Insular volume abnormalities associated with different transition probabilities to psychosis

Background Although individuals vulnerable to psychosis show brain volumetric abnormalities, structural alterations underlying different probabilities for later transition are unknown. The present study addresses this issue by means of voxel-based morphometry (VBM). Method We investigated grey matter volume (GMV) abnormalities by comparing four neuroleptic-free groups: individuals with first episode of psychosis (FEP) and with at-risk mental state (ARMS), with either long-term (ARMS-LT) or short-term ARMS (ARMS-ST), compared to the healthy control (HC) group. Using three-dimensional (3D) magnetic resonance imaging (MRI), we examined 16 FEP, 31 ARMS, clinically followed up for on average 3 months (ARMS-ST, n=18) and 4.5 years (ARMS-LT, n=13), and 19 HC. Results The ARMS-ST group showed less GMV in the right and left insula compared to the ARMS-LT (Cohen's d 1.67) and FEP groups (Cohen's d 1.81) respectively. These GMV differences were correlated positively with global functioning in the whole ARMS group. Insular alterations were associated with negative symptomatology in the whole ARMS group, and also with hallucinations in the ARMS-ST and ARMS-LT subgroups. We found a significant effect of previous antipsychotic medication use on GMV abnormalities in the FEP group. Conclusions GMV abnormalities in subjects at high clinical risk for psychosis are associated with negative and positive psychotic symptoms, and global functioning. Alterations in the right insula are associated with a higher risk for transition to psychosis, and thus may be related to different transition probabilities.

Estimating the Size of the Homeless Population in Budapest

Since 1990, the issue of homelessness has become increasingly important in Hungary as a result of economic and structural changes. Various suggestions as to how the problem may be solved have always been preceded by the question "How many homeless people are there?" and there is still no official consensus as to the answer. Counting of the homeless is particularly difficult because of the bias in the initial sampling frame due to two factors that characterise this population: the definition of homelessness, and its 'hidden' nature. David aimed to estimate the size of the homeless population of Budapest by using two non-standard sampling methods: snowball sampling and the capture-recapture method. Her calculations are based on three data sets: one snowball data set and two independent list data sets. These estimators, supported by other statistical data, suggest that in 1999 there were about 8000-10000 homeless people in Budapest.

Estimating the number of motor units using random sums with independently thinned terms

The problem of estimating the numbers of motor units N in a muscle is embedded in a general stochastic model using the notion of thinning from point process theory. In the paper a new moment type estimator for the numbers of motor units in a muscle is denned, which is derived using random sums with independently thinned terms. Asymptotic normality of the estimator is shown and its practical value is demonstrated with bootstrap and approximative confidence intervals for a data set from a 31-year-old healthy right-handed, female volunteer. Moreover simulation results are presented and Monte-Carlo based quantiles, means, and variances are calculated for N in{300,600,1000}.