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Columbia University’s Data Science Institute Presents:
DATA SCIENCE DAY

Authors/Collaborators are listed in alphabetical order.



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Wednesday, April 6 • 2:00pm - 4:30pm
Describing High-Order Statistical Dependence Using "Concurrence Topology,” with Application to Functional MRI Brain Data [P21]

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We propose a new nonparametric method, "Concurrence Topology (CT)", for describing dependence among dichotomous variables. CT starts by translating the data into a "filtration", i.e., a series of shapes. Holes in the filtration correspond to relatively weak or negative association among the variables. CT uses computational topology to describe the pattern of holes in the filtration. CT is able to describe high-order dependence while avoiding combinatorial explosion. We employed CT to investigate brain functional connectivity based on dichotomized functional MRI data. The data set includes subjects diagnosed with ADHD and healthy controls. In an exploratory analysis, working in both the time and Fourier domains, CT found a number of differences between ADHD subjects and controls in the topology of their filtrations. A paper based on this work has been published.

Demo/Poster Presenter
avatar for Steven Ellis

Steven Ellis

Associate Professor of Clinical Neurobiology in Psychiatry, College of Physicians and Surgeons
Steven P. Ellis, Ph.D. is Associate Prof. of Clinical Neurobiology (in Psychiatry). He is Director, Statistics and Computing Core, Conte Center: Neurobiological and Developmental Antecedents to Suicidal Behavior: The Neurobiology of Suicidal Behavior (CCNDASB). He is also PI on Project 8 of the CCNDASB, "Statistical methods in suicide research." He graduated cum laude, with Honors with Exceptional Distinction in Mathematics, and with a... Read More →

Demo/Poster Collaborator
avatar for Arno Klein

Arno Klein

Director of Neuroimaging and of SIMPL(E) Principal Scientist of Systems Biology, Sage Bionetworks
Arno's current research focuses on the analysis of mobile health sensor data and brain image data. He was the scientific lead on the mPower study that tracks symptoms of Parkinson disease by collecting voice, accelerometer, and tapping data, and am completing work on the mhealthx pipeline for extracting features from such data. He has been engaged in brain imaging research since 1988 and develop brain image analysis software, including the... Read More →


Wednesday April 6, 2016 2:00pm - 4:30pm
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Attendees (3)