Background: Despite its inhibitory control requirements, antisaccade deficits have been consistently associated with working memory impairments in schizophrenia. We investigated whether variance in antisaccade performance could be better accounted for in terms of a specific inhibitory function. Method: We assessed 48 clinically stable out-patients with schizophrenia on an antisaccade task, as well as on measures of spatial and verbal working memory, sustained selective attention, and a simple motoric go0no-go measure of response inhibition. Results: In a stepwise multiple regression analysis, go0no-go task performance accounted for a considerably greater percentage of variance in antisaccade performance (25.3%) than either working memory (8.4%) or sustained selective attention task (9.1%). Discussion: We conclude that antisaccade deficits in schizophrenia appear to be better understood in terms of a specific deficit of inhibitory control than in terms of more general difficulties with context maintenance or goal neglect. (JINS, 2006, 12, 901-906.)

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This paper outlines a methodology to measure the ability of a schizophrenic patient to saccade against a presented stimulus by tracking the patient's electrooculographic (EOG) activity. The overall goal is to investigate a deficit in this anti-saccade task as a cognitive endophenotype for schizophrenia. A portable system for performing this test and a novel biopotential amplifier are presented along with a signal processing method and analysis. Results based on control subjects indicate that a mean anti-saccade task performance of above 89% is achievable. A study on 5 schizophrenic patients indicates that anti-saccade task performance drops to a mean value of 45%. A study to be carried out by the Trinity College Neuropsychiatric Genetics Research Group is currently assessing up to 800 subjects including schizophrenics and their relatives. ______________________________________________________________________________________ I.

This paper describes a platform developed for analysis of Diffusion Tensor Image data using different tractography algorithms. The platform allows these algorithms to be compared both quantitatively and qualitatively. Two specific tractography algorithms were compared, STT (Streamlines Tracking Technique) and TEND (tensor-deflection algorithm). The platform was assessed on a publicly available DTI dataset to analyse and quantify the performance of these tractography algorithms. Based on specific tests using this platform, results indicate that the STT algorithm is better at dealing with fibres containing curves and TEND is more appropriate for straighter fibres. A methodology is also proposed to help differentiate between nerve fibres that meet or cross.