Altered structural connectivity in neonates at genetic risk for schizophrenia: A combined study using morphological and white matter networks
Introduction
Schizophrenia is a chronic, severe, and disabling mental disorder characterized by diverse clinical presentations, such as hallucinations, delusions, anhedonia, avolition and impaired cognitive functions (Schultz and Andreasen, 1999). It is commonly accepted that schizophrenia is not a result of regionally isolated pathologies, but is due to the alteration of interactions between two or more regions (Friston, 1998). However, the vast majority of studies on schizophrenia involved only adult subjects, and the impact of genetic risk of the illness on infants is still poorly understood.
The symptoms of schizophrenia usually start to present at late adolescence and early adulthood, and persist for a lifetime in 1% of the population (Regier et al., 1993). Interestingly, our recent work suggested that the precursor of developmental disorder may occur as early as infancy (Gilmore et al., 2010). We found structural abnormalities, such as significantly larger intracranial, CSF, and lateral ventricular volumes, in (especially male) offspring of schizophrenic parents. No significant differences in diffusion properties, however, were reported for major white matter fiber bundles. Note that having an affected relative increases the risk for schizophrenia. For example, 20–50% of children born to schizophrenic mothers have been found to exhibit developmental abnormalities (Marcus et al., 1993). Their risk of developing schizophrenia is about 10 times higher than that of the general population (Sullivan, 2005). High-risk neonates, with minimal exposure to environmental influences, are ideal candidates for understanding the genetic effects of schizophrenia at a very early stage.
Recent advances in graph-based network analysis techniques allows for a systems-level evaluation of brain interregional interactions (Watts and Strogatz, 1998, Yap et al., 2010). The brain can be modeled as a complex network composed of a collection of nodes that are connected by edges. The nodes represent the anatomical regions of interest (ROIs), and the edges encode the interaction between the nodes. For T1-/T2-weighted images, the structural associations between different GM regions can be evaluated via the correlations of morphological descriptors, such as gray matter (GM) volumes, cortical thickness, and surface areas (Bassett et al., 2008, Gong et al., 2009b, He et al., 2007). Brain morphological networks, formed by collections of such associations, have been successfully employed in many studies, such as asymmetry analysis of normal aging (Zhu et al., 2012) and brain alteration analysis in relation to multiple sclerosis (He et al., 2009). Of note, Bassett et al. (2008) constructed a morphological network on brain multimodal association areas using interregional gray-matter volumes, and reported the loss of frontal and the emergence of nonfrontal hubs, and increased connection distance in schizophrenia adults. Diffusion-weighted imaging, on the other hand, captures local microstructural information of brain tissues via water diffusion, and allows tracing of neuronal fibers in vivo. Fiber connections between each pair of brain regions can be used to construct a so-called brain white matter network (Gong et al., 2009a, He and Evans, 2010, Sporns et al., 2005, Yap et al., 2011). Zalesky et al. (2010) constructed the white matter network of schizophrenia adults and found lines of evidence of disruption of axonal connections comprising medial frontal, parietal/occipital, and the left temporal lobe.
The disconnection hypothesis has gathered increasing support from neurophysiological and neuroimaging studies, suggesting that the core symptoms of schizophrenia are related to aberrant connectivity between distinct brain regions. However, the actual cause of disconnectivity in schizophrenia is still controversial. Disconnectivity may be a result of (1) aberrant synaptic plasticity, and/or (2) the “miswiring” of axonal fibers in white matter (WM) (Friston, 1999, Stephan et al., 2006). Recent studies reveal that synaptic plasticity contributes to macro-scale volumetric changes and thus can be measured by using gray matter volumes in MR brain images (Anderson, 2011, Zatorre et al., 2012). Several structural imaging studies on adults (Glahn et al., 2008, Thompson et al., 2001) provide additional evidence in support of the occurrence of gray matter abnormalities in association with the illness. This motivates us to investigate the impairment of structural associations/interactions in gray matter (i.e., morphological network) for exploring the disconnection hypothesis. On the other hand, the integrity of axonal fibers can be assessed more directly with diffusion-weighted imaging by tracing the white matter connections between brain regions. Combining morphological and white matter networks takes into account the two possible causes of disconnection hypothesis and allows us to perform a more comprehensive examination of the pathologies associated with schizophrenia.
Our aim in the current work is to identify possible neurological alterations in terms of inter-regional interactions in high-risk neonates. We hypothesize that the high-risk neonates may be susceptible to neurological alterations at a systems level. To test this hypothesis, we investigated both morphological and white matter brain networks in neonates who were at genetic risk for schizophrenia. Network properties were extracted, and comparisons were made between 26 high-risk neonates and 26 matched healthy neonates. Details of methods and experiments are described in the following sections.
Section snippets
Participants and MR image acquisition
This study was approved by the institutional review board of the University of North Carolina School of Medicine. New mothers with schizophrenia or schizoaffective disorder were recruited and underwent a Structured Clinical Interview for DSM-IV Axis I Disorders (SCID). Past psychiatric records were obtained and a final consensus diagnosis was assigned. The matched control subjects were selected from a companion study of normal brain development. Potential control mothers were screened for
Small-world network organization
For each subject, a 90 by 90 correlation matrix was computed via the Pearson correlation of the corrected gray-matter volumes between all ROI pairs (top row of Fig. 2). The absolute values of the correlations were taken as indicators of connections (He et al., 2008). As shown in Fig. 3, both high-risk neonates and control neonates demonstrate small-world network properties, such as intermediate network efficiency in between degree-matched random and lattice networks (Figs. 3A–B), and
Discussion
In this study, we investigated brain organization in terms of morphological and white matter networks of the neonates at genetic risk for schizophrenia. These networks may reflect different causes of schizophrenia in support of the disconnection hypotheses in association with GM and WM. Both morphological and white matter networks demonstrate small-world topologies. Results indicate that the high-risk neonates exhibit significantly altered structural associations, such as reduced global
Acknowledgments
This work was supported in part by NIH grants EB006733, EB008760, EB008374, EB009634, MH088520, MH070890, MH064065, NS055754, and HD053000.
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