Physiogenomic Analysis of Localized fMRI Brain Activity in Schizophrenia

Genomas, Inc., Hartford, CT, 06106, USA.
Annals of Biomedical Engineering (Impact Factor: 3.23). 07/2008; 36(6):877-88. DOI: 10.1007/s10439-008-9475-2
Source: PubMed


The search for genetic factors associated with disease is complicated by the complexity of the biological pathways linking genotype and phenotype. This analytical complexity is particularly concerning in diseases historically lacking reliable diagnostic biological markers, such as schizophrenia and other mental disorders. We investigate the use of functional magnetic resonance imaging (fMRI) as an intermediate phenotype (endophenotype) to identify physiogenomic associations to schizophrenia. We screened 99 subjects, 30 subjects diagnosed with schizophrenia, 13 unaffected relatives of schizophrenia patients, and 56 unrelated controls, for gene polymorphisms associated with fMRI activation patterns at two locations in temporal and frontal lobes previously implied in schizophrenia. A total of 22 single nucleotide polymorphisms (SNPs) in 15 genes from the dopamine and serotonin neurotransmission pathways were genotyped in all subjects. We identified three SNPs in genes that are significantly associated with fMRI activity. SNPs of the dopamine beta-hydroxylase (DBH) gene and of the dopamine receptor D4 (DRD4) were associated with activity in the temporal and frontal lobes, respectively. One SNP of serotonin-3A receptor (HTR3A) was associated with temporal lobe activity. The results of this study support the physiogenomic analysis of neuroimaging data to discover associations between genotype and disease-related phenotypes.

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Available from: Andreas Windemuth
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    • "Wildtypes were assigned as a result of the absence of such SNPs. The PG array has been tested on nearly 5,000 patients and has been successfully applied in cardiovascular and neuropsychiatric pharmacogenetic research, resulting in ten publications (de Leon et al., 2008; Liu et al., 2009; Ruaño et al., 2005b, 2007a, 2007b, 2008, 2009, 2010; Seip et al., 2008; Windemuth et al., 2008). Careful manual analysis was performed on the alignments underlying the genotype calls using GenCall and 50 SNPs with even a slight degree of uncertainty about calling accuracy were not included in the analysis, leaving 332 SNPs from 196 genes. "

    Full-text · Chapter · Mar 2012
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    • "Comparisons of affected/unaffected monozygotic and dizygotic twin pairs and healthy control twins indicate that WM impairments vary with the level of genetic susceptibility to the disorder, suggesting that these deficits may provide a behavioral marker of genetic liability (Park et al., 1995). Similarly gradated relationships in both the structural volume (Cannon and Keller, 2006) and functional activation patterns (Meda et al., 2008; Windemuth et al., 2008) of frontal regions that support WM in studies of twin pairs and first-degree unaffected relatives further support the idea of ''genetic loading'' for WM and other cognitive dysfunction. WM deficits have long been associated with dysfunction of the dorsalateral prefrontal cortex (DLPFC) and other constituents of the network that is hypothesized to subserve WM. "
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    ABSTRACT: Deficits in working memory (WM) are a consistent neurocognitive marker for schizophrenia. Previous studies have suggested that WM is the product of coordinated activity in distributed functionally connected brain regions. Independent component analysis (ICA) is a data-driven approach that can identify temporally coherent networks that underlie fMRI activity. We applied ICA to an fMRI dataset for 115 patients with chronic schizophrenia and 130 healthy controls by performing the Sternberg Item Recognition Paradigm. Here, we describe the first results using ICA to identify differences in the function of WM networks in schizophrenia compared to controls. ICA revealed six networks that showed significant differences between patients with schizophrenia and healthy controls. Four of these networks were negatively task-correlated and showed deactivation across the posterior cingulate, precuneus, medial prefrontal cortex, anterior cingulate, inferior parietal lobules, and parahippocampus. These networks comprise brain regions known as the default-mode network (DMN), a well-characterized set of regions shown to be active during internal modes of cognition and implicated in schizophrenia. Two networks were positively task-correlated, with one network engaging WM regions such as bilateral DLPFC and inferior parietal lobules while the other network engaged primarily the cerebellum. Our results suggest that DLPFC dysfunction in schizophrenia might be lateralized to the left and intrinsically tied to other regions such as the inferior parietal lobule and cingulate gyrus. Furthermore, we found that DMN dysfunction in schizophrenia exists across multiple subnetworks of the DMN and that these subnetworks are individually relevant to the pathophysiology of schizophrenia. In summary, this large multisite study identified multiple temporally coherent networks, which are aberrant in schizophrenia versus healthy controls and suggests that both task-correlated and task-anticorrelated networks may serve as potential biomarkers.
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    • "Recent research studies used neuroimaging techniques in linking genes to brain morphology and function. One such study by Windemuth et al. [61] identified endophenotypes associated with schizophrenia. They found 3 single nucleotide polymorphisms in certain genes were significantly associated with fMRI activity. "
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    ABSTRACT: The field of proteomics has made leaps and bounds in the last 10 years particularly in the fields of oncology and cardiovascular medicine. In comparison, neuroproteomics is still playing catch up mainly due to the relative complexity of neurological disorders. Schizophrenia is one such disorder, believed to be the results of multiple factors both genetic and environmental. Affecting over 2 million people in the US alone, it has become a major clinical and public health concern worldwide. This paper gives an update of schizophrenia biomarker research as reviewed by Lakhan in 2006 and gives us a rundown of the progress made during the last two years. Several studies demonstrate the potential of cerebrospinal fluid as a source of neuro-specific biomarkers. Genetic association studies are making headway in identifying candidate genes for schizophrenia. In addition, metabonomics, bioinformatics, and neuroimaging techniques are aiming to complete the picture by filling in knowledge gaps. International cooperation in the form of genomics and protein databases and brain banks is facilitating research efforts. While none of the recent developments described here in qualifies as biomarker discovery, many are likely to be stepping stones towards that goal.
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