Developmental changes in multivariate neuroanatomical patterns that predict risk for psychosis in 22q11.2 deletion syndrome
Introduction
The 22q11.2DS, also known as velocardiofacial syndrome (Shprintzen et al., 1978), is the most common microdeletion syndrome in humans occurring in at least 1–5000 live births (Botto et al., 2003). It has been shown that at least 25% of individuals with 22q11.2DS develop a schizophrenia-like psychosis by young adulthood (Murphy et al., 1999). Being the most common identifiable genetic risk factor for schizophrenia, 22q11.2DS serves as an important model from which to elucidate the path leading from a well defined genetic defect to variation in brain development and eventually to the evolution of psychotic symptoms.
Research has shown links between the development of psychotic symptoms and VIQ decline or catechol-O-methyltransferase (COMT) hemizygosity (Gothelf et al., 2005), but no studies have demonstrated whether neuroanatomical patterns can predict the development of psychotic symptoms in 22q11.2DS. This may be due to the fact that past longitudinal studies have used univariate analysis of more crude volumetric or lobar volume measures (Gothelf et al., 2005) rather than multivariate analysis of voxel-based measures, which could be a more sensitive and powerful measure in detecting subtle regional changes. Indeed, studies have begun to elucidate neuroanatomical patterns that predict disease transition in at-risk mental states of psychosis (Koutsouleris et al., 2009). Therefore, the main purpose of the current study was to identify neuroanatomical patterns that predicted risk of psychotic symptoms with high accuracy using cross-validation support vector machine (SVM) algorithms and to compare that with univariate methods.
Section snippets
Subjects
Time1 and Time2 data included 19 children with 22q11.2DS and 18 typically developing (TD) controls. The presence of the 22q11.2 microdeletion was confirmed in all subjects with 22q11.2DS by fluorescence in situ hybridization (FISH). All controls were screened and were not included in the study if they had a history of major psychiatric disorder or neurological or cognitive impairment. The follow-up interval was 4.9 ± 0.7 for the 22q11.2DS group and 4.9 ± 0.9 years for the controls. The
Between-group differences using univariate analysis
Summaries of baseline (Time1) and follow-up (Time2) brain TTV is presented in Table 1, and results of regional brain volume are presented in Table 3. When repeated measures ANCOVA was performed (total GMV and age as covariates), there was a main effect of diagnostic group, a main effect of time, but no significant interaction (p = 0.01 corrected, Fig. 1a and b). Differences in regional GMV between 22q11.2DS and TD groups were very similar at Time1 and Time2 with the 22q11.2DS group showing
Discussion
In this longitudinal study of 22q11.2DS adolescents, we show that later psychotic symptoms can be predicted by developmental changes in morphometric spatial GMV and WMV patterns with very high accuracy using cross-validated MVPA (94.7 and 100%, respectively), and significantly better than using univariate analysis of the PFC GMV (63.1%). We further show that longitudinal VBM analysis replicates and extends previous findings regarding the developmental neuroanatomical characteristics of
Financial disclosure
None.
Role of funding sources
This study was funded by grants NIMH MH50047-15 (Dr. Reiss), NARSAD Young Investigator Award (Drs. Gothelf and Hoeft), the Child Health Research Program from the Stanford University School of Medicine and NICHD 1K23HD054720-01 (Dr Hoeft). The NIMH, NARSAD and NICHD had no further role in the study design; collection, analysis, and interpretation of data; writing of the report; and decision to submit the paper for publication.
Contributors
Doron Gothelf: author, subject recruitment, data collection, data analysis.
Fumiko Hoeft: author, method development, data analysis.
Takefumi Ueno: author, data analysis.
Lisa Sugiura: data management, data analysis.
Agatha D. Lee & Paul Thompson: method development, data analysis.
Allan Reiss: author, data analysis and oversees the research project.
Conflict of interest
No authors have any conflicts of interest. No authors have any financial ties to any people or organizations that could have influenced this research study.
Acknowledgement
None.
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Shared first authors. Authors contributed equally.