ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2021
A Multi-Factorial Assessment of Functional Human Autistic Spectrum Brain Network Analysis.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- QG-WRN: A Quantum-Enhanced Graph Convolutional Wide Residual Network for ASD Diagnosis via Neuroimaging Sensing Technology.Sensors (Basel, Switzerland) · 2026Article
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Abstract
The variability of the results obtained by the statistical analysis of functional human brain networks depend on multiple factors such as: the source of the fMRI data, the brain parcellations, the graph theory measures, and the threshold values applied to the functional connectivity matrices to obtain adjacency matrices of sparse graphs. Therefore, the brain network used for down-stream analysis is heavily dependent on the methods that are applied to the fMRI data to obtain and analyze such networks. In this paper we present the preliminary results of a multi-factorial assessment of the statistical analysis of functional human brain networks. The assessment was performed in the functional human brain networks obtained from the resting state fMRI data of ten imaging sites provided by the Autism Brain Imaging Data Exchange (ABIDE) preprocessed functional magnetic resonance database, with six different functional brain parcellations, six different graph theory measures, and three different threshold values applied to the corresponding connectivity matrices to obtain sparse graphs. The statistical analysis to detect differences between the networks representing autism and control subjects were performed with four different statistical methods, using the p-values to determine the levels of significance of the analysis. Our main results show a strong dependence of functional human brain networks statistical analysis on the brain parcellations, and on the graph theory measures. Our results further show that the results of these analysis are less dependent on the statistical tests methods and on the threshold values of the sparse graphs for all practical purposes. An additional result is that the levels of significance of the statistical tests obtained for data provided by individual sites were much higher than the global levels of significance obtained by averaging the results of all the sites, implying that the best results on the analysis of functional human brain networks are obtained when the source of the fMRI data is the same for all the data. Since reproducibility and reliability of functional brain network statistical analysis is strongly dependent on the graphs obtained from fMRI data; our expectation is that the novel results presented in this paper would further help researchers in this field to develop methods that are reliable and reproducible.
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