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      VoxelStats: A MATLAB Package for Multi-Modal Voxel-Wise Brain Image Analysis

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          Abstract

          In healthy individuals, behavioral outcomes are highly associated with the variability on brain regional structure or neurochemical phenotypes. Similarly, in the context of neurodegenerative conditions, neuroimaging reveals that cognitive decline is linked to the magnitude of atrophy, neurochemical declines, or concentrations of abnormal protein aggregates across brain regions. However, modeling the effects of multiple regional abnormalities as determinants of cognitive decline at the voxel level remains largely unexplored by multimodal imaging research, given the high computational cost of estimating regression models for every single voxel from various imaging modalities. VoxelStats is a voxel-wise computational framework to overcome these computational limitations and to perform statistical operations on multiple scalar variables and imaging modalities at the voxel level. VoxelStats package has been developed in Matlab ® and supports imaging formats such as Nifti-1, ANALYZE, and MINC v2. Prebuilt functions in VoxelStats enable the user to perform voxel-wise general and generalized linear models and mixed effect models with multiple volumetric covariates. Importantly, VoxelStats can recognize scalar values or image volumes as response variables and can accommodate volumetric statistical covariates as well as their interaction effects with other variables. Furthermore, this package includes built-in functionality to perform voxel-wise receiver operating characteristic analysis and paired and unpaired group contrast analysis. Validation of VoxelStats was conducted by comparing the linear regression functionality with existing toolboxes such as glim_image and RMINC. The validation results were identical to existing methods and the additional functionality was demonstrated by generating feature case assessments (t-statistics, odds ratio, and true positive rate maps). In summary, VoxelStats expands the current methods for multimodal imaging analysis by allowing the estimation of advanced regional association metrics at the voxel level.

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          Most cited references21

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          Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space.

          In both diagnostic and research applications, the interpretation of MR images of the human brain is facilitated when different data sets can be compared by visual inspection of equivalent anatomical planes. Quantitative analysis with predefined atlas templates often requires the initial alignment of atlas and image planes. Unfortunately, the axial planes acquired during separate scanning sessions are often different in their relative position and orientation, and these slices are not coplanar with those in the atlas. We have developed a completely automatic method to register a given volumetric data set with Talairach stereotaxic coordinate system. The registration method is based on multi-scale, three-dimensional (3D) cross-correlation with an average (n > 300) MR brain image volume aligned with the Talariach stereotaxic space. Once the data set is re-sampled by the transformation recovered by the algorithm, atlas slices can be directly superimposed on the corresponding slices of the re-sampled volume. the use of such a standardized space also allows the direct comparison, voxel to voxel, of two or more data sets brought into stereotaxic space. With use of a two-tailed Student t test for paired samples, there was no significant difference in the transformation parameters recovered by the automatic algorithm when compared with two manual landmark-based methods (p > 0.1 for all parameters except y-scale, where p > 0.05). Using root-mean-square difference between normalized voxel intensities as an unbiased measure of registration, we show that when estimated and averaged over 60 volumetric MR images in standard space, this measure was 30% lower for the automatic technique than the manual method, indicating better registrations. Likewise, the automatic method showed a 57% reduction in standard deviation, implying a more stable technique. The algorithm is able to recover the transformation even when data are missing from the top or bottom of the volume. We present a fully automatic registration method to map volumetric data into stereotaxic space that yields results comparable with those of manually based techniques. The method requires no manual identification of points or contours and therefore does not suffer the drawbacks involved in user intervention such as reproducibility and interobserver variability.
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            Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis.

            The quantitative analysis of magnetic resonance imaging (MRI) data has become increasingly important in both research and clinical studies aiming at human brain development, function, and pathology. Inevitably, the role of quantitative image analysis in the evaluation of drug therapy will increase, driven in part by requirements imposed by regulatory agencies. However, the prohibitive length of time involved and the significant intraand inter-rater variability of the measurements obtained from manual analysis of large MRI databases represent major obstacles to the wider application of quantitative MRI analysis. We have developed a fully automatic "pipeline" image analysis framework and have successfully applied it to a number of large-scale, multicenter studies (more than 1,000 MRI scans). This pipeline system is based on robust image processing algorithms, executed in a parallel, distributed fashion. This paper describes the application of this system to the automatic quantification of multiple sclerosis lesion load in MRI, in the context of a phase III clinical trial. The pipeline results were evaluated through an extensive validation study, revealing that the obtained lesion measurements are statistically indistinguishable from those obtained by trained human observers. Given that intra- and inter-rater measurement variability is eliminated by automatic analysis, this system enhances the ability to detect small treatment effects not readily detectable through conventional analysis techniques. While useful for clinical trial analysis in multiple sclerosis, this system holds widespread potential for applications in other neurological disorders, as well as for the study of neurobiology in general.
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              Two-year follow-up of amyloid deposition in patients with Alzheimer's disease.

              Beta amyloid is one of the major histopathological hallmarks of Alzheimer's disease. We recently reported in vivo imaging of amyloid in 16 Alzheimer patients, using the PET ligand N-methyl[11C]2-(4'-methylaminophenyl)-6-hydroxy-benzothiazole (PIB). In the present study we rescanned these 16 Alzheimer patients after 2.0 +/- 0.5 years and have described the interval change in amyloid deposition and regional cerebral metabolic rate for glucose (rCMRGlc) at follow-up. Sixteen patients with Alzheimer's disease were re-examined by means of PET, using PIB and 2-[18F]fluoro-2-deoxy-d-glucose (FDG) after 2.0 +/- 0.5 years. The patients were all on cholinesterase inhibitor treatment and five also on treatment with the N-methyl-d-aspartate (NMDA) antagonist memantine. In order to estimate the accuracy of the PET PIB measurements, four additional Alzheimer patients underwent repeated examinations with PIB within 20 days (test-retest). Relative PIB retention in cortical regions differed by 3-7% in the test-retest study. No significant difference in PIB retention was observed between baseline and follow-up while a significant (P 3 (21.4 +/- 3.5 to 15.6 +/- 3.9, P < 0.01) (AD-progressive) while the rest of the patients were cognitively more stable (MMSE score = 25.6 +/- 3.1 to 25.9 +/- 3.7) (AD-stable) compared with baseline. A positive correlation (P = 0.001) was observed in the parietal cortex between Rey Auditory Verbal Learning (RAVL) test score and rCMRGlc at follow-up while a negative correlation (P = 0.018) was observed between RAVL test and PIB retention in the parietal at follow-up. Relatively stable PIB retention after 2 years of follow-up in patients with mild Alzheimer's disease suggests that amyloid deposition in the brain reaches a plateau by the early clinical stages of Alzheimer's disease and therefore may precede a decline in rCMRGlc and cognition. It appears that anti-amyloid therapies will need to induce a significant decrease in amyloid load in order for PIB PET images to detect a drug effect in Alzheimer patients. FDG imaging may be able to detect a stabilization of cerebral metabolism caused by therapy administered to patients with a clinical diagnosis of Alzheimer's disease.
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                Author and article information

                Contributors
                Journal
                Front Neuroinform
                Front Neuroinform
                Front. Neuroinform.
                Frontiers in Neuroinformatics
                Frontiers Media S.A.
                1662-5196
                15 June 2016
                2016
                : 10
                : 20
                Affiliations
                [1] 1Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill University Montreal, QC, Canada
                [2] 2McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada
                [3] 3McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill University Montreal, QC, Canada
                [4] 4Douglas Hospital Research Center, Douglas Research Institute, McGill University Montreal, QC, Canada
                [5] 5Department of Psychiatry, McGill University Montreal, QC, Canada
                [6] 6Department of Neurology and Neurosurgery, McGill University Montreal, QC, Canada
                [7] 7Department of Epidemiology and Biostatistics, McGill University Montreal, QC, Canada
                Author notes

                Edited by: Qingming Luo, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, China

                Reviewed by: Jussi Tohka, Universidad Carlos III de Madrid, Spain; Anan Li, Huazhong University of Science and Technology, China

                *Correspondence: Pedro Rosa-Neto pedro.rosa@ 123456mcgill.ca
                Article
                10.3389/fninf.2016.00020
                4908129
                27378902
                0251ddaf-b4d3-4b90-a04e-d7512f4cd93b
                Copyright © 2016 Mathotaarachchi, Wang, Shin, Pascoal, Benedet, Kang, Beaudry, Fonov, Gauthier, Labbe, Rosa-Neto for the Alzheimer's Disease Neuroimaging Initiative.

                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
                : 09 February 2016
                : 01 June 2016
                Page count
                Figures: 5, Tables: 2, Equations: 0, References: 40, Pages: 12, Words: 7361
                Funding
                Funded by: Canadian Institutes of Health Research 10.13039/501100000024
                Award ID: MOP-11-51-31
                Funded by: Alzheimer's Association 10.13039/100000957
                Award ID: NIRG-12-92090
                Award ID: NIRP-12-259245
                Funded by: Fonds de Recherche du Québec - Santé 10.13039/501100000156
                Categories
                Neuroscience
                Methods

                Neurosciences
                voxel-wise analysis,multimodal analysis,longitudinal analysis,generalized linear model,mixed effect model,alzheimer's disease,roc analysis

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