Depression severity evaluation for female patients based on a functional MRI model.
OBJECTIVE
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.


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.


CONCLUSIONS
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.
Qing
Lu
L
Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Nanjing, China. luq@seu.edu.cn
Haiteng
Jiang
J
Haiyan
Liu
L
Gang
Liu
L
Gaojun
Teng
T
Zhijian
Yao
Y
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000503:physiopathology*; D003198:Computer Simulation; D003863:Depression / Q000175:diagnosis* / Q000503:physiopathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index