Modulations of functional connectivity in the healthy and schizophrenia groups during task and rest
Highlights
► Functional connectivity is quantified using higher-order spatial information. ► Connectivity during task and rest is studied using graph-theoretical analysis. ► Novel metrics are proposed based on the modules obtained by a clustering approach. ► Meaningful task-induced modulations to connectivity are found in the healthy group. ► Altered topological properties of connectivity are observed in schizophrenia group.
Introduction
In the past two decades, the human brain as a complex system has been successfully analyzed using functional magnetic resonance imaging (fMRI). One of the most active areas in current fMRI research involves examining the functional connectivity, i.e., interactions among distributed regions. More interestingly, dysconnectivity or abnormal connectivity has been hypothesized as the major pathophysiological mechanism of various mental disorders, especially schizophrenia (Bullmore et al., 1997, Friston and Frith, 1995, Palmer et al., 2009, Stephan et al., 2009). Evidences for dysconnectivity in schizophrenia have been found between several brain regions (Honey et al., 2005, Lawrie et al., 2002, Liang et al., 2006, Rotarska-Jagiela et al., 2010, Zhou et al., 2007). For example, (Friston and Frith, 1995) discovered profound disruption of prefrontal–temporal interaction; (Rotarska-Jagiela et al., 2010) reported that schizophrenia patients showed aberrant connectivity in the default-mode network and decreased frontoparietal activity. Recently, further studies using graph-theoretical analysis methods suggested that patients with schizophrenia often present abnormalities in topological properties of the brain network connectivity, including small-worldness, efficiency and modularity (Alexander-Bloch et al., 2010, Bullmore and Sporns, 2009, Guye et al., 2010, Liu et al., 2008, Lynall et al., 2010, Wang et al., 2010, Yu et al., 2011b, Zalesky et al., 2010). For example, significantly decreased local and global efficiency in schizophrenia has been shown in a resting-state fMRI study (Liu et al., 2008); a reduced small-worldness in schizophrenic group has been reported in (Lynall et al., 2010). In general, most previous studies using graph-theoretical analysis methods have been conducted with participants during the resting state (Beckmann et al., 2005, Camchong et al., 2011, De Luca et al., 2005, Jafri et al., 2008, van de Ven et al., 2004, Yu et al., 2011b); while the topological properties of network connectivity as a function of task performance (Rajapakse et al., 2006, Yu et al., 2011a) and also the differences between task and rest have not been studied to the same degree. In this work, we examine the graph-theoretical properties of the brain network connectivity for the patients with schizophrenia and healthy controls during both an auditory oddball task (AOD) and an extended rest.
For fMRI data analysis, functional connectivity can be investigated using seed-based method that relies on temporal correlation between a few predefined seed regions of interest and other remaining regions (Biswal et al., 1995, Biswal et al., 1997, Cordes et al., 2000, Cordes et al., 2002, Moussa et al., 2011). However, the seed-based approach usually requires prior knowledge, for example an anatomical model, to determine the seed regions.
An alternative approach to estimate functional connectivity is using independent component analysis (ICA) (McKeown and Sejnowski, 1998). ICA, as applied to fMRI, separates data into a set of maximally independent components and associated time courses, where each component is a spatially distinct network containing temporally coherent voxels (Calhoun et al., 2001). Without an explicit prior knowledge, ICA provides a promising way to study connectivity on a multivariate level. Based on an ICA decomposition, functional connectivity is generally defined as the correlation between the ICA time courses (Jafri et al., 2008, van de Ven et al., 2004).
However, the time dimension of fMRI data and thus of the ICA time courses typically contain a significantly smaller set of samples relative to the spatial components (hundreds compared to tens of thousands). The inherent small sample size of time courses may reduce the accuracy of the computed statistics. Also, the second-order statistics such as correlation do not take full-order statistical information into account. We note that ICA uses diversity to take higher-order statistics into consideration. Based on its generative model, ICA also naturally takes temporal information into account by providing a clustering of spatial components through the temporal modulations. Hence, we measure the functional connectivity using ICA spatial components and in terms of higher-order statistical dependence, instead of the typically used temporal correlation.
In this study, we examine the functional connectivity in patients with schizophrenia and healthy controls acquired during both an auditory oddball task (AOD) and an extended rest. We construct connectivity maps using the mutual information among the ICA spatial components and calculate the graph-theoretical metrics of these maps. We also define three novel metrics based on the modules of components obtained using our previously proposed clustering approach (Ma et al., 2011a). According to the dysconnectivity hypothesis in schizophrenia, we assume in our study that the graph-theoretical properties of the network connectivity observed in the healthy group would be altered in the schizophrenia group, not only at resting state but also during the AOD task. Our experimental results show that regardless of the brain activation states (AOD task versus rest), the connectivity networks derived from spatial dependencies in schizophrenia patients preserve the small-world property. However, patients present a significantly lower small-worldness during both AOD task and rest relative to controls, indicating a consistent tendency towards a more random network organization. These two groups also present some different task-induced changes in the topological features, for example healthy controls show higher efficiency in the motor regions during the AOD task than at rest while patients exhibit reverse trend. These altered connectivity modulations in patients during different activation states may provide further evidence for the cortical processing deficiency in schizophrenia.
Section snippets
Participants and experimental design
Participants consisted of 28 healthy controls (HC, average age: 33 ± 13; range: 17–62) and 28 schizophrenia patients (SZ, average age: 38 ± 12; range: 19–59). Four patients and one control were left-handed. All patients had chronic schizophrenia and symptoms were also assessed by positive and negative syndrome scale (PANSS). The detailed demographic characteristics for participants are shown in Table 1.
All participants were scanned during both an auditory oddball task (AOD) and an extended rest.
Simulations using fMRI-like data
In order to show that ICA decomposition takes temporal information into account when estimating the spatial components, we first generate a simulated fMRI AOD task-related data set using the SimTB toolbox (Erhardt et al., 2012). Total of 20 super-Gaussian sources are generated, including motor, auditory, frontal, parietal, visual, dorsal attention network (DAN), default mode network (DMN), ventricle and sub-cortical nuclei regions. Each source has 148 × 148 voxels and is independently rotated,
Discussion
In this study, we decompose fMRI data sets acquired during an AOD task and extended resting state into a number of spatial components and associated time courses using group ICA. We measure the functional connectivity using the spatial dependence among the ICA components and construct undirected connectivity maps. Several topological properties of these maps are examined for task versus rest and compared between multiple healthy controls and patients with schizophrenia.
With or without a driving
Acknowledgments
This work was supported by the NSF grant 1117056 and NIH grants R01 EB000840 and R01 EB005846. Tom Eichele was supported through a BILATGRUNN grant from the Norwegian Research Council. We thank the research staff at the Olin Neuropsychiatry Research Center and the Mind Research Network who collected, processed and shared the data. We appreciate the valuable advice given by the members of Machine Learning for Signal Processing Laboratory in University of Maryland, Baltimore County.
References (53)
- et al.
The dysplastic net hypothesis: an integration of developmental and dysconnectivity theories of schizophrenia
Schizophr. Res.
(1997) - et al.
Hierarchical clustering to measure connectivity in fMRI resting-state data
Magn. Reson. Imaging
(2002) - et al.
Attention, motor control and motor imagery in schizophrenia: implications for the role of the parietal cortex
Schizophr. Res.
(2004) - et al.
Mutual information: a measure of dependency for nonlinear time series
Physica A
(2004) - et al.
SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability
NeuroImage
(2012) - et al.
A method for functional network connectivity among spatially independent resting state components in schizophrenia
Neuroimage
(2008) - et al.
Reduced frontotemporal functional connectivity in schizophrenia associated with auditory hallucinations
Biol. Psychiatry
(2002) - et al.
Resting-state functional network correlates of psychotic symptoms in schizophrenia
Schizophr. Res.
(2010) - et al.
Functional resting-state networks are differentially affected in schizophrenia
Schizophr. Res.
(2011) - et al.
Network-based statistic: identifying differences in brain networks
Neuroimage
(2010)
Functional dysconnectivity of the dorsolateral prefrontal cortex in first-episode schizophrenia using resting-state fMRI
Neurosci. Lett.
A resilient, lowfrequency, small-world human brain functional network with highly connected association cortical hubs
J. Neurosci.
Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia
Front. Syst. Neurosci.
A baseline for the multivariate comparison of resting state networks
Front. Syst. Neurosci.
Hierarchical organization of human cortical networks in health and schizophrenia
J. Neurosci.
Investigations into resting-state connectivity using independent component analysis
Philos. Trans. R. Soc. Lond. B Biol. Sci.
An information-maximization approach to blind separation and blind deconvolution
Neural Comput.
Functional connectivity in the motor cortex of resting human brain using echo-planar MRI
Magn. Reson. Med.
Simultaneous assessment of flow and BOLD signals in resting-state functional connectivity maps
NMR Biomed.
Complex brain networks: graph theoretical analysis of structural and functional systems
Nature
A method for making group inferences from functional MRI data using independent component analysis
Hum. Brain Mapp.
Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks
Hum. Brain Mapp.
Altered functional and anatomical connectivity in schizophrenia
Schizophr. Bull.
Equivariant adaptive source separation
IEEE Trans. Signal Process.
Mapping functionally related regions of brain with functional connectivity MR imaging
Am. J. Neuroradiol.
Blood oxygenation level dependent contrast resting state networks are relevant to functional activity in the neocortical sensorimotor system
Exp. Brain Res.
Cited by (57)
The whole-brain connectome landscape in patients with schizophrenia: A systematic review and meta-analysis of graph theoretical characteristics
2023, Neuroscience and Biobehavioral ReviewsReorganization of the functional connectome from rest to a visual perception task in schizophrenia and bipolar disorder
2022, Psychiatry Research - NeuroimagingAnalysis of the functional EEG network in an Ecuadorian schizophrenia sample
2021, European Journal of PsychiatryInterindividual variability of functional connectome in schizophrenia
2021, Schizophrenia Research