Concurrence Topology: Finding High-Order Dependence in Neuropsychiatric Data

Concurrence Topology: Finding High-Order Dependence in Neuropsychiatric Data

The proposed research develops new computational tools to identify, diagnose, and predict treatment response for different mental illnesses. The research will first be applied to publicly available resting state fMRI BOLD data from patients with attentiondeficit hyperactivity disorder and autism....

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Bibliographic Details
Main Authors: Arno Klein, Steven Ellis
Format: Article
Language:English
Published: Pensoft Publishers 2016-04-01
Series:Research Ideas and Outcomes
Subjects:
concurrence topology
homology
brain imaging
Online Access:https://riojournal.com/article/8815/
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spelling doaj-2d23c0855d2c4e8881ff3fcd37f53d5e2020-11-25T01:48:33ZengPensoft PublishersResearch Ideas and Outcomes2367-71632016-04-01211910.3897/rio.2.e88158815Concurrence Topology: Finding High-Order Dependence in Neuropsychiatric DataArno Klein0Steven Ellis1Stony Brook UniversityColumbia University The proposed research develops new computational tools to identify, diagnose, and predict treatment response for different mental illnesses. The research will first be applied to publicly available resting state fMRI BOLD data from patients with attentiondeficit hyperactivity disorder and autism. It will also be applied to existing clinical and biological data concerning suicidality in the context of major depressive disorder. These disorders affect millions of Americans, but these tools can be applied to any mental illness, such as Alzheimer’s disease, bipolar disorder, schizophrenia – indeed to analyze differences in brain, clinical, and biological data between any two populations. https://riojournal.com/article/8815/concurrence topologyhomologybrain imaging
collection DOAJ
language English
format Article
sources DOAJ
author Arno Klein
Steven Ellis
spellingShingle Arno Klein
Steven Ellis
Concurrence Topology: Finding High-Order Dependence in Neuropsychiatric Data
Research Ideas and Outcomes
concurrence topology
homology
brain imaging
author_facet Arno Klein
Steven Ellis
author_sort Arno Klein
title Concurrence Topology: Finding High-Order Dependence in Neuropsychiatric Data
title_short Concurrence Topology: Finding High-Order Dependence in Neuropsychiatric Data
title_full Concurrence Topology: Finding High-Order Dependence in Neuropsychiatric Data
title_fullStr Concurrence Topology: Finding High-Order Dependence in Neuropsychiatric Data
title_full_unstemmed Concurrence Topology: Finding High-Order Dependence in Neuropsychiatric Data
title_sort concurrence topology: finding high-order dependence in neuropsychiatric data
publisher Pensoft Publishers
series Research Ideas and Outcomes
issn 2367-7163
publishDate 2016-04-01
description The proposed research develops new computational tools to identify, diagnose, and predict treatment response for different mental illnesses. The research will first be applied to publicly available resting state fMRI BOLD data from patients with attentiondeficit hyperactivity disorder and autism. It will also be applied to existing clinical and biological data concerning suicidality in the context of major depressive disorder. These disorders affect millions of Americans, but these tools can be applied to any mental illness, such as Alzheimer’s disease, bipolar disorder, schizophrenia – indeed to analyze differences in brain, clinical, and biological data between any two populations.
topic concurrence topology
homology
brain imaging
url https://riojournal.com/article/8815/
work_keys_str_mv AT arnoklein concurrencetopologyfindinghighorderdependenceinneuropsychiatricdata
AT stevenellis concurrencetopologyfindinghighorderdependenceinneuropsychiatricdata
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