# Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder
## 4 Results and Discussion
### Identification of Top FC Features Using ANOVA-SVM Algorithm  
For DFC, the connectivity of 90 regions generates 4005 images for each subject based on WC. This is a relatively large number of images compared to the number of subjects in each class. Selecting the best FCs from the 4005 values not only improves the training time of the CNN, but also improves classification performance. Further, the large number of images complicates the ability of the CNN models to correctly perform the classification task. For SFC, using all 4005 PCC scores may have a negative impact on classification performance. Therefore, the ANOVA-SVM algorithm is used to reduce the size of FC patterns based on the WCF and PCC scores. In addition, we hypothesized that the significant correlation between FCs and SRS scores provides better identification of neuronal biomarkers of ASD. Since only the LEUVEN dataset provides SRS scores, these subjects are used for this experiment. LEUVEN participants were recruited by a multidisciplinary team that included a psychiatrist and a neurologist who used the SRS tool to meet DSM criteria. Table 2 shows the details of the dataset and the SRS scores of the two groups as well as the results of the ANOVA tests showing significant differences (\(p\)-value \(<=0.05\)).  
The ANOVA-SVM algorithm is used to select the best subset of WCF and PCC features based on SVM accuracy, as shown in Section 3.3.2. Table 3 shows the average classification accuracy of each subset of connections using 10-fold CV. Table 3 shows that the SVM accuracy based on WCF ranges from 91.1% to 93.7% and based on PCC scores ranges from 87.6% to 91.8%. On the other hand, the variance of the accuracy values is lower for the WCF method than for the PCC method based on the standard deviations. According to these findings, WCF outperformed PCC in identifying ASD connectivity. This could be due to the fact that WCF better represents the neural activity of the brain, as WCF scores are determined using WC, which is a multivariate estimator. WCF is determined by averaging the correlation between two BOLD signals in the time-frequency domain, while PCC is evaluated in the time domain. Further, WCF overcomes the challenges mentioned by [24; 25] in scaling analysis of the extensive information generated by WC. Compared to other DFC studies [19; 20; 21], the WCF metric is useful in providing information about the lag or lead of coherence between signals from multiple subjects and brain regions based on time-frequency analysis.  
The improved representation of brain neural activity by WCF is useful for identifying neural biomarkers of ASD, which can be shown by correlation analysis with SRS scores in the next section. The high classification accuracy shown in Table 3 suggests that the severity of ASD symptoms is reflected by 212 brain connectivity functions. However, to determine which of these connections should be considered neural biomarkers for ASD, correlation analysis is performed between the WCFs of 212 connections and the ASD symptom scores of the SRS tool.