| 1 | Schizophr. Res. 2011 Jul 129: 141-8 |
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| PMID | 21507613 |
| Title | Genetic influences of cortical gray matter in language-related regions in healthy controls and schizophrenia. |
| Abstract | Individuals with schizophrenia show a broad range of language impairments, including reading difficulties. A recent structural MRI (sMRI) study linked these difficulties to structural abnormalities in language-related regions (Leonard et al., 2008). Similar regions have been implicated in primary reading disability (RD). Major hypotheses of RD implicate abnormal embryonic neuronal migration in the cortex, and genetic linkage and association studies have identified a number of candidate RD genes that are associated with neuronal migration (Paracchini et al., 2007). Interestingly, evidence suggests at least some individuals with schizophrenia also show impaired neuronal migration in the cortex (Akbarian et al., 1996). Thus the aim of this study was to examine the link between RD-related genes and gray matter volumes in healthy controls and schizophrenia. We used parallel independent component analysis (parallel-ICA) to examine the relationship between gray matter volumes extracted using voxel-based morphometry (VBM) and 16 single nucleotide polymorphisms (SNPs) spanning FOXP2 and four RD-related genes, DCDC2, DYX1C1, KIAA0319 and TTRAP. Parallel-ICA identified five sMRI-SNP relationships. Superior and inferior cerebellar networks were related to DYX1C1 and DCDC2/KIAA0319 respectively in both groups. The superior prefrontal, temporal and occipital networks were positively related to DCDC2 in the schizophrenia, but not the control group. The identified networks closely correspond to the known distribution of language processes in the cortex. Thus, reading and language difficulties in schizophrenia may be related to distributed cortical structural abnormalities associated with RD-related genes. |
| SCZ Keywords | schizophrenia |
| 2 | Brain Imaging Behav 2013 Mar 7: 15-27 |
| PMID | 22669497 |
| Title | Genetic influences of resting state fMRI activity in language-related brain regions in healthy controls and schizophrenia patients: a pilot study. |
| Abstract | Individuals with schizophrenia show a broad range of language impairments, similar to those observed in reading disability (RD). Genetic linkage and association studies of RD have identified a number of candidate RD-genes that are associated with neuronal migration. Some individuals with schizophrenia also show evidence of impaired cortical neuronal migration. We have previously linked RD-related genes with gray matter distributions in healthy controls and schizophrenia. The aim of the current study was to extend these structural findings and to examine links between putative RD-genes and functional connectivity of language-related regions in healthy controls (n?=?27) and schizophrenia (n?=?28). Parallel independent component analysis (parallel-ICA) was used to examine the relationship between language-related regions extracted from resting-state fMRI and 16 single nucleotide polymorphisms (SNPs) spanning 5 RD-related genes. Parallel-ICA identified four significant fMRI-SNP relationships. A Left Broca-Superior/Inferior Parietal network was related to two KIAA0319 SNPs in controls but not in schizophrenia. For both diagnostic groups, a Broca-Medial Parietal network was related to two DCDC2 SNPs, while a Left Wernicke-Fronto-Occipital network was related to two KIAA0319 SNPs. A Bilateral Wernicke-Fronto-Parietal network was related to one KIAA0319 SNP only in controls. Thus, RD-genes influence functional connectivity in language-related regions, but no RD-gene uniquely affected network function in schizophrenia as compared to controls. This is in contrast with our previous study where RD-genes affected gray matter distribution in some structural networks in schizophrenia but not in controls. Thus these RD-genes may exert a more important influence on structure rather than function of language-related networks in schizophrenia. |
| SCZ Keywords | schizophrenia |
| 3 | Cortex 2014 Jun 55: 182-91 |
| PMID | 24447899 |
| Title | Category fluency, latent semantic analysis and schizophrenia: a candidate gene approach. |
| Abstract | Category fluency is a widely used task that relies on multiple neurocognitive processes and is a sensitive assay of cortical dysfunction, including in schizophrenia. The test requires naming of as many words belonging to a certain category (e.g., animals) as possible within a short period of time. The core metrics are the overall number of words produced and the number of errors, namely non-members generated for a target category. We combine a computational linguistic approach with a candidate gene approach to examine the genetic architecture of this traditional fluency measure. In addition to the standard metric of overall word count, we applied a computational approach to semantics, Latent Semantic Analysis (LSA), to analyse the clustering pattern of the categories generated, as it likely reflects the search in memory for meanings. Also, since fluency performance probably also recruits verbal learning and recall processes, we included two standard measures of this cognitive process: the Wechsler Memory Scale and California Verbal Learning Test (CVLT). To explore the genetic architecture of traditional and LSA-derived fluency measures we employed a candidate gene approach focused on SNPs with known function that were available from a recent genome-wide association study (GWAS) of schizophrenia. The selected candidate genes were associated with language and speech, verbal learning and recall processes, and processing speed. A total of 39 coding SNPs were included for analysis in 665 subjects. Given the modest sample size, the results should be regarded as exploratory and preliminary. Nevertheless, the data clearly illustrate how extracting the meaning from participants' responses, by analysing the actual content of words, generates useful and neurocognitively viable metrics. We discuss three replicated SNPs in the genes ZNF804A, DISC1 and KIAA0319, as well as the potential for computational analyses of linguistic and textual data in other genomics tasks. |
| SCZ Keywords | schizophrenia |