Summary Background Huntington’s disease (HD) is an autosomal dominant, fully penetrant, neurodegenerative disease that most commonly affects adults in mid-life. Our aim was to identify sensitive and reliable biomarkers in premanifest carriers of mutated HTT and in individuals with early HD that could provide essential methodology for the assessment of therapeutic interventions. Methods This multicentre study uses an extensive battery of novel assessments, including multi-site 3T MRI, clinical, cognitive, quantitative motor, oculomotor, and neuropsychiatric measures. Blinded analyses were done on the baseline cross-sectional data from 366 individuals: 123 controls, 120 premanifest (pre-HD) individuals, and 123 patients with early HD. Findings The first participant was enrolled in January, 2008, and all assessments were completed by August, 2008. Cross-sectional analyses identified significant changes in whole-brain volume, regional grey and white matter differences, impairment in a range of voluntary neurophysiological motor, and oculomotor tasks, and cognitive and neuropsychiatric dysfunction in premanifest HD gene carriers with normal motor scores through to early clinical stage 2 disease. Interpretation We show the feasibility of rapid data acquisition and the use of multi-site 3T MRI and neurophysiological motor measures in a large multicentre study. Our results provide evidence for quantifiable biological and clinical alterations in HTT expansion carriers compared with age-matched controls. Many parameters differ from age-matched controls in a graded fashion and show changes of increasing magnitude across our cohort, who range from about 16 years from predicted disease diagnosis to early HD. These findings might help to define novel quantifiable endpoints and methods for rapid and reliable data acquisition, which could aid the design of therapeutic trials. Funding CHDI/High Q Foundation.

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BackgroundHuntington's disease (HD) is a fatal inherited neurodegenerative disease, caused by a

Adaptive tracking-by-detection methods are widely used in computer vision for tracking arbitrary objects. Current approaches treat the tracking problem as a classification task and use online learning techniques to update the object model. However, for these updates to happen one needs to convert the estimated object position into a set of labelled training examples, and it is not clear how best to perform this intermediate step. Furthermore, the objective for the classifier (label prediction) is not explicitly coupled to the objective for the tracker (estimation of object position). In this paper, we present a framework for adaptive visual object tracking based on structured output prediction. By explicitly allowing the output space to express the needs of the tracker, we avoid the need for an intermediate classification step. Our method uses a kernelised structured output support vector machine (SVM), which is learned online to provide adaptive tracking. To allow our tracker to run at high frame rates, we (a) introduce a budgeting mechanism that prevents the unbounded growth in the number of support vectors that would otherwise occur during tracking, and (b) show how to implement tracking on the GPU. Experimentally, we show that our algorithm is able to outperform state-of-the-art trackers on various benchmark videos. Additionally, we show that we can easily incorporate additional features and kernels into our framework, which results in increased tracking performance.

Huntington's disease is caused by a known genetic mutation and so potentially can be diagnosed many years before the onset of symptoms. Neuropathological changes have been found in both striatum and frontal cortex in the pre-symptomatic stage. Disruption of cortico-striatal white matter fibre tracts is therefore likely to contribute to the first clinical signs of the disease. We analysed diffusion tensor MR image (DTI) data from 25 pre-symptomatic gene carriers (PSCs) and 20 matched controls using a multivariate support vector machine to identify patterns of changes in fractional anisotropy (FA). In addition, we performed probabilistic fibre tracking to detect changes in 'streamlines' connecting frontal cortex to striatum. We found a pattern of structural brain changes that includes putamen bilaterally as well as anterior parts of the corpus callosum. This pattern was sufficiently specific to enable us to correctly classify 82% of scans as coming from a PSC or control subject. Fibre tracking revealed a reduction of frontal cortico-fugal streamlines reaching the body of the caudate in PSCs compared to controls. In the left hemispheres of PSCs we found a negative correlation between years to estimated disease onset and streamlines from frontal cortex to body of caudate. A large proportion of the fibres to the caudate body originate from the frontal eye fields, which play an important role in the control of voluntary saccades. This type of saccade is specifically impaired in PSCs and is an early clinical sign of motor abnormalities. A correlation analysis in 14 PSCs revealed that subjects with greater impairment of voluntary-guided saccades had fewer fibre tracking streamlines connecting the frontal cortex and caudate body. Our findings suggest a specific patho-physiological basis for these symptoms by indicating selective vulnerability of the associated white matter tracts.

Disturbances in the white matter connections of the sensorimotor cortex can be demonstrated not only in manifest HD but also in premanifest gene carriers. Connectivity measures are well related to clinical functioning. DTI measures can be regarded as a potential biomarker for HD, due to their ability to objectify changes in brain structures and their role within brain networks.

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