Whole brain resting state functional connectivity abnormalities in schizophrenia
Introduction
Schizophrenia is a devastating disorder that simultaneously affects multiple cognitive domains including language, memory, attention and executive functioning. Since each of these functions relies on efficient communication between several, often distant, brain regions, schizophrenia has been hypothesized to arise from disruptions in brain connectivity (Konrad and Winterer, 2008). Based on the clinical symptoms, lesion studies and initial in vivo MRI studies, it has been predicted that such abnormalities should affect frontal, temporal and parietal regions, and their connections (Kraepelin et al., 1919, Weinberger et al., 1992, McGuire and Frith, 1996). Since the emergence of functional MRI (fMRI), many studies have investigated functional neuroanatomy and the correlates of the cognitive dysfunctions observed in schizophrenia (Niznikiewicz et al., 2003). However, these studies are limited by several factors, one of them being the low Signal-to-Noise Ratio (SNR), typical of fMRI data derived during experiments involving cognitive tasks, forcing the interpretations of such studies to be based on aggregated data of 10 or more subjects, rather than a single individual. Another factor limiting the clinical application of fMRI is the issue of task difficulty, where participants are limited to those able to perform a given task, and hence, whose cognition is least affected by the disease (Greicius, 2008). Additional difficulty comes with using fMRI data to compare populations, where matching for cognitive performance might lead to removing the data variance due to disease related cognitive decline (Greicius, 2008).
Resting-state fMRI is a relatively new functional imaging method, with the potential to overcome most of the above limitations. I.e. since no cognitive task is involved, there is no need to correct for cognitive performance, or exclude subjects that cannot perform the task (thus biasing the sample). Resting-state functional MR data is collected in the absence of any experimental task; the subject is asked to rest quietly, either with their eyes closed or with their eyes opened and fixating on one point. Initial experiments suggest that various regions of the brain remain active during this process, expressed in low frequency BOLD fluctuations. It is believed that temporal correlations between these fluctuations reveal the intrinsic functional organization of the brain (Biswal et al., 1995, Gusnard and Raichle, 2001, Peltier et al., 2003). Univariate tests and random effects analysis are, to a great extent, the standard in population studies of functional connectivity (Liang et al., 2006, Greicius et al., 2007, Zhou et al., 2007). Using these methods several “resting state networks (RSNs)” can be robustly identified (Beckmann et al., 2005). The method has been also applied to several brain disorders, including Alzheimer's, depression, schizophrenia, ADHD and multiple sclerosis (MS). While results of studies in Alzheimer's are consistent and encouraging, the same is not true for schizophrenia (Greicius, 2008). Several studies describe increased connectivity within the default mode network (one of the most robust of the resting state networks (Zhou et al., 2007, Whitfield-Gabrieli et al., 2009), while others report decreased connectivity within this network (Bluhm et al., 2007, Zhou et al., 2010). Reports on changes in correlations between other RSNs are inconsistent as well (Zhou et al., 2007, Bluhm et al., 2007).
As stated previously, schizophrenia is a multi-dimensional disease, where several separate, but interrelated cognitive domains and processes appear to be affected (Kalkstein et al., 2010). Thus, the clinical symptoms of schizophrenia are most likely not related to any particular brain region brain connection, but rather appear due to instability of communication within and between networks of regions, across the spectrum of cognitive domains. Resting state fMRI data has a potential to map those interactions and their abnormalities in schizophrenia. However, traditional functional connectivity analysis focuses on disruptions of single connections or single cognitive networks, and interactions between them are often ignored, leaving the models and clinical hypotheses that are being tested much too simplified. Specifically, most functional connectivity studies of schizophrenia use t-scores/p-values to identify the significant connections. We believe there are two issues with this approach: (1) the tests are done independently on each connection, and therefore one cannot identify networks of connections that together cause abnormalities, and (2) t-scores/p-values are not necessarily good measures of the relevance of specific connections. Only recently have the multivariate classifier and regression approaches been used successfully in population based functional connectivity analyses (in depression (Craddock et al., 2009), and brain development (Dosenbach et al., 2010)). In this paper, we use a similar multivariate classifier approach (Random Forest classification (Breiman, 2001)). The method has been introduced, and described in detail, including its comparison to univariate tests in Venkataraman et al. (2010). We address the above-mentioned limitations of univariate approaches by using a multi-pattern score to select the relevant features (Gini Importance), and by using prediction as the primary way to validate the results (i.e., can the model predict the diagnosis of a new subject).
Here, we applied the method to the data collected from patients with chronic schizophrenia and their matched healthy controls, intending to identify and characterize patterns of brain connectivities that can differentiate patients with schizophrenia and healthy controls. Based on schizophrenia literature as well as our previous studies, we hypothesized that the connections/networks best predicting schizophrenia diagnosis will involve fronto-temporal connections, at least partially overlapping with the default network. We further expected that the patterns of both hyper as well as hypo connectivity will be represented in schizophrenia.
Section snippets
Subjects
Eighteen male patients diagnosed with chronic schizophrenia (SZ) (using DSM-IV criteria based on SCID-P interviews and a review of the medical records), and 18 male healthy volunteers (NC) were matched on gender, handedness, parental socio-economic status (PSES), age and premorbid IQ. All subjects gave written informed consent prior to participation in the study, and the study was approved by an institutional IRB. Subjects were included in the study if they fulfilled the following criteria:
Schizophrenia group abnormalities
Schizophrenia patients exhibited increased functional connectivity between the medial parietal region, including the posterior cingulate gyrus, and the frontal lobe (pars triangularis and opercularis of the inferior frontal gyrus, and dorsolateral prefrontal cortex). This was true for both the full dataset analysis and for the analysis based on the pre-selected brain regions. Along with the increased functional connectivity, abnormal schizophrenia connectivity pattern also included reduced
Discussion
Our results of whole brain, multivariate analysis of functional connectivity in schizophrenia indicate that when compared to healthy controls, patients with schizophrenia exhibit two distinct patterns of differences. Rather than showing uniformly increased or decreased connectivity, schizophrenia patients, when compared to controls, exhibit abnormally increased connectivity between the medial parietal and frontal lobes, and decreased connectivity between the medial parietal and temporal regions
Role of funding source
This study was supported, in part, by the National Alliance for Medical Image Computing (NA-MIC), supported through the National Institutes of Health Roadmap for Medical Research, Grant U54 EB005149 (MK, CFW, PG); and National Institute of Health (R01 M074794 to CFW and MK). This work was also supported by a grant from the Medical Research Council of Australia (Overseas-Based Biomedical Training Fellowship (NHMRC 520627), through the Melbourne Neuro-Psychiatry Centre at the University of
Contributors
A. Venkataraman designed the study and ran the analyses. Dr Kubicki provided fMRI data. Drs Kubicki, Golland and Westin supervised various aspects of the study. Dr Whitford undertook clinical statistical analyses, Drs Kubicki and Whitford helped interpreting clinical findings, and A. Venkataraman wrote the first draft of the manuscript. All authors contributed to and have approved the final manuscript.
Conflict of interest
The authors report no conflict of interests.
Acknowledgments
We want to thank PNL research assistants (Jorge Alvarado and Tali Swisher) for their help with data organization and endnote libraries.
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