Topological Properties of Resting-State fMRI Functional Networks Improve Machine Learning-Based Autism Classification

Abstract

Automatic algorithms for disease diagnosis are being thoroughly researched for use in clinical settings. They usually rely on pre-identified biomarkers to highlight the existence of certain problems. However, finding such biomarkers for neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) has challenged researchers for many years. With enough data and computational power, machine learning (ML) algorithms can be used to interpret the data and extract the best biomarkers from thousands of candidates. In this study, we used the fMRI data of 816 individuals enrolled in the Autism Brain Imaging Data Exchange (ABIDE) to introduce a new biomarker extraction pipeline for ASD that relies on the use of graph theoretical metrics of fMRI-based functional connectivity to inform a support vector machine (SVM). Furthermore, we split the dataset into 5 age groups to account for the effect of aging on functional connectivity. Our methodology achieved better results than most state-of-the-art investigations on this dataset with the best model for the >30 years age group achieving an accuracy, sensitivity, and specificity of 95, 97, and 95%, respectively. Our results suggest that measures of centrality provide the highest contribution to the classification power of the models.

Current Challenges in Translational and Clinical fMRI and Future Directions

“…This problem is further amplified by the fact that many disorders are spectrum disorders with gradual deviations. However, advanced machinelearning approaches that have been applied to large databases achieved, for example, for the classification of autism spectrum disorders already accuracies of 70% (102) to 90% (103). Interestingly, both studies used the same dataset but different algorithms, indicating that the selection of the algorithms can bias the results.…”

Predicting Sex from Resting-State fMRI Across Multiple Independent Acquired Datasets

“…Thus, investigating sex differences at the level of BOLD fluctuation may reveal if there is strong evidence of sex differences. Recently, machine learning (ML) techniques have been used widely to perform classification and regression on neuroscience data (Al Zoubi, Awad, & Kasabov, 2018;Al Zoubi, Ki Wong, et al, 2018;Campbell et al, 2020;Cohen, Chen, Parker Jones, Niu, & Wang, 2020;Du, Fu, & Calhoun, 2018;Garner et al, 2019;Kazeminejad & Sotero, 2019;Saccà et al, 2019). Some works focused on using ML for classifying subjects into male and female using functional (Ktena et al, 2018;Zhang, Dougherty, Baum, White, & Michael, 2018) and structural data (Chekroud et al, 2016;Feis, Brodersen, von Cramon, Luders, & Tittgemeyer, 2013;Rosenblatt, 2016).…”

Guiding and interpreting brain network classification with transcriptional data

“…Network analysis of functional brain imaging data has been widely applied in studies of neurological disorders and has facilitated the discovery of functional biomarkers in many complex brain diseases [6,10,39], but the molecular and neurobiological mechanisms underlying brain functional connectivity patterns remain elusive [36,37]. Previous studies [23,37,43] on neuroimaging network classification in mental disorders that provided feature selection typically sought to biologically interpret algorithmically selected functional connectivity features post hoc, based on available literature on the known functions of certain brain regions. At the same time, it is known that differential transcription across distinct brain regions has an impact on brain functions [20,30], raising the question of how changes in gene expression associated with a mental disorder may be connected to disrupted brain functional organisation (note that in this paper we sometimes use the phrase "gene expression" when discussing transcriptional data).…”