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      Influence of Group on Individual Subject Maps in SPM Voxel Based Morphometry

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          Abstract

          Voxel based morphometry (VBM) is a widely utilized neuroimaging technique for spatially normalizing brain structural MRI (sMRI) onto a common template. The DARTEL technique of VBM takes into account the spatial intensity distribution of sMRIs to construct a study specific group template. The group template is then used to create final individual normalized tissue maps (FINTM) for each subject in the group. In this study, we investigate the effect of group on FINTM, i.e., we evaluate the variability of a constant subject's FINTM when other subjects in the group are iteratively changed. We examine this variability under the following scenarios: (1) when the demographics of the iterative groups are similar, (2) when the average age of the iterative groups is increased, and (3) when the number of subjects with a brain disorder (here we use subjects with autism) is increased. Our results show that when subject demographics of the group remains similar the mean standard deviation ( SD) of FINTM gray matter (GM) of the constant subject was around 0.01. As the average age of the group is increased, mean SD of GM increased to around 0.03 and at certain brain locations variability was as high as 0.23. A similar increase in variability was observed when the number of autism subjects in the group was increased where mean SD was around 0.02. Further, we find that autism vs. control GM differences are in the range of −0.05 to +0.05 for more than 97% of the voxels and note that the magnitude of the differences are comparable to GM variability. Finally, we report that opting not to modulate during normalization or increasing the size of the smoothing kernel can decrease FINTM variability but at the loss of subject-specific features. Based on the findings of this study, we outline precautions that should be considered by investigators to reduce the impact of group on FINTM and thereby derive more meaningful group differences when comparing two cohorts of subjects.

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          Most cited references15

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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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            Surface-based and probabilistic atlases of primate cerebral cortex.

            Brain atlases play an increasingly important role in neuroimaging, as they are invaluable for analysis, visualization, and comparison of results across studies. For both humans and macaque monkeys, digital brain atlases of many varieties are in widespread use, each having its own strengths and limitations. For studies of cerebral cortex there is particular utility in hybrid atlases that capitalize on the complementary nature of surface and volume representations, are based on a population average rather than an individual brain, and include measures of variation as well as averages. Linking different brain atlases to one another and to online databases containing a growing body of neuroimaging data will enable powerful forms of data mining that accelerate discovery and improve research efficiency.
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              The neuroanatomy of autism: a voxel-based whole brain analysis of structural scans.

              Autism is a biological disorder which affects social cognition, and understanding brain abnormalities of the former will elucidate the brain basis of the latter. We report structural MRI data on 15 high-functioning individuals with autistic disorder. A voxel-based whole brain analysis identified grey matter differences in an amygdala centered system relative to 15 age- and IQ-matched controls. Decreases of grey matter were found in anterior parts of this system (right paracingulate sulcus, left inferior frontal gyrus). Increases were found in posterior parts (amygdala/peri-amygdaloid cortex, middle temporal gyrus, inferior temporal gyrus), and in regions of the cerebellum. These structures are implicated in social cognition by animal, imaging and histopathological studies. This study therefore provides converging evidence of the physiological basis of social cognition.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                02 December 2016
                2016
                : 10
                : 522
                Affiliations
                [1] 1Autism & Developmental Medicine Institute, Geisinger Health System Lewisburg, PA, USA
                [2] 2Institute for Advanced Applications, Geisinger Health System Danville, PA, USA
                [3] 3Department of Radiology, Geisinger Health System Danville, PA, USA
                Author notes

                Edited by: Xi-Nian Zuo, Chinese Academy of Sciences, China

                Reviewed by: Pedro Antonio Valdés Hernández, Cuban Neuroscience Center, Cuba; Baxter P. Rogers, Vanderbilt University, USA

                This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2016.00522
                5134364
                7c936785-bd93-4795-af91-e827fb5624eb
                Copyright © 2016 Michael, Evans and Moore.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 24 February 2016
                : 28 October 2016
                Page count
                Figures: 7, Tables: 4, Equations: 0, References: 21, Pages: 12, Words: 7683
                Categories
                Neuroscience
                Technology Report

                Neurosciences
                structural mri preprocessing,spm dartel,spm vbm,spatial normalization,influence of group

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