Dynamics of Cortical Degeneration Over a Decade in Huntington's Disease

BACKGROUND: Characterizing changing brain structure in neurodegeneration is fundamental to understanding longterm effects of pathology and ultimately providing therapeutic targets. It is well established that Huntington’s disease (HD) gene carriers undergo progressive brain changes during the course of disease, yet the long-term trajectory of cortical atrophy is not well defined. Given that genetic therapies currently tested in HD are primarily expected to target the cortex, understanding atrophy across this region is essential. METHODS: Capitalizing on a unique longitudinal dataset with a minimum of 3 and maximum of 7 brain scans from 49 HD gene carriers and 49 age-matched control subjects, we implemented a novel dynamical systems approach to infer patterns of regional neurodegeneration over 10 years. We use Bayesian hierarchical modeling to map participant- and group-level trajectories of atrophy spatially and temporally, additionally relating atrophy to the genetic marker of HD (CAG-repeat length) and motor and cognitive symptoms. RESULTS: We show, for the first time, that neurodegenerative changes exhibit complex temporal dynamics with substantial regional variation around the point of clinical diagnosis. Although widespread group differences were seen across the cortex, the occipital and parietal regions undergo the greatest rate of cortical atrophy. We have established links between atrophy and genetic markers of HD while demonstrating that specific cortical changes predict decline in motor and cognitive performance. CONCLUSIONS: HD gene carriers display regional variability in the spatial pattern of cortical atrophy, which relates to genetic factors and motor and cognitive symptoms. Our findings indicate a complex pattern of neuronal loss, which enables greater characterization of HD progression

Test-Retest Reliability of Diffusion Tensor Imaging in Huntington's Disease.

Diffusion tensor imaging (DTI) has shown microstructural abnormalities in patients with Huntington's Disease (HD) and work is underway to characterise how these abnormalities change with disease progression. Using methods that will be applied in longitudinal research, we sought to establish the reliability of DTI in early HD patients and controls. Test-retest reliability, quantified using the intraclass correlation coefficient (ICC), was assessed using region-of-interest (ROI)-based white matter atlas and voxelwise approaches on repeat scan data from 22 participants (10 early HD, 12 controls). T1 data was used to generate further ROIs for analysis in a reduced sample of 18 participants. The results suggest that fractional anisotropy (FA) and other diffusivity metrics are generally highly reliable, with ICCs indicating considerably lower within-subject compared to between-subject variability in both HD patients and controls. Where ICC was low, particularly for the diffusivity measures in the caudate and putamen, this was partly influenced by outliers. The analysis suggests that the specific DTI methods used here are appropriate for cross-sectional research in HD, and give confidence that they can also be applied longitudinally, although this requires further investigation. An important caveat for DTI studies is that test-retest reliability may not be evenly distributed throughout the brain whereby highly anisotropic white matter regions tended to show lower relative within-subject variability than other white or grey matter regions

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

An image-based model of brain volume biomarker changes in Huntington's disease

Objective: Determining the sequence in which Huntington's disease biomarkers become abnormal can provide important insights into the disease progression and a quantitative tool for patient stratification. Here, we construct and present a uniquely fine-grained model of temporal progression of Huntington's disease from premanifest through to manifest stages. Methods: We employ a probabilistic event-based model to determine the sequence of appearance of atrophy in brain volumes, learned from structural MRI in the Track-HD study, as well as to estimate the uncertainty in the ordering. We use longitudinal and phenotypic data to demonstrate the utility of the patient staging system that the resulting model provides. Results: The model recovers the following order of detectable changes in brain region volumes: putamen, caudate, pallidum, insula white matter, nonventricular cerebrospinal fluid, amygdala, optic chiasm, third ventricle, posterior insula, and basal forebrain. This ordering is mostly preserved even under cross-validation of the uncertainty in the event sequence. Longitudinal analysis performed using 6 years of follow-up data from baseline confirms efficacy of the model, as subjects consistently move to later stages with time, and significant correlations are observed between the estimated stages and nonimaging phenotypic markers. Interpretation: We used a data-driven method to provide new insight into Huntington's disease progression as well as new power to stage and predict conversion. Our results highlight the potential of disease progression models, such as the event-based model, to provide new insight into Huntington's disease progression and to support fine-grained patient stratification for future precision medicine in Huntington's disease

Cross infection control measures and the treatment of patients at risk of Creutzfeldt Jakob disease in UK general dental practice

AIMS: To determine the suitability of key infection control measures currently employed in UK dental practice for delivery of dental care to patients at risk of prion diseases. MATERIALS AND METHODS: Subjects: Five hundred dental surgeons currently registered with the General Dental Council of the UK. Data collection: Structured postal questionnaire. Analysis: Frequencies, cross-tabulations and chi-squared analysis. RESULTS: The valid response rate to the questionnaire was 69%. 33% of practices had no policy on general disinfection and sterilisation procedures. Only 10 of the 327 responding practices (3%) possessed a vacuum autoclave. 49% of dentists reported using the BDA medical history form but less than 25% asked the specific questions recommended by the BDA to identify patients at risk of iatrogenic or familial CJD. However, 63% of practitioners would refer such patients, if identified, to a secondary care facility. Of the 107 practitioners who were prepared to provide dental treatment, 75 (70%) would do so using routine infection control procedures. CONCLUSIONS: Most of the dental practices surveyed were not actively seeking to identify patients at risk of prion diseases. In many cases, recommended procedures for providing safe dental care for such patients were not in place

A Quantitative Method to Analyze Drosophila Pupal Eye Patterning

BACKGROUND:The Drosophila pupal eye has become a popular paradigm for understanding morphogenesis and tissue patterning. Correct rearrangement of cells between ommatidia is required to organize the ommatidial array across the eye field. This requires cell movement, cell death, changes to cell-cell adhesion, signaling and fate specification. METHODOLOGY:We describe a method to quantitatively assess mis-patterning of the Drosophila pupal eye and objectively calculate a 'mis-patterning score' characteristic of a specific genotype. This entails step-by-step scoring of specific traits observed in pupal eyes dissected 40-42 hours after puparium formation and subsequent statistical analysis of this data. SIGNIFICANCE:This method provides an unbiased quantitative score of mis-patterning severity that can be used to compare the impact of different genetic mutations on tissue patterning

Macroalgae as a sustainable aquafeed ingredient

Macroalgae, commonly known as seaweed, offer a novel and added‐value dietary ingredient in formulated diets for fish. Production of biomass can be achieved without reliance on expensive arable land, as seaweed may be collected from coastal regions or farmed. There are three taxonomic groups represented by the term ‘macroalgae’: Rhodophyta (red), Chlorophyta (green) and Phaeophyta (brown). Like terrestrial plants, nutritional content in macroalgae can vary greatly amongst species, genera, divisions, seasons and locations. Aside from their basic nutritional value, seaweeds contain a number of pigments, defensive and storage compounds, and secondary metabolites that could have beneficial effects on farmed fish. This review appraises the beneficial qualities of these macroalgae compounds and their potential for exploitation in commercial finfish feeds. The current knowledge of the effects of macroalgae inclusion in finfish diets is also addressed. From these >50 fish feeding studies that were analysed, enhancing trends in fish growth, physiology, stress resistance, immune system and fillet muscle quality were reported. However, only a small fraction of algal species have so far been investigated as potential components in finfish diets, and furthermore, this review has identified a number of knowledge gaps that current research has yet to address. To conclude, an appraisal is made of the possible technologies employed to exploit seaweeds to an industrial level through stabilising the algal meal, enhancing the digestibility and functional food properties

Multimodal characterization of the visual network in Huntington's disease gene carriers

Objective A sensorimotor network structural phenotype predicted motor task performance in a previous study in Huntington’s disease (HD) gene carriers. We investigated in the visual network whether structure – function – behaviour relationship patterns, and the effects of the HD mutation, extended beyond the sensorimotor network. Methods We used multimodal visual network MRI structural measures (cortical thickness and white matter connectivity), plus visual evoked potentials and task performance (Map Search; Symbol Digit Modalities Test) in healthy controls and HD gene carriers. Results Using principal component (PC) analysis, we identified a structure – function relationship common to both groups. PC scores differed between groups indicating white matter disorganization (higher RD, lower FA) and slower, and more disperse, VEP signal transmission (higher VEP P100 latency and lower VEP P100 amplitude) in HD than controls while task performance was similar. Conclusions HD may be associated with reduced white matter organization and efficient visual network function but normal task performance. Significance These findings indicate that structure – function relationships in the visual network, and the effects of the HD mutation, share some commonalities with those in the sensorimotor network. However, implications for task performance differ between the two networks suggesting the influence of network specific factors