Matlab r2009a

For all the experiments, the sound stimuli were generated using MATLAB R2009a (MATHWORKS^®), with the sampling frequency of 44.1 kHz, and were presented at a comfortable sound level. Experiments were performed and controlled by the Psychtoolbox 3.0 toolbox (Brainard, 1997 (link); Pelli, 1997 (link)). Sound stimuli were presented through Sennheiser HD215 headphone on a Dell OPTIPLEX380 PC. All the participants performed the experiments in an acoustically shielded sound-proof room.

Continuous variables were presented as means (standard deviation) or medians (interquartile range) and were analyzed using the Mann–Whitney test. SPSS version 17.0 (SPSS Inc., Chicago, IL), MATLAB R2009a (Mathworks, Natick, MA), and R version 2.15.1 (R Foundation, Vienna, Austria) were employed for data analysis.
Performance evaluation was carried out with the leave-one-out cross-validation (LOOCV) strategy to get unbiased estimation of the model performance. In this study with 702 samples, 702 iterations were required for performance evaluation. In iteration, one sample was used as the testing sample while the remaining 701 samples were used for training. The risk score prediction process was repeated 702 times so that each sample was tested individually. Risk scores were then obtained for the entire dataset and a threshold was derived to report sensitivity and specificity.
A package developed in MATLAB was used to analyze ECG for HRV, calculate risk scores, and report prediction performance measures. To further evaluate differences in discrimination between models, pair-wise AUC comparisons using bootstrap method were performed with a ROC comparison package
[32 (link)]. Statistical significance was set at p-value <0.05.

A near infrared hyperspectral imaging (NIR-HSI) system in the spectral range of 874–1734 nm was used as shown in Figure 2. The system contains a lens, an imaging spectrograph (N17E, Specim, Finland), a light source (Oriel Instruments, Irvine, Cal.) that included two 150 W quartz tungsten halogen lamps, a conveyor belt operated by a stepper motor (IRCP0076, Isuzu Optics Corp, Taiwan, China) and a computer. The area CCD array detector of the camera has 320×256 (spatial ×spectral) pixels, and the spectral resolution is 5 nm. The NIR-HSI system scans the sample line by line, and the reflected light was dispersed by the spectrograph and captured by the area CCD array detector in spatial-spectral(x×λ) axes. The ENVI 4.7 software (Research system Inc, Boulder, Co.USA), Unscrambler 9.7 software (Camo, Process, As, Oslo, Norway) and MATLAB R2009a (The Math Works, Natick, USA) software were used to preprocess the raw spectral information and establish identification models in this study.

All data were acquired using a 1.5T MR Philips Intera Gyroscan (Philips Healthcare, Best, The Netherlands) with an 8-channel head (SENSE) third-party coil. For each subject a fast field echo-planar imaging (FFE-EPI) protocol was acquired for rs-fMRI with TR/TE = 3000/60 ms, voxel size = 2.2 × 2.2 × 4 mm³, FOV = 250 × 250 mm², 26 slices, SENSE factor = 3.1, 100 repeated volumes. For anatomical reference a volumetric 3DT1-weighted acquisition was also collected using a fast field echo (FFE) sequence (TR/TE = 8.6/4 ms; flip angle 8°; 170 sagittal slices; slice thickness = 1.2 mm; FOV = 240 mm; acquisition matrix = 192 × 192, reconstructed to 256 × 256; in-plane resolution 1.25 × 1.25 mm², reconstructed to 0.94 × 0.94 mm²).
All the MRI analysis was performed on a workstation with Linux Ubuntu 12.04, running SPM8 (Wellcome Department of Cognitive Neurology, http://www.fil.ion.ucl.ac.uk/), Matlab R2009a (The MathWorks, Natick, Mass, USA http://www.mathworks.com/) and FSL (FMRIB Software Library, version 4.1.9, http://www.fmrib.ox.ac.uk/fsl/).

fMRI data were preprocessed using the BrainVoyager QX 2.8 software package (Brain Innovation, Maastricht, Netherlands) and complementary software written in MATLAB R2009a (The MathWorks, USA). Preprocessing of functional scans successively included: 3D motion correction (no head motion exceeded 2 mm/2 degrees in any of the six movement directions i.e. X, Y, Z translations, X, Y, Z rotations), slice-time correction, band-pass filtering between 0.01 and 0.1 Hz, voxel-to-voxel linear regression46 (link) of spurious signals from the white matter and ventricle regions anatomically defined for each subject, normalization in the Talairach coordinate system in the volume47 , and spatial smoothing with a 6 mm Gaussian filter kernel full-width-at-half-maximum. We did not include global signal removing to avoid the possible introduction of false negative correlations48 (link). For the between-group comparison, we tested and found no significant difference in the maximum head movement49 (link) between the three groups (SMD: 0.29 ± 0.27 mm; RPTV: 0.16 ± 0.08 mm; sighted controls: 0.19 ± 0.18 mm; ANOVA F (2, 35) = 1.5; p = 0.23). We also performed a GLM analysis including the head movement predictors (control analysis; see Supplementary Figures S1, S2, S3 and S4). Including movement predictors in the GLM did not change the main results.

Raw hyperspectral image (I₀) was calibrated by white (W) and dark (B) reference images. The white image was acquired from a standard Teflon tile (~99.9% reflectance); the dark image (~0% reflectance) was obtained by turning off the light source, completely covering the camera leans with its opaque cap and recording the completely response38 . The calibrated image (I) was calculated by the following equation:

A whole cucumber leaf was selected as a region of interest (ROI) of a sample. A spectral matrix of 196 samples ×512 bands was generated. There are many approaches to execute the target image segmentation from hyperspectral images, such as “Manual Mask” and “Automatic Mask”39 (link)40 . A simple threshold segmentation based on a single band grayscale image was finished with the aid of Environment Visualizing Images (ENVI) softwares (ITT Visual Information, Solutins, USA). In addition, this method is also effective in MATLAB R2009a (The MathWorks, Inc., Natick, MA, USA).

A visible and near infrared (VIS-NIR) hyperspectral imaging system covering the spectral wavelengths of 380–1030 nm was used in this study (as shown in Fig. 2). The system includes a lens (OLE-23), an imaging spectrograph (V10E-QE, Specim, Finland), a CCD camera (C8484-05, Hamamatsu City, Japan), two light sources (Oriel Instruments, Irvine, USA) provided by two 150W quartz tungsten halogen lamps, a conveyer belt operated by a stepper motor (IRCP0076, Isuzu Optics Corp., Taiwan, China), and a computer operating the spectral image system V10E software (Isuzu Optics Corp., Taiwan, China). The area CCD array detector of the camera has 672×512 (spatial × spectral) pixels, and the spectral resolution is 2.8 nm. The system scans the samples line by line, and the reflected light was dispersed by the spectrograph and captured by the area CCD array detector in spatial-spectral(x×λ) axes. The ENVI 4.7 (Research system Inc., Boulder, Co., USA), Unscrambler V9.7 (CAMO Process AS, Oslo, Norway) and Matlab R2009a (The Math Works, Inc., Natick, MA, USA) software were used in this study.