Initially, all raw DICOM scans were reviewed and converted into NIFTI format, using MRICRON software (Rorden et al., 2007 (link)). VBM preprocessing was conducted using default settings for Computational Anatomy Toolbox (CAT12, https://dbm.neuro.uni-jena.de/cat/ ), an extension of SPM12 (Wellcome Trust Center for Neuroimaging, London, UK), and MATLAB 9.5 (Mathworks, Inc., Natick, MA). T1-weighted images were bias, noise, and global intensity corrected prior to spatial normalization to the MNI152 template using the DARTEL algorithm (Ashburner, 2007 (link)). Next, normalized images were segmented into GM, WM, and cerebrospinal fluid (CSF; Ashburner and Friston, 2005 (link)) and written as modulated normalized volumes, allowing for interpretation of localized grey matter volume (GMV). Intracranial volumes (ICV) were calculated during segmentation for use as a nuisance variable during statistical analyses. Quality assurance was conducted via visual inspection and an automated quality check protocol embedded in CAT12, leading to the exclusion of one participant. All scans were then spatially smoothed using a 6 mm (FWHM) Gaussian smoothing kernel and resampled into 1.5 × 1.5 × 1.5 mm voxel size.
Matlab 9
Manufactured by MathWorks
Sourced in United States, United Kingdom
MATLAB 9.0 is a high-performance technical computing environment for data analysis, algorithm development, and visualization. It provides a matrix-based programming language for numerical computation, visualization, and programming.
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Lab products found in correlation
Spm12, by MathWorks (3 mentions)
Prism 9, by GraphPad (3 mentions)
Prism 8, by GraphPad (3 mentions)
Image processing toolbox 9, by MathWorks (3 mentions)
Spss v23, by IBM (3 mentions)
Spss 24, by IBM (2 mentions)
Gold 6248r cpu, by NVIDIA (2 mentions)
A100 pcie gpu, by NVIDIA (2 mentions)
Matlab 8, by MathWorks (2 mentions)
Sas 9, by SAS Institute (2 mentions)
Spss 26, by IBM (1 mentions)
Statistical package for the social sciences spss software version 24, by IBM (1 mentions)
Visual3d, by C-Motion (1 mentions)
Instrumented split belt treadmill, by Bertec (1 mentions)
Proteome discoverer v1, by Thermo Fisher Scientific (1 mentions)
Neuroscan system, by Compumedics (1 mentions)
Infinite f nano, by Tecan (1 mentions)
Luperox tbh70x, by Merck Group (1 mentions)
Microfluidic control system mfcs, by Fluigent (1 mentions)
R version 3, by R Project for Statistical Computing (1 mentions)
113 protocols using matlab 9
1
Voxel-Based Morphometry Protocol for Brain Imaging
2
Spatial Normalization of PET Scans
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Individual 18F-Florzolotau PET scans were resampled in the common space of the corresponding T1-weighted images and subsequently linearly and non-linearly registered to the Montreal Neurological Institute (MNI) brain atlas based on the transformation of individual T1-weighted images to the MNI space. Spatially normalized PET images were smoothed using a Gaussian kernel (full-width at half-maximum: 6 mm). The cerebellar grey matter was chosen as reference for intensity normalization [11 (link)]. All image processing was computed using Statistical Parametric Mapping (version 12) implemented in MATLAB 9.5 (MathWorks, Natick, MA, USA).
3
Automated Cortical Surface Estimation
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The estimation of the cortical surface was conducted using an automated processing pipeline implemented in the Computational Anatomy Toolbox (CAT, version 12.6) within SPM12 while running MATLAB 9.5 (R2018b; MathWorks, Natick, MA, USA). Briefly, high-resolution T1 images were bias-field corrected, skull-stripped, aligned to a Montreal Neurological Institute standard space (MNI-152 template) and segmented as gray matter, white matter, and cerebrospinal fluid (Ashburner and Friston 2005 (link)). The cortical thickness and the central surface were calculated in one step based on the projection-based thickness (PBT) approach, which also allowed partial volume information, sulcal blurring and sulcal asymmetries to be managed without explicit sulcus reconstruction via skeleton or thinning methods (Dahnke et al. 2013 (link)). Additionally, the surface stream included topological correction, spherical mapping, and spherical registration (Yotter et al. 2011 (link)). Finally, cortical thickness maps were re-sampled into a common coordinate system and smoothed with a Gaussian kernel of 15 mm (FWHM). The pre-processing steps were visually inspected to ensure that no misalignment of brain structures had occurred.
4
SVM-Based Data Analysis in MATLAB
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Data were analyzed using the SVM algorithm and MATLAB 9.5 (The MathWorks Inc., Natick, MA, USA).
5
Neuroimaging Data Preprocessing Pipeline
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The functional images were slice‐time corrected and re‐aligned. The anatomical image was segmented and co‐registered to the mean functional image in order to transform the grey matter, white matter, and cerebrospinal fluid (CSF) maps to native space. All pre‐processing steps were performed in SPM12 (Wellcome Trust Centre for Neuroimaging, London, UK) with default settings unless otherwise specified. Jenkinson's algorithm was used to calculate framewise displacement (FD) of the head (Jenkinson et al., 2002 (link)), and in‐house software written in MATLAB 9.5 (The MathWorks Inc., Natick, MA, 2018) was used to first binarize the white‐matter and CSF segmentation masks at an (inclusive) probability threshold of >80%, and subsequently erode each of these two masks by 1 voxel in each of the cardinal dimensions x, y, and z in order to avoid partial volume sharing with grey matter (Chai et al., 2012 (link); as in Varkevisser et al., 2017 (link)). Quality control of the pre‐processed data led to the exclusion of one participant due to an unresolved artefact that arose during segmentation. In addition, two participants had to be excluded due to excessive head motion, which we defined as more than 25% of scan volumes with an FD above 0.5 mm (Siegel et al., 2014 (link)).
6
Statistical Analyses of Multivariate Data
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Statistical analyses (Kruskal–Wallis, ANOVA and Spearman’s correlation) were performed in Rstudio 1.1.463 (©2009–2018 Rstudio, Inc.) using dunn.test package and IBM SPSS 26.0 (Armonk, NY: IBM Corp.). p-values below 0.05 were considered statistically significant. Canonical correlation analyses were performed in MATLAB 9.5 (The MathWorks Inc., 2018 ).
7
Spectral Peak Power Analysis
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Spectral power at peak frequencies were estimated by spline interpolation of the double logarithmic plots of the power spectra. The spectral peak amplitude was then whitened by subtracting the estimated power based on the fitted linear function from the peak power containing both arrhythmic and rhythmic activity (Fig. 2 ):
In order to avoid negative amplitudes due to the logarithmic scale, the power values were shifted for being all positive before this subtraction by adding a constant. This latter step was applied for the calculation of the amplitude measures only. As multiple spectral peaks were detected for some of the participants/EEG recording locations, the one with the largest amplitude was determined and used in this study. If no spectral peak was found in the spindle frequency range, peak values were considered as missing data (see Suppl. table8 ). Data analysis was performed by MATLAB 9.5 (Mathworks Inc., https://www.mathworks.com ).
In order to avoid negative amplitudes due to the logarithmic scale, the power values were shifted for being all positive before this subtraction by adding a constant. This latter step was applied for the calculation of the amplitude measures only. As multiple spectral peaks were detected for some of the participants/EEG recording locations, the one with the largest amplitude was determined and used in this study. If no spectral peak was found in the spindle frequency range, peak values were considered as missing data (see Suppl. table
8
Comparing MRI Techniques for Spinal Endplate Assessment
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The one-sample signed rank sum test was applied to compare fat suppression uniformity, artefacts, lesion conspicuity, and signal extent between DixonT2w and STIR. To compare noise, SNR, and CNR, we used Bland–Altman plots with the mean of differences ± 1.96 SD (limits of agreement [LoA]).
Cohen's kappa was calculated for inter-observer agreement on MC-related high signal on DixonT2w at each endplate L4-S1, and for agreement on conclusive findings between DixonT2w and STIR across L4-S1 (480 endplates). Due to the low prevalence of findings (<10%) in levels Th12-L3, kappa was not calculated for these levels (26 (link)). The interpretation of Cohen's kappa was as follows: 0.00–0.20 = poor; 0.21–0.40 = fair; 0.41–0.60 = moderate; 0.61–0.80 = good; and 0.81–1.00 = very good agreement beyond chance (26 (link)).
For height and maximum intensity of the high signal on DixonT2w in percentage points, we calculated means of differences between observers with LoA across all endplates L4-S1.
MedCalc 17.6 (MedCalc Software) was used for analyses and Matlab 9.5 (Mathworks) for plots.
Cohen's kappa was calculated for inter-observer agreement on MC-related high signal on DixonT2w at each endplate L4-S1, and for agreement on conclusive findings between DixonT2w and STIR across L4-S1 (480 endplates). Due to the low prevalence of findings (<10%) in levels Th12-L3, kappa was not calculated for these levels (26 (link)). The interpretation of Cohen's kappa was as follows: 0.00–0.20 = poor; 0.21–0.40 = fair; 0.41–0.60 = moderate; 0.61–0.80 = good; and 0.81–1.00 = very good agreement beyond chance (26 (link)).
For height and maximum intensity of the high signal on DixonT2w in percentage points, we calculated means of differences between observers with LoA across all endplates L4-S1.
MedCalc 17.6 (MedCalc Software) was used for analyses and Matlab 9.5 (Mathworks) for plots.
9
Contrast Ratio Simulation of Tissue Hydration
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To assess the impact of an elevated water content in tissue, we performed simulations of the contrast ratio in both elevated bulk water content and localized water pool scenarios.
To calculate the reflectance spectra of the tissue, Equation (2) was used. To obtain the bulk tissue’s integrated contrast ratio, the reflectance spectra were convolved with the Gaussian spectrum of a hypothetical LED with a peak wavelength at 980 nm and HWHM = 20 nm. To obtain the integrated contrast ratio for the localized water pool, the spectral dependence of the contrast ratio for the horizontal inhomogeneity (Equation (4)) was convoluted with the Gaussian spectrum of a hypothetical LED with a peak wavelength at 980 nm and HWHM = 20 nm. The simulations were performed with the range of parameters described in the next section (model parameters). Calculations were performed using MathCad 2001 Professional (PTC, Boston, MA, USA). Visualization was performed using MATLAB 9.5 (MathWorks, Natick, MA, USA).
To calculate the reflectance spectra of the tissue, Equation (2) was used. To obtain the bulk tissue’s integrated contrast ratio, the reflectance spectra were convolved with the Gaussian spectrum of a hypothetical LED with a peak wavelength at 980 nm and HWHM = 20 nm. To obtain the integrated contrast ratio for the localized water pool, the spectral dependence of the contrast ratio for the horizontal inhomogeneity (Equation (4)) was convoluted with the Gaussian spectrum of a hypothetical LED with a peak wavelength at 980 nm and HWHM = 20 nm. The simulations were performed with the range of parameters described in the next section (model parameters). Calculations were performed using MathCad 2001 Professional (PTC, Boston, MA, USA). Visualization was performed using MATLAB 9.5 (MathWorks, Natick, MA, USA).
10
Deep Brain Nuclei Segmentation and Iron Assessment
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To perform age regression models using QSM and R2* maps, deep brain nuclei characterised by high iron content, were delineated using atlas based masks (Lorio et al., 2016 (link)). The regions analysed were the globus pallidus, putamen, caudate, thalamus, substantia nigra, subthalamic nucleus, red nucleus and cerebellar dentate. The masks were warped from the MNI space to the subject's native space using the deformation fields estimated with SPM12 and Matlab 9.5 (Mathworks, Sherborn, MA, USA). The deformation fields were computed by performing tissue classification on the T1w images using the “segmentation” approach in SPM12 (Ashburner and Friston, 2005 (link)). The suspected FCD patients did not exhibit any lesions in the deep brain nuclei.
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