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Depression severity evaluation for female patients based on a functional MRI model
Author(s) -
Qing Lu,
Haiteng Jiang,
Haiyan Liu,
Gang Liu,
Gaojun Teng,
Zhijian Yao
Publication year - 2010
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.22161
Subject(s) - hamd , depression (economics) , computer science , psychology , medicine , physical medicine and rehabilitation , artificial intelligence , rating scale , developmental psychology , economics , macroeconomics
Abstract Purpose: To develop a functional MRI (fMRI) signal based model that can evaluate depression severity in a numeric form; therefore, depressed patients can be identified during the course of illness, independent from symptoms. Materials and Methods: Data from 20 medication‐free depressed patients and 16 healthy subjects were analyzed. The event‐related fMRI scanning features under sad facial emotional stimuli were extracted as model inputs. Fuzzy logic and a genetic algorithm were used to provide suitable model outputs for numeric estimations of depression. Results: The correlation value r between the model estimations and the professional Hamilton Depression Rating Scales (HAMD) was 0.7886 with P < 0.00016. A typical tracking history for a particular subject has also promised the possibility for early disease warning, when the clinal symptoms are ambiguous or recessive. Conclusion: A numeric and objective estimation for the course of illness can be provided. The model can be used by psychiatrists to track the recovery process. As a simple extended application, the proposed model can be applied to classify subjects into different patterns: major depression, moderate depression, or healthy. J. Magn. Reson. Imaging 2010;31:1067–1074. © 2010 Wiley‐Liss, Inc.