A case control study on psychiatric disorders in Hashimoto disease and euthyroid goitre: not only depressive but also anxiety disorders are associated with thyroid autoimmunity

OBJECTIVE: To evaluate the association between mood and anxiety disorders in Hashimoto disease and Euthyroid Goitre in a case control study. METHODS: Cases included 19 subjects with Hashimoto disease in euthyroid phase, 19 subjects with euthyroid goitre, 2 control groups each of 76 subjects matched (4/1) according to age and sex drawn from the data base of a community based sample. Psychiatric diagnoses were formulated using the International Composite Diagnostic Interview Simplified, according to DSM-IV criteria. All subjects underwent a complete thyroid evaluation including physical examination, thyroid echography and measure of serum free T4 (FT4), free T3 (FT3), thyroid-stimulating hormone (TSH) and anti-thyroid peroxidase autoantibodies (anti-TPO). Results: Subjects with Hashimoto disease showed higher frequencies of lifetime Depressive Episode (OR = 6.6, C.L. 95% 1.2–25.7), Generalized Anxiety Disorders (OR = 4,9 Cl 95% 1.5–25.4) and Social Phobia (OR = 20.0, CL 95% 2.3–153.3) whilst no differences were found between subjects with goitre and controls. CONCLUSION: The study seems to confirm that risk for depressive disorders in subjects with thyroiditis is independent of the thyroid function detected by routine tests and indicates that not only mood but also anxiety disorders may be associated with Hashimoto disease

Robot task-driven attention

Visual attention is a crucial skill in human beings in that it allows optimal deployment of visual processing and memory resources. It turns out to be even more useful in search tasks, since to select salient zones we use top-down priors, depending on the observed scene, along with bottom-up criteria. In this paper we show how we constructed a robotic model of attention, inspired by studies on human attention and gaze shifting. Our model relies on a measure of salience related to the particular type of environment and to the given task. This measure is hierarchically structured and consists of both top-down components, learned from the tutor, and bottom-up components as perceived in the scene by the robot. Hence with such a general model the robot can perform its own scan-path inside a similar environment and report on its findings. Copyright © held by author

Motion saliency maps from spatiotemporal filtering

For artificial systems acting and perceiving in a dynamic world a core ability is to focus on aspects of the environment that can be crucial for the task at hand. Perception in autonomous systems needs to be filtered by a biologically inspired selective ability, therefore attention in dynamic settings is becoming a key research issue. In this paper we present a model for motion salience map computation based on spatiotemporal filtering. We extract a measure of coherent motion energy and select by the center-surround mechanism relevant zones that accumulate most energy and therefore contrast with surroundings in a given time slot. The method was tested on synthetic and real video sequences, supporting biological plausibility. © 2009 Springer Berlin Heidelberg