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      Cerebellar volume and cerebellocerebral structural covariance in schizophrenia: a multisite mega-analysis of 983 patients and 1349 healthy controls.

      Molecular Psychiatry

      Springer Nature

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          Abstract

          Although cerebellar involvement across a wide range of cognitive and neuropsychiatric phenotypes is increasingly being recognized, previous large-scale studies in schizophrenia (SZ) have primarily focused on supratentorial structures. Hence, the across-sample reproducibility, regional distribution, associations with cerebrocortical morphology and effect sizes of cerebellar relative to cerebral morphological differences in SZ are unknown. We addressed these questions in 983 patients with SZ spectrum disorders and 1349 healthy controls (HCs) from 14 international samples, using state-of-the-art image analysis pipelines optimized for both the cerebellum and the cerebrum. Results showed that total cerebellar grey matter volume was robustly reduced in SZ relative to HCs (Cohens's d=-0.35), with the strongest effects in cerebellar regions showing functional connectivity with frontoparietal cortices (d=-0.40). Effect sizes for cerebellar volumes were similar to the most consistently reported cerebral structural changes in SZ (e.g., hippocampus volume and frontotemporal cortical thickness), and were highly consistent across samples. Within groups, we further observed positive correlations between cerebellar volume and cerebral cortical thickness in frontotemporal regions (i.e., overlapping with areas that also showed reductions in SZ). This cerebellocerebral structural covariance was strongest in SZ, suggesting common underlying disease processes jointly affecting the cerebellum and the cerebrum. Finally, cerebellar volume reduction in SZ was highly consistent across the included age span (16-66 years) and present already in the youngest patients, a finding that is more consistent with neurodevelopmental than neurodegenerative etiology. Taken together, these novel findings establish the cerebellum as a key node in the distributed brain networks underlying SZ.Molecular Psychiatry advance online publication, 16 May 2017; doi:10.1038/mp.2017.106.

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          Most cited references 51

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          Effect size, confidence interval and statistical significance: a practical guide for biologists.

          Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.
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            Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies.

            Choosing the appropriate neuroimaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroimaging methods provide numerous potential phenotypes, their role for imaging genetics studies is unclear. Here we examine the relationship between brain volume, grey matter volume, cortical thickness and surface area, from a genetic standpoint. Four hundred and eighty-six individuals from randomly ascertained extended pedigrees with high-quality T1-weighted neuroanatomic MRI images participated in the study. Surface-based and voxel-based representations of brain structure were derived, using automated methods, and these measurements were analysed using a variance-components method to identify the heritability of these traits and their genetic correlations. All neuroanatomic traits were significantly influenced by genetic factors. Cortical thickness and surface area measurements were found to be genetically and phenotypically independent. While both thickness and area influenced volume measurements of cortical grey matter, volume was more closely related to surface area than cortical thickness. This trend was observed for both the volume-based and surface-based techniques. The results suggest that surface area and cortical thickness measurements should be considered separately and preferred over gray matter volumes for imaging genetic studies. Copyright 2009 Elsevier Inc. All rights reserved.
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              Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies.

              Voxel-based morphometry is a method for detecting group differences in the density or volume of brain matter. The authors reviewed the literature on use of voxel-based morphometry in schizophrenia imaging research to examine the capabilities of this method for clearly identifying specific structural differences in patients with schizophrenia, compared with healthy subjects. The authors looked for consistently reported results of relative deficits in gray and white matter in schizophrenia and evaluated voxel-based morphometry methods in order to propose a future strategy for using voxel-based morphometry in schizophrenia research. The authors reviewed all voxel-based morphometry studies of schizophrenia that were published to May 2004 (15 studies). The studies included a total of 390 patients with a diagnosis of schizophrenia and 364 healthy volunteers. Gray and white matter deficits in patients with schizophrenia, relative to healthy comparison subjects, were reported in a total of 50 brain regions. Deficits were reported in two of the 50 regions in more than 50% of the studies and in nine of the 50 regions in one study only. The most consistent findings were of relative deficits in the left superior temporal gyrus and the left medial temporal lobe. Use of a smaller smoothing kernel (4-8 mm) led to detection of a greater number of regions implicated in schizophrenia. This review implicates the left superior temporal gyrus and the left medial temporal lobe as key regions of structural difference in patients with schizophrenia, compared to healthy subjects. The diversity of regions reported in voxel-based morphometry studies is in part related to the choice of variables in the automated process, such as smoothing kernel size and linear versus affine transformation, as well as to differences in patient groups. Voxel-based morphometry can be used as an exploratory whole-brain approach to identify abnormal brain regions in schizophrenia, which should then be validated by using region-of-interest analyses.
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                Journal
                28507318
                10.1038/mp.2017.106

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