomology and disease progression. Motor and 
cognitive functions reflect complex dynamics in/across distributed neural systems. Studying brain-behavior 
relationships in this framework is facilitated by acquisition of multiple types of data to index complementary 
aspects of brain microstructure.  Simultaneous comprehensive assessment of behavioral, physiologic, and 
neural measures (DTI) facilitates a 'systems level' interpretation of brain-behavior relationships underlying 
disease processes and symptomology. Studies in animal models of aging and PD showed that aerobic 
exercise increases plasticity2432,57 and neuroprotective effects.33 However, mechanistic studies to identify 
neuroplastic effects of exercise in human PD are very limited. Our study will fill this gap. 
An important innovation is the use of driving as an outcome measure, capitalizing on our expertise on driving in 
PD.42-44 Driving draws on cognitive and motor functions, which are impaired in PD and can be potentially 
improved by aerobic exercise. Driving impairment signifies a broader loss of independence than just driving 
itself, as the same physical and cognitive deficits that preclude safe driving also reduce people’s ability to use 
public transportation systems.58 We will conduct road testing in a state-of-the-art instrumented vehicle.42-44  
A.4. 
EFFECTS OF AEROBIC EXERCISE IN AGING AND PD:  
A.4.i.   Aging 
Aerobic exercise is thought to benefit the aging brain by increasing plasticity, enhancing circulation,24 
increasing neurotrophins, and decreasing inflammatory processes.57 Several RCTs on aerobic exercise show 
encouraging results on cognition: Aerobic walking in sedentary elderly improved a measure of executive 
control,57 accompanied by increased connectivity and regional brain volumes in executive networks.59 The 
CRF is associated with brain integrity and function in aging independent of habitual level of physical activity.60  
Aerobic exercise on treadmill improved executive functions in MCI.61 Compared to “usual care”, a self-
administered aerobic walking program improved cognition in older adults with memory complaints.62 However, 
the landmark LIFE (Lifestyle Interventions and Independence For Elders) study with its large sample size and 
long duration failed to show cognitive benefits despite improving mobility by a combined exercise regimen 
(predominantly aerobic walking, accompanied by resistance and balance/flexibility training) .27  Still, secondary 
analyses showed potential benefit in executive function composite scores in a subgroup of subjects who were 
older and had poorer baseline physical performance.27 Overall studies in animals and humans suggest that 
aerobic exercise in aging improves cognition and its imaging and electrophysiological biomarkers.32,33    
In summary, aerobic exercise improves CRF and mobility in aging and accumulating evidence 
suggests that it also improves cognition. 
A.4.ii.   PD 
Motor function: Different exercise modalities typically benefit performance of trained domains: For example, 
aerobic exercise improves CRF (VO2max)63,64 and resistance exercise improves muscle strength.33,65 Aerobic 
walking/running has consistently increased gait speed in PD.3,63,64 However, the effects on other, not-directly 

 
 
Figure 6. Still from drive video.1, 42 
trained physical domains is less certain. The effects of aerobic exercise on the severity of parkinsonism (as 
measured by UPDRS scores) have been mixed with reports showing improvement3 or no benefit.63,64  
Cognition: We reported improvement in a measure of executive control. A study of resistance training in PD 
showed that both the resistance training and an active control group improved on various cognitive tests out of 
many. Many of the published intervention studies, including ours, had major shortcomings in design or analysis 
that preclude definitive conclusions about the efficacy of aerobic exercise on cognition. A review on effects of 
exercise on cognition highlighted various limitations in studies using aerobic, resistance, and dance 
interventions.66 Examples of problems in ascertaining cognitive effects from current studies using aerobic 
exercise in PD include: 1) Cognition not tested63,64,67,68, 2) small sample sizes (5-25 group),69-71 3) inadequate 
or no controls,3,63,64,70,71 and 4) lack of declaration of a primary cognitive outcome measure.69,70  Similar 
problems were found in a study of resistance training in PD, in which both the resistance training and an active 
control group showed improvements in various cognitive tests.72 Almost all studies with positive results report 
improvement in one or few out of many cognitive tests and do not report on composite measures or try to link 
the improvements to real life functions. In summary, almost all published PD studies reporting cognitive 
benefits from exercise need to be considered preliminary or exploratory due to mentioned reasons. This 
conclusion could probably be extended to most exercise study results on cognition in aging.73  
Similar to aging, aerobic exercise improves CRF and gait speed, but its effects on global parkinsonian 
severity, cognition, and non-motor features need further investigation. 
B.  Preliminary Studies  
B.1. 
COGNITIVE IMPAIRMENT AND DRIVING IN PD  
In a 2-year longitudinal cohort study (R01 NS044930, PI: Uc), PD patients underwent motor and cognitive 
tests annually for 2 years after  baseline. Thry scored worse in motor function,  all cognitive domains, global 
cognitive function (COGSTAT), and road safety error count 
(Table 1) compared to age matched controls at baseline.51 
COGSTAT is a composite measure of global cognition that is 
calculated by adding up standard T-scores (mean=50, SD=10) 
assigned to eight cognitive tests spanning executive functions, 
spatial perception, working memory, episodic memory, speed of 
processing, and attention.51 
Road test in an instrumented vehicle: At baseline and 2 years 
later, participants drove 18 miles across residential city streets, 
suburban commercial strips, rural two-lane highways, and a 
freeway. The instrumented vehicle had sensors to record 
electronic data (e.g., steering wheel position, vehicle speed) that 
were superimposed on video showing lane position of the car 
and forward view of the road captured by miniature cameras (Fig 6). 1,42 The videos were scored by a certified 
driving instructor for safety errors. Drivers were tested in the medication “on” state, under good visibility and 
road conditions. Drivers with PD committed more safety errors (Table 1), showed poorer navigation skills, 
recognized fewer landmarks on a visual search task, and were burdened more by multitasking at baseline 
compared to controls.42,46-48 
Two years later, 42.8 % drivers with PD and 62.7% control drivers returned for repeat road testing (p<0.01).1 
The baseline total driving error counts of PD returnees was similar to control returnees, but significantly less 
than PD non-returnees (Table 1). The PD returnees had a higher COGSTAT score than PD non-returnees at 
baseline, but still below Control returnees.1 Driving error counts are summarized in Fig 7, and Tables 1 & 2.  
Table 2 shows divergent COGSTAT scores 2 years later, in favor of controls. Please note that Table 2 only 
shows PD patients who remained active drivers and returned for all visits. While the differences in COGSTAT 
trajectory between groups became very obvious 2 years later (Table 2), there was already a significant 
difference at Year 1 after baseline (change in PD -5.4±37.5 vs. Controls 3.8±26.1, p=0.05). 
 
Control 
PD 
Control 
PD 
Measure 
All (n=110) 
All (n=67) 
NR (41) 
R (69) 
NR(39)  
R(28)
Road Error #È (median); 
30.5 
38.0*** 
29.0 
33.0 
40.0 
34.5* 
COGSTATÇ  mean (SD). 
404 (43) 
346 (78)*** 
401 (42) 
406 (44) 
330 (90) 
370 (49)* 
Table 1. Baseline comparisons.1 R=returnee, NR=non-returnee. *p<0.05, ***p<0.001. Arrow direction = better .  

 
 
 
Fig 8. Increased driving  
    cessation in PD.44 
 
Fig 7. Comparison of road errors in PD vs. Controls. 1 
R=Returnee, NR=Non-returnee; *p<0.05, **p<0.01, 
***p<0.001, NS=Not significant 
 
Baseline 
2-Year Follow Up 
Change in 2 Years 
Measure 
Control 
PD 
Control 
PD 
Control 
PD 
Road Error #È 
33.0 
34.5 
33.0 
46.5*** 
3.0 
13.5*** 
COGSTATÇ  
406 (44) 
370 (49)* 
417 (47) 
361 (55)*** 
12 (26) 
-8 (30) ** 
UPDRS-ADLÈ  
 
6.9 (2.5) 
 
9.1 (5.0) 
 
3.0 
Table 2. Data in returnees only. *p<0.05, **p<0.01, ***p<0.001 between groups. 1 
PD was at higher risk of driving cessation 
compared to controls (Fig 8): 2-year cumulative 
incidence of driving cessation was 17.6% in PD 
vs. 3.1% in controls (p<0.0001).44 Due to 
higher driving cessation rates in PD, fewer PD 
patients returned for follow up drive. PD non-
returnees had significantly poorer cognition at 
baseline compared to PD-returnees (Table 1). 
If all baseline participants had returned 2-years 
later regardless of driving status, there would 
have been a wider gulf between PD and 
controls in COGSTAT. Similar to the trajectory 
of COGSTAT, despite drop out of the more 
impaired drivers within the PD cohort, returning 
PD drivers, who drove like non-PD controls at 
baseline, showed many more driving safety 
errors than controls after 2 years (+13.5 vs. 
+3.0, p<0.001).  
Predictors of driving outcomes: Decrease in 
COGSTAT, executive functions, visual acuity and attention, and UPDRS-ADL 
score predicted increase in safety errors and future driving cessation within the 
PD group.44 Motor dysfunction predicted slow reaction times in response to 
crash hazards in the simulator.43 Crashes within the PD group were predicted 
by history of prior citations and postural instability at baseline. Of note, there 
was no difference in crash rates between PD and controls due to attrition of 
high risk PD drivers. Citations were predicted by total road error counts at 
baseline.  
In summary, drivers with PD showed poorer road safety compared to 
aging controls at baseline, which correlated with motor and cognitive 
impairments. A much larger proportion of drivers with PD stopped 
driving during follow-up. Despite attrition of the more impaired drivers 
within the PD cohort, returning PD drivers, who drove like controls at 
baseline, showed many more driving safety errors and a steeper 
cognitive decline than controls after 2 years. 
B.2. 
AEROBIC EXERCISE IN PD: Clinical effects 
We investigated effects of aerobic walking on motor function, cognition, and quality of life in PD (n=60, Hoehn-
Yahr Stage=2.0±0.4.  MMSE=29.1±1.1 at baseline).3 Subjects were required to exercise 45 min/session, 3 
times/week for 6 months. We also compared safety and fitness benefits between continuous and interval 
training The goal for continuous training was to remain within 70-80% of the age predicted maximal heart rate 
(HRmax), and for interval training to alternate between 60-70% and 80-90% of HRmax every 3 minutes. Most 
participants exercised individually on a neighborhood trail or track at their convenience. They wore electronic 
heart rate and walking speed monitors during each session. We randomized 43 participants to continuous or 
interval training in the first 2 years. Three subjects in the interval group dropped out due to knee pain 
(improved with rest), while none of the continuous group subjects had this problem (p=0.196). There was no 
difference in efficacy on aerobic fitness between interval and continuous arms. Therefore, the next 17 
participants in the third year were allocated only to continuous training. Eighty-one percent completed the 
study, 7 of 11 drop-outs were due to reasons unrelated to the study. There were no serious adverse events. 

 
 
Fig 9. Improved (È) rD in white matter, pFDR<0.05.6 
Outcome 
Pre 
Post 
Change 
VO2max (ml/min/kg)n 
25.4±6.6 
27.0±7.0 
1.56±2.74*** 
7m Walk (seconds)p 
9.4±1.5 
8.7±1.4 
-0.85±0.94*** 
MoCAn 
24.5±3.0 
24.8±3.3 
0.5±1.9† 
Flanker (PIS)p 
25.5±10.1 
22.0+±10.2 
-3.7±8.2** 
Fatigue (FSS)p 
4.1±1.1 
3.6±1.3 
-0.5±1.1** 
Depression (GDS)p 
5.3±3.6 
4.5±3.8 
-0.8±2.6* 
Quality of Life p 
41.7± 8.9 
40.6±9.3 
-1.1±4.2** 
UPDRS - Mentalp 
2.1±1.9 
1.6±1.3 
-0.5±1.6* 
UPDRS - ADLp 
9.3±4.9 
8.8±4.6 
-0.2±2.5 
UPDRS - Motorp 
18.8±10.4 
15.9±8.4 
-2.8±7.1** 
Table 3. Select results from our exercise study.3  *p<0.05, 
**p<0.01, ***p<0.001, †p<0.1. Arrow  direction = better
Results: Completers attended 83.3% of the required sessions and achieved a mean exercise intensity of 
46.8% of their heart rate reserve 
(HRR),74 equivalent to 69.7% of age 
predicted HRmax.74 As treatment groups 
did not differ in baseline characteristics, 
adherence, exercise intensity, and 
VO2max gains, we pooled all completers 
for analysis, and observed significant 
improvements in VO2max, gait speed, 
UPDRS scores, executive functions, 
depression, and fatigue scores, and 
quality of life score (Table 3) after 
adjusting for dopaminergic treatment.3  
Increase in VO2max correlated with 
mean exercise intensity and walking 
speed. Multiple linear regression models 
showed that improvements on both the 
flanker task and quality of life score were 
significantly associated with increase in VO2max and tended to be associated with lower VO2max at baseline.
B.3. 
Aerobic exercise in PD: DTI effects
We found that CRF (VO2max) correlated directly 
with FA in putamen, substantia nigra, cingulum, 
hippocampus, and various other white matter 
regions at baseline(p<0.05 to p<0.01).4,5 CRF 
correlated inversely with MD and rD of the putamen, 
superior longitudinal fasciculus, cingulum, posterior 
and superior corona radiata, anterior internal 
capsule at baseline (p<0.05 to p<0.001).4,5   
After the 6-months aerobic exercise, we observed 
decreased MD in the putamen and in the left 
superior longitudinal fasciculus and superior corona 
radiata, and increase in FA in the posterior corona 
radiata (pFDR<0.05, adjusted for multiple 
comparisons using False Rate Discovery-FDR).4,5  
There was tendency for increased FA in the left 
caudate and putamen (p=0.1). 5 Further analysis of 
diffusivity showed significant (pFDR<0.05) decrease in 
the rD in bilateral cingulate gyrus and superior 
longitudinal fasciculus, left anterior limb of internal 
capsule, posterior corona radiata, and superior 
corona radiata (Fig 9), but no significant decrease in 
axial diffusivity (index of axonal integrity) in any 
region, suggesting that aerobic exercise might be primarily benefiting the myelin.6  
The lack of a control group in our preliminary study may have underestimated the improvement of 
microstructural tissue integrity by aerobic exercise, as a control group is expected to show the natural decline 
in tissue integrity.11,53 For example, compared to normal aging, PD was associated with  up to 8.0% increase in 
rD over 1 year.11 Within PD group only, the rD increased up to 4% over 1 year across different regions.11 As 
shown in the Table in the Introduction, we had many regions that showed significant decrease in rD after 
aerobic exercise. Considering the contrast between the significant 1.3-1.5% decrease of rD in our primary 
regions of interest during our exercise study6 and  an average of 1.7%  increase of rD in a usual care group,11 
the effect size in the proposed study likely will be much higher than in the preliminary study.
In summary, our preliminary, uncontrolled, 6-month study suggested that striatal and white matter 
tissue integrity is associated with CRF, and that aerobic exercise might improve clinical symptoms and 
reverse the decline of DTI in PD, suggesting increased tissue integrity (neuroprotection?).

 
 
 
Fig 11. Electronic heart rate monitoring 
output from our preliminary study:3 A 45 
min exercise session with mean HR 119.
C.  Research Design and Methods: 
C.1. 
SUMMARY  
Design: Single-blind, parallel 2-group RCT with outcome measures as in Table 4. Please see Human Subjects 
section for detailed schedule of activities (Table 3) and visit day schedules. 
Target population: Stable, non-demented 
(per Movement Disorder Society Level I 
criteria75), currently driving patients with 
idiopathic PD (age 40) in Hoehn-Yahr 
stages I-III with no contraindication to 
exercise. To increase the yield of the 
interventions, we will select subjects whose 
CRF (VO2max) is <90th percentile of their 
age and gender based norms76 (details in Human Subjects).  These criteria will limit eligibility to those who are 
most likely to benefit, but yet are safely able to participate in the study.  Subjects are allowed to continue 
existing physical training programs or PT as long as they meet the VO2max and other criteria as randomization 
will balance potentially confounding activities between the groups. We will keep track of extra-study 
rehabilitation programs or exercise using patient diaries and biweekly phone calls, and incorporate their 
characteristics in analysis of outcomes. We believe that being too restrictive over a one-year period can hinder 
recruitment or increase risk of withdrawal from the study. We have a plan to manage participants who require 
absences or breaks for travel or illnesses, as documented in the section 04.2.b. in Human Subjects titled 
“Protocol for Managing Illness/Injury and Other Health Problems or Travel”. 
C.2. 
INTERVENTIONS  
C.2.i.   
Active Interventions: Aerobic walking  
We will use self-administered continuous walking exercise at a moderate intensity level, defined as 40-59% of 
heart rate reserve or 64-77% of heart rate at gas exchange threshold (HRGET) by ACSM74  as in our preliminary 
study.3 The HRGET will be determined as the heart rate at VO2max during graded cycle ergometry.3 Using 
measured rather than estimated (e.g., by age) HRmax has the advantage of incorporating the effects of PD, 
comorbidities, and medications (e.g., beta-blockers) into the prescribed target intensity.74 The total duration of 
the exercises will be 150 min/week per 2008 Physical Activity 
Guidelines for Americans and American Heart Association 
recommendations,74 conducted in three 50 min sessions. The 
aerobic walking intervention will take place outdoors (e.g., trails, 
sidewalks, parks) or indoors (e.g., track in a local gym or a mall) 
depending on the preferences of the subject and weather. 
Session duration will be 20 min the first week and will be 
advanced by 5 min per week over 6 weeks. As the sessions are 
not directly supervised, we will have the subjects advance the 
exercise intensity using perceived levels of exertion to avoid 
adverse events.  Using Borg’s scale, that ranges from 6 to 20,77 
participants will be asked to walk at an intensity of 13 (activity 
perception SOMEWHAT HARD), consistent with moderate 
intensity.77 They will be discouraged from exercising at levels 
that approach or exceed 15 (HARD) or drop to a rating of 11 
(LIGHT) or below. Completely sedentary (less than 20 min 
exercise/week), will be started at <12 (light intensity, about 30% 
of HRR).  The heart rate monitor will beep if the subjects exceed 59% off HRR or drop below 30% of HRR to 
CRF 
VO2max 
Motor 
OFF period MDS-UPDRS Motor Subscale score 
Cognition 
Flanker task performance (PIS) 
Driving  
Total road safety error count 
QoL/Non-Motor  PDQ-39; MDS-UPDRS Non-motor EDL scale  
DTI 
rD in putamen, SLF, and cingulum  
Table 4. Main outcome measures for each category.  
Compliance 
Adverse Events 
Fig 10.   
Study design 
A=Aerobic 
C=Control 
N=100 (50/group)
Motor, Driving 
Cognitive, DTI 
VO2max, QoL 
Motor, Driving 
Cognitive, DTI 
VO2max, QoL 
          0                                              6                                      12 months 
A
A
C
C

 
 
provide them with real time guidance during exercise.  The ACSM guidelines for exercise prescriptions74 will be 
followed to avoid exercise related injury.  
Monitoring: The subject will fill a simple diary about each exercise session. They will wear a Polar A300 heart 
rate monitor during training sessions that has been programmed to provide them with exercise duration, their 
current heart rate, and signals to slow down or increase the heart rate to remain within the prescribed window, 
similar to our preliminary study.3 We will also allow aerobic exercise subjects to engage in additional extra-
study physical activity if they desire as this motivation could be an outcome of aerobic exercise itself. 
Fig 11 shows the HR monitor printout of an actual exercise session from our preliminary study, where we 
captured 96% of all sessions electronically, confirming the  exercise intensity  and showing a significant 
correlation between exercise intensity and VO2max gain.3 We will have the subject weekly synchronize the 
heart rate monitor data with Polar Flow web service using a personal computer, tablet, or smartphone.  
Approved study personnel will have access to Polar Flow web service data. We will call the subject biweekly 
for a structured interview to review progress, compliance, and adverse events, and extra-study physical activity 
which will also be reviewed during the study visits every 6 months.  
C.2.ii.   
Control intervention: Usual Care with PD specific health education. 
Recent clinical trials on exercise in PD employed either no control group at all,63 or their control group also 
engaged in an exercise program.64,65 Lack of a non-exercise intervention control group makes it difficult to 
evaluate the true effect of the tested exercise program as other exercise interventions may have provide 
benefits beyond placebo and reduce the effect size of the investigated intervention. Furthermore, a non-
exercise control group would reveal a more realistic effect size of the proposed intervention in the community 
as it reflects the lifestyle of the majority of the population (65% of adults in North America are sedentary).78  
Usual care for PD consists of medical and surgical treatments for motor and non-motor symptoms, and PT/OT 
for specific prescribed indications. There is no standard of care for behavioral interventions such as exercise. 
The usual care for behavioral interventions typically involves patient education by in-person discussion, 
providing pamphlets/booklets and on-line resources to learn about PD and encourage healthy living.  
In this study, patients will receive their usual medical treatment for motor and non-motor symptoms from their 
primary neurologist. We will use a streamlined form of PD specific health education prepared by the VA: My 
Parkinson's Story, which consists of a series of short videos prepared by the VA PADRECCs addressing 
various aspects of PD. These 6-12 minutes long videos are freely available on YouTube79. We can also 
provide them on a CD if subjects desire. They start with a patient testimony about the topic of the episode, 
followed by comments of experts in the field. These videos are popular with our patients and on YouTube 
(some had > 70,000 views. The title of the episodes are: Early Parkinson's, Medications,  Exercise, Memory,  
Visual Disturbances,  Depression, Sleep,  Speech and Swallowing, Impulsive Behaviors, Driving,  Pain, 
Dyskinesias, Deep Brain Stimulation, Advanced Parkinson's Disease, Falls, The Caregiver, Hospitalization, 
Genetics, Environmental Exposure, Atypical Parkinsonism. Dr. Uc was one of the contributors (“Driving” 
episode) to this program. We will ask the subjects to watch one of these videos every 2 weeks that can be 
discussed during biweekly monitoring phone calls. 
The control subjects will be free to continue their existing exercise habits or to get involved in new programs on 
their own. We will also allow aerobic exercise subjects to engage in additional extra-study physical activity if 
they desire as this motivation could be an outcome of aerobic exercise itself. This liberal approach to extra-
study physical activity will likely improve recruitment and retention, and mitigate potential ethical concerns of 
depriving subjects of physical activity. However, we will document extra-study physical activity very carefully by 
providing all subjects with exercise diaries to keep track of their exercise modality, frequency, and intensity. We 
will also inquire about extra-study physical activity during biweekly phone calls. Furthermore, VO2max and 
detailed motor testing at baseline and 12 months will document the change in their aerobic fitness, strength, 
and balance. We will use diary and physical performance test results in exploratory analyses to see if extra-
study physical activities might have affected outcomes. However, CRF is the associated with brain integrity 
and function in aging independent of  physical activity, suggesting that, unless it is a sustained aerobic  
program, extra-study physical activity would have little bearing on our outcomes.60 We realize that this control 
arm is not well matched with the active arm in terms of time spent while engaging in the intervention itself. 
However, we will match social attention between both groups by contacting all subjects from both groups 
biweekly about their physical activity, general health, adverse events, medication changes, and other concerns 
that come up.   

 
 
C.2.iii.   
Concomitant drug treatment and other activities during the study  
Patients will continue to be managed by their treating neurologists and primary care providers. Due to the long 
duration of the study, changes in PD medications and other medications will be allowed. However, we will keep 
track of medication changes in key categories such as dopaminergics, antidepressants, anxiolytics, 
antihypertensives, anticholinergics, and choline esterase inhibitors that can affect the outcome measures. We 
will study the influence of baseline treatments and their changes during the trial using regression analyses. 
Subjects will be allowed to continue their ongoing physical activities, but we will keep track of them using 
physical activity diaries/phone calls, and use this information as covariate in exploratory regression analyses. 
These statistical approaches and randomization are expected to explain and minimize the effect of 
concomitant treatment and activity on the outcomes. 
C.3. 
APPROACH TO SPECIFIC AIMS:  
 
C.3.i.      Aim 1:  Determine the effects of LTAE on clinical features and functional abilities in PD.  
Hypothesis:   
LTAE will improve motor function, cognition, and non-motor symptoms with translation of benefits 
to QoL and IADL.  
Approach: We will analyze differences in changes on clinical outcome measures between the aerobic 
exercise and usual care control groups in a one-year, single blind RCT.  
C.3.i.a. 
Motor Function  
Primary outcome measure: MDS-UPDRS motor examination subscale (Part III) score in the “practically 
defined OFF state”, i.e., after overnight (~12 hours) withdrawal of PD medications.34 
Secondary outcome measures: These measures will be obtained in the ON state. 
1) MDS-UPDRS motor examination subscale (Part III) score and MDS-UPDRS motor experiences of daily 
living subscale (Part II) score (completed by the patient).  The MDS-UPDRS is a validated core instrument 
of NINDS Common Data Elements.80  It is structured on original UPDRS but provides much broader coverage 
of non-motor features and incorporates of patient-reported outcomes to enhance the relevance and validity of 
study results. It shows greater sensitivity for change compared to original UPDRS, and correlates strongly with 
changes in quality of life and non-motor features.80 Please note that we will also score subjects using the 
original UPDRS (subscales I-III) for comparability to past research. This additional scoring will not increase the 
burden on the subject as the exam and questionnaire items are overlapping and the original UPDRS has 
lesser items to score on. 
2) NIH Toolbox motor battery includes 5 subdomains the following valid and reliable measures that are 
simultaneously low-cost and portable:811) Dexterity (9-hole peg board); 2) Strength (grip dynamometry for 
upper-extremity strength); 3) Balance (standing balance test); 4) Locomotion (4-m walk test for gait speed); 
and 5) Endurance (2-minute walk; distances covered over 2 and 6 minutes are highly correlated).82    
C.3.i.b. 
Cognition 
Primary outcome measure: Due to its sensitivity to changes in aerobic fitness57,83 (including in our preliminary 
study3), we chose change in Percent Increase Score (PIS) on Eriksen’s flanker task, which measures the cost 
of conflict resolution between the incongruent and congruent stimuli. Participants will  be asked to identify the 
orientation of a central arrow cue (‘<’ or ‘>’), which is flanked on both sides by two arrow cues that either point 
in the same direction (congruent: <<<<<) or a different direction (incongruent: >><>>). Using reaction times 
(RT) during congruent and incongruent trials, the PIS will be calculated as =((RT_incongruent – RT_congruent) 
/ RT_congruent) * 100.57 
Secondary outcome measures: COGSTAT, a composite measure of cognition, calculated by assigning and 
summing standard T-scores (mean=50, SD=10) to eight tests from the cognitive test battery we used in our 
driving studies,1 will be the main secondary outcome measure. This cognitive battery will enable us to probe 
multiple domains: Complex Figure Test—Copy (CFT-Copy) Version, Block Design for visuospatial 
construction; Trail-making Test (B-A), a measure of set shifting and  Controlled Oral Word Association Test 
(also tests language) for executive functions; Rey Auditory Verbal Learning Test (anterograde verbal memory), 
CFT–Recall is administered 30 minutes after the CFT-Copy (visual memory), Benton Visual Retention Test-
errors for memory;  Judgment of Line Orientation for visual perception.1  
All cognitive and driving tests will be conducted in the ON state. 

 
 
C.3.i.c. 
Non-motor symptoms & quality of life 
MDS-UPDRS Non-motor Experiences of Daily Living subscale (Part I) score will be the main non-motor 
outcome measure. As depression, anxiety, sleep, and fatigue are only covered by one item in this subscale, 
we will employ specific scales measure them further: Geriatric Depression Scale (GDS),51 Beck Anxiety 
Inventory (BAI),84 PD Sleep Scale version 2 (PDSS-2),85 and Fatigue Severity Scale (FSS).3 QoL will be 
measured by the summary index of the Parkinson’s Disease Questionnaire-39 (PDQ-39).86  
C.3.i.d. 
IADL (Driving) 
We will use driving as the outcome for IADL. Driving represents an important symbol for independence, and 
depends on integrity of cognitive and motor systems.  
Road testing: The primary outcome measure will be the total number of safety errors during a standardized 
experimental drive in an instrumented vehicle per our well established protocol described in preliminary data 
section, published papers, and Human Subjects section. The drive videos will be scored by a certified driving 
instructor for safety errors. 
Real world driving outcomes: Time to driving cessation, first citation and crash will be determined as in our 
prior work using Driving Habits Questionnaire,44 clinic and state driving records.44 The earliest evidence of 
driving cessation will be used to calculate elapsed time since baseline. For cases without evidence of driving 
cessation, we will use the last date of known driving as the censoring time for this outcome.  
  
C.3.ii.   
Aim 2: Determine the mechanism of LTAE effects in PD. 
 
Hypotheses:  
1. LTAE will improve brain tissue integrity as indexed by DTI. 
Approach: We will analyze differences in regional rD changes between the aerobic exercise and usual 
care control groups in a one-year, single blind RCT.  
Primary outcome regions of interest:  Putamen, Cingulum, Superior Longitudinal Fasciculus 
Secondary outcome regions of interest:  Diffusion imaging tractography of                                                 
Motor:  Substantia nigralputamen (nigrostriatal tract) and putamenlpremotor cortex 
Cognitive: Dorsal lateral prefrontal cortex (DLPFC)lcaudate and the parietal cortexlprefrontal cortex.   
2. LTAE effects on motor and cognitive function are mediated by changes in tissue integrity of 
critical neural structures on DTI. 
Approach: We will perform regression analyses to determine associations between rD changes in 
primary regions of interest and changes in outcomes for motor and cognitive function across groups.  
The primary outcome measure for motor function is the MDS-UPDRS motor score in the OFF period. 
The primary measure for cognitive function is PIS (Percent Increase Score) on the flanker task. 
3. Physiological processes leading to improved CRF from LTAE are critical to the benefits on the 
brain tissue integrity on DTI and  on motor/cognitive function.  
Approach: We will perform regression analyses to determine associations                                                 
1) between CRF (VO2max) changes and rD changes in primary regions of interest, and                              
2) between CRF changes and changes in primary outcomes for motor/ cognitive functions.                                 
C.3.ii.a. Imaging Methodology 
We will use DTI, a biomarker of structural connectivity and brain tissue integrity, as primary imaging measure 
based on our preliminary data.4,5 DTI will be performed in the practically defined OFF state (withholding 
medication overnight for 12 hours) to avoid potential confounding effects from dopaminergic treatments.8  
Alternatively, positron emission tomography (PET) or single photon emission computed tomography (SPECT) 
of the dopaminergic system could have been used,87 but they are invasive, more expensive, and more likely to 
be affected by concomitant medications. Furthermore, data on other aspects of brain structure and function 
can be collected while subject is in the MRI scanner. For example, we will also acquire MR images for 
secondary neuroimaging measures of functional connectivity (resting-state fMRI BOLD)88 and neuronal
metabolism (T1ȡ),89,90 and volumetry of the striatum  as performed by members of our team in prior work 
(details in Dr. Voss88, Dr. Magnotta30,31,89,90).   

 
 
 
Fig 12. T2 weighted images from 3T (left) and 7T (right). The 
delineation of substantia nigra (SN) is superior at 7T vs. 3T.  
The primary DTI outcome measure will be the change in rD in putamen, cingulum, and SLF (Mori et al, 20087) 
as we have observed significant changes in these nodes in our preliminary study along with significant 
correlations with baseline aerobic fitness4-6 and several clinical features (Table in the Introduction). These 
regions have been shown to have decreased FA and increased diffusivity in PD in cross-sectional and 
longitudinal studies.8-12 Mean diffusivity changes in the white matter in PD were found to be predominantly 
related to an increase in rD,13 which increases importance of our finding that we found improvements mainly of 
rD in various regions  rather than aD.6  The putamen is the rostral terminus of the nigrostriatal pathway, but 
also pathology in cingulum and SLF play an important role in clinical manifestations of PD:  The severity of 
postural instability and gait disorder correlates with increased diffusivity (especially rD) in the SLF;14-16 DTI 
abnormalities in cingulum and SLF are associated with cognitive dysfunction.14,17,18,18-24 Furthermore, white 
matter microstructure mediates the relationship between CRF and cognition in older adults.25 Also, SLF 
integrity is associated with flanker task performance26 as in our preliminary study (Table). Flanker task 
performance is also the primary cognitive outcome measure in this proposed study.   
The proposed motor and cognitive tracts in the prior submission will be used as secondary outcomes. Among 
them is the nigrostriatal track: DTI of SN is abnormal in PD and deteriorates with disease progression.11,53,55 
DTI changes in SN are associated with bradykinesia, cognition,53 and dopaminergic deficit.11 The SN will be 
delineated by 7T MRI. 
Imaging protocol: Participants will undergo anatomical (T1 and T2), resting state fMRI, and DTI using a GE 
750W 3T scanner (Waukesha, Wisconsin). Three-dimension (3D) T1 weighted images will be collected using 
IR-prepped gradient echo sequence (BRAVO) in the coronal plane with the following parameters: TI=450ms, 
TE=3ms, TR=8.5ms, flip angle=10, FOV=256x256x240mm, matrix=256x256x240, bandwidth= 244Hz/pixel, 
acceleration=2. Sagittal 3D T2-weighted images will be collected using a variable flip angle fast spin-echo 
sequence (CUBE) with the following parameters: TE= 90ms, TR= 3000ms, echo train length=130, 
FOV=256x256x176mm, matrix=256x256x176, bandwidth= 488Hz/pixel, acceleration=2. Resting state BOLD 
will be acquired in the axial plane using a T2*-weighted multi-band gradient-echo echo-planar sequence: TE = 
30ms, TR=2000ms, Flip Angle=75°, FOV=256x256mm, Matrix=128x128, slice thickness/gap=2.0/0.0mm, 
BW=2000 Hz/pixel, multi-band factor=3, Acceleration=2. Diffusion weighted images will be collected using a 
twice refocused echo-planar spin-echo sequence with four b0 images: TE=88ms, TR=6000ms, 
FOV=256x256mm, Matrix=128x128, slice thickness/gap=2.0/0.0mm, BW=2000 Hz/pixel, multi-band factor=3, 
Acceleration=2, b-value=1000s/mm2, and # diffusion directions=64.  
7T Imaging protocol: Images will be 
collected on a GE 950 scanner using a 
8Tx/32Rx head coil. The study will 
collect a localizer and high-resolution 
anatomical T2 weighted image for 
delineation of the substantia nigra. The 
T2 weighted sequence will be collected 
using a 3D CUBE sequence in the 
sagittal plane (TE=93, TR=2500, echo 
train length=100, Bandwidth=244 
Hz/pixel, FOV=230x230x192, 
Matrix=384x384x320, acceleration=2.5).  
Example image quality for this scan is 
shown in Figure 12.   
Anatomical Image Analysis: The 
volumetric anatomical T1- and T2-
weighted images will be analyzed using 
BRAINS AutoWorkup.91 The analysis 
will provide automated definitions of subcortical gray matter using the Stable Atlas-based Mapped Prior 
[STAMP] machine-learning segmentation30,31 to identify putamen and caudate nucleus as in our preliminary 
study.5 The original images and the automated pipeline results will be checked for validity by a technician blind 
to group and visit category.  The substantia nigra will be automatically segmented from the 7T datasets using 
an ANTS registration toolbox92 and a basal ganglia atlas,93 which includes the substantia nigra and red 
nucleus. The white matter regions of interest (SLF, cingulum, and others) will be identified per White Matter 
Parcellation Map as described by Mori et al (2008)7 and performed in our preliminary study.4 
SN
SN

 
 
 
Fig 13. Premotorlputamen 
tract (left) from our past work.97
Diffusion Analysis: Quality assurance of the diffusion weighted data will be performed using DTIPrep.94 After 
motion and eddy current correction, data will be fit to a tensor model allowing derivation of rotationally invariant 
scalar measures (FA, MD, aD, rD). Scalar measures will be analyzed using both voxel based and ROI 
approaches based on participation on motor and cognitive functions, as well as documented vulnerability to 
degeneration11,53 and potential for regeneration with exercise in our preliminary study.4-6 
Diffusion Imaging tractography: Whole brain fiber tractography will be performed using an unscented Kalman 
filter.95 The fiber tracts connecting the nodes of the motor and cognitive 
networks will be defined using the White Matter Query Language96 by 
including fiber tracts that start and end within nodes of interest. For the 
motor networks, fiber tracking will be performed for the substantia 
nigralputamen (nigrostriatal tract) and the putamenlpremotor cortex 
regions of interest defined via direct anatomical imaging. The 7T data will 
be used to delineate the substantia nigra due to its increased resolution 
and greater sensitivity to iron concentrations. For the cognitive networks, 
fiber tracking will be generated for the dorsal lateral prefrontal cortex 
(DLPFC)lcaudate and the parietal cortexlprefrontal cortex connection.  
Median rotationally invariant diffusion scalars will be calculated for the 
fiber tracts. In our past work on Huntington’s disease (Fig 13), we 
showed  cross-sectional and longitudinal abnormalities of premotor-
putamen connections.97  
C.3.ii.b. 
Cardiorespiratory Fitness Assessment 
We will test VO2max pre-post intervention to investigate the effect of CRF changes on brain tissue integrity and 
clinical function and to show that our intervention was delivered successfully. Oxygen uptake will be measured 
from expired air samples on a breath-by-breath basis during cycle ergometry. We will verify maximal effort for 
VO2max when 2 of 3 criteria were met: 1) a plateau in VO2 between two or more workloads, 2) respiratory 
exchange ratio 1.10, and 3) heart rate > 85% of the age-predicted maximum heart rate (220-age).3 
C.4. 
STATISTICAL ANALYSIS PLAN 
C.4.i.   Sample Size, Power, Randomization, Descriptive Analyses.  
Design: We will determine the effects of LTAE on clinical features and brain tissue integrity in PD in a 2-arm, 
parallel group, one-year, single-blind, RCT (Fig 10).  
Planned sample size and anticipated power:  
We are planning to recruit N=100 (50/group) that would provide 80% power to detect an effect size of Cohen’s 
d’=0.7 (equivalent to f=0.35 or a correlation of r=0.33 or R2=0.11) at a 2-sided Į=.05 after taking into 
consideration that 10% of subjects may be lost to follow up and 25% might discontinue the intervention.  
The effect size estimates for various outcome measures have been detailed in the Introduction. Briefly, our 
effect size estimates aerobic exercise vs. usual care are Cohen’s d’ ~1.09 for CRF (VO2max), ~0.9 for motor 
UPDRS, ~0.76 for flanker task (PIS), ~0.7 for driving (road test error counts), and ~1.09-1.46 for rD on DTI of 
selected regions.  Thus, we chose Cohen’s d’=0.7 as the lowest common effect size estimate, which would 
require an effective sample size of N=68 (34/group) to provide 80% power at a 2-sided Į=.05.To ensure an 
effective sample size of N=68 at a 25% rate of discontinuation of the intervention, we would need to recruit 
N=90 subjects. If we incorporate 10% of loss to follow up, we would need to recruit N=100. 
Randomization. Block randomization will be used in order to assure that the number in both groups will be 
similar throughout the study period, i.e., a split proportional to 1:1 is constrained within each block. The actual 
size of the blocks will be randomly chosen to be size 4 or 8, so that the assignment of the next participant 
cannot be accurately predicted by the study recruiters. Furthermore, to ensure that study recruitment remains 
unbiased, the assigned group will only be revealed after the participant has signed the consent form to indicate 
a willingness to accept the randomly chosen assignment. 
Descriptive analyses: We will use descriptive statistics (means, SD, medians, etc.) and graphical techniques 
(boxplots, scatter plots, histograms, etc.) to explore the data, check for outliers, and examine distributions. We 
will compare baseline characteristics between groups using Fisher’s Exact tests for categorical variables, and 
analysis of variance or the Kruskal-Wallis test for continuous variables, depending on the normality. 

 
 
C.4.ii.   Analysis of Efficacy and Associations  
C.4.ii.a. 
General considerations: Statistical Models, ITT principle 
The outcome measures will be obtained at baseline and after completing the intervention in 12 months. Mixed 
effects longitudinal regression models, multiple linear regression, and survival analysis methodology (Kaplan-
Meier plots and Cox regression) will be used depending on the outcome measure as detailed in the Analysis 
section of each specific aim. Significance will be set at an overall 2-sided Į level of .05.  
The two groups will be compared using mixed effects longitudinal regression models (a generalization of 
repeated-measures ANCOVA). These models will accommodate flexible correlation patterns of the repeated 
measures and additional covariates and factors such as age, sex, education, LEDD, use of medications that 
can affect the tested study outcome (e.g., antidepressants, antihypertensives, acetylcholine esterase 
inhibitors), extra-study physical activity, and relevant interactions. 
The ANCOVA model with estimated effect size of f=0.35 (equivalent to Cohen’s d’=0.7 or adjusted R2=0.11, 
medium correlation of r=0.33) will allow examination of up to 10 independent variables at 80% power for 
determining the unique variance accounted for by demographic features, CRF and its change, regional rD and 
its change, concomitant dopaminergic and other relevant treatments, extra-study physical activity, and 
additional covariates as needed. Similar models will be used for exploratory analyses of secondary outcome 
measures in each outcome category. 
Intention-to-treat” principle: All subjects will be analyzed according to their randomization group, regardless of 
whether they comply with intervention protocol or not. Those who stop participating in the interventions will be 
invited back at 12 months to provide outcome measures. Based on our experience and the literature,64,65 we 
expect ~70-80% of the subjects to fully complete the study according to the protocol and ~90% of subjects to 
provide 12 month data for ITT approach. In the event of missing data, we will perform “completers only” and 
“compliers only” analyses, as well as other sensitivity analyses, to see whether the results are robust to non-
ignorable missing data mechanisms.  
C.4.ii.b. 
Effects on Clinical Features: Motor and Cognitive Function, Non-motor 
Symptoms and Quality of Life, and IADL (driving) 
Motor function: The dependent variable in the repeated-measures ANCOVA model will be the post-training 
OFF period MDS-UPDRS motor subscale score. The baseline OFF period MDS-UPDRS motor subscale score 
will be entered into the model as a continuous variable and the treatment group (aerobic exercise or usual 
care) will be entered as a categorical variable. To assess possible impact of other relevant characteristics, we 
will enter demographic features, concomitant treatment, extra-study physical activity as covariates and factors 
into the model in supplementary analyses. 
Cognitive function/Driving/Non-motor symptoms and Quality of Life: The dependent variable in the repeated-
measures ANCOVA model will be the post-training value of the primary outcome measure of the tested 
domain as outlined in C.3.i. (e.g., PIS on flanker task for cognition, road test error counts for driving, etc.). 
Similar modeling approach will be used as above. 
Real world driving outcomes: We will use survival analysis methodology (Kaplan-Meier plots and Cox 
regression) to analyze the difference in the time to driving cessation among groups after adjusting for baseline 
characteristics of age, sex, and education.44 Similar methods will be used for time to first crash and citation.44  
C.4.ii.c. 
Effects on regional DTI 
The dependent variable in the repeated-measures ANCOVA model will be the post-training rD value of the 
primary outcome region (e.g., putamen, cingulum, or SLF). The baseline rD value of that region will be entered 
into the model as a continuous variable and the treatment group (aerobic exercise or usual care) will be 
entered as a categorical variable. To assess possible impact of other relevant characteristics, we will enter 
demographic features, concomitant treatment, extra-study physical activity as covariates and factors into the 
model in supplementary analyses. 
This approach will be repeated for other primary and secondary outcome regions. All results will be adjusted 
for multiple comparisons using FDR (p<0.05, corrected). 
C.4.ii.d. 
Association of Clinical Changes with DTI Changes 
Motor function: The dependent variable in the repeated-measures ANCOVA model will be the post-training 
OFF period MDS-UPDRS motor subscale score. The baseline OFF period MDS-UPDRS motor subscale score 

 
 
and the 'rD of putamen (change after intervention) will be entered into the model as continuous variables. This 
will show if change of rD in the putamen predicts (correlates with) change in the motor function. This across 
groups analysis will make use of pooled data from both groups providing increased range and variance to 
detect correlations. To assess possible impact of other relevant characteristics, we will enter the baseline rD of 
putamen, demographic features, concomitant treatment, extra-study physical activity as covariates and factors 
into the model in supplementary analyses. A similar approach will be used for other primary and secondary DTI 
regions specified in C.3.ii.a. Their 'rD values will be entered into to the model instead of putamen. 
Cognitive function: The dependent variable in the repeated-measures ANCOVA model will be the PIS on 
flanker task. Similar modeling approach will be used as above. 
C.4.ii.e. 
Association of Clinical and DTI Changes with CRF Changes 
Motor function: The dependent variable in the repeated-measures ANCOVA model will be the post-training 
OFF period MDS-UPDRS motor subscale score. The baseline OFF period MDS-UPDRS motor subscale score 
and the 'CRF (change of VO2max after intervention) will be entered into the model as continuous variables. To 
assess possible impact of other relevant characteristics, we will enter the baseline CRF, demographic features, 
concomitant treatment, extra-study physical activity as covariates and factors into the model in supplementary 
analyses. A similar approach will be used for other primary and secondary DTI regions specified in C.3.ii.a. 
Cognitive function and DTI: A similar modeling approach will be used as above, but the dependent variable 
and its baseline value will reflect the outcome measure tested. For example, for cognition, the dependent 
variable will be post-training PIS on the flanker task. For DTI, the dependent variable will be post-training rD 
value of the putamen (or one of the other primary regions). 
C.5. 
TIMELINE OF THE STUDY
 
Due to our experience with operational details, subject recruitment will start immediately upon receiving 
funding as shown below (Table 5). We expect to phone screen 15-20 subjects per month, resulting in 6-8 visits 
for in-person screen, and enrollment of 3-4 subjects per month. Intervention and testing will continue 
throughout the study period. 
Activity 
Months 
01-06
07-12
13-18
19-24
25-30 31-36 37-42
43-48
Rolling enrollment/Intervention/Testing 
++++++ 
Data and Safety Monitoring Board meeting
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
Analysis/report, grant writing 
 
+ 
Table 5. Approximate timeline of the study. 
C.6. 
LIMITATIONS, POTENTIAL PITFALLS AND MITIGATION SRATEGIES 
1. Recruitment. We are confident that we can reach our recruitment goal because of the large number of PD 
patients followed bu our institutions. Our VAMC, a VA PD Consortium site, and  our sister institution University 
of Iowa Hospitals & Clinics (UIHC) each saw more than 1,000 unique PD patients in 2017. We will place IRB 
approved newspaper ads and disseminate information in social and educational events for PD.  
2. Attrition. The study has low risk interventions with <3 hr/week time commitment. We will advance exercise 
intensity very gradually to reduce drop-outs due to adverse events and intolerance. We will ensure subject 
comfort during the visits and compensate for their time. Our sample size estimate incorporates attrition risk.  
3. Safety of self-administered physical exercise. We had no serious adverse events in the preliminary 
study.3 Our selection criteria exclude patients at high risk for falls and cardiovascular events. Our exercise 
regimen is cautious and gradual. We will complete a full clinical evaluation including exercise stress test on 
cycle ergometry to rule out contraindications.  
4. Failure to support mechanistic hypotheses using DTI. Our secondary imaging measuressuch as 
functional connectivity, neuronal metabolism, and volumetric analyses will provide alternative approaches to 
investigating the mechanisms of LTAE and can be used to generate testable hypotheses for future studies.  
5. Availability of broadband internet access by patients to upload exercise data. There has been big rise 
in digital access in the US over the last few years: According to Pew Research Center, 67% of seniors (65+), 
87% of 50-64 year olds, and 96% of 30-49 year olds (96%) use the internet.98 Digital access is ~80% in rural 
communities.98 Broad band access can also be found in local public library or schools. The data upload can be 
helped by a technology-savvy family member or friend. 

 
 
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