Open, multimodal, neuroimaging datasets are a key resource for the development of biomarkers and statistical models that can support disease monitoring and treatment planning in Parkinson’s disease (PD). Here we present the first release of Quebec Parkinson Network Neuroimaging Cohort (QPN-NC), a cross-sectional cohort of 202 individuals with PD and 69 older adults with multimodal MRI and extensive clinical and neuropsychological evaluations. The main objective of this data collection is to investigate neural correlates of motor and non-motor symptoms and facilitate comparisons with other PD studies. This QPN-NC release comprises well-characterized clinical assessments and curated imaging data processed through five neuroimaging pipelines. Additionally, we provide harmonized data annotations to facilitate search and cohort matching across other PD datasets. We share the tools and software used in this data release to enable data discovery, reproduce image processing, and help other studies to adopt best practices and community standards. Together with other datasets, QPN-NC will improve our understanding of PD and aid the translation of neuroimaging biomarkers into the clinic.

The causes of neuropsychiatric symptoms in Parkinson’s disease (PD) remain ill-defined. Disruptions in dopamine-dependent reward learning, a consequence of midbrain dopamine loss, potentially represent a mechanism that could underlie the neuropsychiatric symptoms of apathy, depression, and impulsivity, all of which have been proposed to reflect aberrant goal-directed behaviour. However no large-scale investigation that jointly considers these symptoms in PD has ever been undertaken. We aimed to determine if reward learning is associated with apathy, depression and impulsivity symptom in two samples of PD patients. Two samples of PD patients (n sample1 =81, n sample2 =90), tested in their medicated state, completed two widely used reward learning tasks (probabilistic stimulus selection task, probabilistic reward task) from which we derived five summary measures of performance. Apathy, depression, and impulsivity were evaluated using validated self-report questionnaires. Overall, there was poor consistency in the relationship between reward learning and neuropsychiatric symptom severity. Greater depressive symptom severity was associated with slower reward learning performance in sample 1, but with both slower and faster reward learning performance in sample 2. Greater impulsivity symptom severity was associated with slower reward learning performance in sample 1 but not in sample 2. There were no associations between apathy and reward learning. We found inconsistent relationships between symptoms of apathy, depression, and impulsivity and reward learning performance across two samples of PD patients. While this doesn’t rule out the possibility that reward learning impairments contribute to these symptoms, it suggests any effect is likely to be small and overshadowed by other non-measured factors.

Apathy, characterized by reduced motivation and goal-directed behavior, is common in normal aging and may arise from biased cost–benefit evaluation during decision making. While prior research has focused on simple choices involving the trade-off between motor or cognitive effort and monetary reward, real-life decisions of people with apathy likely reflect biases affecting many other dimensions of value. Additionally, the extent to which mood symptoms that frequently co-occur with apathy also contribute to biases in decision making is unclear. To address these gaps, we developed a novel online task completed by 158 older adults who rated everyday activities (e.g., watching TV, going to the park) across 19 value attributes (e.g., enjoyment, social obligation), and made a series of choices between different activities. A factor analysis of attribute ratings identified three latent factors representing reward, effort, and obligation, which strongly predicted choices and choice response times. Apathy was selectively associated with an overweighting of effort-related attributes while controlling for impulsivity and depressive symptoms, with no effect on the weighting of reward or obligation. Interestingly, impulsivity, which correlated positively with apathy, was associated with an underweighting of effort. Hierarchical drift diffusion modelling supported these findings: apathy and impulsivity modulated the influence of effort on drift rate in opposite directions, altering evidence accumulation toward or away from high-effort options. Overall, these results suggest that everyday decisions depend on the integration of a wide range of value attributes and that apathy is associated with a selective overweighting of the perceived cost of effort.

Numerical variability is rarely quantified in neuroimaging despite many measures relying on subtle morphometric differences across individuals. We instrumented FreeSurfer 7.3.1, a widely used neuroimaging pipeline, to simulate numerical differences across computational environments, and used it to measure numerical variability in MRI analyses of Parkinson’s disease patients and controls. In multiple cortical and subcortical regions, numerical variation reached nearly one-third of the population variability, altering statistical conclusions about group differences and clinical associations. To assess the impact of numerical noise in existing studies, we developed a practical tool that estimates the Numerical-Population Variability Ratio (NPVR) in a study, and propagates the resulting numerical variability to common statistics and associated p-values. By applying this framework to thirteen previously published studies reporting MRI measures in Parkinson’s disease, we quantified the probability of numerically induced false positives and false negatives in the literature, highlighting a substantial impact of numerical variability on MRI measures of Parkinson’s disease with an average significance-flip probability of 5% for cross-sectional studies and 10% for longitudinal studies. These results underscore the importance of systematically evaluating numerical stability in neuroimaging and provide a practical framework to do so.

ABSTRACT Background Degeneration in the cholinergic nucleus basalis of Meynert (nbM) is thought to contribute to early cognitive deficits in Parkinson’s disease (PD). However, it is unknown whether this relationship is confounded by the parallel degeneration of the substantia nigra (SN) and the locus coeruleus (LC), and whether this relationship is unique to PD. Methods We conducted a cross-sectional analysis in 112 PD patients (disease duration <10 years) and 46 controls who underwent standard neuropsychological testing, diffusion-weighted and neuromelanin magnetic resonance imaging. Mean diffusivity (MD) of the nbM and neuromelanin signal of the SN and LC were used as proxy measures of neurodegeneration. Results nbM MD did not differ between PD patients and controls (β=0.05, 95% CI [−0.27, 0.37], p =.771), a finding that was replicated using other MRI metrics. Higher nbM MD in PD patients was associated with worse executive function (β=−0.22, 95% CI [−0.39, −0.05], p FDR =.027), controlling for degeneration in the SN and LC. An association with attention did not survive multiple comparisons correction (β=−0.22, 95% CI [−0.41, −0.02], p FDR =.112), and there was no association with memory (β=0.08, 95% CI [−0.15, 0.30], p FDR =.572). When considering both groups jointly, the relationship between increased nbM MD and worse executive function was stronger in PD than controls (β nbM * Group =−0.30, 95% CI [−0.54, −0.06], p FDR =.036). Conclusion Our findings suggest that in early-stage PD, nbM microstructural changes may account for unique variance in executive dysfunction in PD, independent of the effects of LC degeneration, and with a stronger association in PD than controls. KEY MESSAGES What is already known on this topic Although cognitive deficits may stem, at least in part, from degeneration of cholinergic neurons in the nucleus basalis of Meynert (nbM), it is unknown whether this relationship is confounded by the parallel degeneration of the substantia nigra (SN) and the locus coeruleus (LC), and whether this relationship is unique to PD. What this study adds The novelty of our approach is that we considered the effects of the nbM, SN, and LC jointly rather than in isolation. We found that in early-stage PD, loss of nbM microstructural integrity was associated with executive dysfunction in PD, independent of the association of LC and executive dysfunction. How this study might affect research, practice, or policy These findings have important clinical implications because they suggest that the effects of degeneration in the cholinergic and noradrenergic system are at least partially independent and therefore could be targeted separately for remediating cognitive impairments.

BACKGROUND:Alzheimer's disease (AD) co-pathology contributes to dementia in PD, but its role in earlier cognitive impairment remains uncertain. OBJECTIVE:To determine if p-tau217, a biomarker of early AD, is associated with cognitive impairment in PD. METHODS:Plasma p-tau217 levels in 167 PD patients without dementia and 63 controls were related to performance on standard neuropsychological testing, and to cognitive impairment as defined by a MoCA score <26 and by self-report. Plasma GFAP, NfL and APOE ε4 carrier status were also examined. RESULTS:No significant differences in p-tau217, GFAP and NfL level were observed between groups (pFDR > 0.08). Higher p-tau217 was associated with worse visuospatial function and greater self-reported cognitive impairment, but these associations did not survive correction (pFDR > 0.08). There was no association with cognitive impairment (pFDR > 0.08). CONCLUSION:These results suggest that co-morbid AD pathology is not a major contributor to early cognitive changes in this sample of PD patients without dementia. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Abstract Background Neuromelanin-MRI enables in vivo assessment of the substantia nigra (SN) and locus coeruleus (LC) in individuals with Parkinson’s disease (PD), yet longitudinal studies rely on cross-sectional processing that may introduce measurement variability and confound estimates of change over time. Objectives In this paper, a longitudinal neuromelanin-MRI processing framework is presented that is designed and validated to improve measurement stability and reduce processing-related variability across repeated scans. Methods Imaging and clinical data from the Quebec Parkinson Network were analyzed in 268 participants (199 PD, 69 controls), including a longitudinal subset of 74 participants (49 PD, 25 controls) scanned approximately one year apart. Validation experiments evaluated slice-by-slice intensity normalization for slice dependent intensity variation, bias field correction for LC signal asymmetry, and the effects of longitudinal registration on measurement stability and PD-control discrimination. Results Slice-by-slice intensity normalization significantly reduced brainstem intensity variability by 3.6%. A systematic leftward signal asymmetry was observed in the LC and persisted following N4 bias field correction, suggesting a scanner-related effect not captured by conventional bias field modeling. Longitudinal registration reduced annualized change variability by 25-36% for SN CR and 27-34% for LC CR metrics in controls, indicating improved within-subject measurement stability. Residual variability was also reduced for contrast-based metrics by up to 28%. Longitudinal registration generally produced larger PD-control effect sizes at baseline and follow-up, particularly for SN volume metrics. However, no significant method × group × time interactions were observed, indicating that estimated longitudinal trajectories did not differ significantly between longitudinal and conventional cross-sectional processing. Conclusions Longitudinal registration reduced technical variability and improved the precision of NM-MRI measurements. Although it did not significantly enhance detection of longitudinal PD-control differences over the follow-up interval examined here, it provides a more robust framework for longitudinal NM-MRI studies and may improve sensitivity to subtler biological effects in future investigations.

Apathy, characterized by diminished motivation and reduced goal-directed behavior, is prevalent in normal aging. Recent evidence suggests that it may result from biases in the evaluation of costs and benefits during decision making. However, most research has focused on simple decisions involving the trade-off between motor or cognitive effort and monetary reward. Meanwhile, it is possible that the disrupted real-life decisions of people with apathy reflect biases affecting many other dimensions of value. Additionally, the extent to which mood symptoms that frequently co-occur with apathy also contribute to biases in decision making is unclear. To address these gaps, we developed a novel online task completed by 158 older adults who first rated everyday activities (e.g., watching TV, going to the park) across 19 value attributes (e.g., enjoyment, social obligation), and then made a series of choices between random pairs of the different activities. A factor analysis of attribute ratings identified three latent factors representing reward, effort, and obligation, which strongly predicted choices and choice response times. Apathy was selectively associated with an overweighting of effort-related attributes in models controlling for impulsivity, depression, or anhedonia; it did not affect the weighting of reward or obligation-related attributes. Interestingly, impulsivity, which was positively correlated with apathy, was associated with an underweighting of effort. Hierarchical drift diffusion modelling supported these findings, showing that apathy and impulsivity modulated the influence of effort on the rate of evidence accumulation in opposite directions. Overall, these results suggest that decisions about everyday activities rely on the integration of a wide range of value attributes and that apathy is selectively associated with an overweighting of the effort dimension of value, driving choices away from activities that require greater effort.

BackgroundThe substantia nigra (SN) and locus coeruleus (LC) are among the first brain regions to degenerate in Parkinson's disease (PD). This has important implications for early cognitive deficits because these nuclei are sources of ascending neuromodulators (i.e., dopamine and noradrenaline) that support various cognitive functions such as learning, memory, and executive function.ObjectiveOur aim was to investigate the selective and independent contributions of SN and LC degeneration to cognitive deficits in PD.MethodsWe ran a cross-sectional study testing patients with PD and older adults on tasks of positive reinforcement learning, attention/working memory, executive function, and memory to measure cognitive performance in domains thought to be related to dopaminergic and noradrenergic function. Participants also underwent neuromelanin-sensitive magnetic resonance imaging as a measure of degeneration.ResultsReduced SN neuromelanin signal in PD was independently associated with impaired positive reinforcement learning (beta = 0.41, 95% confidence interval [CI]: 0.08, 0.74) controlling for changes in the LC. In contrast, reduced LC neuromelanin signal was independently associated with impairments in attention/working memory (beta = 0.20, 95% CI [-0.47, -0.10]) and executive function (beta = 0.22, 95% CI: -0.57, -0.24), controlling for changes in the SN.ConclusionsThese results suggest that SN and LC degeneration may contribute to different cognitive deficits, potentially explaining the heterogeneity that exists in the cognitive manifestations of PD. These results also highlight the potential value of leveraging brain-behavior relationships to develop performance-based measures of cognition that could be used to characterize the phenotypic differences associated with underlying patterns of neurodegeneration. (c) 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Memory consolidation refers to the process by which newly encoded memories are strengthened and retained over time, and ample evidence indicates that sleep supports this process for both procedural and declarative memories. Although sleep spindles during non-rapid eye movement (NREM) sleep have been associated to consolidation, it remains unclear whether all spindle types contribute equally. Spindles vary in frequency and topography - slow spindles (<=12.5Hz) predominating over frontal regions, whereas fast spindles (>12.5Hz) peak parietally - and recent work suggests that procedural memory consolidation during overnight sleep is related to the temporal organization of spindles in trains (i.e., events occurring <6s apart). Here we investigated whether a similar mechanism operates for declarative memory during daytime naps. Participants were assigned to a Nap (N=23) or No-Nap (N=15) group, and completed an object-spatial location task involving 36 item-location associations. Memory was assessed immediately after learning and again following a 90-minute nap or an equivalent wake period. Results showed that the Nap group exhibited significantly better delayed memory, as measured by combined recall-recognition score, and a greater proportion of participants maintained or improved their performance. In the Nap group, memory performance correlated with local spindle density at frontal and parietal sites, and, critically, with the proportion of slow spindles clustered in trains during NREM2. These findings suggest the temporal organization of slow spindles into clusters support declarative memory consolidation, pointing to a shared spindle-based mechanism across domains. Keywords: sleep, memory consolidation, declarative memory, sleep spindles, spindle trains, naps ### Competing Interest Statement The authors have declared no competing interest. Canadian Institutes of Health Research (CIHR)

Apathy, characterized by reduced motivation and goal-directed behavior, is common in normal aging and may arise from biases cost-benefit evaluation during decision making. While prior research has focused on simple choices involving the trade-off between motor or cognitive effort and monetary reward, real-life decisions of people with apathy likely reflect biases affecting many other dimensions of value. Additionally, the extent to which mood symptoms that frequently co-occur with apathy also contribute to biases in decision making is unclear. To address these gaps, we developed a novel online task completed by 158 older adults who rated everyday activities (e.g., watching TV, going to the park) across 19 value attributes (e.g., enjoyment, social obligation), and made a series of choices between different activities. A factor analysis of attribute ratings identified three latent factors representing reward, effort, and obligation, which strongly predicted choices and choice response times. Apathy was selectively associated with an overweighting of effort-related attributes while controlling for impulsivity and depression, with no effect on the weighting of reward or obligation. Interestingly, impulsivity, which correlated positively with apathy, was associated with an underweighting of effort. Hierarchical drift diffusion modelling supported these findings: apathy and impulsivity modulated the influence of effort on drift rate in opposite directions, altering evidence accumulation toward or away from high-effort options. Overall, these results suggest that everyday decisions depend on the integration of a wide range of value attributes and that apathy is associated with a selective overweighting of the perceived cost of effort.

Parkinson's disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology and no established biomarkers for the diagnosis of early stages and for prediction of disease progression. Several neuroimaging biomarkers have been studied recently, but these are susceptible to several sources of variability related for instance to cohort selection or image analysis. In this context, an evaluation of the robustness of such biomarkers to variations in the data processing workflow is essential. This study is part of a larger project investigating the replicability of potential neuroimaging biomarkers of PD. Here, we attempt to fully reproduce (reimplementing the experiments with the same methods, including data collection from the same database) and replicate (different data and/or method) the models described in (Nguyen et al., 2021) to predict individual's PD current state and progression using demographic, clinical and neuroimaging features (fALFF and ReHo extracted from resting-state fMRI). We use the Parkinson's Progression Markers Initiative dataset (PPMI, ppmi-info.org), as in (Nguyen et al., 2021) and aim to reproduce the original cohort, imaging features and machine learning models as closely as possible using the information available in the paper and the code. We also investigated methodological variations in cohort selection, feature extraction pipelines and sets of input features. Different criteria were used to evaluate the reproduction attempt and compare the results with the original ones. Notably, we obtained significantly better than chance performance using the analysis pipeline closest to that in the original study (R2 > 0), which is consistent with its findings. In addition, we performed a partial reproduction using derived data provided by the authors of the original study, and we obtained results that were close to the original ones. The challenges encountered while attempting to reproduce (fully and partially) and replicating the original work are likely explained by the complexity of neuroimaging studies, in particular in clinical settings. We provide recommendations to further facilitate the reproducibility of such studies in the future.

The speech of people with Parkinson's Disease (PD) has been shown to hold important clues about the presence and progression of the disease. We investigate the factors based on which humans experts make judgments of the presence of disease in speech samples over five different speech tasks: phonations, sentence repetition, reading, recall, and picture description. We make comparisons by conducting listening tests to determine clinicians accuracy at recognizing signs of PD from audio alone, and we conduct experiments with a machine learning system for detection based on Whisper. Across tasks, Whisper performs on par or better than human experts when only audio is available, especially on challenging but important subgroups of the data: younger patients, mild cases, and female patients. Whisper's ability to recognize acoustic cues in difficult cases complements the multimodal and contextual strengths of human experts.

It is increasingly recognized that Alzheimer’s disease (AD) co-pathology contributes to dementia in PD, but its role in earlier stages of cognitive impairment remains uncertain. This study examined whether plasma phosphorylated Tau at threonine 217 (p-tau217), a biomarker of early AD-related pathology, is associated with cognitive impairment in PD. Using cross-sectional data from 167 PD patients without dementia and 63 controls of the Quebec Parkinson Network registry we examined the association between plasma p-tau217 and three measures of cognitive impairment: performance on standard neuropsychological testing, and cognitive impairment as defined by a Montreal Cognitive Assessment (MoCA) score <26 and by self-report. Glial Fibrillary Acidic Protein (GFAP) and Neurofilament Light chain (NfL) were also measured as non-specific markers of inflammation and neurodegeneration. No significant difference in p-tau217 level was observed between groups; GFAP and NfL were higher in PD patients, but these differences did not survive multiple comparison correction ( pFDR > 0.08). Among PD patients, higher p-tau217 was associated with worse visuospatial function (p=0.04) and greater self-reported cognitive impairment (p=0.03), but these associations did not survive multiple comparison correction ( pFDR > 0.08). There was no association with cognitive impairment as defined by a MoCA <26. Plasma p-tau217 was not clearly associated with early cognitive impairment suggesting that co-morbid AD pathology is not a major contributor to early cognitive changes in this sample of PD patients without dementia. Replication of these findings and longitudinal research is needed to determine whether p-tau217 can predict progression to dementia in PD.

INTRODUCTION:A better understanding of the heterogeneity in the cognitive and mood symptoms of Parkinson's disease will require research conducted in large samples of patients. Fully online and remote research assessments present interesting opportunities for scaling up research but the feasibility and reliability of remote and fully unsupervised performance-based cognitive testing in individuals with Parkinson's disease is unknown. This study aims to establish the feasibility and reliability of this testing modality in Parkinson's patients. METHODS:Sixty-seven Parkinson's patients and 36 older adults completed two sessions of an at-home, online battery of five cognitive tasks and three self-report questionnaires. Feasibility was established by examining completion rates and data quality. Test-retest reliability was evaluated using the Intraclass Correlation Coefficient (ICC (2,1)). RESULTS:Overall completion rates and data quality were high with few participant exclusions across tasks. With regards to test-retest reliability, intraclass correlation coefficients were quite variable across measures extracted from a task as well as across tasks, but at least one standard measure from each task achieved moderate to good reliability levels. Self-report questionnaires achieved a higher test-retest reliability than cognitive tasks. Feasibility and reliability were similar between Parkinson's patients and older adults. CONCLUSION:These results demonstrate that remote and unsupervised testing is a feasible and reliable method of measuring cognition and mood in Parkinson's patients that achieves levels of test-retest reliability that are comparable to those reported for standard in-person testing.

Background: Technology is poised to bridge the gap between demand for therapies to improve gait in people with Parkinson’s and available resources. A wearable sensor, Heel2Toe TM , a small device that attaches to the side of the shoe and gives a sound each time the person starts their step with a strong heel strike has been developed and pre-tested by a team at McGill University. The objective of this study was to estimate feasibility and efficacy potential of the Heel2Toe TM sensor in changing walking capacity and gait pattern in people with Parkinson’s. Methods : A pilot study was carried out involving 27 people with Parkinson’s randomized 2:1 to train with the Heel2Toe[TM] sensor and or to train with recommendations from a gait-related workbook. Results: A total of 21 completed the 3-month evaluation, 14 trained with the Heel2Toe[TM] sensor and 7 trained with the workbook. Thirteen of 14 people in the Heel2Toe group improved over measurement error on the primary outcome, the Six Minute Walk Test, (mean change 66.4 m.) and 0 of the 7 in the Workbook group (mean change -19.4 m.): 4 of 14 in the Heel2Toe group made reliable change and 0 of 7 in the Workbook group. Improvements in walking distance were accompanied by improvements in gait quality. 40% of participants in the intervention group were strongly satisfied with their technology experience and an additional 37% were satisfied. Conclusions: Despite some technological difficulties, feasibility and efficacy potential of the Heel2Toe sensor in improving gait in people with Parkinson’s was supported.

Sleep is required for successful memory consolidation. Sleep spindles, bursts of oscillatory activity occurring during non-REM sleep, are known to be crucial for this process and, recently, it has been proposed that the temporal organization of spindles into clusters might additionally play a role in memory consolidation. In Parkinson’s disease, spindle activity is reduced, and this reduction has been found to be predictive of cognitive decline. However, it remains unknown whether alterations in sleep spindles in Parkinson’s disease are predictive of sleep-dependent cognitive processes like memory consolidation, leaving open questions about the possible mechanisms linking sleep and more general cognitive state in Parkinson’s patients. The current study sought to fill this gap by recording overnight polysomnography and measuring overnight declarative memory consolidation in a sample of thirty-five Parkinson’s patients. Memory consolidation was measured using a verbal paired-associates task administered before and after the night of recorded sleep. We found that lower sleep spindle density at frontal leads during non-REM stage 3 was associated with worse overnight declarative memory consolidation. We also found that patients who showed less temporal clustering of spindles exhibited worse declarative memory consolidation. These results suggest alterations to sleep spindles, which are known to be a consequence of Parkinson’s disease, might represent a mechanism by which poor sleep leads to worse cognitive function in Parkinson’s patients. Statement of significance Sleep — particularly spindle activity — is critical for memory consolidation, a core cognitive process. Changes to the architecture and oscillations of sleep are well documented in Parkinson’s disease (PD) and have been associated with worse overall cognition. However, whether altered sleep plays a causal role in this relationship, by directly interfering with sleep-dependent cognitive processes, or whether it represents a mere epiphenomenon of advancing disease, remains unknown. Our study is the first to investigate a possible direct relationship between sleep and cognition in PD. We show that sleep spindles and their temporal clustering into ‘trains’ relate to impairments in overnight declarative memory consolidation in patients. These findings are an important first step towards identifying modifiable sources of cognitive impairment in PD.

CONTEXT:An existing major challenge in Parkinson's disease (PD) research is the identification of biomarkers of disease progression. While magnetic resonance imaging is a potential source of PD biomarkers, none of the magnetic resonance imaging measures of PD are robust enough to warrant their adoption in clinical research. This study is part of a project that aims to replicate 11 PD studies reviewed in a recent survey (JAMA neurology, 78(10) 2021) to investigate the robustness of PD neuroimaging findings to data and analytical variations. OBJECTIVE:This study attempts to replicate the results in Hanganu et al. (Brain, 137(4) 2014) using data from the Parkinson's Progression Markers Initiative (PPMI). METHODS:Using 25 PD subjects and 18 healthy controls, we analyzed the rate of change of cortical thickness and of the volume of subcortical structures, and we measured the relationship between structural changes and cognitive decline. We compared our findings to the results in the original study. RESULTS:(1) Similarly to the original study, PD patients with mild cognitive impairment (MCI) exhibited increased cortical thinning over time compared to patients without MCI in the right middle temporal gyrus, insula, and precuneus. (2) The rate of cortical thinning in the left inferior temporal and precentral gyri in PD patients correlated with the change in cognitive performance. (3) There were no group differences in the change of subcortical volumes. (4) We did not find a relationship between the change in subcortical volumes and the change in cognitive performance. CONCLUSION:Despite important differences in the dataset used in this replication study, and despite differences in sample size, we were able to partially replicate the original results. We produced a publicly available reproducible notebook allowing researchers to further investigate the reproducibility of the results in Hanganu et al. (2014) when more data is added to PPMI.

Image processing software impacts the quantification of brain measures, playing an important role in the search for clinical biomarkers. We investigated the impact of the variability between FreeSurfer releases on the estimation of structural brain measures in Parkinson's disease (PD). Structural brain scans from 106 controls and 209 patients were analyzed with FreeSurfer versions 5.3, 6.0.1, and 7.3.2, including longitudinal data from 125 patients. First, we measured the differences in the estimation of volume, surface area, and cortical thickness between FreeSurfer versions. Second, we focused on the relationship between MRI-derived brain measures and group differences as well as disease severity clinical outcomes, which were evaluated both cross-sectionally and longitudinally. We found high software-induced variability in the estimation of all three structural measures, which impacted clinical outcomes. There were differences between software versions in group differences between patients and healthy controls in subcortical volume and vertex-wise cortical thickness. Software variability also impacted the estimated relationship between brain structure and disease severity in patients. Hence, software variability not only relates to the estimation of structural measures, but it also impacts clinically-relevant MRI measures. Our study provides insight into the reproducibility of structural neuroimaging studies in PD populations. ### Competing Interest Statement The authors have declared no competing interest.