Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging

Abstract

Brain functional imaging data, especially functional magnetic resonance imaging (fMRI) data, have been employed to reflect functional integration of the brain. Alteration in brain functional connectivity (FC) is expected to provide potential biomarkers for classifying or predicting brain disorders. In this paper, we present a comprehensive review in order to provide guidance about the available brain FC measures and typical classification strategies. We survey the state-of-the-art FC analysis methods including widely used static functional connectivity (SFC) and more recently proposed dynamic functional connectivity (DFC). Temporal correlations among regions of interest (ROIs), data-driven spatial network and functional network connectivity (FNC) are often computed to reflect SFC from different angles. SFC can be extended to DFC using a sliding-window framework, and intrinsic connectivity states along the time-varying connectivity patterns are typically extracted using clustering or decomposition approaches. We also briefly summarize window-less DFC approaches. Subsequently, we highlight various strategies for feature selection including the filter, wrapper and embedded methods. In terms of model building, we include traditional classifiers as well as more recently applied deep learning methods. Moreover, we review representative applications with remarkable classification accuracy for psychosis and mood disorders, neurodevelopmental disorder, and neurological disorders using fMRI data. Schizophrenia, bipolar disorder, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease and mild cognitive impairment (MCI) are discussed. Finally, challenges in the field are pointed out with respect to the inaccurate diagnosis labeling, the abundant number of possible features and the difficulty in validation. Some suggestions for future work are also provided.

Dynamic Functional Connectivity Reveals Abnormal Variability and Hyper‐connected Pattern in Autism Spectrum Disorder

“…However, a growing body of evidence suggests that rsfMRI is dynamic in nature [Allen et al, ; Hutchison et al, ; Wee, Yap, & Shen, ]. Recently, researchers have shown more interest in dynamic functional connectivity (DFC) studies and found profound indicators associated with DFC [Du, Fu, & Calhoun, ; Preti, Bolton, & Van De Ville, ]. Meanwhile, DFC was superior to SFC at predicting behavior in a typically developing (TD) population [Chen, Nomi, Uddin, Duan, & Chen, ].…”

Predicting dysfunctional age-related task activations from resting-state network alterations

“…Recent reports have extended towards using restFC to quantitatively predict or classify age-related conditions (Dosenbach et al, 2010;Woo et al, 2017;Du et al, 2018). However, failures of predictive models generalizing out-of-sample (Onoda et al, 2017;Teipel et al, 2017;Fountain-Zaragoza et al, 2019) highlight limitations of entirely data-driven approaches to predicting Alzheimer's-related pathologies, especially as artifactual contaminants of restFC can drive clinical group differences (Siegel et al, 2016;Hodgson et al, 2017).…”

Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network

“…Previous studies on functional connectivity have reported accuracies in the range of 60-95% in classifying schizophrenia versus healthy subjects(Du, Zening, & Calhoun, 2018;Woo, Chang, Lindquist, & Wager, 2017); however, most of them employ ICA to define networks from the whole data, which leads to a bias in the cross validation. Previous studies on functional connectivity have reported accuracies in the range of 60-95% in classifying schizophrenia versus healthy subjects(Du, Zening, & Calhoun, 2018;Woo, Chang, Lindquist, & Wager, 2017); however, most of them employ ICA to define networks from the whole data, which leads to a bias in the cross validation.…”