Longitudinal changes of SARA scale in Friedreich ataxia: Strong influence of baseline score and age at onset

BACKGROUND: The Scale for Assessment and Rating of Ataxia (SARA) is widely used in different types of ataxias and has been chosen as the primary outcome measure in the European natural history study for Friedreich ataxia (FA). METHODS: To assess distribution and longitudinal changes of SARA scores and its single items, we analyzed SARA scores of 502 patients with typical-onset FA (<25 years) participating in the 4-year prospective European FA Consortium for Translational Studies (EFACTS). Pattern of disease progression was determined using linear mixed-effects regression models. The chosen statistical model was re-fitted in order to estimate parameters and predict disease progression. Median time-to-change and rate of score progression were estimated using the Kaplan-Meier method and weighted linear regression models, respectively. RESULTS: SARA score at study enrollment and age at onset were the major predictive factors of total score progression during the 4-year follow-up. To a less extent, age at evaluation also influenced the speed of SARA progression, while disease duration did not improve the prediction of the statistical model. Temporal dynamics of total SARA and items showed a great variability in the speed of score increase during disease progression. Gait item had the highest annual progression rate, with median time for one-point score increase of 1 to 2 years. INTERPRETATION: Analyses of statistical properties of SARA suggest a variable sensitivity of the scale at different disease stages, and provide important information for population selection and result interpretation in future clinical trials

Heuristics for connectivity-based brain parcellation of SMA/pre-SMA through force-directed graph layout

We propose the use of force-directed graph layout as an explorative tool for connectivity-based brain parcellation studies. The method can be used as a heuristic to find the number of clusters intrinsically present in the data (if any) and to investigate their organisation. It provides an intuitive representation of the structure of the data and facilitates interactive exploration of properties of single seed voxels as well as relations among (groups of) voxels. We validate the method on synthetic data sets and we investigate the changes in connectivity in the supplementary motor cortex, a brain region whose parcellation has been previously investigated via connectivity studies. This region is supposed to present two easily distinguishable connectivity patterns, putatively denoted by SMA (supplementary motor area) and pre-SMA. Our method provides insights with respect to the connectivity patterns of the premotor cortex. These present a substantial variation among subjects, and their subdivision into two well-separated clusters is not always straightforward.

Data modelling in corpus linguistics:How low may we go?

Corpus linguistics allows researchers to process millions of words. However, the more words we analyse, i.e., the more data we acquire, the more urgent the call for correct data interpretation becomes. In recent years, a number of studies saw the light attempting to profile some prolific authors' linguistic decline, linking this decline to pathological conditions such as Alzheimer's Disease (AD). However, in line with the nature of the (literary) work that was analysed, numbers alone do not suffice to 'tell the story'. The one and only objective of using statistical methods for the analysis of research data is to tell a story - what happened, when, and how. In the present study we describe a computerised but individualised approach to linguistic analysis - we propose a unifying approach, with firm grounds in Information Theory, that, independently from the specific parameter being investigated, guarantees to produce a robust model of the temporal dynamics of an author's linguistic richness over his or her lifetime. We applied this methodology to six renowned authors with an active writing life of four decades or more: Iris Murdoch, Gerard Reve, Hugo Claus, Agatha Christie, P.D. James, and Harry Mulisch. The first three were diagnosed with probable Alzheimer Disease, confirmed post-mortem for Iris Murdoch; this same condition was hypothesized for Agatha Christie. Our analysis reveals different evolutive patterns of lexical richness, in turn plausibly correlated with the authors' different conditions. (C) 2013 Elsevier Ltd. All rights reserved

Group analyses of connectivity-based cortical parcellation using repeated k-means clustering

K-means clustering has become a popular tool for connectivity-based cortical segmentation using Diffusion Weighted Imaging (DWI) data. A sometimes ignored issue is, however, that the output of the algorithm depends on the initial placement of starting points, and that different sets of starting points therefore could lead to different solutions. In this study we explore this issue. We apply k-means clustering a thousand times to the same DWI dataset collected in 10 individuals to segment two brain regions: the SMA-preSMA on the medial wall, and the insula. At the level of single subjects, we found that in both brain regions, repeatedly applying k-means indeed often leads to a variety of rather different cortical based parcellations. By assessing the similarity and frequency of these different solutions, we show that similar to 256 k-means repetitions are needed to accurately estimate the distribution of possible solutions. Using nonparametric group statistics, we then propose a method to employ the variability of clustering solutions to assess the reliability with which certain voxels can be attributed to a particular cluster. In addition, we show that the proportion of voxels that can be attributed significantly to either cluster in the SMA and preSMA is relatively higher than in the insula and discuss how this difference may relate to differences in the anatomy of these regions. (C) 2009 Elsevier Inc. All rights reserved

Point in polygon problem via homotopy and Hopf's degree Theorem

The current work revisits the point-in-polygon problem by providing a novel solution that explicitly employs the properties of epigraphs and hypographs. Using concepts of epigraphs and hypographs, this manuscript provides a new definition of inaccessibility and inside, to accurately specify the meaning of inclusion of a point within or without a polygon. Via Poincaré's ideas on homotopy and Hopf's Degree Theorem from topology, a relationship between inaccessibility and inside is established and it is shown that consistent results are obtained for peculiar cases of both non-intersectingand self-intersecting polygons while investigating the point inclusion test w.r.t. a polygon. Through illustrative examples, the novel method addresses the issues of • ambiguous solutions given by the Cross Over for both non-intersecting and self-intersecting polygons and • a point being labeled as multi-ply inside a self-intersecting polygon by the Winding Number Rule, by providing an unambiguous and singular result for both kinds of polygons. The proposed solution bridgesthe gap between Cross Over and Winding Number Rule for complex cases