Using classification to identify biomarkers for various brain disorders has become a common practice among the functional MR imaging community. Typical classification pipeline includes taking the time series, extracting features from them, and using them to classify a set of patients and healthy controls. The most informative features are then presented as novel biomarkers. In this paper, we compared the results of single and double cross validation schemes on a cohort of 170 subjects with schizophrenia and healthy control subjects. We used graph theoretic measures as our features, comparing the use of functional and anatomical atlases to define nodes and the effect of prewhitening to remove autocorrelation trends. We found that double cross validation resulted in a 20% decrease in classification performance compared to single cross validation. The anatomical atlas resulted in higher classification results. Prewhitening resulted in a 10% boost in classification performance. Overall, a classification performance of 80% was obtained with a double-cross validation scheme using prewhitened time series and an anatomical brain atlas. However, reproducibility of classification within subjects across scans was surprisingly low and comparable to across subject classification rates, indicating that subject state during the short scan significantly influences the estimated features and classification performance.
Bibliographical noteFunding Information:
The authors would like to thank Dr. Gowtham Atluri, Dr. Jazmin Camchong, and the subjects who participated in this study. Funding. This work was supported by NIH grants MH060662 and DA038894 and NSF grant CMMI: 1634445.
This work was supported by NIH grants MH060662 and DA038894 and NSF grant CMMI: 1634445.
- Double cross validation
- Network measures
- Resting-state fMRI