Neurophysiologic Markers of Abnormal Brain Activity in Schizophrenia

Cortical electrophysiologic event-related potentials are multidimensional measures of information processing that are well-suited for efficiently parsing automatic and controlled components of cognition that span the range of deficits evidenced in schizophrenia patients. These information processes are key cognitive measures that are recognized as informative and valid targets for understanding the neurobiology of schizophrenia. These measures may be used in concert with the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) neurocognitive measures in the development of novel treatments for schizophrenia and related neuropsychiatric disorders. The employment of novel event-related potential paradigms designed to carefully characterize the early spectrum of perceptual and cognitive information processing allows investigators to identify the neurophysiologic basis of cognitive dysfunction in schizophrenia and to examine the associated clinical and functional impairments

Reliable Measurement of Cortical Flow Patterns Using Complex Independent Component Analysis of Electroencephalographic Signals

Complex independent component analysis (ICA) of frequency-domain electroencephalographic (EEG) data [1] is a generalization of real time-domain ICA to the frequency-domain. Complex ICA aims to model functionally independent sources as representing patterns of spatio-temporal dynamics. Applied to EEG data, it may allow non-invasive measurement of flow trajectories of cortical potentials. As complex ICA has a higher complexity and number of parameters than time-domain ICA, it is important to determine the extent to which complex ICA applied to brain signals is stable across decompositions. This question is investigated for the complex ICA method applied to the 5-Hz frequency band of data from a selective attention EEG experiment

The unusual iron sulfur composition of the Acidianus ambivalens succinate dehydrogenase complex.

The succinate dehydrogenase complex of the thermoacidophilic archaeon Acidianus ambivalens was investigated kinetically and by EPR spectroscopy in its most intact form, i.e., membrane bound. Here it is shown that this respiratory complex has an unusual iron-sulfur cluster composition in respect to that of the canonical succinate dehydrogenases known. The spectroscopic studies show that center S3, the succinate responsive [3Fe-4S]1+/0 cluster of succinate dehydrogenases, is not present in membranes prepared from aerobically grown A. ambivalens, nor in partially purified complex fractions. On the other hand, EPR features associated to the remaining centers, clusters S1 ([2Fe-2S]1+/2+) and S2 ([4Fe-4S]2+/1+), could be observed. Similar findings were made in other archaea, namely Acidianus infernus and Sulfolobus solfataricus. Kinetic investigations showed that the A. ambivalens enzyme is reversible, capable of operating as a fumarate reductase - a required activity if this obligate autotroph performs CO2 fixation via a reductive citric acid cycle. Sequencing of the sdh operon confirmed the spectroscopic data. Center S3 ([3Fe-4S]) is indeed replaced by a second [4Fe-4S] center, by incorporation of an additional cysteine, at the cysteine cluster binding motif (CxxYxxCxxxC-->CxxCxxCxxxC). Genomic analysis shows that genes encoding for succinate dehydrogenases similar to the ones here outlined are also present in bacteria, which may indicate a novel family of succinate/fumarate oxidoreductases, spread among the Archaea and Bacteria domains

DIRAC: Detection and identification of rare audio-visual events

The DIRAC project was an integrated project that was carried out between January 1st 2006 and December 31st 2010. It was funded by the European Commission within the Sixth Framework Research Programme (FP6) under contract number IST-027787. Ten partners joined forces to investigate the concept of rare events in machine and cognitive systems, and developed multi-modal technology to identify such events and deal with them in audio-visual applications. This document summarizes the project and its achievements. In Section 2 we present the research and engineering problem that the project set out to tackle, and discuss why we believe that advance made on solving these problems will get us closer to achieving the general objective of building artificial cognitive system with cognitive capabilities. We describe the approach taken to solving the problem, detailing the theoretical framework we came up with. We further describe how the inter-disciplinary nature of our research and evidence collected from biological and cognitive systems gave us the necessary insights and support for the proposed approach. In Section 3 we describe our efforts towards system design that follow the principles identified in our theoretical investigation. In Section 4 we describe a variety of algorithms we have developed in the context of different applications, to implement the theoretical framework described in Section 2. In Section 5 we describe algorithmic progress on a variety of questions that concern the learning of those rare events as defined in our Section 2. Finally, in Section 6 we describe our application scenarios, an integrated test-bed developed to test our algorithms in an integrated way