For sleep scoring, EEG and EMG signals were divided into regular-spaced epochs. Epoch length was set to 4 s for EEG data obtained during in vivo imaging because of the needed high resolution. Epochs of 10-s length were used for all other sleep analyses. Each epoch was scored as either wake, NREM, or REM sleep. EEG signals underwent fast Fourier transformation by custom MATLAB scripts (MATLAB 2018a, The MathWorks, Inc., MA, USA). Scoring was based on the power in the EEG delta band (0.5–4 Hz), the EEG theta band (6–10 Hz), and the ratio of delta to theta band power, as well as the integral of the EMG signal. Epochs with a high integral of EMG signal and low delta power were scored as wake. Epochs with a low integral of EMG signal and high delta power were scored as NREM. Epochs with a very low integral of EMG signal and a high theta to delta power ratio were scored as REM. The scoring was conducted using custom MATLAB programs (MATLAB 2018a, The MathWorks, Inc., MA, USA) (Bjorness et al., 2016 (link)).
Matlab 2018a
Manufactured by MathWorks
Sourced in United States
MATLAB 2018a is a high-performance numerical computing environment and programming language. It provides a wide range of mathematical, engineering, and scientific tools and functions for data analysis, algorithm development, and visualization.
Automatically generated - may contain errors
Lab products found in correlation
Statistica 10, by StatSoft (3 mentions)
Simbiology v 5, by MathWorks (2 mentions)
Eeglab, by MathWorks (2 mentions)
Spss statistic, by IBM (2 mentions)
Net station review, by Electrical Geodesics (2 mentions)
Discovery mr750, by GE Healthcare (2 mentions)
Achro adi 10x 0.25na, by Nikon (2 mentions)
Pl apo 20x 0.75na, by Nikon (2 mentions)
Apo lwd λ s 40x 1.15na, by Nikon (2 mentions)
Prime95b 22mm, by Teledyne (2 mentions)
Eclipse ti2 e, by Nikon (2 mentions)
Spss statistics for macintosh, by IBM (1 mentions)
Adw 4.6 workstation, by GE Healthcare (1 mentions)
Ingenia 3, by Philips (1 mentions)
Spss v28, by IBM (1 mentions)
Igor pro, by Wavemetrics (1 mentions)
Matlab, by GraphPad (1 mentions)
Prism 8, by GraphPad (1 mentions)
Matlab, by MathWorks (1 mentions)
Intera achieva scanner, by Philips (1 mentions)
176 protocols using matlab 2018a
1
Sleep Scoring via EEG and EMG Signals
2
Detailed Statistical Analysis Methodology
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Statistical analyses were carried out with Matlab 2018a (the Mathworks, Natick, Massachusetts, USA) and Statistica 10 (StatSoft Inc., Tulsa, Oklahoma, USA). Post-hoc comparisons were always conducted using the Fisher’s LSD procedure. The significance level was set at p ≤ 0.05, except in the case of correction for multiple comparisons (see below).
3
Measuring Retinal Layer Thickness and Integrity
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OCT was performed with a spectral–domain (SD) system (RTVue-100; Optovue Inc., Fremont, CA, USA). For analysis of ONL and EZ line extent, overlapping OCT scans (30 degrees in length; 1019 longitudinal reflectivity profiles (LRPs), each averaging 17–32) were used to cover the vertical meridian up to 30 degrees eccentricity from the fovea. Post-acquisition processing of the data was performed by one of the authors (A.S.) using custom programs (MATLAB 2018a, MathWorks, Natick, MA, USA). LRPs making up the scans were aligned by straightening the major RPE reflection. ONL layer thicknesses in patients were quantified [44 (link),48 (link)], plotted as a function of eccentricity, and compared with the normal ranges (mean ± 2SD; n = 15; age range 8–62 years). EZ line extent was determined as previously published [44 (link)].
4
Knee Osteoarthritis Surgical Outcomes
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PROs derived from the EPRA were joined based on medical record number and surgical date using MatLab 2018a (MathWorks, Natick, MA). Descriptive statistics were run on all patient entries. Independent t tests were used for evaluating continuous baseline variables, χ2 tests for nominal variables, and Wilcoxon rank-sum test for ordinal baseline values. Univariable and multivariable linear regressions were performed to assess the unadjusted and adjusted primary outcome of interest, respectively. Owing to the limited sample size of patients receiving Worker’s Comp (3 patients) and Medicaid (5 patients), these patients were not included in our statistical analyses.
A power analysis was conducted to demonstrate the minimum number of subjects for adequate study power. Assuming a minimal detectable change (ie, the minimal amount of change required to distinguish a true change in health from normal measurement variability) of 11 points on the KOOS JR. 100-point scale, the minimum number of total subjects needed to detect a difference between the commercial and Medicare cohorts is 61 [21] (link).
A power analysis was conducted to demonstrate the minimum number of subjects for adequate study power. Assuming a minimal detectable change (ie, the minimal amount of change required to distinguish a true change in health from normal measurement variability) of 11 points on the KOOS JR. 100-point scale, the minimum number of total subjects needed to detect a difference between the commercial and Medicare cohorts is 61 [21] (link).
5
Wireless EMG Signal Preprocessing
6
3D Rib Cage Tissue Modeling
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3D anatomical tissue model was generated using anonymized human CT images, by importing the CT image set into a 3D Slicer using embodi3D, open source software platform, for segmentation of the bone. Ribs 7 to 12 of the right side of the model were severed from the spine due to its close proximity to the liver, and imported as STL file into MATLAB 2018a (The MathWorks, Inc., Natick, MA, USA) for modeling where it was meshed and then voxelized for use in ray tracing and acoustic modeling algorithms. The rib cage was positioned at five different depths from the center of the transducer ranging from 40 mm to 100 mm and was aligned such that the beam axis either passed through one of the ribs or the middle of intercostal spacing.
7
LED-Based Sensory Perception Experiment
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An individually addressable light emitting diode (LED) strip (WS2812B ECO LED Strip, BTF-Lighting Technology Co., Ltd., Guangdong, China) three meters in length (180 LEDs, 60 LEDs/m) was centered on a black plastic mat, see Figure 1 . Distance between each LED was 12 mm. During the experiment, individually addressed LEDs were powered for 200 ms and specified to be white light for maximum contrast with the black plastic mat. These colors were selected to better simulate the color contrast of the typical black hockey puck on a white ice surface. A wired Xbox (Microsoft, Inc., Redmond, WA) controller allowed participants to respond during the protocol by clicking either the right or left buttons with their index fingers for “yes” and “no” responses, respectively. A custom MATLAB 2018a (The MathWorks, Inc., Natick, MA) script and an Arduino Nano3 (Arduino LLC., Torino, Italy) were used to play the auditory tone and turn on/off individual LEDs as well as capture participant responses (Xbox button press). All data were analyzed offline using custom MATLAB scripts.
8
Voxel-based Normalization of Electrode Contacts
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The occurrence rate was normalized to the highest occurrence rate channel. Normalized value assignment was performed in every voxel included in the volume defined by a 9 × 9 × 9 mm cube model centered on each electrode contact (David et al., 2011 (link)). The results were smoothed through a [4 4 4] Gaussian kernel, which was overlayed on volumes. The pipeline was developed in MATLAB 2018a (The MathWorks, Inc., Natick, MA, United States) and illustrated in Figure 1 . Codes of the algorithms described in this paper, including the trained neural network, are open-source and openly available2 .
9
Statistical Analysis of Biometric Data
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For biometric, vessel lumen diameter and vessel volume data, normality was assessed by the Lilliefors test based on Kolmogorov–Smirnov test. Normal-distributed quantitative variables were analysed by Student’s t-test. Non-normal distributed variables were analysed with the non-parametric Mann–Whitney U test. Differences were considered significant with probability values of p < 0.05. The statistical analysis was performed in SPSS® (IBM Corp. Released 2016. IBM SPSS Statistics for Macintosh, Version 24.0. Armonk, NY, IBM Corp) and in Matlab 2018a (The Mathworks Inc, Natick, MA, USA).
10
Quantitative Multimodal Brain Mapping
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Raw 3D-PCASL data for the CBF quantitative map were acquired via the GE ADW4.6 workstation, and the raw ESWAN data for the QSM quantitative map were acquired through the MATLAB 2018a (MathWorks, Natick, MA, USA) platform using STI Suite 3.0 software (https://people.eecs.berkeley.edu/chunlei.liu/software.html ). To calculate the quantitative parameters CBF and QSM in different brain regions, we adopted a voxel-based morphometry (VBM) method and employed SPM12 software via the MATLAB 2018a platform (http://www.fil.Ion.ucl.ac.uk/spm/ ); the 3D-T1 sequence structure map was registered with quantitative CBF and QSM maps. Subsequently, the CAT12 toolkit in SPM12 software (http://www.neuro.uni-jena.de/cat/ ) was employed to segment the registered structural quantitative map and ultimately extract the parameter values for each brain area (Figure 1 ).
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