Unsupervised fuzzy clustering analysis supports behavioral cutoff criteria in an animal model of posttraumatic stress disorder

被引：80

作者：

Cohen, H

Zohar, J

Matar, MA

Kaplan, Z

Geva, AB

机构：

[1] Ben Gurion Univ Negev, Fac Hlth Sci, Minist Hlth, Mental Hlth Ctr,Anxiety & Stress Res Unit, IL-84170 Beer Sheva, Israel

[2] Ben Gurion Univ Negev, Dept Elect & Comp Engn, IL-84170 Beer Sheva, Israel

[3] Tel Aviv Univ, Chaim Sheba Med Ctr, Sackler Sch Med, IL-69978 Tel Aviv, Israel

来源：

BIOLOGICAL PSYCHIATRY | 2005年 / 58卷 / 08期

关键词：

posttraumatic stress disorder; animal model; unsupervised fuzzy clustering; anxiety; stress; behavioral criteria;

D O I：

10.1016/j.biopsych.2005.04.002

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

Background: Unsupervised fuzzy cluslering (UFC) analysis is a mathematical technique that groups together objects in the multidimensional feature space according to a specified similarity measurement, thereby yielding clusters of similar data points that can be represented by a set of prototypes or centroids. Methods: Since clinical studies of mental disorders distinguish between affected and unaffected individuals, we designed inclusion/exclusion criteria (cutoff behavioral criteria [CBC]) approach for animal behavioral studies. The effected individuals according to behaviors on two behavioral paradigms was study population into clearly affected versus clearly ana statistically significant. Conclusions: The UFC anaalysis yielded data that support the conceptual contention of the CBC and lends additional validity to our previous behavioral studies.

引用

页码：640 / 650

页数：11

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