# Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder
## 5 Conclusions and Future Works  
In this paper, an ASD classification framework comprising principal wavelet coherence (PWC) and a 3L-CNN is developed and thoroughly evaluated. In essence, PWC connectivity provides the neuronal biomarkers that are input to the 3L-CNN, which extracts and classifies features. The advantage of PWC to represent dynamic connectivity lies in the fact that it provides both time and frequency domain features that carry critical characteristic for discriminating ASD from NC subjects. Further, PWC is fundamentally the most discriminative feature of WC for ASD classification, as determined by a three-stage process: First, by ANOVA-SVM, providing 212 FCs, which were further distilled using correlation analysis between WCF and SRS scores, resulting in 11 FC. Lastly, application of SVD on the 11 FCs extracted the first principal subspace that sensed the maximal orientation energy of the PWC images. In other words, the input images of the 3L-CNN carry the most significant time-frequency information of the PWC and can be used as the biomarker for diagnosis of ASD. Evaluation of the proposed framework was conducted using the full ABIDE dataset, which includes 1035 individuals. PWC + 3 LCNN achieved classification with the highest accuracy of 95.2% with a low error rate (4.8%). Comparison with state-of-the-art ASD classification models show the PWC method performs better than recently developed approaches.  
Although PWC is promising for using fMRI features for ASD subtype diagnosis, it still has space for improvement. Furthermore, there is evidence that ASD and ADHD have overlapping symptoms, such as social withdrawal and communication difficulties [25,49]. ADHD is defined by a chronic pattern of inattention and/or hyperactivity and impulsivity that interferes with development. The overlap of symptoms in different brain disorders makes clinical diagnosis challenging, and classification models with multiple classes are needed for future studies of neurodevelopmental disorders. Future research on PWC images using CNN models could lead to a method for diagnosing ASD individuals using rs-fMRI data. To improve performance in multi-class classification, the use of different brain atlases, such as Craddock (CC200, CC400), that extract more information from the BOLD signals could be considered. Moreover, PWC images can also be trained and tested on other CNN architectures such as residual or inception blocks to achieve better classification of ASD subtypes. In addition, the proposed technique sheds light on computer-aided diagnosis of other psychological problems such as depression, schizophrenia and early Alzheimer's disease.  
[PERSON] conducted the research and investigation process, finalised the methodology, proposed the algorithm and results analysis, and completed the manuscript writing. [PERSON] formulated the research objective, provided supervision, validated the results and methodology, revised the manuscript and managed the funding of the project. [PERSON] provided some valuable suggestions and supervised the project. [PERSON] and [PERSON] were involved in writing, reviewing and editing the final manuscript. All authors read and agreed to the published version of the manuscript.  
This research is supported by two research grants: (1) The Ministry of Education of Malaysia under the Higher Institutional Centre of Excellence (HICoE) Scheme awarded to the Centre for Intelligent Signal and Imaging Research (CISIR); and (2) the Yayasan Universiti Teknologi PETRONAS under grant number YUTP-FRG 015 LC0-292.  
Not applicable.  
The authors declare no conflict of interest.