Irritability in Huntington’s Disease:factor analysis of Snaith’s Irritability scale

Background Elevated levels of irritability are reported to occur in a number of neurological conditions, including Huntington's disease (HD), a genetic neurodegenerative disorder. Snaith's Irritability Scale (SIS) is used within HD research, but no psychometric evaluation of this instrument has previously been undertaken. Therefore, the current study aimed to analyze the factor structure of this scale among an HD population. Methods Exploratory and confirmatory factor analysis were used to examine the structural properties of SIS using responses from 1,264 HD gene expansion carriers, across 15 European countries, who were engaged in the REGISTRY 3 study. Results An exploratory factor analysis of a subsample of the data suggested a two-factor interpretation of the data comprising “temper” and “self-harm.” Eight possible models were tested for goodness of fit using confirmatory factor analysis. Two bifactor models, testing general and group factors in the structure of the scale, provided an equivocal “good” fit to the data. The first comprised a general irritability factor and two group factors (as originally proposed using SIS): outward irritability and inward irritability. The second comprised a general irritability factor and two group factors (as proposed by the exploratory factor analysis): temper and self-harm. The findings from both models suggested that the loadings of items were higher on the general factor. Conclusions Bifactor models are proposed to best consider the structure of the SIS, with findings suggesting that an overall score should be used to measure irritability within HD populations

Large-scale screening of clinical assessments to distinguish between states in the Integrated HD Progression Model (IHDPM)

BackgroundUnderstanding the sensitivity and utility of clinical assessments across different HD stages is important for study/trial endpoint selection and clinical assessment development. The Integrated HD Progression Model (IHDPM) characterizes the complex symptom progression of HD and separates the disease into nine ordered disease states.ObjectiveTo generate a temporal map of discriminatory clinical measures across the IHDPM states.MethodsWe applied the IHDPM to all HD individuals in an integrated longitudinal HD dataset derived from four observational studies, obtaining disease state assignment for each study visit. Using large-scale screening, we estimated Cohen’s effect sizes to rank the discriminative power of 2,472 clinical measures for separating observations in disease state pairs. Individual trajectories through IHDPM states were examined. Discriminative analyses were limited to individuals with observations in both states of the pairs compared (N = 3,790).ResultsDiscriminative clinical measures were heterogeneous across the HD life course. UHDRS items were frequently identified as the best state pair discriminators, with UHDRS Motor items – most notably TMS – showing the highest discriminatory power between the early-disease states and early post-transition period states. UHDRS functional items emerged as strong discriminators from the transition period and on. Cognitive assessments showed good discriminative power between all state pairs examined, excepting state 1 vs. 2. Several non-UHDRS assessments were also flagged as excellent state discriminators for specific disease phases (e.g., SF-12). For certain state pairs, single assessment items other than total/summary scores were highlighted as having excellent discriminative power.ConclusionBy providing ranked quantitative scores indicating discriminatory ability of thousands of clinical measures between specific pairs of IHDPM states, our results will aid clinical trial designers select the most effective outcome measures tailored to their study cohort. Our observations may also assist in the development of end points targeting specific phases in the disease life course, through providing specific conceptual foci

Epidemiology

Epidemiology is a methodological, well grounded and versatile tool-kit to conduct evidence-based quantitative research in all health sciences. It integrates a wide spectrum of case studies and examples from the different disciplines thereby fostering the multi-disciplinary approach in the health sciences. It follows a two level 'methods based' approach differentiating between "basic" knowledge that all students of epidemiology should be familiar with and "beyond the basics" information for the interested or more advanced reader

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

The development and validation of a scale to measure the impact of Huntington's Disease on the quality of life of spousal carers.

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Fetal striatal grafting slows motor and cognitive decline of Huntington's disease

OBJECTIVE: To assess the clinical effect of caudate-putaminal transplantation of fetal striatal tissue in Huntington's disease (HD). METHODS: We carried out a follow-up study on 10 HD transplanted patients and 16 HD not-transplanted patients. All patients were evaluated with the Unified HD Rating Scale (UHDRS) whose change in motor, cognitive, behavioural and functional capacity total scores were considered as outcome measures. Grafted patients also received morphological and molecular neuroimaging. RESULTS: Patients were followed-up from disease onset for a total of 309.3 person-years (minimum 5.3, median 11.2 years, maximum 21.6 years). UHDRS scores have been available since 2004 (median time of 5.7 years since onset, minimum zero, maximum 17.2 years). Median post-transplantation follow-up was 4.3 years, minimum 2.8, maximum 5.1 years. Adjusted post-transplantation motor score deterioration rate was reduced compared to the pretransplantation period, and to that of not-transplanted patients by 0.9 unit/years (95% CI 0.2 to 1.6). Cognitive score deterioration was reduced of 2.7 unit/years (95% CI 0.1 to 5.3). For grafted patients the 2-year post-transplantation [(18)F]fluorodeoxyglucose positron emission tomography (PET) showed striatal/cortical metabolic increase compared to the presurgical evaluation; 4-year post-transplantation PET values were slightly decreased, but remained higher than preoperatively. [(123)I]iodobenzamide single photon emission CT demonstrated an increase in striatal D2-receptor density during postgrafting follow-up. CONCLUSIONS: Grafted patients experienced a milder clinical course with less pronounced motor/cognitive decline and associated brain metabolism improvement. Life-time follow-up may ultimately clarify whether transplantation permanently modifies the natural course of the disease, allowing longer sojourn time at less severe clinical stage, and improvement of overall survival

Development and evaluation of biomarkers in Huntington’s Disease: furthering our understanding of the disease and preparing for clinical trials

Huntington’s Disease (HD) is a devastating hereditary neurodegenerative disease for which there are currently only symptomatic treatments. Several potentially curative pharmaceutical and genetic therapies are however in varying stages of development and therefore an increasing number of large-scale clinical trials of disease-modifying therapies are imminent. There is consequently a need for biomarkers which are sensitive to beneficial attenuation of disease-related changes. Functional, neuroimaging and biochemical biomarkers have been developed in HD (Andre et al. 2014;Weir et al. 2011). Neuroimaging biomarkers are strong candidates based on their clear relevance to the neuropathology of disease, proven precision and superior sensitivity compared with some standard functional measures (Tabrizi et al. 2011;Tabrizi et al. 2012). Their use in early-stage clinical trials, as surrogate end-points providing initial evidence of biological effect, is becoming increasingly common. Comparison of biomarkers in HD will help to clarify which measures, over varying time intervals, are most sensitive to disease progression. Additionally, the identification of robust fully-automated methods, comparable to manual and semi-automated gold-standards, would facilitate large-scale volumetric analysis. These methods however require validation in observational studies of neurodegenerative disease before they can be applied to sensitive clinical trial data. This thesis will develop and evaluate biomarkers for use in HD; both furthering our understanding of the disease and in preparation for use as end-points in clinical trials. A direct comparison of the sensitivity of diffusion and volumetric imaging biomarkers to HD-related change will be reported for the first time. Several exploratory imaging investigations are also described which enhance current knowledge of the relationship between neuroimaging metrics, brain functioning and behaviour, additionally strengthening the argument for the clinical relevance of neuroimaging measures as surrogate end-points in HD. The thesis will conclude with a comprehensive biomarker evaluation in early-stage HD, along with suggested strategies for selection of primary and secondary trial end-points based on effect sizes and corresponding sample size requirements

Study Protocol for the Development of a European eHealth Platform to Improve Quality of Life in Individuals With Huntington's Disease and Their Partners (HD-eHelp Study): A User-Centered Design Approach

Background: Huntington's disease (HD) is an autosomal dominant neurodegenerative disease that affects the quality of life (QoL) of HD gene expansion carriers (HDGECs) and their partners. Although HD expertise centers have been emerging across Europe, there are still some important barriers to care provision for those affected by this rare disease, including transportation costs, geographic distance of centers, and availability/accessibility of these services in general. eHealth seems promising in overcoming these barriers, yet research on eHealth in HD is limited and fails to use telehealth services specifically designed to fit the perspectives and expectations of HDGECs and their families. In the European HD-eHelp study, we aim to capture the needs and wishes of HDGECs, partners of HDGECs, and health care providers (HCPs) in order to develop a multinational eHealth platform targeting QoL of both HDGECs and partners at home.Methods: We will employ a participatory user-centered design (UCD) approach, which focusses on an in-depth understanding of the end-users' needs and their contexts. Premanifest and manifest adult HDGECs (n = 76), partners of HDGECs (n = 76), and HCPs (n = 76) will be involved as end-users in all three phases of the research and design process: (1) Exploration and mapping of the end-users' needs, experiences and wishes; (2) Development of concepts in collaboration with end-users to ensure desirability; (3) Detailing of final prototype with quick review rounds by end-users to create a positive user-experience. This study will be conducted in the Netherlands, Germany, Czech Republic, Italy, and Ireland to develop and test a multilingual platform that is suitable in different healthcare systems and cultural contexts.Discussion: Following the principles of UCD, an innovative European eHealth platform will be developed that addresses the needs and wishes of HDGECs, partners and HCPs. This allows for high-quality, tailored care to be moved partially into the participants' home, thereby circumventing some barriers in current HD care provision. By actively involving end-users in all design decisions, the platform will be tailored to the end-users' unique requirements, which can be considered pivotal in eHealth services for a disease as complex and rare as HD

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction☆

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome