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.

Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing.

Longitudinal neuroimaging analysis methods have remarkably advanced our understanding of early postnatal brain development. However, learning predictive models to trace forth the evolution trajectories of both normal and abnormal cortical shapes remains broadly absent. To fill this critical gap, we pioneered the first prediction model for longitudinal developing cortical surfaces in infants using a spatiotemporal current-based learning framework solely from the baseline cortical surface. In this paper, we detail this prediction model and even further improve its performance by introducing two key variants. First, we use the varifold metric to overcome the limitations of the current metric for surface registration that was used in our preliminary study. We also extend the conventional varifold-based surface registration model for pairwise registration to a spatiotemporal surface regression model. Second, we propose a morphing process of the baseline surface using its topographic attributes such as normal direction and principal curvature sign. Specifically, our method learns from longitudinal data both the geometric (vertices positions) and dynamic (temporal evolution trajectories) features of the infant cortical surface, comprising a training stage and a prediction stage. In the training stage, we use the proposed varifold-based shape regression model to estimate geodesic cortical shape evolution trajectories for each training subject. We then build an empirical mean spatiotemporal surface atlas. In the prediction stage, given an infant, we select the best learnt features from training subjects to simultaneously predict the cortical surface shapes at all later timepoints, based on similarity metrics between this baseline surface and the learnt baseline population average surface atlas. We used a leave-one-out cross validation method to predict the inner cortical surface shape at 3, 6, 9 and 12 months of age from the baseline cortical surface shape at birth. Our method attained a higher prediction accuracy and better captured the spatiotemporal dynamic change of the highly folded cortical surface than the previous proposed prediction method.

Diagnostic efficacy of panoramic radiography in detection of osteoporosis in post-menopausal women with low bone mineral density.

OBJECTIVE: The aim of the study was to evaluate panoramic radiograph, a commonly taken dental radiograph as a screening tool to detect early osseous changes (normal, mildly or severely eroded) of the mandibular inferior cortex and measure the mandibular cortical width (CW) in post-menopausal women and correlate it with the bone mineral density (BMD) measured by the ultrasound bone sonometer at the mid-shaft tibia region. MATERIALS AND METHODS: The study included females between 45 years and 65 years of age in their post-menopausal stage (no menstruation for at least 6-12 months). Mandibular indices (mandibular CW and mandibular cortical shape) were evaluated from panoramic radiographs. The BMD assessment was carried out at the mid-shaft tibia region, exactly half-way between the heel and the knee joint perpendicular to the direction of the bone, using an ultrasound bone sonometer. It is a non-invasive device designed for quantitative measurement of the velocity of ultrasound waves as speed of sound in m/s, capable of measuring bone density at one or more skeletal sites. Using 1994 WHO criteria the study subjects were categorized as Group 1: Normal, Group 2: Osteopenia, Group 3: Osteoporosis. (WHO T score for tibia BMD can be used as a standard). RESULTS: The diagnostic efficacy of the panoramic radiograph in detecting osseous changes in post-menopausal women with low BMD was shown to have 96% specificity and 60% sensitivity with mandibular cortical shape and 58% specificity and 73% sensitivity with mandibular CW measurement. Factorial ANOVA analysis carried out indicated a significant correlation of BMD classification with mandibular cortical shape (F = 29.0, P < 0.001, partial eta squared [η(2)] =0.85), a non-significant correlation with mandibular CW, (F = 1.6, P = 0.23, η(2) = 0.86), and a more significant correlation with combined cortical shape and width (F = 3.3, P < 0.05, η(2) = 0.70). CONCLUSION: The study concludes that the combined mandibular cortical findings (P < 0.05) and mandibular cortical shape erosion alone (P < 0.001) on panoramic radiograph are effective indicators of osteoporosis in post-menopausal women.