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.

Keywords: biomarker; brain disorders; classification; fMRI; functional connectivity.

Figure 1

The primary functional connectivity analysis methods and possible connectivity features used for classification/prediction problem.

Figure 2

Summary of the existing application studies (included in Tables 1–6). (A) Total number of papers for 2-year intervals for each disease type. The legend shows the color code for each disease type. This legend also applies to subfigure (B,D). (B) Scatter plot of the reported classification accuracy vs. the total sample size. In the subfigure (B), square shape indicates study using features from one modality, while circle shape represent study using features from multiple modalities. (C) Histogram of the sample sizes (including all patients and healthy controls) of the surveyed studies. Vertical dashed lines indicate mean (red) and median (blue) of the sample size among all studies. (D) Disorder specific boxplot plots of reported classification accuracies of the surveyed papers. For each disease type, the accuracies in different studies are shown using a boxplot. Green shape means a 95% confidence interval for the mean while orange shape means standard deviation.

Figure 3

Flowchart of one study (Du et al., 2015b) that includes classifying HCs, SZ patients, BPP patients, SADM patients, and SADD patients. The spatial network maps of the training set computed from GIG-ICA were used as the features in a multiclass (five-class) SVM classifier, that yielded 68.75% classification accuracy for the new coming subjects. The figure is reused with permission from Du et al. (2015b).

Figure 4

Relationship between those original subjects evaluated using network measures in the study of Du et al. (2015b). (A) Distance matrix computed using the feature vectors of 93 subjects. The x-axis and y-axis denote subject ID. Subjects with ID 1–20 are HCs, subjects with ID 21–40 are SZ patients, subjects with ID 41–60 are BP patients, subjects with ID 61–80 are SADM patients, and subjects with ID 81–93 are SADD patients. (B) The mean distance matrix obtained by averaging the values in each inter-group and intra-group related sub-block of the distance matrix. (C) The projection results of 93 subjects using t-distributed stochastic neighbor embedding (t-SNE) method. Each point denotes one subject, and different colors denote different groups. Each ellipse reflects mean (center) and standard deviation for one group. (D) The linkage results from the hierarchical clustering method. The x-axis denotes the subject ID, which is as same as that in (A). In (D), “HC” denotes that most of the subjects clustered into the related group are healthy controls. “SZ,” “BP,” “SADM,” and “SADD” have similar meanings. The figure is reused with permission from Du et al. (2015b).