PAIR Comparison between Two Within-Group Conditions of Resting-State fMRI Improves Classification Accuracy

Classification approaches have been increasingly applied to differentiate patients and normal controls using resting-state functional magnetic resonance imaging data (RS-fMRI). Although most previous classification studies have reported promising accuracy within individual datasets, achieving high levels of accuracy with multiple datasets remains challenging for two main reasons: high dimensionality, and high variability across subjects. We used two independent RS-fMRI datasets, comparing eyes closed (EC; n = 31) and eyes open (EO; n = 46) conditions. For each dataset, we first reduced the number of features to a small number of brain regions with paired t-tests, using the amplitude of low frequency fluctuation (ALFF) as a metric. Second, we employed a new method for feature extraction, named the PAIR method, examining EC and EO as paired conditions rather than independent conditions. Specifically, for each dataset-1, we obtained EC minus EO (EC − EO) maps of ALFF from half of the subjects (n = 15 for dataset-1, n = 23 for dataset-2) and obtained EO − EC maps from the other half (n = 16 for dataset-1, n = 23 for dataset-2). A support vector machine (SVM) method was used for classification of EC RS-fMRI mapping and EO mapping. The mean classification accuracy of the PAIR method was 91.40% for dataset-1, and 92.75% for dataset-2 in the conventional frequency band of 0.01–0.08 Hz. For cross-dataset validation, we applied the classifier from dataset-1 directly to dataset-2, and vice versa. The mean accuracy of cross-dataset validation was 94.93% for dataset-1 to dataset-2, and 90.32% for dataset-2 to dataset-1 in the 0.01–0.08 Hz range. The mean classification accuracy of the PAIR method was substantially higher than that of the UNPAIR method (76% for within-dataset validation and 64% for cross-dataset validation). In conclusion, for within-group design studies (e.g., paired conditions or follow-up studies), we recommend the PAIR method for feature extraction. In addition, dimension reduction with strong prior knowledge of specific brain regions should also be considered for feature selection in neuroimaging studies.