From ed.vessel at nyu.edu Mon Jul 28 10:20:58 2014 From: ed.vessel at nyu.edu (Ed Vessel) Date: Mon, 28 Jul 2014 10:20:58 -0400 Subject: [CBIAnnounce] CBI Workshop: Intro fMRI Analysis, Sept. 18-19, 2014 Message-ID: CBI WORKSHOP Introductory fMRI Analysis Sept. 18-19, 2014 This workshop will introduce the basics of a standard fMRI analysis, combining explanation with hands-on processing of real data. Description: Each session, the first hour will be devoted to an overview of a standard set of analysis steps and data flow. This is geared for people who have never done any fMRI analysis or are only just beginning. After a short break, the rest of the session will be devoted to more in-depth explanations of each analysis with hands-on tutorials. I'll first give some theoretical background on why and when one would perform a specific analysis, and then will show how to do the analysis. I'll be illustrating these analyses using FSL (http://www.fmrib.ox.ac.uk/fsl/) and SPM (http://www.fil.ion.ucl.ac.uk/spm/). If time allows, I may also try to include some examples of matlab scripting. These tutorials will utilize datasets provided by me, and offer you a "cooking-show" style interactive tutorial - you will work through loading the datasets into the software and performing the processing. Pre-processed versions of the data will also be provided to save time for processes which require too much time to do in real time. SCHEDULE: Session I: Thursday 9/18 9am-12 * Preprocessing - motion/slicetime correction, correcting for field inhomogeneities, temporal filtering / detrending, spatial filtering Session II: Thursday 9/18 1pm-4pm * Individual voxelwise statistical analysis - model fitting (GLM's), statistical inference, correcting for multiple comparisons Session III: Friday 9/19 10am - 1pm * Registration & group analysis * ROI analysis TO RESERVE A SPOT: Please email me (ed.vessel at nyu.edu) with your name, lab affiliation, position, and your level of experience with fMRI. Seats in the lab are limited, and priority will be given to students who are part of labs that scan at the NYU Center for Brain Imaging (at Washington Sq.). -- Ed Vessel Center for Brain Imaging New York University ed.vessel at nyu.edu 4 Washington Place, Rm. 156 New York, NY 10003 http://www.cns.nyu.edu/~vessel (212) 998-8217 From pablo.velasco at nyu.edu Tue Jul 29 09:45:40 2014 From: pablo.velasco at nyu.edu (Pablo Velasco) Date: Tue, 29 Jul 2014 09:45:40 -0400 Subject: [CBIAnnounce] Fwd: [NYU Data Science] Fwd: RSS - Ashish Raj - Invitation In-Reply-To: References:

Message-ID: FYI... ---------- Forwarded message ---------- From: Eero Simoncelli Date: Tue, Jul 29, 2014 at 2:27 AM Subject: Fwd: [NYU Data Science] Fwd: RSS - Ashish Raj - Invitation To: cns.events at cns.nyu.edu Dear Colleagues, This may be of interest to some of you: From: "Claudio T. Silva" Subject: [NYU Data Science] Fwd: RSS - Ashish Raj - Invitation Date: 28 July2014 at 3:12:43 PM EDT To: datascience-pull-push at cs.nyu.edu Please join us on Aug 1, at 12:00 PM for a research seminar and discussion with Ashish Raj from Cornell Medical School. Abstract: Whole brain connectivity networks are now routinely derived from diffusion MRI, followed by tractography and connectivity estimation. Functional connectivity networks can also be obtained from functional MRI data, where connectivity between any two brain regions is defined by the statistical covariance between their respective neuronal signals, typically measured by functional MRI scans. Graph theory methods applied to these networks allow us to interrogate various network-level features of both healthy and diseased brains. In this talk I will show that: a) Connectivity networks from diffusion MRI reveal hierarchical but hub-free organization in the brain b) A simple model of network spread, graph diffusion, captures the relationship between functional networks (i.e. obtained from covariance structure of neuronal activity) and structural networks (from fiber tracts) c) Graph diffusion processes can reproduce the spatiotemporal spread of neurodegenerative diseases, including Alzheimer?s, dementia, Parkinson?s and epilepsy. These results point to the breadth and importance of graph theory and network modeling in neuroscience and neurology. I will finish by pointing to some open questions in the field that could benefit from graphbased filtering or other graph theoretic approaches. Bio: I am Associate Professor of Computer Science in Radiology and Co-Director of IDEAL, both at Weill Cornell Department of Radiology. I graduated with a PhD in 2005 from Cornell University in Electrical and Computer Engineering. I have more than 40 journal papers ranging from microwave engineering, superconductivity, image/signal processing, vision, graph theory and neuroscience. My current research encompasses many computational problems in NEUROIMAGING, particularly the modeling of dementias using graph theory. We extract brain networks from neuroimaging data like Diffusion Tendor Imaging, functional MRI and MRI brain morphometry. The goal is to find network characteristics that distinguish healthy brains from pathological brains for a number of brain disorders like Alzheimer's, Epilepsy, Autism, Schizophrenia and Stroke. Aug 1, 2014, 12:00 pm Jacobs Seminar Room Center for Urban Science and Progress New York University 1 Metrotech Center, 19th Floor Brooklyn, NY 11201 To RSVP: https://www.surveymonkey.com/s/DYKNZSC About CUSP The Center for Urban Science and Progress (CUSP) is a unique public-private research center of NYU that uses New York City as its laboratory and classroom to help cities around the world become more productive, livable, equitable, and resilient. CUSP observes, analyzes, and models cities to optimize outcomes, prototype new solutions, formalize new tools and processes, and develop new expertise/experts. The CUSP research seminars aim to promote an intellectual community around urban informatics, an intrinsically multidisciplinary field, by facilitating discussions on various research topics related to the intersection of big data and urban planning. Please send inquiries to abhishekgupta at nyu.edu _______________________________________________ Datascience-pull-push mailing list Datascience-pull-push at cs.nyu.edu http://cs.nyu.edu/mailman/listinfo/datascience-pull-push