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Mricron

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MRIcroN is a software tool designed for the visualization and analysis of neuroimaging data, particularly magnetic resonance imaging (MRI) scans. It provides a user-friendly interface for loading, viewing, and manipulating MRI images, allowing researchers and clinicians to explore and analyze brain structure and function.

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Lab products found in correlation

Matlab, by MathWorks (9 mentions) Spm12, by MathWorks (2 mentions) Matlab based custom routines, by MathWorks (2 mentions)

12 protocols using mricron

1

Continuous Mapping of Shape Processing

Cited 1 time
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fMRI raw data are available at https://doi.org/10.1184/R1/c.3889873.v1 and were processed using BrainVoyager 20.2 software (Brain Innovation, Maastricht, Netherlands; RRID:SCR_013057), MRIcron (RRID:SCR_002403), complementary in-house software written in Matlab (The MathWorks, Inc, Natick, MA, USA; RRID:SCR_001622; see source code) and R Development Core Team (2009) . Preprocessing included 3D-motion correction and filtering of low temporal frequencies (cutoff frequency of 2 cycles per run). No spatial smoothing was applied to allow the voxel-wise analysis. All scans were transformed to Montreal Neurological Institute (MNI) space (Fonov et al., 2011 (link)). Three main analytical approaches were employed: a novel voxel-wise approach that allows a continuous mapping of shape processing along the two pathways, a more traditional ROI analysis and a multivariate representational similarity analysis (RSA).
Freud E., Culham J.C., Plaut D.C, & Behrmann M. (2017). The large-scale organization of shape processing in the ventral and dorsal pathways. eLife, 6, e27576.
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2

Multimodal MRI Data Processing and Analysis

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We used the statistical parametric mapping package (SPM12, http://www.fil.ion.ucl.ac.uk/spm/), MRIcroN, and MATLAB-based (The MathWorks Inc., Natick, MA) custom software for data processing and analyses.
We visually examined high-resolution T1-, PD-, and T2-weighted images of all subjects for any serious brain pathology, such as tumors, cysts, or major brain infarcts. Arterial spin labeling images were also examined for any potential head motion-related or other imaging artifacts. None of the subjects included in this study showed any such brain pathology or imaging artifacts, which may affect regional CBF values.
Roy B., Woo M.A., Wang D.J., Fonarow G.C., Harper R.M, & Kumar R. (2017). Reduced Regional Cerebral Blood Flow in Patients with Heart Failure. European journal of heart failure, 19(10), 1294-1302.
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3

Structural Brain Imaging Analysis Protocol

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The statistical parametric mapping package (SPM12, http://www.fil.ion.ucl.ac.uk/spm/), MRIcroN, 28 (link) and MATLAB-based (The MathWorks Inc., Natick, MA, USA) custom software were used for data processing and analyses. Data processing steps were followed as described in previous publications.29 Two high-resolution T1-weighted images were reoriented to remove any potential variation from head motion and averaged to increase signal-to-noise ratio. The averaged T1-weighted images were partitioned into gray matter, white matter, and cerebrospinal fluid tissue types. The Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra algorithm (DARTEL) toolbox 30 (link) was used to generate the flow fields which are nonlinear deformations applied for warping all the gray matter images to match each other and template images which were implemented for normalization of gray matter maps to Montreal Neurological Institute (MNI) space (voxel size: 1×1×1 mm3). The unmodulated normalized maps were smoothed using a Gaussian filter (kernel, 10 mm).
The average T1-weighted images from each T2DM and control subjects were normalized to MNI space. The normalized images obtained from each subject were averaged to create background images for structural identification.
CHOI S.E., ROY B., FREEBY M., MULLUR R., WOO M.A, & KUMAR R. (2020). Prefrontal Cortex Brain Damage and Glycemic Control in Patients with Type 2 Diabetes. Journal of diabetes, 12(6), 465-473.
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4

Voxel-Based Morphometry of Brain Structure

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We used the statistical parametric mapping package SPM12 (Wellcome Department of Cognitive Neurology, UK), MRIcroN, and MATLAB-based (The MathWorks Inc., Natick, MA, USA) custom software for data processing and analyses. Voxel-based morphometry (VBM) analyses were performed as described in an earlier report (Roy et al., 2021 (link); Singh et al., 2018 (link)). Two high-resolution T1-weighted images were realigned and averaged for each subject to remove any potential variations between the scans. The Diffeomorphic Anatomic Registration through Exponentiated Lie algebra algorithm (DARTEL) toolbox was used to improve inter-subject image registration (Ashburner, 2007 (link)). The averaged images were segmented into gray matter, white matter, and cerebrospinal fluid tissue types using DARTEL toolbox, and flow fields and a series of template images were generated. The flow fields and final template image were used to normalize the gray matter maps (unmodulated, re-sliced to 0.7 × 0.7 × 0.7 mm3) and smoothed using a Gaussian filter (10 mm full width at half maximum).
Bounds D., Woo M.A., Nyamathi A., Kehoe P., Roy B., Yadav K., Cabrera-Mino C, & Kumar R. (2023). Brain Changes Linked to Cognitive Symptomatology in Homeless Youth. Clinical Nursing Research, 32(6), 932-946.
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5

Neuroimaging Analysis of Putamen Volume

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High-resolution T1-weighted, PD-, and T2-weighted scans were visually-examined to assess any major brain lesions (e.g., cystic lesions, major infarcts, or space occupying lesions). Both high-resolution T1-weighted scans were also examined for any imaging artifacts before data processing.
We used the statistical parametric mapping package SPM8 (http://www.fil.ion.ucl.ac.uk/spm/), MRIcroN, and MATLAB-based (The MathWorks Inc., Natick, MA) custom software for data processing, manually-outlining putamen structures, and determination of putamen volumes.
Kumar R., Farahvar S., Ogren J.A., Macey P.M., Thompson P.M., Woo M.A., Yan-Go F.L, & Harper R.M. (2014). Brain putamen volume changes in newly-diagnosed patients with obstructive sleep apnea. NeuroImage : Clinical, 4, 383-391.
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6

Image Pre-processing and Analysis for OSA Study

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We used various software applications for image pre-processing, analysis, and image visualization, which included the statistical parametric mapping package (SPM 12; WellCome Department of Cognitive Neurology, UK; http://www.fil.ion.ucl.ac.uk/spm), MRIcroN (Rorden et al. 2007 (link)), and MATLAB (The MathWorks Inc, Natick, MA). High-resolution T1-weighted, PD-, and T2-weighted images of all OSA subjects were examined visually to ensure absence of any serious anatomical defects, including cysts, tumors, or any other lesions before data processing. No OSA subjects included here showed any of these abnormalities on visual examination. Both images, with and without MT contrast, were also assessed visually for image quality, including motion artifacts for further processing.
Tummala S., Roy B., Park B., Kang D.W., Woo M.A., Harper R.M, & Kumar R. (2016). Associations between Brain White Matter Integrity and Disease Severity in Obstructive Sleep Apnea. Journal of neuroscience research, 94(10), 915-923.
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7

Preprocessing Pipeline for Structural MRI Analysis

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The statistical parametric mapping package (SPM12),1 MRIcroN, and MATLAB-based (The MathWorks Inc., Natick, MA, USA) custom software were used for data processing and analyses. T1-weighted images were partitioned into gray matter, white matter, and cerebrospinal fluid tissue types. The Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra algorithm (DARTEL) toolbox (Ashburner, 2007 (link)) was used to generate the flow field maps, which are non-linear deformations applied for warping all the gray matter images to match each other and template images that were implemented for normalization of gray matter maps to Montreal Neurological Institute (MNI) space (voxel size: 0.46 mm3 × 0.46 mm3 × 6 mm3). The modulated and normalized gray matter maps were smoothed using a Gaussian filter, and the smoothed gray matter (GM) maps were used for further statistical analyses.
Vandiver M.S., Roy B., Mahmud F., Lavretsky H, & Kumar R. (2022). Functional comorbidities and brain tissue changes before and after lung transplant in adults. Frontiers in Cellular Neuroscience, 16, 1015568.
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8

Neuroimaging Data Processing and Analysis

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The statistical parametric mapping package SPM12 (http://www.fil.ion.ucl.ac.uk/spm/), Diffusion Toolkit [21 (link)], MRIcroN, and MATLAB-based (http://www.mathworks.com/) custom software were used for data processing and analyses, as well as visualization. Diffusion and non-diffusion weighted images of all SVHD and controls were also assessed for any head-motion related or other imaging artifacts before MD, AD, and RD calculations.
Singh S., Roy B., Pike N., Daniel E., Ehlert L., Lewis A.B., Halnon N., Woo M.A, & Kumar R. (2019). Altered Brain Diffusion Tensor Imaging Indices in Adolescents with the Fontan Palliation. Neuroradiology, 61(7), 811-824.
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9

Multimodal Neuroimaging Data Analysis for OSA

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We used various software for image visualization, data pre-processing, and analyses that included the statistical parametric mapping package SPM8 (Wellcome Department of Cognitive Neurology, UK; http://www.fil.ion.ucl.ac.uk/spm), Diffusional Kurtosis Estimator (DKE),20 (link) MRIcroN,21 (link) and MATLAB-based custom routines (The MathWorks Inc, Natick, MA). For all OSA and control subjects, acquired structural images ((T1-, T2-, and PD-weighted) were examined visually to ensure that no serious anatomical defects are apparent. No subjects included here showed any serious brain pathology on visual examination of structural brain images. DKI data were also inspected visually for any motion or other imaging artifacts to ensure that images were acceptable for further analysis.
Tummala S., Roy B., Vig R., Park B., Kang D.W., Woo M.A., Aysola R., Harper R.M, & Kumar R. (2017). Non-Gaussian Diffusion Imaging Shows Brain Myelin and Axonal Changes in Obstructive Sleep Apnea. Journal of computer assisted tomography, 41(2), 181-189.
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10

Automated Brain Tissue Segmentation and Normalization

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The statistical parametric mapping package (SPM12, http://www.fil.ion.ucl.ac.uk/spm/), MRIcroN, and MATLAB-based (The MathWorks Inc., Natick, MA) custom software were used for data processing and analyses. T1-weighted image volumes were partitioned into gray matter, white matter, and cerebrospinal fluid (CSF) tissue types. The Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra algorithm (DARTEL) toolbox was used to generate the flow fields, which are nonlinear deformations applied for warping all the gray matter images to match each other and template images that were implemented for normalization of gray matter maps to Montreal Neurological Institute (MNI) space (voxel size: 1 × 1 × 1 mm 3 ). The modulated and normalized maps were smoothed using a Gaussian filter, and the smoothed gray matter maps were used for further statistical analyses.
Roy B., Ogren J.A., Allen L.A., Diehl B., Sankar R., Lhatoo S.D., Kumar R, & Harper R.M. (2024). Brain gray matter changes in children at risk for sudden unexpected death in epilepsy. Pediatric research.
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