Most neuroimaging-based studies of schizophrenia focus on showing aberrations of some features (structural or functional) in a patient group by comparing them to a control group. Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations. doi: 10.1006/nimg.1997.0315 Pubmed Abstract | Pubmed Full Text | Cross Ref Full Text Lui, S., Li, T., Deng, W., Jiang, L., Wu, Q., Tang, H., et al. Short-term effects of antipsychotic treatment on cerebral function in drug-naive first-episode schizophrenia revealed by “resting state” functional magnetic resonance imaging. While many of these findings are statistically significant in the average sense, discrimination ability of those features is under question for classification purposes on a case-by-case basis.
Most previous studies considered structural MRI, diffusion tensor imaging and task-based f MRI for this purpose.
However, resting-state data has been rarely used in discrimination of schizophrenia patients from healthy controls.
The correlation between these time-courses made the feature vector for each subject.
By using feature selection and dimensionality reduction techniques, they reduced the dimensionality down to three where they classified patients from controls with a high accuracy (93% for patients and 75% for healthy controls).
Advances in neuroimaging technologies in the past two decades have opened a new window into the structure and function of the healthy human brain as well as illuminating many brain disorders such as schizophrenia.
Schizophrenia is among the most prevalent mental disorders affecting about 1% of the population worldwide (Wyatt et al., 1995; Bhugra, 2005).
To our knowledge our effects represent the first use of resting-state functional network connectivity (FNC) features to classify schizophrenia.
Population studies show that lifetime prevalence of all psychotic disorders is as high as 4% ( Pubmed Abstract | Pubmed Full Text Melgani, F., and Bruzzone, L. Classification of hyperspectral remote sensing images with support vector machines.
Moreover, more accurate connectivity maps can be detected using rf MRI data compared to task-based f MRI data (Xiong et al., 1999).