Plasma neurofilament light levels show elevation two years prior to diagnosis of amyotrophic lateral sclerosis in the UK Biobank.

OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative disease with profound unmet need. In patients carrying genetic mutations, elevations in neurofilament light (NfL) have been shown to precede symptom onset, however, the natural history of NfL in general ALS patients is less characterized. METHODS: We performed a secondary analysis of the UK Biobank Pharma Proteomics Project (UKB-PPP), a subset of the UK Biobank, a population-based cohort study in the United Kingdom, to examine plasma NfL levels in 237 participants subsequently diagnosed with ALS. We applied logistic and Cox proportional hazards regression to compare cases to 42,752 population-based and 948 age and sex-matched controls. Genetic information was obtained from exome and genotype array data.Results and Conclusions: We observed that NfL was 1.42-fold higher in cases vs population-based controls. At two to three years pre-diagnosis, NfL levels in patients exceeded the 95th percentile of age and sex-matched controls. A time-to-diagnosis analysis showed that a 2-fold increase in NfL levels was associated with a 3.4-fold risk of diagnosis per year, with NfL being most predictive of case status at two years (AUC = 0.96). Participants with genetic variation that might put them at risk for familial disease (N = 46) did not show a different pattern of association than those without (N = 191). DISCUSSION: Our findings show that NfL is elevated and discriminative of future ALS diagnosis up to two years prior to diagnosis in patients with and without genetic risk variants.

Electrical impedance myography detects dystrophin-related muscle changes in mdx mice.

BACKGROUND: The lack of functional dystrophin protein in Duchenne muscular dystrophy (DMD) causes chronic skeletal muscle inflammation and degeneration. Therefore, the restoration of functional dystrophin levels is a fundamental approach for DMD therapy. Electrical impedance myography (EIM) is an emerging tool that provides noninvasive monitoring of muscle conditions and has been suggested as a treatment response biomarker in diverse indications. Although magnetic resonance imaging (MRI) of skeletal muscles has become a standard measurement in clinical trials for DMD, EIM offers distinct advantages, such as portability, user-friendliness, and reduced cost, allowing for remote monitoring of disease progression or response to therapy. To investigate the potential of EIM as a biomarker for DMD, we compared longitudinal EIM data with MRI/histopathological data from an X-linked muscular dystrophy (mdx) mouse model of DMD. In addition, we investigated whether EIM could detect dystrophin-related changes in muscles using antisense-mediated exon skipping in mdx mice. METHODS: The MRI data for muscle T2, the magnetic resonance spectroscopy (MRS) data for fat fraction, and three EIM parameters with histopathology were longitudinally obtained from the hindlimb muscles of wild-type (WT) and mdx mice. In the EIM study, a cell-penetrating peptide (Pip9b2) conjugated antisense phosphorodiamidate morpholino oligomer (PPMO), designed to induce exon-skipping and restore functional dystrophin production, was administered intravenously to mdx mice. RESULTS: MRI imaging in mdx mice showed higher T2 intensity at 6 weeks of age in hindlimb muscles compared to WT mice, which decreased at ≥ 9 weeks of age. In contrast, EIM reactance began to decline at 12 weeks of age, with peak reduction at 18 weeks of age in mdx mice. This decline was associated with myofiber atrophy and connective tissue infiltration in the skeletal muscles. Repeated dosing of PPMO (10 mg/kg, 4 times every 2 weeks) in mdx mice led to an increase in muscular dystrophin protein and reversed the decrease in EIM reactance. CONCLUSIONS: These findings suggest that muscle T2 MRI is sensitive to the early inflammatory response associated with dystrophin deficiency, whereas EIM provides a valuable biomarker for the noninvasive monitoring of subsequent changes in skeletal muscle composition. Furthermore, EIM reactance has the potential to monitor dystrophin-deficient muscle abnormalities and their recovery in response to antisense-mediated exon skipping.

Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study.

Digital health technologies can provide continuous monitoring and objective, real-world measures of Parkinson's disease (PD), but have primarily been evaluated in small, single-site studies. In this 12-month, multicenter observational study, we evaluated whether a smartwatch and smartphone application could measure features of early PD. 82 individuals with early, untreated PD and 50 age-matched controls wore research-grade sensors, a smartwatch, and a smartphone while performing standardized assessments in the clinic. At home, participants wore the smartwatch for seven days after each clinic visit and completed motor, speech and cognitive tasks on the smartphone every other week. Features derived from the devices, particularly arm swing, the proportion of time with tremor, and finger tapping, differed significantly between individuals with early PD and age-matched controls and had variable correlation with traditional assessments. Longitudinal assessments will inform the value of these digital measures for use in future clinical trials.

Urine titin as a novel biomarker for Duchenne muscular dystrophy.

Duchenne muscular dystrophy (DMD) is the most severe form of muscular dystrophy that is caused by lack of dystrophin, a critical structural protein in skeletal muscle. DMD treatments, and quantitative biomarkers to assess the efficacy of potential treatments, are urgently needed. Previous evidence has shown that titin, a muscle cell protein, is increased in the urine of patients with DMD, suggesting its usefulness as a DMD biomarker. Here, we demonstrated that the elevated titin in urine is directly associated with the lack of dystrophin and urine titin responses to drug treatment. We performed a drug intervention study using mdx mice, a DMD mouse model. We showed that mdx mice, which lack dystrophin due to a mutation in exon 23 of the Dmd gene, have elevated urine titin. Treatment with an exon skipper that targets exon 23 rescued muscle dystrophin level and dramatically decreased urine titin in mdx mice and correlates with dystrophin expression. We also demonstrated that titin levels were significantly increased in the urine of patients with DMD. This suggests that elevated urine titin level might be a hallmark of DMD and a useful pharmacodynamic marker for therapies designed to restore dystrophin levels.

Qualitative measures that assess functional disability and quality of life in ALS.

BACKGROUND: Selection of appropriate trial endpoints and outcome measures is particularly important in rare disease and rapidly progressing disease such as amyotrophic lateral sclerosis (ALS) where the challenges to conducting clinical trials, are substantial: patient and disease heterogeneity, limited understanding of exact disease pathophysiology, and lack of robust and available biomarkers. To address these challenges in ALS, the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised version (ALSFRS-R) was developed and has become a key primary endpoint in ALS clinical trials to assess functional disability and disease progression, often replacing survival as a primary outcome. However, increased understanding of the ALS disease journey and improvements in assistive technology for ALS patients have exposed issues with the ALSFRS-R, including non-linearity, multidimensionality and floor and ceiling effects that could challenge its continued utility as a primary outcome measure in ALS clinical trials. Recently, other qualitative scale measures of functioning disability have been developed to help address these issues. With this in mind, we conducted a literature search aimed at identifying both established and promising new measures for potential use in clinical trials. METHODS: We searched PubMed, Google, Google Scholar, and the reference sections of key studies to identify papers that discussed qualitative measures of functional status for potential use in ALS studies. We also searched clinicaltrials.gov to identify functional status and health-related quality of life (HRQoL) measures that have been used in ALS interventional studies. RESULTS: In addition to the ALSFRS-R, we identified several newer qualitative scales including ALSFRS-EX, ALS-MITOS, CNS-BFS, DALS-15, MND-DS, and ROADS. Strengths and limitations of each measure were identified and discussed, along with their potential to act as a primary or secondary outcome to assess patient functional status in ALS clinical trials. CONCLUSION: This paper serves as a reference guide for researchers deciding which qualitative measures to use as endpoints in their ALS clinical trials to assess functional status. This paper also discusses the importance of including ALS HRQoL and ALS cognitive screens in future clinical trials to assess the value of a new ALS therapy more comprehensively.

A Descriptive Review of Global Real World Evidence Efforts to Advance Drug Discovery and Clinical Development in Amyotrophic Lateral Sclerosis.

Understanding patient clinical progression is a key gateway to planning effective clinical trials and ultimately enabling bringing treatments to patients in need. In a rare disease like amyotrophic lateral sclerosis (ALS), studies of disease natural history critically depend on collaboration between clinical centers, regions, and countries to enable creation of platforms to allow patients, caregivers, clinicians, and researchers to come together and more fully understand the condition. Rare disease registries and collaborative platforms such as those developed in ALS collect real-world data (RWD) in standardized formats, including clinical and biological specimen data used to evaluate risk factors and natural history of disease, treatment patterns and clinical (ClinROs) and patient- reported outcomes (PROs) and validate novel endpoints. Importantly, these data support the development of new therapeutics by supporting the evaluation of feasibility and design of clinical trials and offer valuable information on real-world disease trajectory and outcomes outside of the clinical trial setting for comparative purposes. RWD may help to accelerate therapy development by identifying and validating outcome measures and disease subpopulations. RWD can also make potential contributions to the evaluation of the safety and effectiveness of new indications for approved products and to satisfy post-approval regulatory and market access requirements. There is a lack of amalgamated information on available registries, databases, and other sources of real-world data on ALS; thus, a global review of all available resources was warranted. This targeted review identifies and describes ALS registries, biobanks and collaborative research networks that are collecting and synthesizing RWD for the purposes of increasing patient awareness and advancing scientific knowledge with the hope of expediting future development of new therapies.

ALSUntangled 59: Tamoxifen.

Here we use the ALSUntangled methodology to review Tamoxifen as an ALS treatment. We show that it has plausible mechanisms, a positive preclinical study, a case report and 2 small trials suggesting benefits. We show that it appears reasonably safe, though there is a small risk of developing cancer with long term use. While we cannot yet endorse this as an ALS treatment, there is enough evidence to warrant another larger ALS trial.

Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science.

Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson's Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.

Design and analysis of a clinical trial using previous trials as historical control.

BACKGROUND/AIMS: For single arm trials, a treatment is evaluated by comparing an outcome estimate to historically reported outcome estimates. Such a historically controlled trial is often analyzed as if the estimates from previous trials were known without variation and there is no trial-to-trial variation in their estimands. We develop a test of treatment efficacy and sample size calculation for historically controlled trials that considers these sources of variation. METHODS: We fit a Bayesian hierarchical model, providing a sample from the posterior predictive distribution of the outcome estimand of a new trial, which, along with the standard error of the estimate, can be used to calculate the probability that the estimate exceeds a threshold. We then calculate criteria for statistical significance as a function of the standard error of the new trial and calculate sample size as a function of difference to be detected. We apply these methods to clinical trials for amyotrophic lateral sclerosis using data from the placebo groups of 16 trials. RESULTS: We find that when attempting to detect the small to moderate effect sizes usually assumed in amyotrophic lateral sclerosis clinical trials, historically controlled trials would require a greater total number of patients than concurrently controlled trials, and only when an effect size is extraordinarily large is a historically controlled trial a reasonable alternative. We also show that utilizing patient level data for the prognostic covariates can reduce the sample size required for a historically controlled trial. CONCLUSION: This article quantifies when historically controlled trials would not provide any sample size advantage, despite dispensing with a control group.

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development.

Laquinimod Treatment Improves Myelination Deficits at the Transcriptional and Ultrastructural Levels in the YAC128 Mouse Model of Huntington Disease.

Laquinimod, an immunomodulatory agent under clinical development for Huntington disease (HD), has recently been shown to confer behavioural improvements that are coupled with prevention of atrophy of the white matter (WM)-rich corpus callosum (CC) in the YAC128 HD mice. However, the nature of the WM improvements is not known yet. Here we investigated the effects of laquinimod on HD-related myelination deficits at the cellular, molecular and ultrastructural levels. We showed that laquinimod treatment improves motor learning and motor function deficits in YAC128 HD mice, and confirmed its antidepressant effect even at the lowest dose used. In addition, we demonstrated for the first time the beneficial effects of laquinimod on myelination in the posterior region of the CC where it reversed changes in myelin sheath thickness and rescued Mbp mRNA and protein deficits. Furthermore, the effect of laquinimod on myelin-related gene expression was not region-specific since the levels of the Mbp and Plp1 transcripts were also increased in the striatum. Also, we did not detect changes in immune cell densities or levels of inflammatory genes in 3-month-old YAC128 HD mice, and these were not altered with laquinimod treatment. Thus, the beneficial effects of laquinimod on HD-related myelination abnormalities in YAC128 HD mice do not appear to be dependent on its immunomodulatory activity. Altogether, our findings describe the beneficial effects of laquinimod treatment on HD-related myelination abnormalities and highlight its therapeutic potential for the treatment of WM pathology in HD patients.

Predicting disease progression in amyotrophic lateral sclerosis.

OBJECTIVE: It is essential to develop predictive algorithms for Amyotrophic Lateral Sclerosis (ALS) disease progression to allow for efficient clinical trials and patient care. The best existing predictive models rely on several months of baseline data and have only been validated in clinical trial research datasets. We asked whether a model developed using clinical research patient data could be applied to the broader ALS population typically seen at a tertiary care ALS clinic. METHODS: Based on the PRO-ACT ALS database, we developed random forest (RF), pre-slope, and generalized linear (GLM) models to test whether accurate, unbiased models could be created using only baseline data. Secondly, we tested whether a model could be validated with a clinical patient dataset to demonstrate broader applicability. RESULTS: We found that a random forest model using only baseline data could accurately predict disease progression for a clinical trial research dataset as well as a population of patients being treated at a tertiary care clinic. The RF Model outperformed a pre-slope model and was similar to a GLM model in terms of root mean square deviation at early time points. At later time points, the RF Model was far superior to either model. Finally, we found that only the RF Model was unbiased and was less subject to overfitting than either of the other two models when applied to a clinic population. INTERPRETATION: We conclude that the RF Model delivers superior predictions of ALS disease progression.

Epidemiology of Amyotrophic Lateral Sclerosis: A Population-Based Study in Israel.

BACKGROUND: Globally, the annual incidence and prevalence of amyotrophic lateral sclerosis (ALS) are estimated at 1.9 and 4.5 per 100,000 population, respectively. This study is aimed at describing the epidemiology of ALS in Israel in a real-world setting. METHODS: A retrospective study was performed using the databases of Maccabi Healthcare Services (MHS), a 2-million-member health maintenance organization in Israel. The study included all MHS adults diagnosed with ALS between 1997 and 2013. In 2013, characteristics of ALS patients were compared to those of age-sex-matched patients without ALS. Survival after ALS diagnosis was assessed until death and until tracheostomy or death (follow-up through 2014). RESULTS: In 2013 (n = 158), the prevalence of ALS was 8.1 per 100,000 population in MHS. In 1997-2013, a total of 375 ALS patients were diagnosed, corresponding to an average annual incidence of 1.8 per 100,000 population in MHS. The median survival from diagnosis to death was 3.5 years (95% CI 2.9-4.1), with approximately 28% surviving at least 10 years. Median tracheostomy-free survival was 2.5 years (95% CI 2.1-2.9). CONCLUSIONS: Results suggest that there is a relatively high prevalence of ALS in Israel. Further research is needed to investigate factors that may contribute to the survival of patients with ALS in Israel.

Big data to smart data in Alzheimer's disease: Real-world examples of advanced modeling and simulation.

Many disease-modifying clinical development programs in Alzheimer's disease (AD) have failed to date, and development of new and advanced preclinical models that generate actionable knowledge is desperately needed. This review reports on computer-based modeling and simulation approach as a powerful tool in AD research. Statistical data-analysis techniques can identify associations between certain data and phenotypes, such as diagnosis or disease progression. Other approaches integrate domain expertise in a formalized mathematical way to understand how specific components of pathology integrate into complex brain networks. Private-public partnerships focused on data sharing, causal inference and pathway-based analysis, crowdsourcing, and mechanism-based quantitative systems modeling represent successful real-world modeling examples with substantial impact on CNS diseases. Similar to other disease indications, successful real-world examples of advanced simulation can generate actionable support of drug discovery and development in AD, illustrating the value that can be generated for different stakeholders.

Item response theory analysis of the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised in the Pooled Resource Open-Access ALS Clinical Trials Database.

Our objective was to examine dimensionality and item-level performance of the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) across time using classical and modern test theory approaches. Confirmatory factor analysis (CFA) and Item Response Theory (IRT) analyses were conducted using data from patients with amyotrophic lateral sclerosis (ALS) Pooled Resources Open-Access ALS Clinical Trials (PRO-ACT) database with complete ALSFRS-R data (n = 888) at three time-points (Time 0, Time 1 (6-months), Time 2 (1-year)). Results demonstrated that in this population of 888 patients, mean age was 54.6 years, 64.4% were male, and 93.7% were Caucasian. The CFA supported a 4* individual-domain structure (bulbar, gross motor, fine motor, and respiratory domains). IRT analysis within each domain revealed misfitting items and overlapping item response category thresholds at all time-points, particularly in the gross motor and respiratory domain items. Results indicate that many of the items of the ALSFRS-R may sub-optimally distinguish among varying levels of disability assessed by each domain, particularly in patients with less severe disability. Measure performance improved across time as patient disability severity increased. In conclusion, modifications to select ALSFRS-R items may improve the instrument's specificity to disability level and sensitivity to treatment effects.

Being PRO-ACTive: What can a Clinical Trial Database Reveal About ALS?

Advancing research and clinical care, and conducting successful and cost-effective clinical trials requires characterizing a given patient population. To gather a sufficiently large cohort of patients in rare diseases such as amyotrophic lateral sclerosis (ALS), we developed the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) platform. The PRO-ACT database currently consists of >8600 ALS patient records from 17 completed clinical trials, and more trials are being incorporated. The database was launched in an open-access mode in December 2012; since then, >400 researchers from >40 countries have requested the data. This review gives an overview on the research enabled by this resource, through several examples of research already carried out with the goal of improving patient care and understanding the disease. These examples include predicting ALS progression, the simulation of future ALS clinical trials, the verification of previously proposed predictive features, the discovery of novel predictors of ALS progression and survival, the newly identified stratification of patients based on their disease progression profiles, and the development of tools for better clinical trial recruitment and monitoring. Results from these approaches clearly demonstrate the value of large datasets for developing a better understanding of ALS natural history, prognostic factors, patient stratification, and more. The increasing use by the community suggests that further analyses of the PRO-ACT database will continue to reveal more information about this disease that has for so long defied our understanding.

Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in its clinical presentation. This makes diagnosis and effective treatment difficult, so better tools for estimating disease progression are needed. Here, we report results from the DREAM-Phil Bowen ALS Prediction Prize4Life challenge. In this crowdsourcing competition, competitors developed algorithms for the prediction of disease progression of 1,822 ALS patients from standardized, anonymized phase 2/3 clinical trials. The two best algorithms outperformed a method designed by the challenge organizers as well as predictions by ALS clinicians. We estimate that using both winning algorithms in future trial designs could reduce the required number of patients by at least 20%. The DREAM-Phil Bowen ALS Prediction Prize4Life challenge also identified several potential nonstandard predictors of disease progression including uric acid, creatinine and surprisingly, blood pressure, shedding light on ALS pathobiology. This analysis reveals the potential of a crowdsourcing competition that uses clinical trial data for accelerating ALS research and development.

The PRO-ACT database: design, initial analyses, and predictive features.

OBJECTIVE: To pool data from completed amyotrophic lateral sclerosis (ALS) clinical trials and create an open-access resource that enables greater understanding of the phenotype and biology of ALS. METHODS: Clinical trials data were pooled from 16 completed phase II/III ALS clinical trials and one observational study. Over 8 million de-identified longitudinally collected data points from over 8,600 individuals with ALS were standardized across trials and merged to create the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database. This database includes demographics, family histories, and longitudinal clinical and laboratory data. Mixed effects models were used to describe the rate of disease progression measured by the Revised ALS Functional Rating Scale (ALSFRS-R) and vital capacity (VC). Cox regression models were used to describe survival data. Implementing Bonferroni correction, the critical p value for 15 different tests was p = 0.003. RESULTS: The ALSFRS-R rate of decline was 1.02 (±2.3) points per month and the VC rate of decline was 2.24% of predicted (±6.9) per month. Higher levels of uric acid at trial entry were predictive of a slower drop in ALSFRS-R (p = 0.01) and VC (p < 0.0001), and longer survival (p = 0.02). Higher levels of creatinine at baseline were predictive of a slower drop in ALSFRS-R (p = 0.01) and VC (p < 0.0001), and longer survival (p = 0.01). Finally, higher body mass index (BMI) at baseline was associated with longer survival (p < 0.0001). CONCLUSION: The PRO-ACT database is the largest publicly available repository of merged ALS clinical trials data. We report that baseline levels of creatinine and uric acid, as well as baseline BMI, are strong predictors of disease progression and survival.

Multiple kernel learning captures a systems-level functional connectivity biomarker signature in amyotrophic lateral sclerosis.

There is significant clinical and prognostic heterogeneity in the neurodegenerative disorder amyotrophic lateral sclerosis (ALS), despite a common immunohistological signature. Consistent extra-motor as well as motor cerebral, spinal anterior horn and distal neuromuscular junction pathology supports the notion of ALS a system failure. Establishing a disease biomarker is a priority but a simplistic, coordinate-based approach to brain dysfunction using MRI is not tenable. Resting-state functional MRI reflects the organization of brain networks at the systems-level, and so changes in of motor functional connectivity were explored to determine their potential as the substrate for a biomarker signature. Intra- as well as inter-motor functional networks in the 0.03-0.06 Hz frequency band were derived from 40 patients and 30 healthy controls of similar age, and used as features for pattern detection, employing multiple kernel learning. This approach enabled an accurate classification of a group of patients that included a range of clinical sub-types. An average of 13 regions-of-interest were needed to reach peak discrimination. Subsequent analysis revealed that the alterations in motor functional connectivity were widespread, including regions not obviously clinically affected such as the cerebellum and basal ganglia. Complex network analysis showed that functional networks in ALS differ markedly in their topology, reflecting the underlying altered functional connectivity pattern seen in patients: 1) reduced connectivity of both the cortical and sub-cortical motor areas with non motor areas 2)reduced subcortical-cortical motor connectivity and 3) increased connectivity observed within sub-cortical motor networks. This type of analysis has potential to non-invasively define a biomarker signature at the systems-level. As the understanding of neurodegenerative disorders moves towards studying pre-symptomatic changes, there is potential for this type of approach to generate biomarkers for the testing of neuroprotective strategies.