From dongxu@sciences.sdsu.edu Sat Aug 18 06:43:05 2007 From: dongxu@sciences.sdsu.edu (Dong Xu) Date: Fri, 17 Aug 2007 22:43:05 -0700 Subject: [compsci] CSRC Colloquium on Friday -- NEUROINFORMATICS FOR AUTISM RESEARCH. A DISCUSSION OF THE AUTISM TISSUE PROGRAM (ATP) INFORMATICS PORTAL Message-ID: <14da35910708172243x3088b271s9dadf003572a2ec6@mail.gmail.com> Title: NEUROINFORMATICS FOR AUTISM RESEARCH. A DISCUSSION OF THE AUTISM TISSUE PROGRAM (ATP) INFORMATICS PORTAL Date: August 24, 2007 Time: 3:30 PM Location: GMCS 214 Speaker: Richard Pickett San Diego State University Abstract: This research portal (http://www.atpportal.org) is used by autism researchers around the world to access information on individuals who have donated their brains to the ATP. Based upon Oracle technology, researcher can submit proposals as well as obtain information on cases in the database and research documents related to those cases. This information includes both structured and unstructured data. Data sets include autopsies, neuropath reports, MRIs, digital brain images as well as structured data such as medical disorders, immunization history, causes of death, medical history and other information. Host: Jose E. Castillo For future events, please visit our web site at: http://www.csrc.sdsu.edu/csrc/events/colloquium/ ******************************************* Dong Xu CSRC/SDSU From aaulia@sciences.sdsu.edu Mon Aug 20 17:45:31 2007 From: aaulia@sciences.sdsu.edu (aaulia@sciences.sdsu.edu) Date: Mon, 20 Aug 2007 12:45:31 -0400 Subject: [compsci] CSRC Colloquium this Friday -- NEUROINFORMATICS FOR AUTISM RESEARCH. A DISCUSSION OF THE AUTISM TISSUE PROGRAM (ATP) INFORMATICS PORTAL Message-ID: <380-220078120164531555@M2W042.mail2web.com> Title: NEUROINFORMATICS FOR AUTISM RESEARCH. A DISCUSSION OF THE AUTISM TISSUE PROGRAM (ATP) INFORMATICS PORTAL Date: August 24, 2007 Time: 3:30 PM Location: GMCS 214 Speaker: Richard Pickett San Diego State University Abstract: This research portal (http://www.atpportal.org) is used by autism researchers around the world to access information on individuals who have donated their brains to the ATP. Based upon Oracle technology, researcher can submit proposals as well as obtain information on cases in the database and research documents related to those cases. This information includes both structured and unstructured data. Data sets include autopsies, neuropath reports, MRIs, digital brain images as well as structured data such as medical disorders, immunization history, causes of death, medical history and other information. Host: Jose E. Castillo For future events, please visit our web site at: http://www.csrc.sdsu.edu/csrc/events/colloquium/ ******************************************* Akmal Aulia PhD Student Computational Science Research Center San Diego State University Phone: 619-594-3505 Email: aaulia@sciences.sdsu.edu _______________________________________________ SDSU Computational Science Research Center Mailing List -------------------------------------------------------------------- myhosting.com - Premium Microsoft® Windows® and Linux web and application hosting - http://link.myhosting.com/myhosting From castillo@myth.sdsu.edu Tue Aug 21 18:25:04 2007 From: castillo@myth.sdsu.edu (Jose Castillo) Date: Tue, 21 Aug 2007 10:25:04 -0700 Subject: [compsci] CSRC Colloquium on Friday -- NEUROINFORMATICS FOR AUTISM RESEARCH. A DISCUSSION OF THE AUTISM TISSUE PROGRAM (ATP) INFORMATICS PORTAL Message-ID: Title: NEUROINFORMATICS FOR AUTISM RESEARCH. A DISCUSSION OF THE AUTISM TISSUE PROGRAM (ATP) INFORMATICS PORTAL Date: August 24, 2007 Time: 3:30 PM Location: GMCS 214 Speaker: Richard Pickett Senior Director & CIO Business and Faculty Affairs San Diego State University Abstract: This research portal (http://www.atpportal.org) is used by autism researchers around the world to access information on individuals who have donated their brains to the ATP. Based upon Oracle technology, researcher can submit proposals as well as obtain information on cases in the database and research documents related to those cases. This information includes both structured and unstructured data. Data sets include autopsies, neuropath reports, MRIs, digital brain images as well as structured data such as medical disorders, immunization history, causes of death, medical history and other information. Host: Jose E. Castillo For future events, please visit our web site at: http://www.csrc.sdsu.edu/csrc/events/colloquium/ ******************************************* ______________________________________________ Mailing List -- Jose E. Castillo Ph.D. Director / Professor Computational Science Research Center 5500 Campanile Dr San Diego State University San Diego CA 92182-1245 619 5947205/3430, Fax 619-594-2459 castillo@myth.sdsu.edu http://www.csrc.sdsu.edu From aaulia@sciences.sdsu.edu Sun Aug 26 22:24:52 2007 From: aaulia@sciences.sdsu.edu (aaulia@sciences.sdsu.edu) Date: Sun, 26 Aug 2007 17:24:52 -0400 Subject: [compsci] CSRC Colloquium this Friday -- SPATIAL-TEMPORAL COMMON MODES ANALYSIS OF NEUROPHYSIOLOGICAL DATA: AN INITIAL APPROACH Message-ID: <380-22007802621245287@M2W035.mail2web.com> Title: SPATIAL-TEMPORAL COMMON MODES ANALYSIS OF NEUROPHYSIOLOGICAL DATA: AN INITIAL APPROACH Date: Friday, August 31, 2007 Time: 3:30 PM Location: GMCS 214 Speaker: Mark E. Pflieger Source Signal Imaging Inc. Abstract: The spatiotemporal statistical organization of natural brain activity (reflected by neurophysiological measures such as scalp EEG, extracranial MEG, intracranial EEG, or local field potentials) may not conform to the ideal assumptions of some analysis, such as exclusive Gaussianity (PCA), exclusive non-Gaussianity (ICA), trial-to-trial homogeneity of phase-locked responses (event-related averages), additive non-phaselocked activity (event-related variances), and one-one correspondences between spatial and temporal modes (PARAFAC). Spatial-temporal common modes analysis (CMA, for short; a method currently under development) is a data-driven alternative which avoids these assumptions by seeking “most efficient” re-representations of multi-epoch data. Each epoch originally is represented as a matrix of measurements (such as electric potential differences or oriented magnetic field components) with rows corresponding to spatial channels (such as EEG electrodes using a common reference, or MEG sensors) and with columns corresponding to time points (such as latencies relative to stimulus or behavioral events). A change of spatial and temporal coordinates will alter the representation of each epoch so that, compared with the original representation, more or less information (as quantified by some measure) may be required to describe the signal. Thus, the problem of CMA is to find fixed spatial and temporal coordinate systems (basis vectors) that minimize the average information required to represent epochs in a collection. By solving this problem: (a) the complete dataset is represented most efficiently (whether statistics are Gaussian, non-Gaussian, or mixed); (b) the spatial and temporal mode vectors encode maximum common information across epochs (without averaging); (c) the minimum information representations retain essential trial-to-trial variations (without computing variances); and (d) the mutual information between spatial and temporal modes is maximized (without requiring exact one-one correspondences). Variants of the CMA problem depend on the specific measure of representational information, and whether modes are orthogonally constrained. Each variant requires that a continuous nonlinear constrained optimization problem be solved jointly for spatial and temporal matrices of basis vectors. An initial approach using a gradient projection algorithm will be presented. Host: Jose E. Castillo For future events, please visit our web site at: http://www.csrc.sdsu.edu/csrc/events/colloquium/ ******************************************* Akmal Aulia PhD Student Computational Science Research Center San Diego State University Phone: 619-594-3505 Email: aaulia@sciences.sdsu.edu _______________________________________________ SDSU Computational Science Research Center Mailing List -------------------------------------------------------------------- mail2web.com – What can On Demand Business Solutions do for you? http://link.mail2web.com/Business/SharePoint From aaulia@sciences.sdsu.edu Thu Aug 30 00:12:17 2007 From: aaulia@sciences.sdsu.edu (aaulia@sciences.sdsu.edu) Date: Wed, 29 Aug 2007 19:12:17 -0400 Subject: [compsci] Reminder -> CSRC Colloquium this Friday -- SPATIAL-TEMPORAL COMMON MODESANALYSIS OF NEUROPHYSIOLOGICAL DATA: AN INITIAL APPROACH Message-ID: <380-220078329231217709@M2W014.mail2web.com> Title: SPATIAL-TEMPORAL COMMON MODES ANALYSIS OF NEUROPHYSIOLOGICAL DATA: AN INITIAL APPROACH Date: Friday, August 31, 2007 Time: 3:30 PM Location: GMCS 214 Speaker: Mark E. Pflieger Source Signal Imaging Inc. Abstract: The spatiotemporal statistical organization of natural brain activity (reflected by neurophysiological measures such as scalp EEG, extracranial MEG, intracranial EEG, or local field potentials) may not conform to the ideal assumptions of some analysis, such as exclusive Gaussianity (PCA), exclusive non-Gaussianity (ICA), trial-to-trial homogeneity of phase-locked responses (event-related averages), additive non-phaselocked activity (event-related variances), and one-one correspondences between spatial and temporal modes (PARAFAC). Spatial-temporal common modes analysis (CMA, for short; a method currently under development) is a data-driven alternative which avoids these assumptions by seeking “most efficient” re-representations of multi-epoch data. Each epoch originally is represented as a matrix of measurements (such as electric potential differences or oriented magnetic field components) with rows corresponding to spatial channels (such as EEG electrodes using a common reference, or MEG sensors) and with columns corresponding to time points (such as latencies relative to stimulus or behavioral events). A change of spatial and temporal coordinates will alter the representation of each epoch so that, compared with the original representation, more or less information (as quantified by some measure) may be required to describe the signal. Thus, the problem of CMA is to find fixed spatial and temporal coordinate systems (basis vectors) that minimize the average information required to represent epochs in a collection. By solving this problem: (a) the complete dataset is represented most efficiently (whether statistics are Gaussian, non-Gaussian, or mixed); (b) the spatial and temporal mode vectors encode maximum common information across epochs (without averaging); (c) the minimum information representations retain essential trial-to-trial variations (without computing variances); and (d) the mutual information between spatial and temporal modes is maximized (without requiring exact one-one correspondences). Variants of the CMA problem depend on the specific measure of representational information, and whether modes are orthogonally constrained. Each variant requires that a continuous nonlinear constrained optimization problem be solved jointly for spatial and temporal matrices of basis vectors. An initial approach using a gradient projection algorithm will be presented. Host: Jose E. Castillo For future events, please visit our web site at: http://www.csrc.sdsu.edu/csrc/events/colloquium/ ******************************************* Akmal Aulia PhD Student Computational Science Research Center San Diego State University Phone: 619-335-7187 Email: aaulia@sciences.sdsu.edu _______________________________________________ SDSU Computational Science Research Center Mailing List -------------------------------------------------------------------- mail2web - Check your email from the web at http://link.mail2web.com/mail2web