Longitudinal imaging highlights preferential basal ganglia circuit atrophy in Huntington's disease.

Huntington's disease is caused by a CAG repeat expansion in the Huntingtin gene (HTT), coding for polyglutamine in the Huntingtin protein, with longer CAG repeats causing earlier age of onset. The variable 'Age' × ('CAG'-L), where 'Age' is the current age of the individual, 'CAG' is the repeat length and L is a constant (reflecting an approximation of the threshold), termed the 'CAG Age Product' (CAP) enables the consideration of many individuals with different CAG repeat expansions at the same time for analysis of any variable and graphing using the CAG Age Product score as the X axis. Structural MRI studies have showed that progressive striatal atrophy begins many years prior to the onset of diagnosable motor Huntington's disease, confirmed by longitudinal multicentre studies on three continents, including PREDICT-HD, TRACK-HD and IMAGE-HD. However, previous studies have not clarified the relationship between striatal atrophy, atrophy of other basal ganglia structures, and atrophy of other brain regions. The present study has analysed all three longitudinal datasets together using a single image segmentation algorithm and combining data from a large number of subjects across a range of CAG Age Product score. In addition, we have used a strategy of normalizing regional atrophy to atrophy of the whole brain, in order to determine which regions may undergo preferential degeneration. This made possible the detailed characterization of regional brain atrophy in relation to CAG Age Product score. There is dramatic selective atrophy of regions involved in the basal ganglia circuit-caudate, putamen, nucleus accumbens, globus pallidus and substantia nigra. Most other regions of the brain appear to have slower but steady degeneration. These results support (but certainly do not prove) the hypothesis of circuit-based spread of pathology in Huntington's disease, possibly due to spread of mutant Htt protein, though other connection-based mechanisms are possible. Therapeutic targets related to prion-like spread of pathology or other mechanisms may be suggested. In addition, they have implications for current neurosurgical therapeutic approaches, since delivery of therapeutic agents solely to the caudate and putamen may miss other structures affected early, such as nucleus accumbens and output nuclei of the striatum, the substantia nigra and the globus pallidus.

The temporal event-based model: Learning event timelines in progressive diseases.

Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here, we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer's disease (AD) and Huntington's disease (HD). In both diseases, the TEBM not only recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve a power of 80% with less than half the cohort size compared with random selection. The application of the TEBM naturally extends to a wide range of progressive conditions.

Death Anxiety in Huntington Disease: Longitudinal Heath-Related Quality-of-Life Outcomes.

Objective: Death anxiety, represented by the HDQLIFE™ Concern with Death and Dying (CwDD) patient-reported outcome (PRO) questionnaire, captures a person's worry about the death and dying process. Previous work suggests that death anxiety remains an unremitting burden throughout all stages of Huntington disease (HD). Although palliative interventions have lessened death anxiety among people with advanced cancer, none has yet to undergo testing in the HD population. An account of how death anxiety is associated with longitudinal changes to aspects of health-related quality of life (HRQoL) would help optimize neuropalliative interventions for people with HD. Methods: HDQLIFE collected PROs concerning physical, mental, social, and cognitive HRQoL domains and clinician-rated assessments from people with HD at baseline and 12 and 24 months. Linear mixed-effects models were created to determine how baseline death anxiety was associated with follow-up changes in HRQoL PROs after controlling for baseline death anxiety and other disease and sociodemographic covariates. Results: Higher baseline HDQLIFE CwDD is associated with 12- and 24-month declines in HDQLIFE Speech Difficulties, neurology quality of life (NeuroQoL) Depression, Suicidality, HDQLIFE Meaning and Purpose, and NeuroQoL Positive Affect and Well-being. Interpretation: Death anxiety may be a risk factor for worsening mental health and speech difficulty. A further prospective study is required to evaluate whether interventions on death anxiety or mental health generally can reduce declines in HRQoL for people with HD over time.

Factors Associated With the Place of Death in Huntington Disease: Analysis of Enroll-HD.

Background: Most people prefer to die at home. Hospice is the standard in end-of-life care for people with Huntington disease (HD), a neurodegenerative genetic disorder that affects people in middle adulthood. Yet, we have little knowledge regarding the place of death for people with HD. Therefore, the current state of knowledge limits HD clinicians' ability to conduct high-quality goals of care conversations. Objectives: We sought to determine the factors associated with the place of death in people with HD. Design: We obtained cross-sectional data from Enroll-HD and included participants with a positive HD mutation of 36 or more CAG repeats. Results: Out of 16,120 participants in the Enroll-HD study, 536 were reported as deceased. The mean age at death was 60. The leading place of death was home (29%), followed by the hospital (23%). The adjusted odds ratio (aOR) of dying at a skilled nursing facility was significantly lower for those partnered (aOR: 0.48, confidence interval [95% CI]: 0.26-0.86). The aOR for dying on hospice compared to home was increased in a person with some college and above (aOR: 2.40, 95% CI: 1.21-4.75). Conclusions: Our data further suggest that models that predict the place of death for serious illnesses do not appear to generalize to HD. The distribution in the places of death within HD was not uniform. Our findings may assist HD clinicians in communication during goals of care conversations.

Vascular contributions to Alzheimer's disease.

Alzheimer's disease (AD) is the most common cause of dementia and is characterized by progressive neurodegeneration and cognitive decline. Understanding the pathophysiology underlying AD is paramount for the management of individuals at risk of and suffering from AD. The vascular hypothesis stipulates a relationship between cardiovascular disease and AD-related changes although the nature of this relationship remains unknown. In this review, we discuss several potential pathological pathways of vascular involvement in AD that have been described including dysregulation of neurovascular coupling, disruption of the blood brain barrier, and reduced clearance of metabolite waste such as beta-amyloid, a toxic peptide considered the hallmark of AD. We will also discuss the two-hit hypothesis which proposes a 2-step positive feedback loop in which microvascular insults precede the accumulation of Aß and are thought to be at the origin of the disease development. At neuroimaging, signs of vascular dysfunction such as chronic cerebral hypoperfusion have been demonstrated, appearing early in AD, even before cognitive decline and alteration of traditional biomarkers. Cerebral small vessel disease such as cerebral amyloid angiopathy, characterized by the aggregation of Aß in the vessel wall, is highly prevalent in vascular dementia and AD patients. Current data is unclear whether cardiovascular disease causes, precipitates, amplifies, precedes, or simply coincides with AD. Targeted imaging tools to quantitatively evaluate the intracranial vasculature and longitudinal studies in individuals at risk for or in the early stages of the AD continuum could be critical in disentangling this complex relationship between vascular disease and AD.

Comprehensive shape analysis of the cortex in Huntington's disease.

The striatum has traditionally been the focus of Huntington's disease research due to the primary insult to this region and its central role in motor symptoms. Beyond the striatum, evidence of cortical alterations caused by Huntington's disease has surfaced. However, findings are not coherent between studies which have used cortical thickness for Huntington's disease since it is the well-established cortical metric of interest in other diseases. In this study, we propose a more comprehensive approach to cortical morphology in Huntington's disease using cortical thickness, sulcal depth, and local gyrification index. Our results show consistency with prior findings in cortical thickness, including its limitations. Our comparison between cortical thickness and local gyrification index underscores the complementary nature of these two measures-cortical thickness detects changes in the sensorimotor and posterior areas while local gyrification index identifies insular differences. Since local gyrification index and cortical thickness measures detect changes in different regions, the two used in tandem could provide a clinically relevant measure of disease progression. Our findings suggest that differences in insular regions may correspond to earlier neurodegeneration and may provide a complementary cortical measure for detection of subtle early cortical changes due to Huntington's disease.

Huntington's disease age at motor onset is modified by the tandem hexamer repeat in TCERG1.

Huntington's disease is caused by an expanded CAG tract in HTT. The length of the CAG tract accounts for over half the variance in age at onset of disease, and is influenced by other genetic factors, mostly implicating the DNA maintenance machinery. We examined a single nucleotide variant, rs79727797, on chromosome 5 in the TCERG1 gene, previously reported to be associated with Huntington's disease and a quasi-tandem repeat (QTR) hexamer in exon 4 of TCERG1 with a central pure repeat. We developed a method for calling perfect and imperfect repeats from exome-sequencing data, and tested association between the QTR in TCERG1 and residual age at motor onset (after correcting for the effects of CAG length in the HTT gene) in 610 individuals with Huntington's disease via regression analysis. We found a significant association between age at onset and the sum of the repeat lengths from both alleles of the QTR (p = 2.1 × 10-9), with each added repeat hexamer reducing age at onset by one year (95% confidence interval [0.7, 1.4]). This association explained that previously observed with rs79727797. The association with age at onset in the genome-wide association study is due to a QTR hexamer in TCERG1, translated to a glutamine/alanine tract in the protein. We could not distinguish whether this was due to cis-effects of the hexamer repeat on gene expression or of the encoded glutamine/alanine tract in the protein. These results motivate further study of the mechanisms by which TCERG1 modifies onset of HD.

Exome sequencing of individuals with Huntington's disease implicates FAN1 nuclease activity in slowing CAG expansion and disease onset.

The age at onset of motor symptoms in Huntington's disease (HD) is driven by HTT CAG repeat length but modified by other genes. In this study, we used exome sequencing of 683 patients with HD with extremes of onset or phenotype relative to CAG length to identify rare variants associated with clinical effect. We discovered damaging coding variants in candidate modifier genes identified in previous genome-wide association studies associated with altered HD onset or severity. Variants in FAN1 clustered in its DNA-binding and nuclease domains and were associated predominantly with earlier-onset HD. Nuclease activities of purified variants in vitro correlated with residual age at motor onset of HD. Mutating endogenous FAN1 to a nuclease-inactive form in an induced pluripotent stem cell model of HD led to rates of CAG expansion similar to those observed with complete FAN1 knockout. Together, these data implicate FAN1 nuclease activity in slowing somatic repeat expansion and hence onset of HD.

Genetic modifiers of Huntington disease differentially influence motor and cognitive domains.

Genome-wide association studies (GWASs) of Huntington disease (HD) have identified six DNA maintenance gene loci (among others) as modifiers and implicated a two step-mechanism of pathogenesis: somatic instability of the causative HTT CAG repeat with subsequent triggering of neuronal damage. The largest studies have been limited to HD individuals with a rater-estimated age at motor onset. To capitalize on the wealth of phenotypic data in several large HD natural history studies, we have performed algorithmic prediction by using common motor and cognitive measures to predict age at other disease landmarks as additional phenotypes for GWASs. Combined with imputation with the Trans-Omics for Precision Medicine reference panel, predictions using integrated measures provided objective landmark phenotypes with greater power to detect most modifier loci. Importantly, substantial differences in the relative modifier signal across loci, highlighted by comparing common modifiers at MSH3 and FAN1, revealed that individual modifier effects can act preferentially in the motor or cognitive domains. Individual components of the DNA maintenance modifier mechanisms may therefore act differentially on the neuronal circuits underlying the corresponding clinical measures. In addition, we identified additional modifier effects at the PMS1 and PMS2 loci and implicated a potential second locus on chromosome 7. These findings indicate that broadened discovery and characterization of HD genetic modifiers based on additional quantitative or qualitative phenotypes offers not only the promise of in-human validated therapeutic targets but also a route to dissecting the mechanisms and cell types involved in both the somatic instability and toxicity components of HD pathogenesis.

Moving beyond disclosure: Stages of care in preclinical Alzheimer's disease biomarker testing.

Alzheimer's disease (AD) begins with an asymptomatic "preclinical" phase, in which abnormal biomarkers indicate risk for developing cognitive impairment. Biomarker information is increasingly being disclosed in research settings, and is moving toward clinical settings with the development of cheaper and non-invasive testing. Limited research has focused on the safety and psychological effects of disclosing biomarker results to cognitively unimpaired adults. However, less is known about how to ensure equitable access and robust counseling for decision-making before testing, and how to effectively provide long-term follow-up and risk management after testing. Using the framework of Huntington's disease, which is based on extensive experience with disclosing and managing risk for a progressive neurodegenerative condition, this article proposes a conceptual model of pre-disclosure, disclosure, and post-disclosure phases for AD biomarker testing. Addressing research questions in each phase will facilitate the transition of biomarker testing into clinical practice.

Traumatic Brain Injury in Children and Adolescents: Psychiatric Disorders 24 Years Later.

OBJECTIVE: The investigators aimed to extend findings regarding predictive factors of psychiatric outcomes among children and adolescents with traumatic brain injury (TBI) from 2 to 24 years postinjury. METHODS: Youths aged 6-14 years who were hospitalized following TBI from 1992 to 1994 were assessed at baseline for TBI severity and for preinjury psychiatric, adaptive, and behavioral functioning; family functioning; family psychiatric history; socioeconomic status; and intelligence within weeks of injury. Predictors of psychiatric outcomes following pediatric TBI at 3, 6, 12, and 24 months postinjury have previously been reported. In this study, repeat psychiatric assessments were completed at 24 years postinjury with the same cohort, now adults aged 29-39 years, with the outcome measure being presence of a psychiatric disorder not present before the TBI ("novel psychiatric disorder"). RESULTS: Fifty participants with pediatric TBI were initially enrolled, and the long-term outcome analyses focused on data from 45 individuals. Novel psychiatric disorder was present in 24 out of 45 (53%) participants. Presence of a current novel psychiatric disorder was independently predicted by the presence of a preinjury lifetime psychiatric disorder and by severity of TBI. CONCLUSIONS: Long-term psychiatric outcome (mean=23.92 years [SD=2.17]) in children and adolescents hospitalized for TBI can be predicted at the point of the initial hospitalization encounter by the presence of a preinjury psychiatric disorder and by greater injury severity.

Longitudinal subcortical segmentation with deep learning.

Longitudinal information is important for monitoring the progression of neurodegenerative diseases, such as Huntington's disease (HD). Specifically, longitudinal magnetic resonance imaging (MRI) studies may allow the discovery of subtle intra-subject changes over time that may otherwise go undetected because of inter-subject variability. For HD patients, the primary imaging-based marker of disease progression is the atrophy of subcortical structures, mainly the caudate and putamen. To better understand the course of subcortical atrophy in HD and its correlation with clinical outcome measures, highly accurate segmentation is important. In recent years, subcortical segmentation methods have moved towards deep learning, given the state-of-the-art accuracy and computational efficiency provided by these models. However, these methods are not designed for longitudinal analysis, but rather treat each time point as an independent sample, discarding the longitudinal structure of the data. In this paper, we propose a deep learning based subcortical segmentation method that takes into account this longitudinal information. Our method takes a longitudinal pair of 3D MRIs as input, and jointly computes the corresponding segmentations. We use bi-directional convolutional long short-term memory (C-LSTM) blocks in our model to leverage the longitudinal information between scans. We test our method on the PREDICT-HD dataset and use the Dice coefficient, average surface distance and 95-percent Hausdorff distance as our evaluation metrics. Compared to cross-sectional segmentation, we improve the overall accuracy of segmentation, and our method has more consistent performance across time points. Furthermore, our method identifies a stronger correlation between subcortical volume loss and decline in the total motor score, an important clinical outcome measure for HD.

MRI subcortical segmentation in neurodegeneration with cascaded 3D CNNs.

The subcortical structures of the brain are relevant for many neurodegenerative diseases like Huntington's disease (HD). Quantitative segmentation of these structures from magnetic resonance images (MRIs) has been studied in clinical and neuroimaging research. Recently, convolutional neural networks (CNNs) have been successfully used for many medical image analysis tasks, including subcortical segmentation. In this work, we propose a 2-stage cascaded 3D subcortical segmentation framework, with the same 3D CNN architecture for both stages. Attention gates, residual blocks and output adding are used in our proposed 3D CNN. In the first stage, we apply our model to downsampled images to output a coarse segmentation. Next, we crop the extended subcortical region from the original image based on this coarse segmentation, and we input the cropped region to the second CNN to obtain the final segmentation. Left and right pairs of thalamus, caudate, pallidum and putamen are considered in our segmentation. We use the Dice coefficient as our metric and evaluate our method on two datasets: the publicly available IBSR dataset and a subset of the PREDICT-HD database, which includes healthy controls and HD subjects. We train our models on only healthy control subjects and test on both healthy controls and HD subjects to examine model generalizability. Compared with the state-of-the-art methods, our method has the highest mean Dice score on all considered subcortical structures (except the thalamus on IBSR), with more pronounced improvement for HD subjects. This suggests that our method may have better ability to segment MRIs of subjects with neurodegenerative disease.

Revealing the Timeline of Structural MRI Changes in Premanifest to Manifest Huntington Disease.

BACKGROUND AND OBJECTIVES: Longitudinal measurements of brain atrophy using structural MRI (sMRI) can provide powerful markers for tracking disease progression in neurodegenerative diseases. In this study, we use a disease progression model to learn individual-level disease times and hence reveal a new timeline of sMRI changes in Huntington disease (HD). METHODS: We use data from the 2 largest cohort imaging studies in HD-284 participants from TRACK-HD (100 control, 104 premanifest, and 80 manifest) and 159 participants from PREDICT-HD (36 control and 128 premanifest)-to train and test the model. We longitudinally register T1-weighted sMRI scans from 3 consecutive time points to reduce intraindividual variability and calculate regional brain volumes using an automated segmentation tool with rigorous manual quality control. RESULTS: Our model reveals, for the first time, the relative magnitude and timescale of subcortical and cortical atrophy changes in HD. We find that the largest (∼20% average change in magnitude) and earliest (∼2 years before average abnormality) changes occur in the subcortex (pallidum, putamen, and caudate), followed by a cascade of changes across other subcortical and cortical regions over a period of ∼11 years. We also show that sMRI, when combined with our disease progression model, provides improved prediction of onset over the current best method (root mean square error = 4.5 years and maximum error = 7.9 years vs root mean square error = 6.6 years and maximum error = 18.2 years). DISCUSSION: Our findings support the use of disease progression modeling to reveal new information from sMRI, which can potentially inform imaging marker selection for clinical trials.

A Multi-Study Model-Based Evaluation of the Sequence of Imaging and Clinical Biomarker Changes in Huntington's Disease.

Understanding the order and progression of change in biomarkers of neurodegeneration is essential to detect the effects of pharmacological interventions on these biomarkers. In Huntington's disease (HD), motor, cognitive and MRI biomarkers are currently used in clinical trials of drug efficacy. Here for the first time we use directly compare data from three large observational studies of HD (total N = 532) using a probabilistic event-based model (EBM) to characterise the order in which motor, cognitive and MRI biomarkers become abnormal. We also investigate the impact of the genetic cause of HD, cytosine-adenine-guanine (CAG) repeat length, on progression through these stages. We find that EBM uncovers a broadly consistent order of events across all three studies; that EBM stage reflects clinical stage; and that EBM stage is related to age and genetic burden. Our findings indicate that measures of subcortical and white matter volume become abnormal prior to clinical and cognitive biomarkers. Importantly, CAG repeat length has a large impact on the timing of onset of each stage and progression through the stages, with a longer repeat length resulting in earlier onset and faster progression. Our results can be used to help design clinical trials of treatments for Huntington's disease, influencing the choice of biomarkers and the recruitment of participants.

Mild Cognitive Impairment as an Early Landmark in Huntington's Disease.

As one of the clinical triad in Huntington's disease (HD), cognitive impairment has not been widely accepted as a disease stage indicator in HD literature. This work aims to study cognitive impairment thoroughly for prodromal HD individuals with the data from a 12-year observational study to determine whether Mild Cognitive Impairment (MCI) in HD gene-mutation carriers is a defensible indicator of early disease. Prodromal HD gene-mutation carriers evaluated annually at one of 32 worldwide sites from September 2002 to April 2014 were evaluated for MCI in six cognitive domains. Linear mixed-effects models were used to determine age-, education-, and retest-adjusted cut-off values in cognitive assessment for MCI, and then the concurrent and predictive validity of MCI was assessed. Accelerated failure time (AFT) models were used to determine the timing of MCI (single-, two-, and multiple-domain), and dementia, which was defined as MCI plus functional loss. Seven hundred and sixty-eight prodromal HD participants had completed all six cognitive tasks, had MRI, and underwent longitudinal assessments. Over half (i.e., 54%) of the participants had MCI at study entry, and half of these had single-domain MCI. Compared to participants with intact cognitive performances, prodromal HD with MCI had higher genetic burden, worsened motor impairment, greater brain atrophy, and a higher likelihood of estimated HD onset. Prospective longitudinal study of those without MCI at baseline showed that 48% had MCI in subsequent visits and data visualization suggested that single-domain MCI, two-domain MCI, and dementia represent appropriate cognitive impairment staging for HD gene-mutation carriers. Findings suggest that MCI represents an early landmark of HD and may be a sensitive enrichment variable or endpoint for prodromal clinical trials of disease modifying therapeutics.

Meaning and purpose in Huntington's disease: a longitudinal study of its impact on quality of life.

OBJECTIVE: Previous work in Huntington's disease (HD) has shown that a sense of meaning and purpose (M&P) is positively associated with positive affect and well-being (PAW); however, it was unknown whether HD-validated patient-reported outcomes (PROs) influence this association and how M&P impacts PROs in the future. Our study was designed to examine if HD-validated PROs moderate the relationship between M&P and PAW and to evaluate if baseline M&P predicts 12- and 24-month changes in HD-validated PROs. METHODS: This was a longitudinal, multicenter study to develop several PROs (e.g., specific for the physical, emotional, cognitive, and social domains) for people with HD (HDQLIFE). The sample consisted of 322 people with HD (n = 50 prodromal, n = 171 early-stage manifest, and n = 101 late-stage manifest HD). A single, multivariate linear mixed-effects model was performed with PAW as the outcome predicted by main effects for M&P and several moderators (i.e., an HD-validated PRO) and interactions between M&P and a given PRO. Linear-mixed models were also used to assess if baseline M&P predicted HD-validated PROs at 12 and 24 months. RESULTS: Higher M&P was positively associated with higher PAW regardless of the magnitude of symptom burden, as represented by HD-validated PROs, and independent of disease stage. In our primary analysis, baseline M&P predicted increased PAW and decreased depression, anxiety, anger, emotional/behavioral disruptions, and cognitive decline at 12 and 24 months across all disease stages. INTERPRETATION: These findings parallel those seen in the oncology population and have implications for adapting and developing psychotherapeutic and palliative HD interventions.

Predicting an optimal composite outcome variable for Huntington's disease clinical trials.

While there is no known cure for Huntington's disease (HD), there are early-phase clinical trials aimed at altering disease progression patterns. There is, however, no obvious single outcome for these trials to evaluate treatment efficacy. Currently used outcomes are, while reasonable, not optimal in any sense. In this paper we derive a method for constructing a composite variable via a linear combination of clinical measures. Our composite variable optimizes the signal-to-noise ratio (SNR) within the context of a longitudinal study design. We also demonstrate how to induce sparsity using a soft-approximation of an L 1 penalty on the coefficients of the composite variable. We applied our method to data from the TRACK-HD study, a longitudinal study aimed at establishing good outcome measures for HD, and found that compared to the existing composite measurement our composite variable provides a larger SNR and allows clinical trials with smaller sample sizes to achieve equivalent power.

Tracking Huntington's Disease Progression Using Motor, Functional, Cognitive, and Imaging Markers.

BACKGROUND: Potential therapeutic targets and clinical trials for Huntington's disease have grown immensely in the last decade. However, to improve clinical trial outcomes, there is a need to better characterize profiles of signs and symptoms across different epochs of the disease to improve selection of participants. OBJECTIVE: The objective of the present study was to best distinguish longitudinal trajectories across different Huntington's disease progression groups. METHODS: Clinical and morphometric imaging data from 1082 participants across IMAGE-HD, TRACK-HD, and PREDICT-HD studies were combined, with longitudinal times ranging between 1 and 10 years. Participants were classified into 4 groups using CAG and age product. Using multivariate linear mixed modeling, 63 combinations of markers were tested for their sensitivity in differentiating CAG and age product groups. Next, multivariate linear mixed modeling was applied to define the best combination of markers to track progression across individual CAG and age product groups. RESULTS: Putamen and caudate volumes, individually and/or combined, were identified as the best variables to both differentiate CAG and age product groups and track progression within them. The model using only caudate volume best described advanced disease progression in the combined data set. Contrary to expectations, combining clinical markers and volumetric measures did not improve tracking longitudinal progression. CONCLUSIONS: Monitoring volumetric changes throughout a trial (alongside primary and secondary clinical end points) may provide a more comprehensive understanding of improvements in functional outcomes and help to improve the design of clinical trials. Alternatively, our results suggest that imaging deserves consideration as an end point in clinical trials because of the prospect of greater sensitivity. © 2021 International Parkinson and Movement Disorder Society.