![]() – Productivity gains – How much time do you save people on the activities that they perform? Depending on whom you are selling to, how much time you save, and how expensive your solution is, your customer will value this time either very highly, or not so highly, and will give you financial credit for these savings. For example, a billing automation vendor saves it customers at least 49 cents per invoice, since you don’t have to pay money on stamps. ![]() – Hard cost savings – How much money do you actually save your customer from having to pay to other third parties? This is a direct impact on bottom line. First, consider several categories of benefit: It’s key to be thoughtful here in a couple of ways. Step 1: Determine your 3 primary areas of benefit Follow these easy steps and you’ll soon have a presentation that sales reps can consistently use to present the quantitative value of your solution in an engaging, collaborative way. Building a value-based ROI presentation doesn’t have to be hard. # self-computed mask bin_p_values = log_p_values != 0 # VT mask mask_vt_filename = haxby_dataset. log_p_values_img = new_img_like ( fmri_img, log_p_values ) # Now, we visualize log p-values image on functional mean image as background # with coordinates given manually and colorbar on the right side of plot (by # default colorbar=True) plot_stat_map ( log_p_values_img, mean_img, title = "p-values", cut_coords = cut_coords ) # This new image will have same header information as reference image. from nilearn.image import new_img_like # Visualize statistical p-values using plotting function `plot_stat_map` from otting import plot_stat_map # First argument being a reference image # and second argument should be p-values data # to convert to a new image as output. log10 ( p_values ) # NAN values to zero log_p_values = 0.0 log_p_values = 10.0 # Before visualizing, we transform the computed p-values to Nifti-like image # using function `new_img_like` from nilearn. ttest_ind ( fmri_data, fmri_data, axis =- 1, ) # Use a log scale for p-values log_p_values = - np. The lower the # p-value, the more discriminative is the voxel in distinguishing the two # conditions (faces and houses). Import numpy as np from scipy import stats # This test returns p-values that represent probabilities that the two # time-series were not drawn from the same distribution. Potential anisotropy in the image affine (i.e., non-indentical Using a Gaussian function with 4mm to 12mmįull-width at half-maximum (this is where the FWHMĬomes from). When using methods that are not robust to noise, it is useful to apply a Smoothing: Functional MRI data have a low signal-to-noise ratio. Build a statistical test to find voxels of interest # Simple pre-processing step called as image smoothing on functional imagesĪnd then build a statistical test on smoothed images. We have the datasets in hand especially paths to the locations. Labels of haxby dataset (text file) is located at: /home/runner/work/nilearn/nilearn/nilearn_data/haxby2001/subj2/labels.txt Of brain connected networks given in 4D image.įirst subject anatomical nifti image (3D) located is at: /home/runner/work/nilearn/nilearn/nilearn_data/haxby2001/subj2/įirst subject functional nifti image (4D) is located at: /home/runner/work/nilearn/nilearn/nilearn_data/haxby2001/subj2/ See also Regions Extraction of Default Mode Networks using Smith Atlas for automatic ROI extraction Visualization & results checking be possible at each step. Pre-image operations to post-image operations. Here we give clear guidelines about these steps, starting with These chains of operations are easy to set up using Nilearn and Scipy Python Image operations can be used before and after computing ROI to improve the We demonstrate how to compute a ROI mask using T-test and then how simple ![]() Problems that arise in the context of high-dimensional input variables). They represent a means for “data folding”, i.e., extracting and thenĪnalyzing brain data from a subset of voxels rather than whole brain images.Įxample can also help alleviate curse of dimensionality (i.e., statistical This example shows manual steps to create and further modify an ROI spatial To download the full example code or to run this example in your browser via Binder Computing a Region of Interest (ROI) mask manually # nilearn.image: Image Processing and Resampling Utilities._level.make_second_level_design_matrix.
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