Elsevier

NeuroImage

Volume 134, 1 July 2016, Pages 645-657
NeuroImage

Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity

https://doi.org/10.1016/j.neuroimage.2016.04.051Get rights and content

Highlights

  • Performed classification using static and dynamic connectivity features in schizophrenia and bipolar disorder during rest.

  • Classification using connectivity features discriminates subjects into appropriate diagnostic groups with high accuracy.

  • Classification using dynamic connectivity features has significantly higher predictive accuracy than static FNC.

  • Combining both connectivity features does not add significant information for classification purposes.

Abstract

Recently, functional network connectivity (FNC, defined as the temporal correlation among spatially distant brain networks) has been used to examine the functional organization of brain networks in various psychiatric illnesses. Dynamic FNC is a recent extension of the conventional FNC analysis that takes into account FNC changes over short periods of time. While such dynamic FNC measures may be more informative about various aspects of connectivity, there has been no detailed head-to-head comparison of the ability of static and dynamic FNC to perform classification in complex mental illnesses. This paper proposes a framework for automatic classification of schizophrenia, bipolar and healthy subjects based on their static and dynamic FNC features. Also, we compare cross-validated classification performance between static and dynamic FNC. Results show that the dynamic FNC significantly outperforms the static FNC in terms of predictive accuracy, indicating that features from dynamic FNC have distinct advantages over static FNC for classification purposes. Moreover, combining static and dynamic FNC features does not significantly improve the classification performance over the dynamic FNC features alone, suggesting that static FNC does not add any significant information when combined with dynamic FNC for classification purposes. A three-way classification methodology based on static and dynamic FNC features discriminates individual subjects into appropriate diagnostic groups with high accuracy. Our proposed classification framework is potentially applicable to additional mental disorders.

Introduction

Functional connectivity (FC) can be quantified using a variety of different neuroimaging techniques. A commonly used measure is functional magnetic resonance imaging (fMRI), which measures synchronized brain activity via blood oxygenation and infers functional interactions among different brain regions (Craddock et al., 2013). FC, defined as temporal correlation (or other types of statistical dependency) among spatially distant brain regions (Friston, 2002a), has recently been used to examine the functional organization and temporal dependencies among these remote brain regions. Different analytic tools have been applied to resting-state fMRI data to describe brain functional connectivity. Two widely used FC approaches are (i) seed-based analysis (Biswal et al., 1995, Greicius et al., 2003) and (ii) purely data-driven methods, such as ICA (Calhoun and Adali, 2012, Calhoun et al., 2001a, Calhoun et al., 2009, Damoiseaux et al., 2006, Fox and Raichle, 2007, Hyvärinen and Oja, 2000). FC can also be investigated at the network level using spatial independent component analysis (ICA), and connectivity among spatial components is referred to as functional network connectivity (FNC) (Jafri et al., 2008).

The majority of FNC studies are primarily based on the assumption that FNC is stationary throughout the entire scan session (or at least stationary during a given task or resting-state condition) (Camchong et al., 2011, Greicius, 2008, Meda et al., 2012, Sorg et al., 2013). Static FNC analysis overlooks the fact that individual subjects are likely to engage in slightly different mental activities at different instances in time (Arieli et al., 1996, Makeig et al., 2004, Onton and Makeig, 2006). Also evidence of dynamic fluctuation in FC from several studies supports the idea of dynamic changes in FC during the experimental period. More recently, studies have started utilizing the powerful information contained within the temporal features of spontaneous FC of BOLD signals. Connectivity dynamics capture uncontrolled but reoccurring patterns of interactions among intrinsic networks during task engagement or at rest (Allen et al., 2012, Calhoun et al., 2014, Hutchison et al., 2013, Rashid et al., 2014, Sakoğlu et al., 2010). These studies provide results that cannot be detected with static functional connectivity analyses.

There is an increasing interest in designing robust and accurate techniques to classify subjects into groups using functional imaging data. For example, previous studies showed the use of functional connectivity-based features for classification of schizophrenia and bipolar patients at the individual level (Arbabshirani et al., 2013b, Shen et al., 2010, Su et al., 2013). Shen et al. (2010) used an atlas-based method to extract mean time-courses of 116 brain regions in the resting-state for both healthy controls and schizophrenia subjects. The correlation between these time-courses made the feature vector for each subject. By applying feature selection and dimensionality reduction methods, they reduced the dimensionality down to three where they classified patients from controls with a high accuracy. Shinkareva et al. (2006) proposed a classification approach for schizophrenia patients based on fMRI time-series from the voxels showing between-group temporal dissimilarity using leave-one-out cross-validation method. Another study combined both structural and functional MRI data for classification of schizophrenia patients and created a training set by projecting the high dimensional data onto a lower dimensional space using the principle component analysis (PCA), achieving a high classification accuracy (Ford et al., 2002a). A recent study performed automatic classification of schizophrenia using both structural and functional MRI features, and showed that better classification accuracy could be achieved by using both MRI features, compared to using only a single feature (Silva et al., 2014). However, only a few studies have focused on classification analyses of both schizophrenia and bipolar disorder patients (Arribas et al., 2010, Calhoun et al., 2008b, Costafreda et al., 2011). In Calhoun et al. (2008c)) temporal lobe and default mode networks were used as features using a leave-one-out cross-validation framework, and classified schizophrenia and bipolar patients at individual level. In another classification study (Costafreda et al., 2011), a support vector machine (SVM) was applied on the verbal fluency task-based patterns of regional brain responses to identify schizophrenia and bipolar patients at the individual level. To our best knowledge, no such study has provided a detailed comparison of both static and dynamic FNC features in a cross-validated classification analysis.

In this work, we conducted a classification study of schizophrenia, bipolar and healthy subjects using static and dynamic FNC features, as well as combined FNC features from both FNC analyses. Several previous studies have shown that schizophrenia and bipolar patients can be discriminated at group-level by using the information on dysfunctional integration of the brain (Allen et al., 2012, Arbabshirani et al., 2013a, Damaraju et al., 2014, Friston, 2002b, Rashid et al., 2014).We hypothesized that disrupted functional integration in schizophrenia and bipolar patients as captured by FNC analysis reveals powerful information for automatic discriminative analysis at subject-level. We expected some connectivity measures to be better captured in a static model and others in a dynamic model (Damaraju et al., 2014). Static FNC provides information about the overall mean connectivity and may be more optimal for connectivity that is persistent across the entire experiment than a dynamic FNC approach. On the other hand, information on local connectivity changes at different time windows will be better captured by dynamic FNC. Thus, we hypothesize that both static and dynamic FNC methods capture complementary aspects of connectivity, and combining static and dynamic FNC features will improve classification performance beyond the achievable performance from each type of these features individually. We present machine learning techniques to effectively combine these two types of features for accurate classification of schizophrenia, bipolar and healthy controls. However, our results indicated that static FNC features didn’t contribute additional information when combined with dynamic FNC features for classification purposes.

Section snippets

Participants

Before preprocessing, we had raw resting-state fMRI data from 273 subjects (HC = 135, SZ = 87 and BP = 51). After matching for age, and based on our exclusion criterion (see Section 2.3 for details), we eliminated 114 subjects from the final analysis and had 159 subjects in total. We assessed these 159 subjects comprising 61 screened healthy controls [HC, age 35.44 ± 11.57 (range), 28 females], 60 patients diagnosed with schizophrenia or schizoaffective disorder (SZ, age 35.85 ± 12.01, 13 females) and 38

Intrinsic connectivity networks

ICA was successfully used to decompose the functionally homogeneous cortical and subcortical regions with temporally coherent activity. Out of the 100 components obtained, we characterized 49 components as intrinsic connectivity networks (ICNs) that depicted peak cluster locations in gray matter with minimal overlap with white matter, ventricles and edges of the brain and also exhibit higher low frequency temporal activity. We used the time-courses of these 49 ICNs to compute static and dynamic

Discussion

Our results suggest that, classification using dynamic FNC and static + dynamic FNC features significantly outperforms classification using static FNC features (p = 3.229 × 10 6 and p = 8.653 × 10 8, respectively, for overall accuracy). This is also supported by non-overlapping confidence intervals (static FNC: ([51 67]; dynamic FNC: [78 90]; combined FNC: [83 93]). Several group-wise statistical measures (sensitivity, specificity, PPV and NPV) also showed that both dynamic FNC and static + dynamic FNC

Limitations and future directions

There are several methodological and experimental limitations associated with sliding-window analysis method and result interpretations. One issue for sliding-window analysis is the choice of appropriate window size. Is has been reported in (Sakoğlu et al., 2010) that the ideal window size should be able to estimate FC variability, capture lowest frequencies of interest in the signal, and detect interesting short-term effects. Our dynamic FNC approach was based on an empirically validated fixed

Acknowledgment

This study was supported by NIH/NIBIB: 2R01 EB000840-06, NIH P20GM103472, R01EB020407 and NSF 1539067.

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