fMRI_tutorial_1504.ipynb

{"metadata":{"kernelspec":{"name":"octave","display_name":"Octave","language":"octave"},"language_info":{"mimetype":"text/x-octave","name":"octave","file_extension":".m","version":"4.2.2","help_links":[{"text":"GNU Octave","url":"https://www.gnu.org/software/octave/support.html"},{"text":"Octave Kernel","url":"https://github.com/Calysto/octave_kernel"},{"text":"MetaKernel Magics","url":"https://metakernel.readthedocs.io/en/latest/source/README.html"}]},"pycharm":{"stem_cell":{"cell_type":"raw","metadata":{"collapsed":false},"source":[]}}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Introduction to experimental design and data analysis in fMRI","metadata":{}},{"cell_type":"markdown","source":"In this lesson we will learn the very basics of experimental design in fMRI and also you'll have the chance to analyse data from one or two subjects in order to extract brain activation patterns.\n\nThis is done via a [Jupyter notebook](https://jupyter.org/) running an [Octave kernel](https://github.com/Calysto/octave_kernel) on [Binder](https://mybinder.org/), which allows cloud-based computation using SPM12 and Matlab scripts. It is therefore a reproducible environment for fMRI analysis using [SPM12](https://www.fil.ion.ucl.ac.uk/spm/software/download/). Anyone with internet access can run this same code on the same data and should get the same results. Besides being a useful tool for tutorials, this environment allows sharing work with collaborators and, maybe most importantly, represents a tool to share the data and methods underlying your scientific results.\n\nIn order to illustrate how we design and analyse data from an fMRI experiment, we will use data from from a non-VR study. Typically, VR-fMRI studies will first perform a VR experiment outside the scanner, with the hopes that that will elicit changes in how the brain functions that we can then detect using fMRI. The other approach, which is fortunately becoming more and more common, is to do a in-scanner VR study, where the equipment is MRI-compatible and won't be destroyed once you step a foot in. Then, you can scan **live** how your brain works when immersed in a virtual environment. \n\nThese studies are still rare, and open, raw data isn't readily available to the public yet. This is why we will use scans from a non-VR study, but that can be easily thought as a real in-scanner VR study.","metadata":{}},{"cell_type":"markdown","source":"## How to use this notebook","metadata":{}},{"cell_type":"markdown","source":"First, we will explain briefly how [experiment design](#design) works in fMRI experiments through as simple paradigm where subjects see food and non-food images. Then we will overview a \"typical\" data [preprocessing pipeline](#preprocessing), going from the raw data acquired on the scanner all the way to the identification of common pattern of activation between two subjects, using the appropiate [statistical analysis](#stats). \n\nThe output of each code cell is already available. This was done since the whole preprocessing pipeline takes around 50 minutes to complete, with some of the steps taking quite a while. Nevertheless, you are free to re-run it, change some parameters or even write more efficient code (I wish I was more proficient!). You could, for example, click on Run --> Run All Cells and let the notebook do its magic while we advance through this lesson. \n\nIf at some point the kernel dies or it just stops working you can always go Kernel --> Restart Kernel. Worst case scenario would be to relaunch this instance again. \n\nAlso, any cell can be collapsed (and expanded) if you click on the blue bar that appears to the left (some cells will print a very extense output, so it helps keeping the notebook \"clean\").","metadata":{}},{"cell_type":"markdown","source":"<a name=\"design\">\n</a>\n\n## 1) Experimental design\n\n### The experiment\n\nFor this tutorial we will use an open dataset from [OpenNeuro](https://www.openneuro.org), a data repository where you can find hundreds of raw neuroimaging (fMRI, MEG, EEG) datasets, standardized with [BIDS](https://bids.neuroimaging.io/) and freely available to anyone with an account. You can find the article to the study [here](https://www.ncbi.nlm.nih.gov/pubmed/23578759).\n\nThe authors were interested on wheter images of palatable food (chocolate, candy, etc.) evoked a brain response from visual areas and areas associated with self-regulation (a mechanism that would inhibit indulgence). Technicalities aside, the experimental paradigm was quite simple: inside the MRI scanner, subjects underwent a **task-based** experiment design where alternating **blocks** of visual stimuli containing either \"food\" images or \"non-food\" where shown to them, along with rest periods in between.\n\n<center>\n<img src=\"attachment:smeets2013_paradigm.png\"  width=\"66%\" height=\"66%\">\n</center>\n\nHere we see that the sequence Food->Rest->Non-Food->Rest is repeated 8 times over the course of the experiment, and that each food/non-food block consists of 8 different images, shown for 2.5 seconds with a inter-stimulus interval (ISI) of 0.5 seconds.\n\n### Some basic concepts\n\nThere are a couple of term that might be worth explaining. A **task-based** design is one where subjects are expected to perform a certain action in response to the stimulus that is presented: watch, listen, read, move, think, imagine... This allows the identification of brain regions that are functionally involved in a specific task performance. On the other hand, there is the **resting-state** design, where subjects are supposed to do nothing (whatever that is) while they're being scanned. This method is employed whenever the researcher is interested in the spontaneus patters of brain activity and the compared them over time (i.e. after cognitive training) or between populations (i.e. young vs. old subjects). \n\nIn our experiment, subjects were asked to watch the images, hoping that the content of the images (delicious chocolate vs. a pencil) would activate areas associated with complex visual stimuli and self-regulation (occipital cortex, basal ganglia, amygdala).\n\nAnd to do that, we would *theoretically* need to \"substract\" the activation associated with food images from that of non-food images. That way, the resulting activation pattern (again, *theoritically*) would belong to *what makes the act of watching food different from watching other things (like its palatability or the need to self-regulate)*. \n\nAnd this is exactly what we are going to do with out data!","metadata":{},"attachments":{"smeets2013_paradigm.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"<a name=\"preprocessing\">\n</a>\n\n## 2) Preprocessing pipeline\n\nOnce data collection is finished, the first aproach towards the analysis of fMRI data consists on a series of preprocessing steps. In short, preprocessing aims to get rid of unwanted fluctuations in the signal, both within and between subjects, in order to improve the signal-to-noise ratio.\n\nThere several sources of unwanted fluctuations or noise: head movements (and from other body parts as well), heartbeat, respiration and random drifts, to name the most common ones. \n\nSo, throughout the following section we'll review some of the most important steps included in most preprocessing pipelines (beware that this tutorial is not a comprehensive tool to analyze any fMRI dataset, fine-tuning might be required).","metadata":{}},{"cell_type":"markdown","source":"### STEP 0: Getting your data ready","metadata":{}},{"cell_type":"markdown","source":"This notebook will enable you to do a complete round of preprocessing and statistical analysis of 1 or 2 subjects' raw data, right from the MRI scanner. The initial dataset of each subject consists of the following:\n\n- 1 anatomical volume: This is a high resolution image of the brain, which will be used as a reference to the other images.\n- 170 functional volumes: These are T2* (tee-two-star) weighted images, which refers to a different acqusition protocol which best captures small changes in brain tissue oxygenation (more on this, later).\n- 1 event file: Includes relevant time information about when were the stimuli shown to the subject (**onsets**) and for how long (**durations**).\n\nYou can find these files if you navigate the folder `ds000157-download` which can be found in the file explorer to the left.\n\nNow, what steps are involved in a (minimal) preprocessing pipeline? This diagram might give you an idea:\n\n<center>\n<img src=\"attachment:3c8e0144-4049-4ab2-9338-534bd3963afa.png\"  width=\"75%\" height=\"75%\">\n</center>\n\nWe can see that structural and functional images follow separate as well as mutual preprocessing steps. Also, note that after each step a new file is written, including a prefix in its name (r, w, s) which is more or less standardized in the field, and indicates in what preprocessing stage that volume currently is.\n\nWith this in mind, run the code below in order to extract the required image files, move them to the correct preprocessing folders, setup the desired directory structure, and initialize all required variables. Wait until the output prints \"Done\".","metadata":{},"attachments":{"3c8e0144-4049-4ab2-9338-534bd3963afa.png":{"image/png":"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"}}},{"cell_type":"code","source":"% Preprocessed and analysed data will be in \"results/\" folder\ndata_dir = fullfile(getenv('HOME'),'ds000157-download');\nspm_dir = fullfile(getenv('HOME'),'spm12');\npreproc_dir = fullfile(getenv('HOME'),'preproc_data');\nresults_dir = fullfile(getenv('HOME'),'results');\nstats_dir = [results_dir filesep 'stats'];\nprocessing_dir = [results_dir filesep 'preproc_data'];\nif ~exist(stats_dir,'dir')\n    mkdir(stats_dir);\nend\nif ~exist(processing_dir,'dir')\n    mkdir(processing_dir);\nend\n\n% Initialize subject data; extract; copy to preprocessing folder\n\n% count subjects in folder; can be expanded if downloaded more\ndir_sub = dir(data_dir);\nn_sub = length(dir_sub) - 2;\n\nfunc_files = cell(n_sub,1);\nanat_files = cell(n_sub,1);\n\nfor i = 1:n_sub\n    if i < 10\n        sub_path = '0%d';\n        sub = sprintf(sub_path, i);\n    else\n        sub = i;\n    end\n\n    s_raw_fn = fullfile(data_dir, ['sub-' sub], 'anat', ['sub-' sub '_T1w.nii.gz']);\n    if exist(s_raw_fn, 'file')\n        out_fns = gunzip(s_raw_fn);\n        s_fn = out_fns{1};\n    else\n        s_fn = strrep(s_raw_fn, '.gz', '');\n    end\n    f_raw_fn = fullfile(data_dir, ['sub-' sub], 'func', ['sub-' sub '_task-passiveimageviewing_bold.nii.gz']);\n    if exist(f_raw_fn, 'file')\n        out_fns = gunzip(f_raw_fn);\n        f_fn = out_fns{1};\n    else\n        f_fn = strrep(f_raw_fn, '.gz', '');\n    end\n\n    % Create preprocessing subfolders\n    anat_dir = fullfile(processing_dir, ['sub-' sub], 'anat'); \n    func_dir = fullfile(processing_dir, ['sub-' sub], 'func'); \n    stats_dir = fullfile(processing_dir, ['sub-' sub], 'stats');\n    if ~exist(anat_dir, 'dir')\n        mkdir(anat_dir);\n    end\n    if ~exist(func_dir, 'dir')\n        mkdir(func_dir);\n    end\n    if ~exist(stats_dir, 'dir')\n        mkdir(stats_dir);\n    end\n    \n    % Copy extracted files to preprocessing subfolders\n    anat_fn = fullfile(processing_dir, ['sub-' sub], 'anat', ['sub-' sub '_T1w.nii']);\n    anat_files(i,1) = anat_fn; \n    func_fn = fullfile(processing_dir, ['sub-' sub], 'func', ['sub-' sub '_task-passiveimageviewing_bold.nii']);\n    func_files(i,1) = func_fn; \n    if ~exist(anat_fn, 'file')\n        copyfile(s_fn, anat_dir);\n    end\n    if ~exist(func_fn, 'file')\n        copyfile(f_fn, func_dir);\n    end\nend\n    % Initialize processing variables\nfwhm = 6;  % mm\ndisp('Done')","metadata":{"scrolled":true,"jupyter":{"source_hidden":true}},"execution_count":1,"outputs":[{"output_type":"stream","text":"Done\n","name":"stdout"}]},{"cell_type":"markdown","source":"Before actually starting preprocessing, it is good practice to manually inspect the images. You should look for gross displacement, signal alterations or even brain abnormalities (which might be a exclusion criteria). \n\nSince this notebook's setup doesn't allow for any fancy command-line image visualization, what we will do instead is print coronal, axial and sagital cuts of our subjects' structural and functional images, which should be enough for us to notice if there something really wrong with our data. Notice how brains already differ between subjects.","metadata":{}},{"cell_type":"code","source":"%% QUICK INSPECTION OF ANATOMICAL DATA\n% Sub-01\n[p_anat, frm1, rg1, dim1] = fmrwhy_util_readNifti(char(anat_files(1,1)));\nanat_3Dimg = p_anat.nii.img;\n\n[Ni, Nj, Nk] = size(anat_3Dimg);\nsubplot(231); imagesc(rot90(squeeze(anat_3Dimg(round(Ni/2),35:245,1:Nk/2+30)))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\nsubplot(232); imagesc(rot90(squeeze(anat_3Dimg(:,round(Nj/2),1:Nk/2+30)))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\nsubplot(233); imagesc(rot90(squeeze(anat_3Dimg(:,35:245,round(Nk*0.35))))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\n\n% Sub-02\n[p_anat, frm1, rg1, dim1] = fmrwhy_util_readNifti(char(anat_files(2,1)));\nanat_3Dimg = p_anat.nii.img;\n\n[Ni, Nj, Nk] = size(anat_3Dimg);\nsubplot(234); imagesc(rot90(squeeze(anat_3Dimg(round(Ni/2),35:245,1:Nk/2+30)))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\nsubplot(235); imagesc(rot90(squeeze(anat_3Dimg(:,round(Nj/2),1:Nk/2+30)))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\nsubplot(236); imagesc(rot90(squeeze(anat_3Dimg(:,35:245,round(Nk*0.35))))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);","metadata":{"jupyter":{"source_hidden":true}},"execution_count":2,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Image 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\n"},"metadata":{}}]},{"cell_type":"code","source":"%% QUICK INSPECTION OF FUNCTIONAL DATA\n% Sub-01\n[p_func, frm1, rg1, dim1] = fmrwhy_util_readNifti(char(func_files(1,1)));\nfunc_4Dimg = p_func.nii.img;\n\n[Ni, Nj, Nk, Nt] = size(func_4Dimg);\nsubplot(231); imagesc(rot90(squeeze(func_4Dimg(round(Ni/2),8:58,:,1)))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\nsubplot(232); imagesc(rot90(squeeze(func_4Dimg(12:53,round(Nj/2),:,1)))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\nsubplot(233); imagesc(rot90(squeeze(func_4Dimg(12:53,8:58,round(Nk/2),1)))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\n\n% Sub-02 \n[p_func, frm1, rg1, dim1] = fmrwhy_util_readNifti(char(func_files(2,1)));\nfunc_4Dimg = p_func.nii.img;\n\n[Ni, Nj, Nk, Nt] = size(func_4Dimg);\nsubplot(234); imagesc(rot90(squeeze(func_4Dimg(round(Ni/2),8:58,:,1)))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\nsubplot(235); imagesc(rot90(squeeze(func_4Dimg(12:53,round(Nj/2),:,1)))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\nsubplot(236); imagesc(rot90(squeeze(func_4Dimg(12:53,8:58,round(Nk/2),1)))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);","metadata":{"pycharm":{"name":"#%%\n"},"jupyter":{"source_hidden":true}},"execution_count":3,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Image 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5D9rnJ61U8eLHRBtRb7rDkObm9whbsQnz33Xf7aE0voqorVkFOnjypgxdffDG3V2lXX18NCCSAynbDJVpoU0nG7AsDuuLrnlY0i80ZQH03tnIdYGUQZQcAAEOAhQQAi8HGYkkkKkW1Y+OUc3VtR8fJKdf3Q1JRH1ufdrUVe/Qzn/lMPvUwihXr/Kdy+5yRbZedrntUJerPMXs33XRTbtBLCi0UBBJAZc5XVho4Bldl0GKz0uBJxP1ovnB7L0ho9rRn7+WXX87tOyvLggQorFYgERP8kUNMMIxDWcC78cYbdWoTQcaEr1vqy3LqiTsqb1p0iGji36aPbK9iYEXEtddem69bp1GHLqIqw66EQri9Iyas3CgRqncri823L53VCqT46GKCs5r8USmnVxbRm3dfnnMFfEwQEwwAHzdrFkgAH5RJH11ft7jzzjsjbSrqBQA55Xr0nRRb3+7tSuWys5p86tQpHTz33HORPHhz2oY7nKurBrAsEEh7KCUa8wLjlW0VLNM7m+eltzyFyY2wZXKZq/zYxz/XA8Ci8a+6CGOfluW3Y8eO6cDBDnrLegh+fuUjBTWUF9ZOM/nu5vY9cvuyoFhmGLvmejHWcvutt94amwJC0aIeem3Zhb7+CCRYP/0tnSspVqYDTXOuY/bggw/q4Pjx45HmMm+zVuqbqWxabBz9ruj83nvv5f4dOuVRPfDAA5HmUJd21mQ693EWOgcBGATSHrYszEy+7ZpQcq64vDFOC9ecpWnO66iRFDpNhbkQpMo+llzLnEF5mSaUJqmSGb6y/SUBYE0gkABgARSXmjWtso+RzVZd8XKdjWBho/add97RwXXXXRdTvq9ye8n4mXMhelnR1zUw++hK5SH3U2z04tt3oSA3yxptv31x7LRAyr85u0eEfq/d4NDvzLGkd911l0/1U9Dv1b9CxYaqnzfffLM3EDrVb0vuGnt7dJfQvXqQbvHr9/Of/zz21tHSQ0ucaLe3dpMyu5Vd1JxX9PDDD+e7yqzhP75DfsuXW9YP7LIrWxuYMtm5LJvHc+bMmdiEPPSP41lpLmQZYHB2WiABwFIowQulyKmve0FOK389GVlKQFFBoglvJwxJust+ilSpQfEFlv0+kM7qZtZCSjMd9NgHfRA3Kx+zj1aPu7KQqwGhlh0AAAzBSuTqh0cRB2U7yKx3SEPRjvcKsopNEXjdK6VG0aJFLdK9WZexgpZdyXq6dCX3kP1vUvek+skZaGeRFMMcoqp+Xn/9dTVQUktWM0tI2C5EapUViPJ1x+Y7ktfU7d3Mari+Pq8TOERY7ZWrH+2P2UOT5zIK9O27DpsbaCNRu5effvrp3OFqYn9hZ0EgAcACmPNKlWCEEgpvl52jGLK6FklLKOt5RXdx7IMXd6UXlq3H3bM1RVNcjjqYqxXrwVhhveGGG2LKZacb7VFc+k4lCKT/q5srl7HzzvIqsWIWJnNOtaytH2v+Qe8bk+33od+lPssPK6vq+gkqisHNZLoJvYRaEn/ttdd0Udq0svlyyEYJr1jcL/jKKCWfHab/xBNPRPp23nrrrZgvdWrKekOfOvVXteXkL11foi0h282aX+bmytOnT+vADb73ve/FlCUHsCxYQwIAgCHYLQup2PUygKQFT6YFZDvp0UcfjY0OW4L9t9hG0lWlGudg60kDaEsPuVm/4os5ROftt9+OFDqs/3r22Wdjo4DnMHQ/N2+vsiPWEiyI/puUc6yXxy7WqhfeysZC3WkmvC5Ywtu8uKuX2uuINn91Y1+eLGkYau9h2/VXhu2pRka8CghF8nyorKL7mbOql8JuCSTYTeZW+/0aP/bYYzqQt7Mkb3VXmGYN3+5SQKWIRhH2bl/qpOUSHvlBPaS47Mbm/KRXX301Ip5//vncrdsvPVkSdofdEkhlwTO/9kWh0MT0yCOPRMRXvvKV2CQWaJEpJ6uavErpKUMXs9JUEsu7xlcWXXOzbEiVgK78iXSvHuehSpOSwfTtb387NiZUmaO7nbTvCgrA1ccvjqMMRMkHcvqRX6hSD3AuMWjuupEW4nyjuapjJfrA3WpOmCs80XWInJgfqRK8+nG5RVtOCw2aZQ0JAACGYP0W0mS0qNSlYqzkxvLhfP3rX49NTJoCouR1UfpR7N2oUbqPNCZrbYpqk76TU8onY0zzUIubaM42Kr7jvBJWbCxtlKlgPFl+WjyznyerVBpwcRAtiOKjK5VqY/Oh/D2WoMfiQ5sryaOfRLQ/kUN+54ZhtVo4pHgu4rFct/psffnee++NzVecG2hgc7XMAUZj/QIJANZHkdY+tY/6xIkTMeU6U1TCXLk/Kx9zOfJFwd031L74AMu+Ss4f6uuFpdui09hHp+gMn3r8ZdhL8d2tXyBNGkB5Q7yM0+jy16/4Fv2O9fWXxH79CGQtqVn51XYrZ9LjnO2kyaWd/qMv/eTF9rKaJbQGJsvg8ccfj4i///u/139p2wuht0Wdy6j63//931gOZRpympFjq3T9/vvv16n/sLIaS1pij5jqaY+5fTGAopXncFDDZMilG3iBsFTTKIWlY5OZZHtda4Tl6f1BS5mkYHdgDQkAAIZg/RYSAKyGHlIvbDXa7THpzIiNvetotMmdMLc81wd5HTpfV9mOuXGWfrqpreG5eIetXjlvXEXTnklF/H/1q1/VqVevyUMalMmghuxkyzkiio2OjU/Wq8SxN2jbvpcc1KAe9K+z5/LPIrsBtxTsKY6U7seTJ6e7AUszvZmlRlbeUUklkVxIVBf1omYXk4poDU7xc/pAzkk71u2yU7iKv+5yo2OFi1PXzfQVOP3IlMnIlC9rS8RKeZCwi0+zaq87pW/qa1/7mk61bVJspi335pWG/LsFGIf1CyQAWAGTilrszbqLJHR13ZGQvp7/N9/4QaNJFY/QKzhoBbEHKZSKD+VDObqh7OXo9Uj1Y+3QupSUj7mAzMWxKwKp7Cucfxb6CmUuKDIn9poI2SoqW4KWciCx14iZZPJ3vyWLrV+Uvjy552yOOCpVBoT+AoozVmP/Te64447Yu/NsDm6+/vrr5z4RAMBHwq4IJNgpLK0lTV3QzzXRFV9nNVlFK2IqnSum1g+KS83VvrUkYK3cwl7tnW9k5rZUcIfC6nN2C2f0xJMnT+r0m9/8pg4eeuih/DGtbuOygzFZs0DKZkcxQfrOe8eOHYsUH5w3r8vZhZPFVbNdMhmHna2iyWZ9VNsvRst5zIHaOSW2bIGcR6L50Y1lHSoL+Ny5c753iYmxsHr8w9ZqvzeitC6iaqo9OkDCu5T7i/aqbinuJeRV8/+WjZH6XZPOOj9u300dNX6fOqdb2RqekUre0uK2Nl/YcAEmKWLer6USdP7jP/5Dp9r7KjamkkOtSj+2bEqdw5IcVjaK9YGfXjy3PSKrnLofhUv4uqMnpC703dvEiy++qAPrVTINHZFFpQYYnF0RSGWmyAmksgwUbGa9Qw30JitZcrKfHMk2GURX9KBogVXdhJrMXsx2nnS6Mtn12kKlInXWmHSx+IXmikguNH4UABbHrggkAFg0k9VPYqNdeRXQyAi2lVkW8OaWAzulmoZdc2VLkb6X+eT4iyewG8ElWMnrkUUVLtVD+vZOCy3DsSsCadLsyJFp8tL4Z638Mv2bV4CLuZDLj+q/5MYp6y5qlq0lO08mywV18r0aj/51MKteP70euTRqSdDLNfCLjaUOc+bN+KtHc74yo2/QfwTvb1ayGv116MUuk45dc7Yp59YJ9C14dnDwrvDfsxR1tQnuBwl/QZNmbh5n/iVHclFqk4K+e9tC9yaA1bMrAgkAFk2xVMr6mTXFo0eP5gZePyvKipcJrWSUIq2lfqDZt5RD9orHVCHETDdoipro4Un19LCd+1wU3BLYubigBmrZAQDAECxMfn4gegltays5SFpBDUqBtsvOrtuI2XiB2Ov6yP4WJ47INZx32SkbwubOpaPJy1SiHnLZ6Ry3as1ImqDSe6UlTboNsx+vxIjnR+QAh0U4dopH3lXbdeCKYQ6rky9LQWuR9Nmid5fservI5Ivzt+yv/uzZs5Ecg8770VfW9yXS9a50l+1Bu8+tILPA7e0q1HU/1weKx9uuvANcfdYskABgNVymD6psCm6doOyy2EP25SubUw769eLiK/13HWLLjjOTB8LjzNt75maTS8WdpegcaxZI+astJX90ILeskjakLZYqnDrVV641/8nvW1+2vL36AZVVcZHVbTfIURK6mEcVmzcql3aVGSSTqNh8+jeHWviPIK1cDbKVZutBXmnp7Ev5+ZoS1GDPuz6j/wgON9Bfw6UNbBDnAlEdWzaavPrOsMpz8l9P+wXHJmzhtttu0+lcZeiCh1E2/+2Wk7IjHeHiz/Xkk09G+rOopGxstgme26QO4KBgDQkAAIZgzRZSpyTGaglBdonMDq8KaO1BCrX04l5ArHQrfbko17pLC1T6Ly1TWeeVVisVVQ8tu4tmEyqXLyq7S6jzvF+t/i1FjtVMSwu6V8pyaZDXKlCf4WDpi7gxsx4cySrNXpBo5TbsynN+Ut7VJZoPre+rlB0n0Zb3ilEbm8mkdOtT2/RysZSk9f4xvUqqlv4UkztNL4jdEkiwVkpFjDI7eNYoCUDFUxd7Rb7pWYeT28wb+/09G6qimq+XfKPyKWJ+ciwxvlaSVLett//Wt74VEU899VTpXwPz5xo/4Qx2hDULpF7LpzjN9Vrqv6RYlTWkrl+oQcnKzhVXi2KSV0Tzpg9liuk6YElEz1NkThEvn0hqlOykMilrzJq59GGfeeaZSJqjFqXyp9hiFAJcZeaCGvRb9Qviyu45rDRmNtWMthFReVzsnUAi5TnpdZ6r+OBTV3ctYbFzwQt6o73xq15Y0w0+mUoK74xk+eXeFgRrSAAAMARrtpCyyiOFwv7fnKljN06kilJ54xnZFrkej8kXpfLI+CgVhnLyth5a6lJnLayEmeaRayQ5YLRYSOpBzXKdodhoUhqetuiWFeiclZwmJcZXr0qMb6nSHZsvyNe1yUhsUoWUjZT7KStzhbkQ3jKeUmGoj7YUNOshv/od9vbFZefx5LjKfKO+3LLcEvsFEwIcFGsWSACwGub27JhMAI+9OlwkpURSXDpZNJ9bUXFiI7y9taP7l4u760Dq0IqgnW+KbCqxFX3VU83smpvTjexOV6aBT52KoGEvrtYqAgkWjF+zYpLa6s02ZaTZQQGWJZbBV7xtUrFg+nMLZTORstNrv6tMpmX66JvIaXrqkV1lhG6f40j7gxY3W8HqWb9AmnRw5drYWjbUq+sGOfo5RxmUfvpuRiXYVPqO7lLA95aXf7JUT1a48jxVwkB1qtI4mgftfhSl8nTMBG4srmgQAKyG9QskAFgNfZ2sLPvN1f0rwXW+vdQQcrFtm6dyylnFdKx8eUpZFS7Bq7GxzkuFoR6kl5eB+/WuI5aNVIr1vDidcs0CKX8ZxUehX8Px48ej2TRCIQD6drM31uU4hXw7aiADSCaR0c8xe1Ry9dLYa37l+kDFG6P/yjaNei7VZXII++TetcWuiubtWahtVD6FpwPF5vp/HURbIuPtu9OBv+WSLeBuZX06eGHuz1V2XXPO5lzhstKPjdryaymxOdG+Nf+Y77zzzkgxwZ6tiodzcV83rJU1CyQAWD2lmGkJKbRsLhaV25ctZft6oa7YMHIydal2ur2IajSf+dwynprZUCvbAhS9OZrFtl3FGZ/dEkj+ehSLoi9PaqN+XrbfswqpH0G2k4pbQD8aFR8qSf5Ki9Pjcp/WefNyd16/cYNcvsjmf2zeK/9ec2GSPEi/Jz2qe3v5YQCAqwyzD6yBUjqoyFprCWUT8V4wsCRv6boLhZUKYw6xLc/t6xyljpnJe1BFcsGVBQ9T9soqCUn9upyEd999d/mYZX1lcXo0rJVdEUiTaaRaKshb202G4eZqQNlair2LOnrbNUmV4LeegViq8uhBubiqpyTtLJDHP7nlYDd9ymLv3OJQWVZlboLRmLTa/cvXm9I9ZnlFNpIvoSgNvm71QpSyxWXVzfhxpQRRT5MqHoiiQxQ8qlI6qFckUkLVXJGzuWEPy64IJFglkzUDIzn6pWeYp59+Wgdq6bpn3km2RB+of3fixymA5Uc/+pFOXQAi127P4ynBEZ7sir5iLUT9+LlWgF555ZVIs1JRRzyLucPz58/nj2+PdMlMQguBQdgVgdRrmMZm5wWpEtq7rGRKqn02NSbzkHql90mzY8vmfrk8q7hw4UJ+RK4D+4EqvpQqNfvuK4GdBAAHxa4IJABYKJPqkdfbZA13l510OIer2eotmQ9lwc/ty1YgjuC/THWwj7ms25VT68oewCS9slF5XI8eXBYIJFgDk/UmYpOCY9eWXG2x2Sio1GCNTdHV4hNTvlqkgmaKiehpTHmH+GgxwT39qKxPuGSRVgjswbNHUVuh91BjHeh/M4oh9h/HFZXUM8VVYTTWL5ByCMDkQuWZM2d85ZZbblEDxVPJ537ixInYqB4l6j+XDprcda1780qGdm4g36BUubJvSi4dNOlVm8ufKCPZMgd1lQr3HYzA5C+w5DPYYCqZOnMV1stpbEyTuZzlbsGUDclKy5IgH21H6VIYvnyuPjx9QBdjtBJjy09sr444PuyHBAAAQ7B+C0l0SyX2OlLk27FCoWDK2267LfYWM520b6T7lHrvuYHIcai+nssObXnEXJ9bPuxkROkHUpSWolWZrg9qx6O+behcJTF9ESUczjn2r7/+ug4UVlcM7mhRJ3NBvSV2plcTUEiLH1ciXPpvI2/fFa2gmfu36w9gTHZFIAHACuj7A80lI5fNDEs+UI8CmAygjXnf3VyofenWEfYaia8XD2FhLve564gvvfTSZA8LXSBcv0CajMbWt6UfU95f9cUXX1QDbU2vteW8lDK51WYP2vbvSY/oJot7yCtGH6Zsz2UW/sk/68nqq8uyivZ1keu6840U6O/rnk08TZQvQqf213ty0bfm5EQvPc5ZMCXroNjHfe7Q9bkPVSpGu6WHVwbQ+1/idw27AGtIAAAwBOu3kEQJGJPOKwVZ4StyuzuITiqqDCYlOijkV40dPZxLB+VAvmLIdytt0qAuQcNXTHZrlHUOnUq1/9rXvhZJrZbtr+UKCq3CaMytzuqV9KlfYdXykasjmivPUXk2f1W4y6+DD8r+RnNWrykbGnmu0JzgCkMl7nffd00D8LBL8Y5SrDlw2QFcfeZK/ZfZ59y5c+W6Zh9PXmU/tNJPz/sRnms8WyljaS4Ev7vsNDxvv+REIulAfavyfNeWv4MfpOnJk7I7xFkHY7IrAmnLGzi56YMONONkR38pVpanHltFl/Pcssea1JycbDTJZSYGTdo3qtIoBU3HepwnKU1eyp1Ufeu5yrAAV5/ys7cOoTfIv1IVI47NOp+SCCOlCSr32ek7VkpKXlGpvlqcK25fVJ9o750TkqQD2bLRsHtutd7NEssQm5nHvT3zzDP5uXPDWJzmwRoSAAAMwZotpMOHD19Os+uvv97/Fv74xz9GxP/8z/9ExH/+539+pKPbw5/+6Z9GxBe/+MWP7xH6LL/5zW8i4r/+678uZzyTf5NFUPTZvmBQtisszjQbu2VVb98Y3Lw2Ga34f/GkxUY9t7Zedne8/DT7uQYaQEljAhiW1QqkixcvHvQQAOAjxqJ33z0Gn3zyyUguOzu7Sv3AUniw7xEu9WJOllu3KDuc9X5KlkXJZZ7M2Y+Noy82C4H21OnTuWXJW+gdLoXVCiQA4/UGVxe10SC271LYdzkroZKe7ObWG3Ln/cC3u32xzIqpdPkG0+L2Z4MdhzUkAAAYAiwkAFgek1VXIhmFCrdTLf+IuP/++3Wggn7eGtxRdsXq9bqjzFOf2pnm8NTczPTSQSUKd67ykA5KhSF/nCeeeKL0X8KDt9v644NAghUyl5fj6qLlNZ67UfjlLx6wMim4gf3+xefW66eVzeLm0qr2/Vzb553F7UEAOwsCCQCWx5yFVJSDBx98UKc2cU6dOhVp8d+lE3KNktxhCbwsZSrLtky+0ovAHj16NDY7A+T2wns/KgXQy4pvvvmmDhTFUNKYou3TtnTlA4EEC+YyI/s/bhRMH5t4+n2j6uf4WOP+AcYHgQRLhch+gJWBQAKA5VF8U/ZZeQFP2NX2l3/5lzr4/ve/HxH33HOPTu37UsuSThQbl2DxsEUrWeQb5azzXuMep3xx9qSpwLE9eK5sJLwPjsswyndXnhJtp5Kym8ziIOwbAACGAIEEAABDgMsOABaPPWMl76eXCPqHf/iHSFt+2MUnb5hPb7zxxnzdLkEz50PTHiI9kUgDKPsh2eP34x//WAdnz56NTfn/aJ5JVyQqW7P3at8LBQsJAACGAAsJAJbH5J5kkUwEmUo2KWwqqcLCd77zndKPWjrY4eTJkzrQ/kk9WEBBDU4YcvTB008/HRGnT5/W6R133KGDUiH+Rz/6UUQ899xzOrVJJNOqfzoNz/ZfsYQWGsLQwUICAIAhQCABAMAQ/Mn7779/0GMAAJjmyJEjly5d2tJATi271HqZUXHTTTfl6y6NWrYq76V3FN1gz5t3MBGllmtEvPzyy7FJM4pUxVUPcjDF22+/HVNbmM/VclVURS97+IGKqB4+fHjwdHIsJAAAGAIsJAAYl30tJNEtJNG3ZNWVvvmhbJ0eNl3al6KuPfqgxGGXkvBur/CE3qwUgHAUw2XuzbgdLCQAAIDLAoEEAABDQB4SACyeOV9Wd8E5fEDY1zfnyislFUqHPi39eDx+nK4U11xPb1K0RXEMRktjWitYSAAAMAQIJAAAGAJcdgCweC7fl6WWPWpOvrXusps89ZUSRBdt8/JyWp5bHHrRoumWviX5BwULCQAAhgALCQBWy76GhaMG5oq0FotqX8tpLmGo5C1tH+euGUYGCwkAAIYAgQQAAEOAyw4AVkhxpu3r+5pztamf7pr7oHuzzgVTXNlo1woWEgAADAECCQAAhgCXHQCskO1erw/qE/ugDrrumutOvw8znrWChQQAAEOAhQQAQ3P48OGDHgJcJdigDwAAhgCXHQAADAECCQAAhgCBBAAAQ4BAAgCAIUAgAQDAECCQAABgCBBIAAAwBAgkAAAYAgQSAAAMAQIJAACGAIEEAABDgEACAIAhQCABAMAQIJAAAGAIEEgAADAECCQAABgCBBIAAAwBAgkAAIYAgQQAAEOAQAIAgCFAIAEAwBAgkAAAYAgQSAAAMAQIJAAAGAIEEgAADAECCQAAhgClqDuTAAAfC0lEQVSBBAAAQ4BAAgCAIUAgAQDAECCQAABgCBBIAAAwBAgkAAAYAgQSAAAMAQIJAACGAIEEAABDgEACAIAhQCABAMAQIJAAAGAIEEgAADAECCQAABgCBBIAAAwBAgkAAIYAgQQAAEOAQAIAgCFAIAEAwBAgkAAAYAgQSAAAMAQIJAAAGAIEEgAADAECCQAAhgCBBAAAQ4BAAgCAIUAgAQDAECCQAABgCBBIAAAwBAgkAAAYAgQSAAAMAQIJAACGAIEEAABDgEACAIAh+MRBDwAAYJYjR44c9BBWxcWLFw96CNtAIAHA0Fy6dOkAn/6JT+yZJP/whz8c1Eg+PIcPHz7oIewDLjsAABgCBBIAAAwBLjsAgMoH9dTNtV+Tx+8qgIUEAABDgIUEAFApJk4xdHylWzy9JVw+WEgAADAECCQAABgCrEsAgP9jzjX3+9//vjSQs64HL7jlXIewBSwkAAAYAgQSAAAMAeYkAMD/MRc199nPfrZc/93vfhcRn/zkJ3Xqg/y/+bquFI9fd+jteKISFhIAAAwBFhIA7BzFUunXP/WpT+mg2EalfQlhiHnTRx3acppDFlXvdkfAQgIAgCFAIAEAwBDgsgOA9WMf2lxeUSkF9OlPf1oHN9xwQz79yU9+ooPrrrsuUsyC+5FT7r333tOpO9RBCXaYS2/qHsW5SkUrAwsJAACGAIEEAABDgMsOAFZL8chF83rZ5ybv2Ze+9CWd3nzzzTo4fvx4RFx77bWlH0Xf2YN37NgxHfziF7+IiF/96lc6/cIXvpAf99Zbb+ng5z//eUT8+te/1qkPimuuROutPm8JCwkAAIYACwkAVsucqeEghdLg6NGjOj158qQOZCG98cYbOr311lt1INPKzRyecOjQoYj46U9/qtPPfe5z+Yk2vF555ZWIePnll3XqtKef/exnW4bdP9fKwEICAIAhQCABAMAQ4LIDgBWyfYvxUvM0Iu6///5IrjNHN6iHz3/+8zp18IJ8dG5v35qCGtzeKAjCzdS/6xI9++yzOrAvsQyvgMsOAADgYwSBBAAAQ4DLDpbKkSNHDnoIa+PixYsHPYQrZC4araQZRSsd9P3vf18HShiyK8yJRIqvswfP1+Wss8vOvrXic3OCkVx57ufOO++MiJtuuin3FhE//OEP8/DKsNfqqTMIJFgwly5dugpP6TmVZTe2K94soPQzN93MBQF/tNPT4cOHP8LeAK4ABBIALJ4us3Wl7+iqg1OnTun0xIkTub0KKESrsjqnc1xzzTXluUXJcCkHxS/YwCrdnj59WgcvvfRSRFy4cGGyt9VbSKwhAQDAEGAhAfw/pOda25UCO6d9O2bX2fhvv/12pPWAst7QUf9d+54raLZ6BRl2HAQSACyey99ASL4yqwgq4RMb55hjEEzZoMg+NPXw7rvv6tRBDTrorkL14JAHxTj4Lkc3/PM//3NE/NVf/ZVOi+/Ow7YSszIdBYEEu8icCWKjR9jEEZ4O7r333khzjQuaPf/885EqOvt2devlCvejA90VEWfPntVBSYcswRRlLziA1bBagURM8EfOcmOCYfV03UK2SJfZ8q9++ctfLu1V1XRuJ9keVi5towQdeCS2tEocuU+Lt9bXZSpJ44mIb33rWzooukjZiHY1rFYgxWXHBOcfnFXX/DXruCwn5Hs/jt/EvssGk+E3H2Yk2z8LMcEA8HGzZoEEMMec3J10gllNue+++3SgoGFvSSDlOjbat9trncBXfvvb3+q0BE14RwMlS0bEmTNnIrnyilrd2ZGsSVg9CKQ9i5Z9SbM0u0zKmoTmI80mmvI88eWnd7Nsrttoc5MMJv17ZUsLTGewROb2VC3vlD0KqpXQowNKUEPptugWbtArQZTHGTVwP3M1XqXcuISrN5xVg7V66gwCCXaRsjBQTJYSIvXXf/3XOr377rt1oFnDzeZmhxtvvFEHisXypGNTSVe8KVwxxRwroXIyEfHaa6/1TxEfolQEwFDsqEDKK0b6d9Jw6bdki8fNND1pmVRl5x3cqZVMbR+pxvovV6e/7rrrYjO1/fKXv/QYSjGryRWsTJ5JNci5HJfSDzkuADAIOyqQAGBN9MB9KVjWHW+//XYdqEiPXWH2oclZ16PsSriQdVY9qLv41KHbS8uMiGuvvTaaUW4j28MutV9d4ugHP/hBJEW2jGc12uRuCST/GvQz1a8hB9GZvCSTy/eqWUlPkW10yy23RKv1W352MbP+nFeSivNav/js4H7nnXc8hrkaxv6MJWcld/7hA/OWSwmqLNOKX3tF33ouc/BCCeHtKwEF2cHGX9Pkd+TrTpZ8/PHHdfDCCy9ExJNPPjnZj4fRY44BFsFuCSQAWCUWxi7jJD+51+f+7u/+TgcS0o6QtMwuAU2+/t5778VGC4y2vmgdpVhULtJqpUfriLKTTFcdSmGIogz5caUwxGqguCoAAAzBrlhIxXUmtUXKSM64LjVdpL/ccMMNsfHF5XiB2Ovh6d4wX9S9uYH1mrlI7jJUnaqBblEPKlYfm/ir7H4stbOyg460FX32HsOiwLavf/3rOtXXXeqPxUZR7esK0oI/85nP6LTEBPeguOKkLS479+/nfuMb34ikZZ87d04HL7/8cmxNWgBYBLsikABgxVj2W3t48803I9Xgsa5pZ51w0IF2Nup5RRL/VgLUbbRo2KKD+nHWTuT0m2tWlBI3cwPpRi4Fa89kWc9eOjsqkHKQd7Yqyuq09GUFukj9LPsI5F+DfM0lRKJHK2yJw+6xD/4vvS3+ccfGbnNJLi13v/rqq7GpEJxD0mOvlbYyv/PlM7fdmbdHe+KJJyItGGjycgSUv33ZxN57zSWfywpBmVbm0oZKbqYP/DW5gWbDr371qzq96667dCBTqQc7lAWJXbaJYRGwhgQAAEOwWxbSXChLNG++GqjIWF/7cRKDzCYryNGyDbJbPz+0WFq9aFDZRqUvUBUl+oEHHvB/Sa9XPbQXX3wx35VLIJf4ctRnGJNeaTtfLLkZ5RXunrqyfGijtiRsuJqGuO2223TgDr1TUUH92JlRfHF+XFnVLjume96wdS5fiGt2uP6hwvlW47jbLYEEu0mfrTQ7eIOip556KjfwukIJLih5P8YN5OvrIci60Z7AohgpQDk2btho6W4qvxbN5ehhPPjgg/lxjzzySP44fV0E5QPGZLcEkl9gvaJdrfAbK31EWpL2pZZqk+sMxWamUzMpNYcOHYqZfd76QlFM2WrCV5RTqQf1jTBKwrmSc1VyTc082alBTrOlABosgsm08WIh2SLxu6lXw1VKS8ZPf+PmAi+LcuA3Ts91AGQJi3V7ayF6YtlhtgdY6k3vKpQ+SDHjIuLmm2+eHPZCdQ7WkAAAYAjWbyFtqceTlQjFTTlo7fjx4/4vOVLymo3VH2lDuTCMXLrO0xbZNiopKaLE+OWxSaXK+0VmHapktOhz6V8FvH7lK19Rg7/5m7+JvY7vfe2kFaQrlY9Q9FBn79t0ljU8V5BpLqvf36b09OLQcw8KlcwNyt/WrkId2INXCp31kkW64hBnpSVFxLPPPhvL15phd1i/QAKAFbBdmlrGW1mUL6v7xEoJx6JtdN1FPTjE304zL+wJKwdy4s1txmjm9k8qrjx/HBU29KqnNyLRFif++CWjY3EqCAIJVkhZaeglnLU3qwudOdtRlL0/PCnMlUJw6FQxOss6Rw+p0vShDLZMSdIsn8sWuRcIdd2zlXeedVBWGc/i5inYEdYvkLYkpea4AL3DxWWXK8nnlNKiduWq4SUxdjLIO+ZnzGg+Rs1N+UE5wKEkqOeoMPXpSeqb3/xmRDz00ENuMOn023fXWgCAj4n1CyQAWDElIc9alHxc3WU3l7KjG+1qKzV+HB1nc7Zs+VH6mXMJlvY9kUDD61XApT4qjDYizp49qwNZ+X0jlYVqk+sXSPrKS7hkNnT0XapKUImizs2ydVJ+0/kRxf2SzY58POe5Lo1LPz1Aozhh8kgUb+qwVJmAqmYvB7Q+Xam+qh/0CsIZRPne/Wd/+OGHI+17ZI+8/ib+o03au6bsUBCbYBZfd2KQKBHAxh6/8uPpIcXlt1FmN18vQcNutpr0SVgr6xdIALAC5vKQsjc70nKgghqsTBQh7WaldEIxjKL5+ed2ki11C728128UOXq20zeiLVUoyzCchmUlpoxqKaxfIOXFlfI161+tG8k2KtUtdZe+45zc6jBc/Vh7qdbyM92yN2vfUrYsgG8vHWRy7desRzsAXdq6osBz9dXi61iNbQQAi2P9Agl2gaJ+FsWibHfkAyso3pdTCUDWphXs24PTitZs11xx2ZWEoS7mVdejbKcUrWr4W2+9pYPic/OKQomacUSyrAQ/F5cdDM6aBVI2cvsWebGZVlRoR6+uMwrLRnyx1yIpwf76r7LfSX7Elk0ockrsZA5vXz3Ks1tZFO1Zrk6uFFoC1cVs3vWBlQEAHBSTv8BSoM86RPkB24VVktm70lDe3OKy61uEiLmtxLvyMbke6bvcrWaeHqOhK5qmIjlp9AHLSnC0P9pSfB7MNbAGymp/sRgslRV5FZvpyf5MZ/bINCnqQqe87a7roQPtShXJAlPslg0pF1gTbj9XqXruuaWovCdfHxSHcKkQQQUHGI31C6Scq1hePHk2tHpUXvWsd+S40kk1p2cgFb0mT5eTy5h5OafMrVKF5NvJzXIZodg7eeVVqFKIWk4ezctad93XAGK2AoCrw/oFEgAsmrn63EKx+72IhlQ0a2y2GrUg111bWsCbyx/q+Uzq0JH6ZX1uTo2bc6kV7JFzyaKytbmNfqmVTmDYdwCDg0CCFeLZRGtmnq38Git4oS/y6/0vCUA9D6lUyD1//rwOZHp6Mzc/VxvJ24r1vvKTRXhj4zn0ZFqmm7LeEBtnoK97V0Z9TJU76+Nf6JwFK2b9AmnSk5adbGVVUOQV/hxxUFY4hXSrHCNQXnV1LrdbXxhw57lit6cqdai0iRyMrlmmTDE5e7x8ZGUqqJp1blYWY+f2iQE4WMrv0MJeL4J1C9fKkrT2db/gerP6jq6uxV4o0QelDL+nAo9nLrVIzCVZ9wSj/CmiBTv4oGxcUIq0Lg72QwIAgCHYFQtJ1onTp7OFJHNBDcwbb7wRe+2hHClQ1BA1mCwFna0ZPU62TokilfqmWC/d4jKvuksjl6tHDyoFobO5kw0gq065B/0rP08ZdnZGLc426mFjxUwsGxf1Mt5qX3YWKJ4347pnrgkk7du/Jd8ow9SFqfqAy/DK37+UG/B1j0cuPv+8f/zjH+tAv4eynVI0NXxxXzSslfULJABYDd03JWntnGKH4Evo9mVCCe/uOZ8rpzIXUlECbu0iK3lO2z+Ih10i8ud2Uu87mJTTOZ1mKaxZIGUDqG85Y/KajX8WUkX1Xz0R1T8LLRrndaOyDK4epLrm6qX+wWUjTDHZJbs+G3BqJr1YWrkfpBcvh7BPvjC51JUV/DyS/KdbEHMqfynJ7BrJp0+fjqlsStlGJWp/zu9vyuTojV9twRSHfrGP5/7avYhq31lY6AfmOhTOc5Jl5v57hTSAoWANCQAAhmDNilLPWi2qaF430vqNDalcjySHz0k5LSV1c50hKaH22ve9+3KybezVVXP9VttqWR1W55PLPHpit9OtpCugWQHB6ic/bt8/IMAI9GIcpeRViXEvzWLeOtweljZ3Vy8KXlx822v5uGhhadZXQ/UiuxPXHJGnpLv4FsqaBRLsDnMbBemK9QMn6Jw7dy5SwpCL+uj19ixW4vvdrZr1yahMjs4fmhPtJZ9pMi0h2gKDkyXdf45ViRQRoysedtnuC2A0EEgTOzvEXoNDlkROEjJScPTCaxbICUO+V6fqM69LFaT1TC6i5vUn9VPixHLnupgTmCLiBz/4gS+WpZH+19iyXwbACBSLp9ROjfn1NjXoJkVZbiyJQb7udUcpMd2uUoO5qASTsxszOaEwmq/FON1KhSdKSEW0l3cp7zJrSAAAMARYSLAG5qLsSgnaYrm6yHe5se8dXrqd3GQkmtrr/ufal8Fbz5XlPVdXzVgdVnymo/vc/vnnn4+Ie+65R6eTqXIxv0ACcJVZ/w8xRz+XN18vsEOBIy0q5DiFnHNappJccEi3FINdjjVF5Wo6mJw680Q2GSLcAysUp+7xvPLKK+5NVrxSHEoeRt/habJy+dwVgINibuuNct0/eC2wWWYXYTxXYsc6RHGCuQpi8d31dT7d6FVJo5ZWGuZyonN5sC2P80ylLdz0+ucbpVQtTtVY2HABJpnbMVZ4MnIFswceeCBSwUDPYnrh7bgvEY/uRwd9VzRhGe9ZSeNRVlDMW0il8rRPy/Acm+ABaBp1t3fddZcOTp06FRs7KeYtLXQOGIQ1C6QcHVBW6XOEwgsvvBCbl9w1Y3J4d7ZRNDeVlcOcr6q5zA1czSU2qlMJauh1TtVnmYPyhCudqJR5zcZTVvo8t+Yg78kJiFkJAA6WNQskAFgNc2ao1C8bi07Qyf8bUwtywrpjcW5bBVTllGI0u2dbvcUl6Nu1AUrsLf4SERcuXIjkwbMxXaJ5y6bAPrWVXDINttcaH59dEUjlV6tvV37hvBjz+uuvq4Ec0Pk3qnvLz6Vv4VqWmrIdk98Ej0Ttc3S4Oi/lPvUg/Yj1CpVUkm4baTzOTcnFuy7TGBrfZioZPN1jrj+j83X8FSga3n+TMiv5rb7MKIBe7bRcV4fF6u3DngtlLnsoeHhOSNI4PUl5GFpN7KHAc58L4GDZFYEEAKtEJRmt4ZWIym46SGmzLJ/sLVIUQ6nRbp3vtddei6ltkEqgppWM22+/PZKlpeVMW0iusqoDV3AwUqqKqz+aBlyCI8bXKQtrFki9xmj5tmRu6xuVbeQUd33fOSdu0tbJqz75u5+rzhl7DanYG18nRTjXTo3NT1MavZrpt77lB5fH06sAAACMyZoFEqyeuWix4jK1XmmP//e+972IuO+++3TqYJZSE12390I7Up9diKjorR5GibLru4tKnbe26zpsuYhitOg+O5b9ceSas2dPrl33Y2Xfrryy8AAwCGsWSN0mKLkFeiFlC+faPEbNcnxayXyUga//UuitrpRFpi1lXnNsXu8zUmhy7A2lK+QgwDwLTxpGeTyYULAg+s+1hOBbyioYwaeOdy2bFlpIz6UYFiVDMa7RnC7WWoqLz9eVKiTHnQdgR4g3UC/LhMUD6dVQ49pg5VMs9HVes0ACkI++WAbRtkHzikKJJijJjDY1NJvceeedOvW0kqsI5htl+vRQAs0yJcIq9mo/kaYhWUK2zDw55ruihZb1IrAAY7JbAskvpGaWbPropbW6oSlA/+ZJSo09AeUlotzPZBRW3p+ilICU9qRHlF23+yRSVDORl6ayQ2ay2eRyFwDAwbJbAgkAVoZ0u259StOyp25LDa2MrVL79HRw/vx5ndoFJ2eaT92/6gp2q1QNZOPG/L5ouuLr6i327jkQaR1Rxn2vhLRQRROBBGtGr7EnC7+umhd++MMf6rRMH45xkDPNuY0lP2lua4M+2em5fZslXfcyYYm99GqE28s0f/XVV/On80Feicz0LMuFzlawetYskLp7yi+2gv3zf2kOKqUS8yZDmix0Y3nDtXyaHWWeYrL/Le9FVMqaqcNJp98VVDi9zMbUToXF0dfJiot7LoKxaAk9crIEAfnGN954IzZZR5G0kFIq3uPRckDZTskd+sXX8Hq+UUks8cfRXORVzKeffrqMp5wudL2Q/ZAAAGAI1mwhiaxBTMZESpeRlWOPbS7Pk4Oti1UhLSlHcsuEKp5c9aOYq+LLnvN9bzFcJkvKz7Xf1wDa4sZZgQmlb9xhaf6wMmGdHm/FU0gpjs1nd+ht2QLcWf29JpDwz0Ad+qdYDhwEaJ9bqeFrtfqll16KZH+X0ORS9yz2GuV9eACjsX6BBAArYG6HkVLFsZQT7HUCy3qbr5diQo4a0LqdNy4pHfrU647lupUGXbGPTlqFT8sGSH3nkVJF00EWZYf17opcFmsWSNnUKLEr+l7zEpECvu+44w41KIvhsTc/zpqmdOQcYD2phKrBZFT3FVshH2ihaPLefTfvWoFCXfY38rSlFKJe6Ew/hlKpYW4vOPemHUxi8xUfOnRIp8WE8qRjg8yJSuLuu+/ODfr6h9r3dQI90ePx59Vs6MgugMFhDQkAAIZgzRaSmDRBslmt7NfnnnsuUu69dEz9V96mT0xmreZCq+VBQgpv2TL5iq2Qy7Rvyg4I+S4KmsFyKT4GW5N6kWOzKbBj4Up4ntaMIyUqlXqDTn5X6H/evSV3WN4v03c2KafF1VY8fn1a0I2uUuhhl/ZL92qsXyDB7rCvkPZ0c+7cuYg4ffq0Tj095cqBsXm9z549q1MHO6gfO9xKjcS8TbDIYf0xU+owIp555pn8QW699Vad/sVf/IUONL16krVr7vjx47EJeYhUcIR9j2BZrF8gZZWh5EXL9Dl58mRsXt3vfOc7avDtb3879uYz5oAlrw10faRMMXLi33XXXbFZJtVDP8waUm68ZQouGl+uuJofWvZxWbqGBWtl+8Knf8au7/fss89GxMMPP6xTv7MS89Y8vE5ciqiW/KH+oDKeknjUk6ZL+cQSGFlmDK8yeth6f51+ZMp2NkvPfWYNCQAAhmDNFtLhw4e3N/izP/uziPjv//7viLj++usj4o9//KP+62//9m8/plH9+Z//+cfU8y4zpziXBQavEaqarfOTTp06pQMH6YpcR8N3+Up/qNqX/eljXo0VfQFADZx+9OSTT+pA2r1v9/i1tOCI567mAyyC1f5eL168eNBDAICPmO6Jks/NQQHmG9/4RqRcZuscEttWMry1R1EySj6T+7GLb7Kavpkr0m8UK2HPnrOg9CCnPflx//iP/xgpwaCkNy3UQddZrUCCnWJ7oQq/7WVjJN/12GOP5esnTpzQqba0d1qSFx5KGVM32G4JleWHmF+3y4lr+UbZTGVOjL0LhL1/s/QFBlg9rCEBAMAQYCEBwGLoLrJSOqiUFHrkkUd06vU2JRj10kFqX4LutgygWLdun+u/RKsraFdbMbLdj1x2tsWdCaBy4/sa2Uu3fRFIsH56cG1ZALArTy+8kytVdKcUFjI9KXJ7lrH/d19X3tyNGuec5+2KC+wCDAICCQAWw9x6m6+XIAXrEA8++KAO/u3f/i3Ssp+TiG+88cZIqomjJNSDr5dSCz1dqeQnWdso+zOV2z1s3a4kqmh7SLo399PryCwaBBIsmH0j+z8kv/nNb/xvRHzxi1/8WB8HsOMgkGCpENkPsDIQSACweOyzsi9OPjdvveGSQg899FBEPPXUUzq100xRA3aF2YemKAMHO5TlRlOcaaWGcrSUADU7evSoTl3L9cyZM5FiGcrtZq2bLhL2DQAAQ4BAAgCAIcBlBwCLp+cnlZJC9rkppt/Rd48++mjuQbF2EfHuu+/mG3vUnJxmx44d06mrgchJ6EpFLkhYiuuXnUq++93v6kAbo/SMglKM3ONfWekgLCQAABgCLCQAWDBzxoFsCAcpFMvGmy66dMK999472V5REm7mKqvarfHQoUM6/fd//3cdqGdtmRipIoPG6eCL8+fPx8YeilRIQg18V8lbcoxGSfFeDVhIAAAwBAgkAAAYgj95//33D3oMAADTHDly5NKlS5fffq5OYKnN09OJtPnhLbfcolNnCCkswq6zsoWj+/mnf/onHSij6Oabb9apfXdy/cnRF5u0J/sb7XnTeHpFInkaP2T60eHDhwdPJ8dCAgCAIUAgAQDAEOCyA4BxuWKXXam6beSC66WG5Bzr2yBpN3GnJV133XU6UCDchQsXdFr2PbInsGx9UqLj5rZxcpSdu3Ue1YcBlx0AAMBlQR4SAKyHfVf7FYbg2IQS7NANERkx7tYNtKmrTZyy+2KPVijIEiq7Jfl2221mZRUZ5sBCAgCAIUAgAQDAEOCyA4D1UIIa7OMq1+15K04zRxMYte/7G+nK3BbmpkQrlNt9sVRNLQ7A3u1awUICAIAhQCABAMAQ4LIDgPVQfHQ+Ldd7tWxF35X9h3zFUXAlUemaa67RgROV1GHf0Eg+OucVlaf3cU6erh4sJAAAGAIsJABYIXO2xdz1UtPBUQyyaXpwxNztMnp6s8kbV7ab0YcHCwkAAIYAgQQAAEOAyw4Ado59HXrFwzbnqdv3xrkn7lq0wmWChQQAAEOAhQQAQ3P48OGDHgJcJdgPCQAAhgCXHQAADAECCQAAhgCBBAAAQ4BAAgCAIUAgAQDAECCQAABgCBBIAAAwBAgkAAAYAgQSAAAMAQIJAACGAIEEAABDgEACAIAhQCABAMAQIJAAAGAIEEgAADAECCQAABgCBBIAAAwBAgkAAIYAgQQAAEOAQAIAgCFAIAEAwBAgkAAAYAgQSAAAMAQIJAAAGAIEEgAADAECCQAAhgCBBAAAQ4BAAgCAIUAgAQDAECCQAABgCBBIAAAwBAgkAAAYAgQSAAAMAQIJAACGAIEEAABDgEACAIAhQCABAMAQIJAAAGAIEEgAADAECCQAABgCBBIAAAwBAgkAAIYAgQQAAEOAQAIAgCFAIAEAwBAgkAAAYAgQSAAAMAQIJAAAGAIEEgAADMH/D6yDxORRd1fWAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"markdown","source":"We'll take this chance to compare how structural and functional images differ from one to another. Run the next cell. ","metadata":{}},{"cell_type":"code","source":"% Comparing Sub-01 anatomical and functional volumes\n\n% Sub-02 anatomical\n[Ni, Nj, Nk] = size(anat_3Dimg);\nsubplot(121); imagesc(rot90(anat_3Dimg(:,35:245,round(Nk*0.35)))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]); \n\n% Sub-02 functional\n[Ni, Nj, Nk, Nt] = size(func_4Dimg);\nsubplot(122); imagesc(rot90(func_4Dimg(12:53,8:58,round(Nk/2),1))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]); ","metadata":{"jupyter":{"source_hidden":true}},"execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Image 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"Notice how the structual image (on the left) clearly shows many encephalic structures (the gray matter in the cortex, inner white matter, the ventricles). Its voxel resolution is 1mm<sup>3</sup>, so some level of detail is to be expected.\n\nConversely, the functional image (on the right) looks quite grainy and the contrast look somehow inverted. This is explained by the volume's lower resolution (2.5mm<sup>3</sup>) and the fact that it is a T2*-weighted scan. Explaining MRI sequences is beyond the scope of this lesson (you can read more about them [here](http://mri-q.com/t2-vs-t2.html)), but just consider that this sequence is the best we have to measure small changes in **deoxygenated hemoglobin** (the blood protein that carries oxygen), which is what we assume happens when some part of the brain gets activated. This results in some signal intensity variations that we call the **BOLD (Blood Oxygen Level Dependent) contrast**","metadata":{}},{"cell_type":"markdown","source":"But what are we trying to measure? BOLD-fMRI signal fluctuations can be detected on a voxel-level if we take enough samples. On the next cell you can see how that signal behaves across time in a single voxel (you can change its coordinates and see how (if) it changes.","metadata":{}},{"cell_type":"code","source":"% BOLD-timeseries of a random voxel from V1 (primary visual cortex) vs. random voxel from AC (anterior cingulate cortex)\n\n[Ni, Nj, Nk, Nt] = size(func_4Dimg);\nsubplot(211); plot(func_4Dimg(37,15,round(Nk/2),:))\nsubplot(212); plot(func_4Dimg(Ni/2,47,round(Nk/2),:))","metadata":{"jupyter":{"source_hidden":true}},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Image 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"In the example above, you can see how the signal goes different ways between regions (signal from the primary visual cortex voxel increases over time, whereas that of the anterior cingulate voxel decreases over time).","metadata":{}},{"cell_type":"markdown","source":"### STEP 1: Realignment\n\nOnce we visually inspected or data and made sure there weren't any problems, we are finally ready to start our preprocessing pipeline. The first step is called **realigment** or motion correction of functional images, which tries to take care of the main source of the largest source of noise in fMRI which is head movement. From a starting volume of reference, it measures three directions of displacement (x, y, z) and three axes of rotation (raw, pitch, yaw) and registers how they change over the course of the experiment with each functional volume that is acquired. \n\nRunning the following cell will realign each subject's functional volumes and create a `.txt` file that included the 6 motion parameters of each of the 170 volumes. ","metadata":{}},{"cell_type":"code","source":"% Realign all subjects (correct script name)\npreproc_data = struct;\nfor k = 1:n_sub\n    preproc_data(k) = spm_realign_ler(char(func_files(k,1)), char(anat_files(k,1)), fwhm, spm_dir);\nend","metadata":{"jupyter":{"outputs_hidden":true,"source_hidden":true},"collapsed":true},"execution_count":7,"outputs":[{"output_type":"stream","text":"Step 1 -- Realign all volumes to first functional volume\n\n\n------------------------------------------------------------------------\n24-Mar-2021 22:42:52 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 22:42:52 - Running 'Realign: Estimate & Reslice'\n\nSPM12: spm_realign (v7141)                         22:42:52 - 24/03/2021\n========================================================================\nCompleted                               :          22:44:16 - 24/03/2021\n\nSPM12: spm_reslice (v7141)                         22:44:16 - 24/03/2021\n========================================================================\nCompleted                               :          22:45:04 - 24/03/2021\n24-Mar-2021 22:45:05 - Done    'Realign: Estimate & Reslice'\n24-Mar-2021 22:45:05 - Done\n\nStep 1 - Done!\nStep 1 -- Realign all volumes to first functional volume\n\n\n------------------------------------------------------------------------\n24-Mar-2021 22:45:21 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 22:45:21 - Running 'Realign: Estimate & Reslice'\n\nSPM12: spm_realign (v7141)                         22:45:21 - 24/03/2021\n========================================================================\nCompleted                               :          22:46:50 - 24/03/2021\n\nSPM12: spm_reslice (v7141)                         22:46:50 - 24/03/2021\n========================================================================\nCompleted                               :          22:47:38 - 24/03/2021\n24-Mar-2021 22:47:38 - Done    'Realign: Estimate & Reslice'\n24-Mar-2021 22:47:38 - Done\n\nStep 1 - Done!\n","name":"stdout"}]},{"cell_type":"markdown","source":"In healthy and younger populations there is little movement that needs to be corrected for. It usually becomes a problem with children or in patients with certain conditions (Parkinson's disease or other sources of tremors). Since movements revolves around a few milimiters in either direction, visually inspecting how volumes are displaced is quite hard. Fortunately, if we just plot those motion parameters over time, we'll get a better glance of how much that subject moved during that session.","metadata":{}},{"cell_type":"code","source":"% Plot motion parameters (left column = displacement parameters; right colums = axes of rotation)\n\n% Sub-01 motion parameters\nm_par = preproc_data(1).mp_fn;\nmotion = load(m_par);\nsubplot(221); plot(motion(:,1:3)) % rotational x,y,z\nsubplot(222); plot(motion(:,4:6)) % traslational raw, pitch, yaw\n\n% Sub-02 motion parameters\nm_par = preproc_data(2).mp_fn;\nmotion = load(m_par);\nsubplot(223); plot(motion(:,1:3)) % rotational x,y,z\nsubplot(224); plot(motion(:,4:6)) % traslational raw, pitch, yaw\n","metadata":{"jupyter":{"source_hidden":true}},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Image 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"As we can see, subjects almost didn't move at all (other that a couple milimiters or small fraction of a radian). Notice, though, that Sub-02 data shows a couple of spikes of movement. This might become important later on.","metadata":{}},{"cell_type":"markdown","source":"### STEP 2: Structural and functional co-registration\n\nNext, we will bring both the structural and functional images to the same space in a process called **co-registration**. It basically just aligns and overlays each subject's functional images with its corresponding structural image. The purpose of this step is that brain activations detected in the functional data are mapped as accurately as possible to its corresponding anatomical structures.","metadata":{}},{"cell_type":"code","source":"% Co-register anatomical and functional \nfor k = 1:n_sub\n    spm_coregister_ler(char(func_files(k,1)), char(anat_files(k,1)), fwhm, spm_dir);\nend","metadata":{"jupyter":{"source_hidden":true,"outputs_hidden":true},"collapsed":true},"execution_count":9,"outputs":[{"output_type":"stream","text":"Step 2 -- Coregister structural image to first dynamic image\n\n\n------------------------------------------------------------------------\n24-Mar-2021 22:47:56 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 22:47:56 - Running 'Coregister: Estimate'\n\nSPM12: spm_coreg (v7320)                           22:47:56 - 24/03/2021\n========================================================================\nCompleted                               :          22:48:13 - 24/03/2021\n24-Mar-2021 22:48:13 - Done    'Coregister: Estimate'\n24-Mar-2021 22:48:13 - Done\n\nStep 2 - Done!\nStep 2 -- Coregister structural image to first dynamic image\n\n\n------------------------------------------------------------------------\n24-Mar-2021 22:48:28 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 22:48:28 - Running 'Coregister: Estimate'\n\nSPM12: spm_coreg (v7320)                           22:48:28 - 24/03/2021\n========================================================================\nCompleted                               :          22:48:47 - 24/03/2021\n24-Mar-2021 22:48:47 - Done    'Coregister: Estimate'\n24-Mar-2021 22:48:47 - Done\n\nStep 2 - Done!\n","name":"stdout"}]},{"cell_type":"markdown","source":"### STEP 3: Segmentation\n\nWhen we scan someone's head we don't only acquire an image of his/her brain; other tissues, such as bone, fat or even the eyes are part of that image. Since we will be looking exclusively at neural tissue (namely the gray matter and white matter) it makes sense to try and get rid of those other structures that we're not interested in. \n\nIn this structural-exclusive step, we will separate or **segment** the anatomical image into its 6 tissue types: gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), bone, soft tissue and air. Not all of them are necessary (GM, WM and CSF are usually enough), but we will segment all of them to better illustrate how the algorithm very accurately picks each of them.","metadata":{}},{"cell_type":"code","source":"% Segment anatomical \nfor k = 1:n_sub\n    spm_segmentation_ler(char(func_files(k,1)), char(anat_files(k,1)), fwhm, spm_dir);\n    [d, f, e] = fileparts(char(anat_files(k,1)));\n    preproc_data(k).forward_transformation = [d filesep 'y_' f e];\n    preproc_data(k).inverse_transformation = [d filesep 'iy_' f e];\n    preproc_data(k).gm_fn = [d filesep 'c1' f e];\n    preproc_data(k).wm_fn = [d filesep 'c2' f e];\n    preproc_data(k).csf_fn = [d filesep 'c3' f e];\n    preproc_data(k).bone_fn = [d filesep 'c4' f e];\n    preproc_data(k).soft_fn = [d filesep 'c5' f e];\n    preproc_data(k).air_fn = [d filesep 'c6' f e];\nend\n\n% Reslice\nfor k = 1:n_sub\n    spm_reslice_ler(char(func_files(k,1)), char(anat_files(k,1)), fwhm, spm_dir);\n    [d, f, e] = fileparts(char(anat_files(k,1)));\n    preproc_data(k).rstructural_fn = [d filesep 'r' f e];\n    preproc_data(k).rgm_fn = [d filesep 'rc1' f e];\n    preproc_data(k).rwm_fn = [d filesep 'rc2' f e];\n    preproc_data(k).rcsf_fn = [d filesep 'rc3' f e];\n    preproc_data(k).rbone_fn = [d filesep 'rc4' f e];\n    preproc_data(k).rsoft_fn = [d filesep 'rc5' f e];\n    preproc_data(k).rair_fn = [d filesep 'rc6' f e];\nend","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true,"source_hidden":true}},"execution_count":10,"outputs":[{"output_type":"stream","text":"Step 3 -- Segmentation\n\n\n------------------------------------------------------------------------\n24-Mar-2021 22:49:04 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 22:49:04 - Running 'Segment'\n\nSPM12: spm_preproc_run (v7670)                     22:49:04 - 24/03/2021\n========================================================================\nSegment /home/jovyan/results/preproc_data/sub-01/anat/sub-01_T1w.nii\nCompleted                               :          22:56:15 - 24/03/2021\n24-Mar-2021 22:56:15 - Done    'Segment'\n24-Mar-2021 22:56:15 - Done\n\nStep 3 - done!\nStep 3 -- Segmentation\n\n\n------------------------------------------------------------------------\n24-Mar-2021 22:56:32 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 22:56:33 - Running 'Segment'\n\nSPM12: spm_preproc_run (v7670)                     22:56:33 - 24/03/2021\n========================================================================\nSegment /home/jovyan/results/preproc_data/sub-02/anat/sub-02_T1w.nii\nCompleted                               :          23:03:34 - 24/03/2021\n24-Mar-2021 23:03:34 - Done    'Segment'\n24-Mar-2021 23:03:34 - Done\n\nStep 3 - done!\n","name":"stdout"}]},{"cell_type":"markdown","source":"Once this long step is done (it should take between 15-20 minutes for both subjects), run this next cell to visualize all Sub-01's segmented tissues. The order of presentation is as follows: GM-WM-CSF-Bone-Soft tissue-Air\n\nYou can try to change to Sub-02 segmentations and see how its tissue anatomy changes.","metadata":{}},{"cell_type":"code","source":"% Visualize tissue images\n\nc1_fn = spm_vol(preproc_data(1).gm_fn);\nc1 = spm_read_vols(c1_fn);\n[Ni, Nj, Nk] = size(c1);\nsubplot(231); imagesc(rot90(squeeze(c1(:,:,round(Nk/3),1)))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\n\nc2_fn = spm_vol(preproc_data(1).wm_fn);\nc2 = spm_read_vols(c2_fn);\n[Ni, Nj, Nk] = size(c2);\nsubplot(232); imagesc(rot90(squeeze(c2(:,:,round(Nk/3),1)))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\n\nc3_fn = spm_vol(preproc_data(1).csf_fn);\nc3 = spm_read_vols(c3_fn);\n[Ni, Nj, Nk] = size(c3);\nsubplot(233); imagesc(rot90(squeeze(c3(:,:,round(Nk/3),1)))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\n\nc4_fn = spm_vol(preproc_data(1).bone_fn);\nc4 = spm_read_vols(c4_fn);\n[Ni, Nj, Nk] = size(c4);\nsubplot(234); imagesc(rot90(squeeze(c4(:,:,round(Nk/3),1)))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\n\nc5_fn = spm_vol(preproc_data(1).soft_fn);\nc5 = spm_read_vols(c5_fn);\n[Ni, Nj, Nk] = size(c5);\nsubplot(235); imagesc(rot90(squeeze(c5(:,:,round(Nk/3),1)))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\n\nc6_fn = spm_vol(preproc_data(1).air_fn);\nc6 = spm_read_vols(c6_fn);\n[Ni, Nj, Nk] = size(c6);\nsubplot(236); imagesc(rot90(squeeze(c6(:,:,round(Nk/3),1)))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);","metadata":{"jupyter":{"source_hidden":true}},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Image 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"### STEP 4: Normalization\n\nIt is known that not all brains are equal, just as intracraneal volume or body size aren't either. If we are thinking of running a study with several participants, how do we make sure that all those brains are comparable between each other? Since obtaining exactly equal brains is impossible, the next big thing we have is to try approximate or **normalize** their dimensions to a common reference template. \n\nTwo of the most used templates are the MNI (Montreal Neurological Institute) or the Talairach templates which are made out of hundreds of young, healthy brains. This sort of standardization also helps scientists to share and compare their results, since we would all have the same topological coordinate systems. ","metadata":{}},{"cell_type":"code","source":"% Normalize\nfor k = 1:n_sub\n    spm_normalize_ler(char(func_files(k,1)), char(anat_files(k,1)), fwhm, spm_dir, preproc_data(k));\n    [d1, f1, e1] = fileparts(preproc_data(k).rfunctional_fn);\n    preproc_data(k).wrfunctional_fn = [d1 filesep 'w' f1 e1];\nend","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true,"source_hidden":true}},"execution_count":13,"outputs":[{"output_type":"stream","text":"Step 5 -- Normalize (estimate and reslice) functionals to MNI\n\n\n------------------------------------------------------------------------\n24-Mar-2021 23:04:54 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 23:04:54 - Running 'Normalise: Estimate & Write'\n\nSPM12: spm_preproc_run (v7670)                     23:04:54 - 24/03/2021\n========================================================================\nSegment /home/jovyan/results/preproc_data/sub-01/anat/rsub-01_T1w.nii\nCompleted                               :          23:06:53 - 24/03/2021\n24-Mar-2021 23:08:42 - Done    'Normalise: Estimate & Write'\n24-Mar-2021 23:08:42 - Done\n\nStep 5 - Done!\nStep 5 -- Normalize (estimate and reslice) functionals to MNI\n\n\n------------------------------------------------------------------------\n24-Mar-2021 23:08:59 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 23:09:00 - Running 'Normalise: Estimate & Write'\n\nSPM12: spm_preproc_run (v7670)                     23:09:00 - 24/03/2021\n========================================================================\nSegment /home/jovyan/results/preproc_data/sub-02/anat/rsub-02_T1w.nii\nCompleted                               :          23:10:48 - 24/03/2021\n24-Mar-2021 23:12:34 - Done    'Normalise: Estimate & Write'\n24-Mar-2021 23:12:34 - Done\n\nStep 5 - Done!\n","name":"stdout"}]},{"cell_type":"markdown","source":"Since we are **normalizaing** our subjects functional images, we would expect a change in appearance, concretely to one that more closely resembles the template that was used. To compare the previously realigned imaged to a normalized one, run the next cell.\n\nOn the left you will see the normalized image. ","metadata":{}},{"cell_type":"code","source":"% Visualize normalized functional image\n\nnorm_fn = spm_vol(fullfile(processing_dir, 'sub-01', 'func', 'wrsub-01_task-passiveimageviewing_bold.nii,1'));\nnorm_func = spm_read_vols(norm_fn);\n[Ni, Nj, Nk] = size(norm_func);\nsubplot(121); imagesc(rot90(squeeze(norm_func(:,:,round(Nk/2))))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\n\nnon_norm_fn = spm_vol(fullfile(processing_dir, 'sub-01', 'func', 'rsub-01_task-passiveimageviewing_bold.nii,1'));\nnon_norm_func = spm_read_vols(non_norm_fn);\n[Ni, Nj, Nk] = size(non_norm_func);\nsubplot(122); imagesc(rot90(squeeze(non_norm_func(:,:,round(Nk/2))))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\nsubplot(122); imagesc(rot90(squeeze(non_norm_func(12:53,8:58,round(Nk/2),1))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\n","metadata":{"jupyter":{"source_hidden":true}},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Image 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"### STEP 5: Smoothing\n\nSo far we have taken care of some of the sources of noise in our data (head movement), extracted the tissues we're interested in out of the structural image, registrated each subject's own functional and structural images and brought all of them to a common space so that they can be compared with each other and with other studies. \n\nDespite all of this, this whole process isn't perfect, and we're bound to still encounter with noise that may interfere when performing statistical analysis and therefore when making inferences about our findings, specially when many subjects are involved. One solution to this problem has been to **smooth** each voxel's signal by averaging it with that of its neighboring voxels. This usually leads to an increased signal-to-noise ratio, at the cost of spatial specificity. ","metadata":{}},{"cell_type":"code","source":"% Smooth\nfor k = 1:n_sub\n    spm_smooth_ler(char(func_files(k,1)), char(anat_files(k,1)), fwhm, spm_dir, preproc_data(k));\n    [d, f, e] = fileparts(preproc_data(k).wrfunctional_fn);\n    preproc_data(k).swrfunctional_fn = [d filesep 's' f e];\nend","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true,"source_hidden":true}},"execution_count":15,"outputs":[{"output_type":"stream","text":"Step 6 -- Gaussian kernel smoothing of realigned data\n\n\n------------------------------------------------------------------------\n24-Mar-2021 23:12:53 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 23:12:53 - Running 'Smooth'\n24-Mar-2021 23:16:00 - Done    'Smooth'\n24-Mar-2021 23:16:00 - Done\n\nStep 6 - done!\nStep 6 -- Gaussian kernel smoothing of realigned data\n\n\n------------------------------------------------------------------------\n24-Mar-2021 23:16:16 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 23:16:16 - Running 'Smooth'\n24-Mar-2021 23:18:54 - Done    'Smooth'\n24-Mar-2021 23:18:54 - Done\n\nStep 6 - done!\n","name":"stdout"}]},{"cell_type":"markdown","source":"If we compare a normalized functional image against a smoothed one, you'll be able to notice the difference right away. If the activation effect we're looking for is robust enough, it will survive the smoothing and appear on posterior analyses.","metadata":{}},{"cell_type":"code","source":"% Visualize smoothed functional image\n\nsm_fn = spm_vol(fullfile(processing_dir, 'sub-01', 'func', 'swrsub-01_task-passiveimageviewing_bold.nii,1'));\nsmooth_func = spm_read_vols(sm_fn);\n[Ni, Nj, Nk] = size(smooth_func);\nsubplot(121); imagesc(rot90(squeeze(smooth_func(:,:,round(Nk/2))))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);\n\nnorm_fn = spm_vol(fullfile(processing_dir, 'sub-01', 'func', 'wrsub-01_task-passiveimageviewing_bold.nii,1'));\nnorm_func = spm_read_vols(norm_fn);\n[Ni, Nj, Nk] = size(norm_func);\nsubplot(122); imagesc(rot90(squeeze(norm_func(:,:,round(Nk/2))))); colormap gray; axis image; set(gca,'xtick',[],'ytick',[]);","metadata":{"jupyter":{"source_hidden":true}},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Image 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"<a name=\"stats\">\n</a>\n\n## 3) Statistical analysis","metadata":{}},{"cell_type":"markdown","source":"## STEP 4: Create 1st level statistical design","metadata":{}},{"cell_type":"markdown","source":"Here we create the first level statistical design to analyse the preprocessed data. This is done with `spm_specify1stlevel_jsh`.\n\nWe set the timing units as seconds because that is how we want to specify the task stimulus onsets and durations (the other unit option is ‘scans’). The repetition time is set to 1.6 seconds, as specified in the `task-passiveimageviewing_bold.json` file supplied together with the dataset. The task stimulus onsets and durations are given in the `sub-01_task-passiveimageviewing_events.tsv` file supplied with the `sub-01` data. The `.tsv` file also gives the type of stimulus, either food or non-food pictures. For this exercise, we are not interested in the differences in brain responses between food and non-food picture viewing, rather we just want to see which voxels in the brain are likely involved in any type of picture viewing experience vs only looking at a black screen with a cursor. This is why we specified vectors of all onset times and durations (except for the break period) for the respective parameters. Finally we also supply the text file with the movement parameters (`preproc_data.mp_fn`; resulting from the 6 DOF rigid body transformations during the realignment step) to be included as regressors in the design matrix.","metadata":{}},{"cell_type":"code","source":"% Set up statistical design parameters, based on task data\n\n    sess_params = struct;\n    sess_params.timing_units = 'secs';\n    sess_params.timing_RT = 1.6;\n    sess_params.cond_name = 'Pictures';\n    sess_params.cond_onset = [0; 40.1; 77.2; 111.3; 143.3; 179.4; 218.5; 251.5; 299.6; 334.7; 374.8; 411.9; 445.9; 478.0; 514.1; 553.2];\n    sess_params.cond_duration = [24.1000; 24.06; 24.07; 24.06; 24.06; 24.07; 24.04; 24.06; 24.07; 24.10; 24.06; 24.06; 24.09; 24.09; 24.06; 24.07];\n    \nfor i = 1:n_sub\n    if i < 10\n        sub_path = '0%d';\n        sub = sprintf(sub_path, i);\n    else\n        sub = i;\n    end\n    \n    stats_dir = fullfile(processing_dir, ['sub-' sub], 'stats');\n    \n    % Call script to set up design\n    spm_specify1stlevel_jsh(stats_dir, preproc_data(i).swrfunctional_fn, preproc_data(i).mp_fn, sess_params);\n    \n    % Display/explore design matrix \n    load([stats_dir filesep 'SPM.mat']);\n    spm_DesRep('fMRIDesMtx',SPM,1,1)\n    saveas(gcf, [processing_dir filesep 'des_mat.png'])\nend","metadata":{},"execution_count":17,"outputs":[{"output_type":"stream","text":"Can not initialise pmod value(s): job is not a struct.\nCan not initialise regress value(s): job is not a struct.\nCan not initialise fact value(s): job is not a struct.\n\n\n------------------------------------------------------------------------\n24-Mar-2021 23:19:12 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 23:19:12 - Running 'fMRI model specification'\n\nSPM12: spm_fMRI_design (v7739)                     23:19:12 - 24/03/2021\n========================================================================\nSaving fMRI design                      :               ...SPM.mat saved\n\nSPM12: spm_fmri_spm_ui (v7738)                     23:19:13 - 24/03/2021\n========================================================================\nMapping files                           :                        ...done\nCalculating globals                     :                        ...done\nSaving SPM configuration                :               ...SPM.mat saved\nSaving SPM configuration                :               ...SPM.mat saved\nCompleted                               :          23:19:23 - 24/03/2021\n24-Mar-2021 23:19:23 - Done    'fMRI model specification'\n24-Mar-2021 23:19:23 - Done\n\nwarning: Unable to open display.\nwarning: called from\n    spm at line 656 column 9\n    spm at line 577 column 4\n    spm at line 610 column 5\n    spm_figure at line 662 column 6\n    spm_figure at line 175 column 3\n    spm_figure at line 221 column 19\n    spm_DesRep at line 890 column 8\nwarning: Unable to open display.\nwarning: called from\n    spm at line 656 column 9\n    spm at line 577 column 4\n    spm at line 696 column 8\n    spm_figure at line 664 column 6\n    spm_figure at line 175 column 3\n    spm_figure at line 221 column 19\n    spm_DesRep at line 890 column 8\nwarning: Unable to open display.\nwarning: called from\n    spm at line 656 column 9\n    spm_figure at line 665 column 6\n    spm_figure at line 175 column 3\n    spm_figure at line 221 column 19\n    spm_DesRep at line 890 column 8\n           Regressors for Pictures          128 second High-pass filter        \n                                                                               \n          |------------------------|         |------------------------|        \n        1 |************************|     0.6 |$+%+%%%+%%%+%%%+%%%+%%++|        \n      0.8 |************************|     0.5 |$+%%%%%%%%%%%%%%%%%%%%+%|        \n      0.6 |************************|     0.4 |$+%%%%%%%%%%%%%%%%%%%%+%|        \n    relative spectral density******|     0.3 |$+%%%%%%%%%%%%%%%%%%%%+%|        \n      0.4 |************************|         |$  %   %   %   %   %   %|        \n      0.2 |*****************#******|     0.2 |$+%%%%%%%%%%%%%%%%%%%%+%|        \n        0 |***#*************#******|     0.1 |$*%+%%%+%%%+%%%+%%%+%%++|        \n     Basis set and peristimulus sampling   0 |------------------------|        \n            50  10015hrf0 250300350          0 0.05 0.10.15 0.20.25 0.3        \n                    scan                          Frequency (Hz)               \n     0.02 |------------------------|                                           \n          | ***+  + +  +  + +  +  +|                                           \n    0.015 |-*$*$$$$$$$$$$$$$$$$$$+$|                                           \n          | *$*$  $ $  $  $ $  $  $|                                           \n     0.01 |**$**$$$$$$$$$$$$$$$$$+$|                                           \n          |* $ *  $ $  $  $ $  $  $|                                           \n    0.005 |*+$$**$$$$$$$$$$$$$$$$+$|                                           \n        0 |GGGGGGGGGGGGGGGGGGGGGGGG|                                           \n          |------------------------|                                           \n          0  510 1520 25 3035 40 45                                            \n                                                                               \nCan not initialise pmod value(s): job is not a struct.\nCan not initialise regress value(s): job is not a struct.\nCan not initialise fact value(s): job is not a struct.\n\n\n------------------------------------------------------------------------\n24-Mar-2021 23:19:41 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 23:19:41 - Running 'fMRI model specification'\n\nSPM12: spm_fMRI_design (v7739)                     23:19:42 - 24/03/2021\n========================================================================\nSaving fMRI design                      :               ...SPM.mat saved\n\nSPM12: spm_fmri_spm_ui (v7738)                     23:19:42 - 24/03/2021\n========================================================================\nMapping files                           :                        ...done\nCalculating globals                     :                        ...done\nSaving SPM configuration                :               ...SPM.mat saved\nSaving SPM configuration                :               ...SPM.mat saved\nCompleted                               :          23:19:53 - 24/03/2021\n24-Mar-2021 23:19:53 - Done    'fMRI model specification'\n24-Mar-2021 23:19:53 - Done\n\n           Regressors for Pictures          128 second High-pass filter        \n                                                                               \n          |------------------------|         |------------------------|        \n        1 |************************|     0.6 |$+%+%%%+%%%+%%%+%%%+%%++|        \n      0.8 |************************|     0.5 |$+%%%%%%%%%%%%%%%%%%%%+%|        \n      0.6 |************************|     0.4 |$+%%%%%%%%%%%%%%%%%%%%+%|        \n    relative spectral density******|     0.3 |$+%%%%%%%%%%%%%%%%%%%%+%|        \n      0.4 |************************|         |$  %   %   %   %   %   %|        \n      0.2 |*****************#******|     0.2 |$+%%%%%%%%%%%%%%%%%%%%+%|        \n        0 |***#*************#******|     0.1 |$*%+%%%+%%%+%%%+%%%+%%++|        \n     Basis set and peristimulus sampling   0 |------------------------|        \n            50  10015hrf0 250300350          0 0.05 0.10.15 0.20.25 0.3        \n                    scan                          Frequency (Hz)               \n     0.02 |------------------------|                                           \n          | ***+  + +  +  + +  +  +|                                           \n    0.015 |-*$*$$$$$$$$$$$$$$$$$$+$|                                           \n          | *$*$  $ $  $  $ $  $  $|                                           \n     0.01 |**$**$$$$$$$$$$$$$$$$$+$|                                           \n          |* $ *  $ $  $  $ $  $  $|                                           \n    0.005 |*+$$**$$$$$$$$$$$$$$$$+$|                                           \n        0 |GGGGGGGGGGGGGGGGGGGGGGGG|                                           \n          |------------------------|                                           \n          0  510 1520 25 3035 40 45                                            \n                                                                               \n           Regressors for Pictures          128 second High-pass filter        \n                                                                               \n          |------------------------|         |------------------------|        \n        1 |************************|     0.6 |$+%+%%%+%%%+%%%+%%%+%%++|        \n      0.8 |************************|     0.5 |$+%%%%%%%%%%%%%%%%%%%%+%|        \n      0.6 |************************|     0.4 |$+%%%%%%%%%%%%%%%%%%%%+%|        \n    relative spectral density******|     0.3 |$+%%%%%%%%%%%%%%%%%%%%+%|        \n      0.4 |************************|         |$  %   %   %   %   %   %|        \n      0.2 |*****************#******|     0.2 |$+%%%%%%%%%%%%%%%%%%%%+%|        \n        0 |***#*************#******|     0.1 |$*%+%%%+%%%+%%%+%%%+%%++|        \n     Basis set and peristimulus sampling   0 |------------------------|        \n            50  10015hrf0 250300350          0 0.05 0.10.15 0.20.25 0.3        \n                    scan                          Frequency (Hz)               \n     0.02 |------------------------|                                           \n          | ***+  + +  +  + +  +  +|                                           \n    0.015 |-*$*$$$$$$$$$$$$$$$$$$+$|                                           \n          | *$*$  $ $  $  $ $  $  $|                                           \n     0.01 |**$**$$$$$$$$$$$$$$$$$+$|                                           \n          |* $ *  $ $  $  $ $  $  $|                                           \n    0.005 |*+$$**$$$$$$$$$$$$$$$$+$|                                           \n        0 |GGGGGGGGGGGGGGGGGGGGGGGG|                                           \n          |------------------------|                                           \n          0  510 1520 25 3035 40 45                                            \n                                                                               \n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Image 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"## STEP 5: Estimate the model","metadata":{}},{"cell_type":"markdown","source":"Estimating the model fit, i.e. running the general linear model with the specified design. This is done with `spm_estimateModel_jsh`.","metadata":{}},{"cell_type":"code","source":"for i = 1:n_sub\n    if i < 10\n        sub_path = '0%d';\n        sub = sprintf(sub_path, i);\n    else\n        sub = i;\n    end\n    \n    stats_dir = fullfile(processing_dir, ['sub-' sub], 'stats');\n    spm_estimateModel_jsh(stats_dir)\n    disp('Subject')\nend\ndisp('Done')","metadata":{},"execution_count":18,"outputs":[{"output_type":"stream","text":"\n\n------------------------------------------------------------------------\n24-Mar-2021 23:20:57 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 23:20:57 - Running 'Model estimation'\n\nSPM12: spm_spm (v7738)                             23:20:57 - 24/03/2021\n========================================================================\n\nSPM12: spm_est_non_sphericity (v7577)              23:21:00 - 24/03/2021\n========================================================================\nChunk   1/2                             :                  ...processi                       ...doChunk   2/2                             :                  ...processi                       ...done\nTemporal non-sphericity (179311 voxels) :             ...ReML estimation\n  ReML Block 1                \n  ReML Iteration 1                      :        ...1.540325e+02 [+4.25]\n  ReML Iteration 2                      :        ...6.668615e+01 [+4.50]\n  ReML Iteration 3                      :        ...1.978618e+00 [+4.75]\n  ReML Iteration 4                      :        ...1.976287e-01 [+5.00]\n  ReML Iteration 5                      :        ...1.841265e-02 [+5.25]\n\nSPM12: spm_spm (v7738)                             23:22:20 - 24/03/2021\n========================================================================\nChunk   1/2                             :                  ...processi                       ...doChunk   2/2                             :                  ...processi                       ...done\nSpatial non-sphericity (over scans)     :      ...estimating derivativ ...writing resels/voxel ima                       ...done\nSaving SPM.mat                          :                     ...writi                       ...done\nCompleted                               :          23:23:05 - 24/03/2021\nDone\n24-Mar-2021 23:23:05 - Done    'Model estimation'\n24-Mar-2021 23:23:05 - Done\n\nSubject\n\n\n------------------------------------------------------------------------\n24-Mar-2021 23:23:19 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 23:23:19 - Running 'Model estimation'\n\nSPM12: spm_spm (v7738)                             23:23:20 - 24/03/2021\n========================================================================\n\nSPM12: spm_est_non_sphericity (v7577)              23:23:22 - 24/03/2021\n========================================================================\nChunk   1/2                             :                  ...processi                       ...doChunk   2/2                             :                  ...processi                       ...done\nTemporal non-sphericity (190022 voxels) :             ...ReML estimation\n  ReML Block 1                \n  ReML Iteration 1                      :        ...1.071491e+02 [+4.25]\n  ReML Iteration 2                      :        ...4.568893e+00 [+4.50]\n  ReML Iteration 3                      :        ...3.061710e-02 [+4.75]\n\nSPM12: spm_spm (v7738)                             23:24:39 - 24/03/2021\n========================================================================\nChunk   1/2                             :                  ...processi                       ...doChunk   2/2                             :                  ...processi                       ...done\nSpatial non-sphericity (over scans)     :      ...estimating derivativ ...writing resels/voxel ima                       ...done\nSaving SPM.mat                          :                     ...writi                       ...done\nCompleted                               :          23:25:24 - 24/03/2021\nDone\n24-Mar-2021 23:25:24 - Done    'Model estimation'\n24-Mar-2021 23:25:24 - Done\n\nSubject\nDone\n","name":"stdout"}]},{"cell_type":"markdown","source":"## STEP 6: Setup the task contrast","metadata":{}},{"cell_type":"markdown","source":"Here we create the task contrast used as input for the statistical testing. This is done with `spm_setupTaskContrast_jsh`.","metadata":{}},{"cell_type":"code","source":"[Ntt, Nregr] = size(SPM.xX.X);\ncontrast_params = struct;\ncontrast_params.weights = zeros(1, Nregr); \ncontrast_params.weights(1) = 1;\ncontrast_params.name = 'Picture viewing';\n\nfor i = 1:n_sub\n    if i < 10\n        sub_path = '0%d';\n        sub = sprintf(sub_path, i);\n    else\n        sub = i;\n    end\n    \n    stats_dir = fullfile(processing_dir, ['sub-' sub], 'stats');\n    spm_setupTaskContrast_jsh(stats_dir, contrast_params)\n    disp('Subject')\nend\ndisp('Done')","metadata":{},"execution_count":20,"outputs":[{"output_type":"stream","text":"\n\n------------------------------------------------------------------------\n24-Mar-2021 23:28:13 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 23:28:13 - Running 'Contrast Manager'\n\nSPM12: spm_contrasts.m                             23:28:13 - 24/03/2021\n========================================================================\nContrasts folder                        :    ./preproc_data/sub-01/stats\n\tcontrast image  1               :                   ...computi       ...written con_0001.nii\n\tspm{T} image  1                 :                   ...computi      ...written spmT_0001.nii\n\tSaving SPM.mat                  :                     ...writi              ...SPM.mat saved\nCompleted                               :          23:28:26 - 24/03/2021\n24-Mar-2021 23:28:26 - Done    'Contrast Manager'\n24-Mar-2021 23:28:26 - Done\n\nSubject\n\n\n------------------------------------------------------------------------\n24-Mar-2021 23:28:41 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 23:28:41 - Running 'Contrast Manager'\n\nSPM12: spm_contrasts.m                             23:28:41 - 24/03/2021\n========================================================================\nContrasts folder                        :    ./preproc_data/sub-02/stats\n\tcontrast image  1               :                   ...computi       ...written con_0001.nii\n\tspm{T} image  1                 :                   ...computi      ...written spmT_0001.nii\n\tSaving SPM.mat                  :                     ...writi              ...SPM.mat saved\nCompleted                               :          23:28:53 - 24/03/2021\n24-Mar-2021 23:28:53 - Done    'Contrast Manager'\n24-Mar-2021 23:28:53 - Done\n\nSubject\nDone\n","name":"stdout"}]},{"cell_type":"markdown","source":"## STEP 7: Generate results","metadata":{}},{"cell_type":"markdown","source":"Finally, we apply statistical tests and some correction factors to generate a 3D map of thresholded t-values (resulting from t-tests), which indicate voxels that are likely to be involved in the task. This is done with ``\n`spm_runResults_jsh`.","metadata":{}},{"cell_type":"code","source":"for i = 1:n_sub\n    if i < 10\n        sub_path = '0%d';\n        sub = sprintf(sub_path, i);\n    else\n        sub = i;\n    end\n    \n    stats_dir = fullfile(processing_dir, ['sub-' sub], 'stats');\n    spm_runResults_jsh(stats_dir, 'FWE', 0.05)\n    disp('Subject')\nend","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true}},"execution_count":22,"outputs":[{"output_type":"stream","text":"\n\n------------------------------------------------------------------------\n24-Mar-2021 23:33:17 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 23:33:17 - Running 'Results Report'\n\tSPM computation                 :                ...initialisi           ...height thresho           ...extent thresho                       ...done\n\n\nStatistics: p-values adjusted for search volume\n================================================================================\nset\tset\tcluster\tcluster\tcluster\tcluster\tpeak\tpeak\tpeak\tpeak\tpeak\t\np\tc\tp(FWE-corr)\tp(FDR-corr)\tequivk\tp(unc)\tp(FWE-corr)\tp(FDR-corr)\tT\tequivZ\tp(unc)\tx,y,z {mm}\n--------------------------------------------------------------------------------\n0.000\t29\t0.000\t0.000\t5734\t0.000\t0.000\t0.000\t 25.27\t  Inf\t0.000\t 38  -84  -4 \t\n\t\t\t\t\t\t0.000\t0.000\t 22.24\t  Inf\t0.000\t 22  -92   2 \t\n\t\t\t\t\t\t0.000\t0.000\t 19.18\t  Inf\t0.000\t 48  -76 -16 \t\n\t\t0.000\t0.000\t6516\t0.000\t0.000\t0.000\t 23.94\t  Inf\t0.000\t-26  -88  -6 \t\n\t\t\t\t\t\t0.000\t0.000\t 23.57\t  Inf\t0.000\t-30  -82   0 \t\n\t\t\t\t\t\t0.000\t0.000\t 23.41\t  Inf\t0.000\t-50  -80  -6 \t\n\t\t0.000\t0.000\t305\t0.000\t0.000\t0.000\t  9.44\t  Inf\t0.000\t 56  -26  50 \t\n\t\t\t\t\t\t0.000\t0.000\t  7.55\t 7.27\t0.000\t 48  -28  60 \t\n\t\t\t\t\t\t0.000\t0.000\t  7.40\t 7.13\t0.000\t 38  -38  68 \t\n\t\t0.000\t0.012\t39\t0.002\t0.000\t0.000\t  6.77\t 6.57\t0.000\t -6    0  62 \t\n\t\t0.002\t0.135\t13\t0.046\t0.000\t0.000\t  6.72\t 6.52\t0.000\t 30   72   6 \t\n\t\t0.000\t0.020\t31\t0.004\t0.000\t0.000\t  6.60\t 6.41\t0.000\t 22  -38  78 \t\n\t\t\t\t\t\t0.000\t0.008\t  6.03\t 5.88\t0.000\t 24  -28  76 \t\n\t\t\t\t\t\t0.015\t0.331\t  5.17\t 5.07\t0.000\t 16  -32  82 \t\n\t\t0.005\t0.188\t9\t0.091\t0.000\t0.001\t  6.43\t 6.25\t0.000\t 20  -58  74 \t\n\t\t0.002\t0.122\t16\t0.029\t0.000\t0.008\t  6.04\t 5.89\t0.000\t 64  -12  28 \t\n\t\t0.002\t0.135\t13\t0.046\t0.001\t0.025\t  5.79\t 5.66\t0.000\t -8   74  14 \t\n\t\t0.013\t0.377\t4\t0.247\t0.001\t0.028\t  5.77\t 5.63\t0.000\t 10  -46  80 \t\n\t\t0.004\t0.184\t10\t0.076\t0.002\t0.050\t  5.63\t 5.51\t0.000\t-32   58   0 \t\n\t\t0.000\t0.017\t34\t0.003\t0.002\t0.050\t  5.63\t 5.51\t0.000\t-44   -2  30 \t\n\t\t0.002\t0.135\t13\t0.046\t0.002\t0.063\t  5.57\t 5.45\t0.000\t-12   14  70 \t\n\t\t\t\t\t\t0.011\t0.250\t  5.23\t 5.13\t0.000\t-18   18  64 \t\n\t\t0.010\t0.337\t5\t0.197\t0.003\t0.075\t  5.53\t 5.41\t0.000\t 54   42  -4 \t\n\t\t0.006\t0.197\t8\t0.109\t0.003\t0.083\t  5.50\t 5.39\t0.000\t 16   20  66 \t\n\t\t0.006\t0.197\t8\t0.109\t0.004\t0.097\t  5.46\t 5.35\t0.000\t -6   74   6 \t\n\t\t0.003\t0.144\t12\t0.055\t0.005\t0.109\t  5.43\t 5.32\t0.000\t-48    0  54 \t\n\t\t0.005\t0.188\t9\t0.091\t0.006\t0.146\t  5.36\t 5.25\t0.000\t 58   14  30 \t\n\t\t0.016\t0.435\t3\t0.315\t0.007\t0.158\t  5.34\t 5.24\t0.000\t-22   22  58 \t\n\t\t0.013\t0.377\t4\t0.247\t0.010\t0.229\t  5.25\t 5.15\t0.000\t 60  -52  42 \t\n\t\t0.021\t0.501\t2\t0.414\t0.015\t0.319\t  5.18\t 5.08\t0.000\t -8  -36  82 \t\n\t\t0.029\t0.574\t1\t0.574\t0.020\t0.433\t  5.10\t 5.01\t0.000\t 32   68  -2 \t\n\t\t0.021\t0.501\t2\t0.414\t0.025\t0.538\t  5.05\t 4.96\t0.000\t 26  -32 -26 \t\n\t\t0.029\t0.574\t1\t0.574\t0.030\t0.630\t  5.01\t 4.92\t0.000\t-16  -32  -6 \t\n\t\t0.016\t0.435\t3\t0.315\t0.031\t0.645\t  5.00\t 4.91\t0.000\t 52   -6  50 \t\n\t\t0.021\t0.501\t2\t0.414\t0.037\t0.752\t  4.96\t 4.88\t0.000\t-26  -64  -8 \t\n\t\t0.029\t0.574\t1\t0.574\t0.038\t0.779\t  4.95\t 4.87\t0.000\t-14   46  46 \t\n\t\t0.029\t0.574\t1\t0.574\t0.043\t0.867\t  4.92\t 4.84\t0.000\t  2  -40  60 \t\n\t\t0.029\t0.574\t1\t0.574\t0.048\t0.950\t  4.90\t 4.82\t0.000\t 14  -42  54 \t\n\ntable shows 3 local maxima more than 8.0mm apart\n--------------------------------------------------------------------------------\nHeight threshold: T = 4.89, p = 0.000 (0.050)\nExtent threshold: k = 0 voxels\nExpected voxels per cluster, <k> = 3.215\nExpected number of clusters, <c> = 0.05\nFWEp: 4.886, FDRp: 5.638, FWEc: 1, FDRc: 31\nDegrees of freedom = [1.0, 358.0]\nFWHM = 10.1 9.9 8.7 mm mm mm; 5.0 5.0 4.4 {voxels}\nVolume: 1621840 = 202730 voxels = 1703.1 resels\nVoxel size: 2.0 2.0 2.0 mm mm mm; (resel = 109.17 voxels)\n================================================================================\n\n24-Mar-2021 23:33:29 - Done    'Results Report'\n24-Mar-2021 23:33:29 - Done\n\nSubject\n\n\n------------------------------------------------------------------------\n24-Mar-2021 23:33:44 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 23:33:44 - Running 'Results Report'\n\tSPM computation                 :                ...initialisi           ...height thresho           ...extent thresho                       ...done\n\n\nStatistics: p-values adjusted for search volume\n================================================================================\nset\tset\tcluster\tcluster\tcluster\tcluster\tpeak\tpeak\tpeak\tpeak\tpeak\t\np\tc\tp(FWE-corr)\tp(FDR-corr)\tequivk\tp(unc)\tp(FWE-corr)\tp(FDR-corr)\tT\tequivZ\tp(unc)\tx,y,z {mm}\n--------------------------------------------------------------------------------\n0.000\t61\t0.000\t0.000\t5347\t0.000\t0.000\t0.000\t 24.58\t  Inf\t0.000\t-28 -82   2 \t\n\t\t\t\t\t\t0.000\t0.000\t 23.31\t  Inf\t0.000\t-38 -66 -18 \t\n\t\t\t\t\t\t0.000\t0.000\t 22.10\t  Inf\t0.000\t-20 -86  10 \t\n\t\t0.000\t0.000\t5390\t0.000\t0.000\t0.000\t 23.84\t  Inf\t0.000\t 30 -78 -14 \t\n\t\t\t\t\t\t0.000\t0.000\t 22.86\t  Inf\t0.000\t 26 -78  -6 \t\n\t\t\t\t\t\t0.000\t0.000\t 21.93\t  Inf\t0.000\t 42 -76   6 \t\n\t\t0.000\t0.000\t829\t0.000\t0.000\t0.000\t 16.08\t  Inf\t0.000\t 36 -60  52 \t\n\t\t\t\t\t\t0.000\t0.000\t  8.51\t 7.70\t0.000\t 34 -32  34 \t\n\t\t\t\t\t\t0.000\t0.000\t  8.33\t 7.83\t0.000\t 44 -46  56 \t\n\t\t0.000\t0.000\t2521\t0.000\t0.000\t0.000\t 12.52\t  Inf\t0.000\t-40 -46  58 \t\n\t\t\t\t\t\t0.000\t0.000\t 12.49\t  Inf\t0.000\t-56 -18  26 \t\n\t\t\t\t\t\t0.000\t0.000\t 11.80\t  Inf\t0.000\t-28 -52  42 \t\n\t\t0.000\t0.000\t215\t0.000\t0.000\t0.000\t  9.76\t  Inf\t0.000\t 40  22  22 \t\n\t\t\t\t\t\t0.000\t0.000\t  6.82\t 6.61\t0.000\t 52  10  24 \t\n\t\t\t\t\t\t0.024\t0.500\t  5.10\t 5.00\t0.000\t 40  30  14 \t\n\t\t0.000\t0.001\t51\t0.000\t0.000\t0.000\t  9.55\t  Inf\t0.000\t 16 -68 -50 \t\n\t\t0.000\t0.000\t55\t0.000\t0.000\t0.000\t  9.14\t 7.73\t0.000\t-12 -68 -50 \t\n\t\t\t\t\t\t0.018\t0.389\t  5.17\t 5.07\t0.000\t -4 -72 -46 \t\n\t\t0.000\t0.000\t194\t0.000\t0.000\t0.000\t  8.91\t  Inf\t0.000\t-26  -6  42 \t\n\t\t0.000\t0.000\t336\t0.000\t0.000\t0.000\t  8.18\t  Inf\t0.000\t 40  -4  44 \t\n\t\t\t\t\t\t0.000\t0.000\t  7.87\t 7.54\t0.000\t 44  -2  52 \t\n\t\t\t\t\t\t0.000\t0.000\t  7.39\t 7.12\t0.000\t 38  -6  60 \t\n\t\t0.000\t0.000\t334\t0.000\t0.000\t0.000\t  7.64\t 7.36\t0.000\t -2   4  52 \t\n\t\t\t\t\t\t0.001\t0.018\t  5.90\t 5.76\t0.000\t  2  -4  58 \t\n\t\t0.000\t0.000\t159\t0.000\t0.000\t0.000\t  7.59\t 7.30\t0.000\t-42  20   2 \t\n\t\t\t\t\t\t0.000\t0.000\t  7.32\t 7.06\t0.000\t-56  30   4 \t\n\t\t\t\t\t\t0.000\t0.000\t  7.13\t 6.89\t0.000\t-50  12   0 \t\n\t\t0.001\t0.035\t17\t0.014\t0.000\t0.000\t  7.36\t 7.10\t0.000\t -8  48 -20 \t\n\t\t0.004\t0.143\t8\t0.075\t0.000\t0.000\t  7.23\t 6.98\t0.000\t-40  54  16 \t\n\t\t0.000\t0.002\t42\t0.000\t0.000\t0.000\t  6.81\t 6.60\t0.000\t 54 -18  48 \t\n\t\t\t\t\t\t0.016\t0.343\t  5.20\t 5.10\t0.000\t 62 -18  44 \t\n\t\t0.001\t0.033\t18\t0.012\t0.000\t0.000\t  6.72\t 6.52\t0.000\t-22  38  46 \t\n\t\t0.000\t0.022\t21\t0.007\t0.000\t0.000\t  6.67\t 6.48\t0.000\t-18  64 -12 \t\n\t\t0.000\t0.011\t27\t0.003\t0.000\t0.001\t  6.60\t 6.41\t0.000\t 32 -48  60 \t\n\t\t0.000\t0.004\t36\t0.001\t0.000\t0.001\t  6.58\t 6.39\t0.000\t 24  54  36 \t\n\t\t\t\t\t\t0.000\t0.016\t  5.94\t 5.79\t0.000\t 24  60  30 \t\n\t\t0.006\t0.180\t6\t0.118\t0.000\t0.001\t  6.43\t 6.26\t0.000\t-44  -8  56 \t\n\t\t0.001\t0.040\t16\t0.016\t0.000\t0.004\t  6.23\t 6.07\t0.000\t-36  26  46 \t\n\t\t0.000\t0.001\t49\t0.000\t0.000\t0.007\t  6.10\t 5.94\t0.000\t 58   0  40 \t\n\t\t\t\t\t\t0.003\t0.070\t  5.59\t 5.47\t0.000\t 60   4  32 \t\n\t\t0.000\t0.000\t64\t0.000\t0.000\t0.008\t  6.08\t 5.93\t0.000\t 16  62  -8 \t\n\t\t\t\t\t\t0.018\t0.392\t  5.16\t 5.07\t0.000\t 24  60 -12 \t\n\t\t0.001\t0.046\t15\t0.020\t0.000\t0.008\t  6.07\t 5.92\t0.000\t 12  12  66 \t\n\t\t0.001\t0.035\t17\t0.014\t0.000\t0.011\t  6.01\t 5.86\t0.000\t 22  42  50 \t\n\t\t0.000\t0.013\t25\t0.004\t0.000\t0.011\t  6.00\t 5.85\t0.000\t  6  58 -12 \t\n\t\t0.004\t0.143\t8\t0.075\t0.001\t0.018\t  5.90\t 5.76\t0.000\t 52  40  10 \t\n\t\t0.002\t0.089\t11\t0.041\t0.001\t0.019\t  5.89\t 5.75\t0.000\t  6  16  -6 \t\n\t\t0.010\t0.244\t4\t0.196\t0.001\t0.020\t  5.87\t 5.73\t0.000\t-46  44   6 \t\n\t\t0.008\t0.204\t5\t0.151\t0.001\t0.031\t  5.78\t 5.65\t0.000\t-60  -2  -6 \t\n\t\t0.000\t0.011\t27\t0.003\t0.001\t0.031\t  5.77\t 5.64\t0.000\t 24  48 -12 \t\n\t\t0.005\t0.163\t7\t0.094\t0.001\t0.039\t  5.73\t 5.60\t0.000\t-16  42  28 \t\n\t\t0.000\t0.020\t22\t0.006\t0.001\t0.039\t  5.72\t 5.60\t0.000\t-26  68   6 \t\n\t\t0.013\t0.294\t3\t0.260\t0.001\t0.039\t  5.72\t 5.60\t0.000\t 46  20 -20 \t\n\t\t0.006\t0.180\t6\t0.118\t0.001\t0.040\t  5.72\t 5.59\t0.000\t 50  18 -20 \t\n\t\t0.005\t0.163\t7\t0.094\t0.001\t0.041\t  5.71\t 5.58\t0.000\t-20  20  62 \t\n\t\t0.000\t0.025\t20\t0.008\t0.002\t0.043\t  5.70\t 5.57\t0.000\t -4  68   8 \t\n\t\t0.004\t0.143\t8\t0.075\t0.002\t0.046\t  5.68\t 5.56\t0.000\t-50  38  -2 \t\n\t\t0.010\t0.244\t4\t0.196\t0.003\t0.072\t  5.58\t 5.46\t0.000\t 40  50 -12 \t\n\t\t0.002\t0.076\t12\t0.034\t0.003\t0.078\t  5.56\t 5.44\t0.000\t 18 -16  74 \t\n\t\t0.006\t0.180\t6\t0.118\t0.003\t0.078\t  5.56\t 5.44\t0.000\t 54  34  20 \t\n\t\t0.005\t0.163\t7\t0.094\t0.003\t0.081\t  5.55\t 5.43\t0.000\t -2  30  46 \t\n\t\t0.008\t0.204\t5\t0.151\t0.004\t0.095\t  5.51\t 5.40\t0.000\t-34  26 -16 \t\n\t\t0.013\t0.294\t3\t0.260\t0.004\t0.096\t  5.51\t 5.39\t0.000\t 60  18   2 \t\n\t\t0.018\t0.377\t2\t0.358\t0.004\t0.101\t  5.49\t 5.38\t0.000\t 32  44  38 \t\n\t\t0.010\t0.244\t4\t0.196\t0.006\t0.146\t  5.41\t 5.30\t0.000\t 66  -8  32 \t\n\t\t0.003\t0.128\t9\t0.061\t0.006\t0.154\t  5.39\t 5.28\t0.000\t-36  -2   2 \t\n\t\t0.006\t0.180\t6\t0.118\t0.008\t0.180\t  5.36\t 5.25\t0.000\t-34  22 -28 \t\n\t\t0.026\t0.524\t1\t0.524\t0.008\t0.193\t  5.33\t 5.23\t0.000\t-22  14 -28 \t\n\t\t0.013\t0.294\t3\t0.260\t0.008\t0.193\t  5.33\t 5.23\t0.000\t 10  34  58 \t\n\t\t0.008\t0.204\t5\t0.151\t0.010\t0.222\t  5.30\t 5.20\t0.000\t 12 -18  76 \t\n\t\t0.010\t0.244\t4\t0.196\t0.010\t0.226\t  5.30\t 5.19\t0.000\t 24 -12  54 \t\n\t\t0.006\t0.180\t6\t0.118\t0.011\t0.250\t  5.27\t 5.17\t0.000\t-34  50  28 \t\n\t\t0.018\t0.377\t2\t0.358\t0.019\t0.401\t  5.16\t 5.06\t0.000\t 24 -40 -50 \t\n\t\t0.008\t0.204\t5\t0.151\t0.019\t0.405\t  5.15\t 5.06\t0.000\t 16  -6  72 \t\n\t\t0.008\t0.204\t5\t0.151\t0.020\t0.428\t  5.14\t 5.04\t0.000\t-46 -42  16 \t\n\t\t0.018\t0.377\t2\t0.358\t0.027\t0.546\t  5.07\t 4.98\t0.000\t 26 -30  16 \t\n\t\t0.013\t0.294\t3\t0.260\t0.027\t0.549\t  5.07\t 4.98\t0.000\t 38 -58  20 \t\n\t\t0.013\t0.294\t3\t0.260\t0.028\t0.569\t  5.06\t 4.97\t0.000\t 28  34 -10 \t\n\t\t0.026\t0.524\t1\t0.524\t0.035\t0.708\t  5.01\t 4.92\t0.000\t-24  22  56 \t\n\t\t0.018\t0.377\t2\t0.358\t0.043\t0.867\t  4.96\t 4.87\t0.000\t 46 -12 -18 \t\n\t\t0.026\t0.524\t1\t0.524\t0.047\t0.941\t  4.94\t 4.85\t0.000\t 30   4 -24 \t\n\ntable shows 3 local maxima more than 8.0mm apart\n--------------------------------------------------------------------------------\nHeight threshold: T = 4.92, p = 0.000 (0.050)\nExtent threshold: k = 0 voxels\nExpected voxels per cluster, <k> = 2.554\nExpected number of clusters, <c> = 0.05\nFWEp: 4.923, FDRp: 5.683, FWEc: 1, FDRc: 15\nDegrees of freedom = [1.0, 358.0]\nFWHM = 9.6 9.4 7.9 mm mm mm; 4.8 4.7 4.0 {voxels}\nVolume: 1562664 = 195333 voxels = 2043.8 resels\nVoxel size: 2.0 2.0 2.0 mm mm mm; (resel = 88.73 voxels)\n================================================================================\n\n24-Mar-2021 23:33:56 - Done    'Results Report'\n24-Mar-2021 23:33:56 - Done\n\nSubject\n","name":"stdout"}]},{"cell_type":"markdown","source":"## STEP 8: Explore and visualise results\n\nThis part is still to be expanded...","metadata":{}},{"cell_type":"code","source":"% Display contrast image\n\ncon_fn = spm_vol(fullfile(processing_dir, 'sub-01', 'stats', 'spmT_0001.nii'));\ncontrast = spm_read_vols(con_fn);\n[Ni, Nj, Nk] = size(contrast);\nsubplot(231); imagesc(rot90(squeeze(contrast(round(Ni/2),:,:,1)))); colormap jet; axis image; set(gca,'xtick',[],'ytick',[]);\nsubplot(232); imagesc(rot90(squeeze(contrast(:,round(Nj/2),:,1)))); colormap jet; axis image; set(gca,'xtick',[],'ytick',[]);\nsubplot(233); imagesc(rot90(squeeze(contrast(:,:,round(Nk/2),1)))); colormap jet; colorbar; axis image; set(gca,'xtick',[],'ytick',[]);\n\ncon_fn = spm_vol(fullfile(processing_dir, 'sub-02', 'stats', 'spmT_0001.nii'));\ncontrast = spm_read_vols(con_fn);\n[Ni, Nj, Nk] = size(contrast);\nsubplot(234); imagesc(rot90(squeeze(contrast(round(Ni/2),:,:,1)))); colormap jet; axis image; set(gca,'xtick',[],'ytick',[]);\nsubplot(235); imagesc(rot90(squeeze(contrast(:,round(Nj/2),:,1)))); colormap jet; axis image; set(gca,'xtick',[],'ytick',[]);\nsubplot(236); imagesc(rot90(squeeze(contrast(:,:,round(Nk/2),1)))); colormap jet; colorbar; axis image; set(gca,'xtick',[],'ytick',[]);","metadata":{},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Image 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\n"},"metadata":{}}]},{"cell_type":"code","source":"% Second level analysis (one sample t-test)\n    \nfor i = 1:n_sub \n    if i < 10\n        sub_path = '0%d';\n        sub = sprintf(sub_path, i);\n    else\n        sub = i;\n    end\n    preproc_data(i).con1 = fullfile(processing_dir, ['sub-' sub], 'stats', 'con_0001.nii');\nend\n\n% Call script to set up design\nspm_specify2ndlevel_ler(results_dir, preproc_data);","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true}},"execution_count":24,"outputs":[{"output_type":"stream","text":"warning: function name 'spm_specify2nd_ler' does not agree with function filename '/home/jovyan/scripts/spm_specify2ndlevel_ler.m'\n\n\n------------------------------------------------------------------------\n24-Mar-2021 23:36:47 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 23:36:47 - Running 'Factorial design specification'\nMapping files                           :                        ...done\nDesign configuration                    :                        ...done\nSaving SPM configuration                :               ...SPM.mat saved\nCompleted                               :          23:36:48 - 24/03/2021\n24-Mar-2021 23:36:48 - Done    'Factorial design specification'\n24-Mar-2021 23:36:48 - Done\n\n","name":"stdout"}]},{"cell_type":"code","source":"% Estimate second level analysis\n\nspm_estimateModel_jsh(results_dir)","metadata":{},"execution_count":25,"outputs":[{"output_type":"stream","text":"\n\n------------------------------------------------------------------------\n24-Mar-2021 23:39:30 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 23:39:30 - Running 'Model estimation'\n\nSPM12: spm_spm (v7738)                             23:39:30 - 24/03/2021\n========================================================================\nChunk   1/1                             :                  ...processi                       ...done\nSpatial non-sphericity (over scans)     :      ...estimating derivativ ...writing resels/voxel ima                       ...done\nSaving SPM.mat                          :                     ...writi                       ...done\nCompleted                               :          23:39:32 - 24/03/2021\nDone\n24-Mar-2021 23:39:32 - Done    'Model estimation'\n24-Mar-2021 23:39:32 - Done\n\n","name":"stdout"}]},{"cell_type":"code","source":"% Create contrast maps\n\ncontrast_params = struct;\ncontrast_params.weights = zeros(1); \ncontrast_params.weights(1) = 1;\ncontrast_params.name = 'Positive';\nspm_setupTaskContrast_jsh(results_dir, contrast_params)","metadata":{},"execution_count":26,"outputs":[{"output_type":"stream","text":"\n\n------------------------------------------------------------------------\n24-Mar-2021 23:39:55 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 23:39:55 - Running 'Contrast Manager'\n\nSPM12: spm_contrasts.m                             23:39:55 - 24/03/2021\n========================================================================\nContrasts folder                        :           /home/jovyan/results\n\tcontrast image  1               :                   ...computi       ...written con_0001.nii\n\tspm{T} image  1                 :                   ...computi      ...written spmT_0001.nii\n\tSaving SPM.mat                  :                     ...writi              ...SPM.mat saved\nCompleted                               :          23:39:55 - 24/03/2021\n24-Mar-2021 23:39:55 - Done    'Contrast Manager'\n24-Mar-2021 23:39:55 - Done\n\n","name":"stdout"}]},{"cell_type":"code","source":"% Create thresholded maps (FWE, 0.05)\n\nspm_runResults_jsh(results_dir, 'FWE', 0.05)","metadata":{},"execution_count":29,"outputs":[{"output_type":"stream","text":"\n\n------------------------------------------------------------------------\n24-Mar-2021 23:42:22 - Running job #1\n------------------------------------------------------------------------\n24-Mar-2021 23:42:23 - Running 'Results Report'\n\tSPM computation                 :                ...initialisi           ...height thresho           ...extent thresho                       ...done\n\n\nStatistics: p-values adjusted for search volume\n================================================================================\nset\tset\tcluster\tcluster\tcluster\tcluster\tpeak\tpeak\tpeak\tpeak\tpeak\t\np\tc\tp(FWE-corr)\tp(FDR-corr)\tequivk\tp(unc)\tp(FWE-corr)\tp(FDR-corr)\tT\tequivZ\tp(unc)\tx,y,z {mm}\n--------------------------------------------------------------------------------\n0.000\t576\t0.050\t1.000\t1\t1.000\t0.981\t77.327\t41369.12\t 4.32\t0.000\t 42 -56   4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.981\t77.161\t41211.31\t 4.32\t0.000\t-30  -2 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.933\t52.924\t28171.85\t 4.24\t0.000\t-52  24   8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.926\t50.904\t27044.98\t 4.23\t0.000\t -6 -42  50 \t\n\t\t0.050\t1.000\t3\t1.000\t0.768\t28.707\t15160.11\t 4.10\t0.000\t-30 -48 -22 \t\n\t\t0.050\t1.000\t2\t1.000\t0.603\t18.158\t9516.76\t 3.99\t0.000\t 26  60  -6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.570\t16.611\t8678.26\t 3.97\t0.000\t-34 -12  34 \t\n\t\t0.050\t1.000\t1\t1.000\t0.355\t8.642\t4431.70\t 3.80\t0.000\t-46 -62 -20 \t\n\t\t0.050\t1.000\t3\t1.000\t0.300\t7.038\t3573.02\t 3.75\t0.000\t 38  30  -6 \t\n\t\t0.050\t1.000\t4\t1.000\t0.285\t6.650\t3360.97\t 3.73\t0.000\t 30 -86 -12 \t\n\t\t0.050\t1.000\t1\t1.000\t0.266\t6.143\t3086.85\t 3.71\t0.000\t 36 -62 -38 \t\n\t\t0.050\t1.000\t1\t1.000\t0.245\t5.589\t2788.69\t 3.69\t0.000\t 42   0  28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.238\t5.399\t2683.11\t 3.68\t0.000\t 56   8 -12 \t\n\t\t0.050\t1.000\t5\t1.000\t0.234\t5.322\t2637.95\t 3.67\t0.000\t-10 -82 -12 \t\n\t\t0.050\t1.000\t5\t1.000\t0.232\t5.279\t2610.31\t 3.67\t0.000\t-46 -70  -6 \t\n\t\t0.050\t1.000\t2\t1.000\t0.231\t5.250\t2590.30\t 3.67\t0.000\t 22 -84   4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.226\t5.120\t2517.65\t 3.66\t0.000\t-32  58  22 \t\n\t\t0.050\t1.000\t3\t1.000\t0.225\t5.120\t2512.99\t 3.66\t0.000\t-10 -94  -8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.219\t4.974\t2432.05\t 3.65\t0.000\t 20 -24  50 \t\n\t\t0.050\t1.000\t18\t1.000\t0.210\t4.748\t2309.53\t 3.64\t0.000\t-38 -52 -34 \t\n\t\t\t\t\t\t0.066\t1.645\t525.12\t 3.24\t0.001\t-46 -50 -24 \t\n\t\t0.050\t1.000\t1\t1.000\t0.205\t4.637\t2247.47\t 3.63\t0.000\t-28 -76  -8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.204\t4.619\t2234.11\t 3.63\t0.000\t-34 -88 -16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.189\t4.245\t2035.08\t 3.60\t0.000\t-40 -60  10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.187\t4.208\t2012.43\t 3.60\t0.000\t 22 -64 -28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.186\t4.191\t1999.46\t 3.60\t0.000\t 24 -42 -32 \t\n\t\t0.050\t1.000\t1\t1.000\t0.186\t4.184\t1992.27\t 3.60\t0.000\t 10 -30 -36 \t\n\t\t0.050\t1.000\t1\t1.000\t0.176\t3.952\t1868.61\t 3.58\t0.000\t 18 -74   8 \t\n\t\t0.050\t1.000\t2\t1.000\t0.176\t3.952\t1866.96\t 3.58\t0.000\t 22 -30  74 \t\n\t\t0.050\t1.000\t1\t1.000\t0.168\t3.756\t1760.48\t 3.57\t0.000\t 52 -34  52 \t\n\t\t0.050\t1.000\t1\t1.000\t0.166\t3.711\t1733.84\t 3.56\t0.000\t 22 -50  34 \t\n\t\t0.050\t1.000\t5\t1.000\t0.159\t3.545\t1645.39\t 3.55\t0.000\t-34  -8  20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.155\t3.469\t1603.07\t 3.54\t0.000\t 36 -74  14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.155\t3.458\t1594.23\t 3.54\t0.000\t-18  42  32 \t\n\t\t0.050\t1.000\t1\t1.000\t0.147\t3.285\t1502.14\t 3.52\t0.000\t-44 -54 -30 \t\n\t\t0.050\t1.000\t2\t1.000\t0.144\t3.215\t1460.50\t 3.52\t0.000\t-48 -36  56 \t\n\t\t0.050\t1.000\t1\t1.000\t0.142\t3.182\t1440.94\t 3.51\t0.000\t-22 -46 -16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.139\t3.115\t1403.74\t 3.51\t0.000\t 20 -68  60 \t\n\t\t0.050\t1.000\t1\t1.000\t0.136\t3.043\t1364.49\t 3.50\t0.000\t 42 -42  -8 \t\n\t\t0.050\t1.000\t7\t1.000\t0.136\t3.042\t1361.30\t 3.50\t0.000\t-38 -76   4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.135\t3.023\t1348.84\t 3.50\t0.000\t 32   6  22 \t\n\t\t0.050\t1.000\t2\t1.000\t0.134\t2.999\t1333.86\t 3.49\t0.000\t-40 -46 -20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.131\t2.946\t1304.09\t 3.49\t0.000\t 50 -54 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.130\t2.919\t1288.09\t 3.48\t0.000\t-30 -80  -8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.127\t2.863\t1256.93\t 3.48\t0.000\t 22 -54 -10 \t\n\t\t0.050\t1.000\t7\t1.000\t0.127\t2.851\t1248.51\t 3.48\t0.000\t 24 -84 -22 \t\n\t\t0.050\t1.000\t1\t1.000\t0.124\t2.785\t1212.50\t 3.47\t0.000\t  8 -26  54 \t\n\t\t0.050\t1.000\t1\t1.000\t0.121\t2.725\t1179.99\t 3.46\t0.000\t-28   2  66 \t\n\t\t0.050\t1.000\t1\t1.000\t0.119\t2.687\t1158.38\t 3.46\t0.000\t  4  72  18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.119\t2.687\t1156.05\t 3.45\t0.000\t-12 -40  48 \t\n\t\t0.050\t1.000\t1\t1.000\t0.116\t2.610\t1114.94\t 3.45\t0.000\t-32 -12  -2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.115\t2.605\t1110.11\t 3.44\t0.000\t  4 -80  -6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.112\t2.538\t1074.15\t 3.43\t0.000\t 26 -20  28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.111\t2.516\t1060.67\t 3.43\t0.000\t-48   0 -32 \t\n\t\t0.050\t1.000\t1\t1.000\t0.111\t2.510\t1055.18\t 3.43\t0.000\t-26 -46 -28 \t\n\t\t0.050\t1.000\t2\t1.000\t0.110\t2.510\t1053.25\t 3.43\t0.000\t 52 -18  54 \t\n\t\t0.050\t1.000\t1\t1.000\t0.109\t2.474\t1032.82\t 3.42\t0.000\t 46 -24  44 \t\n\t\t0.050\t1.000\t1\t1.000\t0.107\t2.447\t1017.38\t 3.42\t0.000\t-40 -10  52 \t\n\t\t0.050\t1.000\t1\t1.000\t0.107\t2.444\t1013.53\t 3.42\t0.000\t-16 -90  -4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.107\t2.444\t1012.09\t 3.42\t0.000\t 32 -62  60 \t\n\t\t0.050\t1.000\t1\t1.000\t0.106\t2.435\t1004.67\t 3.42\t0.000\t-34 -88   6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.105\t2.409\t989.31\t 3.41\t0.000\t  2  72  10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.105\t2.403\t984.54\t 3.41\t0.000\t-28  -8 -34 \t\n\t\t0.050\t1.000\t2\t1.000\t0.104\t2.396\t978.61\t 3.41\t0.000\t-10   0  56 \t\n\t\t0.050\t1.000\t6\t1.000\t0.103\t2.377\t967.10\t 3.41\t0.000\t 16 -82 -16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.102\t2.362\t957.47\t 3.40\t0.000\t 12 -34  44 \t\n\t\t0.050\t1.000\t2\t1.000\t0.100\t2.313\t931.21\t 3.40\t0.000\t 24 -50 -32 \t\n\t\t0.050\t1.000\t1\t1.000\t0.100\t2.300\t923.06\t 3.39\t0.000\t-50  36  -8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.099\t2.291\t914.23\t 3.39\t0.000\t-12  70  14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.098\t2.277\t905.18\t 3.39\t0.000\t-50 -58  -8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.098\t2.275\t902.44\t 3.39\t0.000\t 54   8  40 \t\n\t\t0.050\t1.000\t2\t1.000\t0.097\t2.257\t891.34\t 3.38\t0.000\t-50   2  44 \t\n\t\t0.050\t1.000\t1\t1.000\t0.095\t2.222\t872.43\t 3.38\t0.000\t  8 -22   4 \t\n\t\t0.050\t1.000\t2\t1.000\t0.094\t2.191\t854.88\t 3.37\t0.000\t-30 -74  14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.093\t2.176\t845.71\t 3.37\t0.000\t -2  72   2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.092\t2.151\t831.58\t 3.37\t0.000\t 12 -88  -2 \t\n\t\t0.050\t1.000\t2\t1.000\t0.089\t2.074\t792.38\t 3.35\t0.000\t-56 -12  36 \t\n\t\t0.050\t1.000\t1\t1.000\t0.088\t2.066\t786.34\t 3.35\t0.000\t 38  28  18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.086\t2.018\t761.45\t 3.34\t0.000\t-28 -64  40 \t\n\t\t0.050\t1.000\t1\t1.000\t0.085\t2.009\t755.14\t 3.34\t0.000\t 38 -48 -12 \t\n\t\t0.050\t1.000\t5\t1.000\t0.084\t1.988\t743.04\t 3.33\t0.000\t-28  26  18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.084\t1.988\t742.30\t 3.33\t0.000\t-36  20 -28 \t\n\t\t0.050\t1.000\t4\t1.000\t0.084\t1.987\t738.95\t 3.33\t0.000\t 46 -56 -26 \t\n\t\t0.050\t1.000\t1\t1.000\t0.084\t1.987\t737.05\t 3.33\t0.000\t 34 -28 -14 \t\n\t\t0.050\t1.000\t6\t1.000\t0.083\t1.974\t728.75\t 3.33\t0.000\t-26  58   2 \t\n\t\t0.050\t1.000\t2\t1.000\t0.082\t1.940\t710.65\t 3.32\t0.000\t-56  14   4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.082\t1.939\t708.12\t 3.32\t0.000\t-50   2  34 \t\n\t\t0.050\t1.000\t2\t1.000\t0.081\t1.923\t698.98\t 3.32\t0.000\t 36  36  12 \t\n\t\t0.050\t1.000\t3\t1.000\t0.079\t1.887\t679.51\t 3.31\t0.000\t-24 -62  32 \t\n\t\t0.050\t1.000\t1\t1.000\t0.077\t1.850\t658.40\t 3.30\t0.000\t-46 -52 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.077\t1.834\t649.01\t 3.30\t0.000\t-24 -22  -4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.074\t1.770\t616.21\t 3.28\t0.001\t-22  -8  32 \t\n\t\t0.050\t1.000\t1\t1.000\t0.073\t1.765\t612.07\t 3.28\t0.001\t 36   2 -14 \t\n\t\t0.050\t1.000\t3\t1.000\t0.073\t1.760\t608.08\t 3.28\t0.001\t 46 -62 -16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.072\t1.747\t599.92\t 3.27\t0.001\t-34  20 -14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.072\t1.732\t591.36\t 3.27\t0.001\t 34   4 -26 \t\n\t\t0.050\t1.000\t2\t1.000\t0.071\t1.729\t586.55\t 3.27\t0.001\t-28 -22 -12 \t\n\t\t0.050\t1.000\t1\t1.000\t0.070\t1.708\t574.90\t 3.26\t0.001\t-36 -16 -22 \t\n\t\t0.050\t1.000\t2\t1.000\t0.070\t1.691\t564.83\t 3.26\t0.001\t-54   2   0 \t\n\t\t0.050\t1.000\t1\t1.000\t0.069\t1.686\t560.96\t 3.25\t0.001\t-44 -60   6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.069\t1.686\t559.05\t 3.25\t0.001\t-20 -72  56 \t\n\t\t0.050\t1.000\t1\t1.000\t0.069\t1.682\t555.62\t 3.25\t0.001\t-18 -90   2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.068\t1.675\t550.83\t 3.25\t0.001\t-30 -60  38 \t\n\t\t0.050\t1.000\t1\t1.000\t0.068\t1.675\t549.79\t 3.25\t0.001\t-34  52  -4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.068\t1.667\t543.42\t 3.25\t0.001\t 42  16 -26 \t\n\t\t0.050\t1.000\t1\t1.000\t0.068\t1.663\t539.95\t 3.24\t0.001\t-62 -28  44 \t\n\t\t0.050\t1.000\t1\t1.000\t0.067\t1.657\t535.61\t 3.24\t0.001\t-36   6  36 \t\n\t\t0.050\t1.000\t1\t1.000\t0.067\t1.657\t535.44\t 3.24\t0.001\t-16  42  48 \t\n\t\t0.050\t1.000\t2\t1.000\t0.066\t1.645\t526.77\t 3.24\t0.001\t 20 -28   2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.066\t1.645\t523.33\t 3.24\t0.001\t 52 -36  56 \t\n\t\t0.050\t1.000\t1\t1.000\t0.066\t1.644\t521.32\t 3.23\t0.001\t 52  16  -4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.065\t1.615\t505.93\t 3.23\t0.001\t-16 -26   6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.063\t1.579\t487.02\t 3.21\t0.001\t 24  38  46 \t\n\t\t0.050\t1.000\t1\t1.000\t0.063\t1.576\t484.04\t 3.21\t0.001\t 38  36  20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.063\t1.570\t479.84\t 3.21\t0.001\t-32 -46   4 \t\n\t\t0.050\t1.000\t2\t1.000\t0.062\t1.565\t475.88\t 3.21\t0.001\t-22  66  18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.062\t1.556\t469.63\t 3.20\t0.001\t 30 -26  40 \t\n\t\t0.050\t1.000\t1\t1.000\t0.062\t1.556\t469.32\t 3.20\t0.001\t 42  32  10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.062\t1.556\t468.99\t 3.20\t0.001\t 38 -66 -20 \t\n\t\t0.050\t1.000\t8\t1.000\t0.061\t1.556\t465.07\t 3.20\t0.001\t-36 -74 -28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.061\t1.541\t456.54\t 3.20\t0.001\t-32 -60  52 \t\n\t\t0.050\t1.000\t1\t1.000\t0.060\t1.535\t451.78\t 3.19\t0.001\t 26 -40 -28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.060\t1.534\t449.90\t 3.19\t0.001\t-22 -44  72 \t\n\t\t0.050\t1.000\t2\t1.000\t0.060\t1.534\t449.30\t 3.19\t0.001\t-32 -52 -30 \t\n\t\t0.050\t1.000\t1\t1.000\t0.060\t1.525\t442.59\t 3.19\t0.001\t-26  14  62 \t\n\t\t0.050\t1.000\t1\t1.000\t0.059\t1.511\t434.12\t 3.18\t0.001\t-26  18   6 \t\n\t\t0.050\t1.000\t5\t1.000\t0.059\t1.511\t433.22\t 3.18\t0.001\t-24 -48 -26 \t\n\t\t0.050\t1.000\t1\t1.000\t0.059\t1.511\t432.26\t 3.18\t0.001\t-40 -58 -32 \t\n\t\t0.050\t1.000\t1\t1.000\t0.059\t1.511\t430.52\t 3.18\t0.001\t-48  10  18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.058\t1.503\t422.52\t 3.17\t0.001\t-16 -30  -4 \t\n\t\t0.050\t1.000\t2\t1.000\t0.058\t1.503\t422.34\t 3.17\t0.001\t 20  42  44 \t\n\t\t0.050\t1.000\t1\t1.000\t0.058\t1.503\t420.67\t 3.17\t0.001\t-22 -32 -24 \t\n\t\t0.050\t1.000\t1\t1.000\t0.058\t1.503\t419.28\t 3.17\t0.001\t-34  22  50 \t\n\t\t0.050\t1.000\t1\t1.000\t0.057\t1.494\t412.26\t 3.17\t0.001\t-38 -14  60 \t\n\t\t0.050\t1.000\t1\t1.000\t0.057\t1.490\t407.13\t 3.16\t0.001\t-58 -18  40 \t\n\t\t0.050\t1.000\t1\t1.000\t0.057\t1.490\t407.03\t 3.16\t0.001\t -6 -32  62 \t\n\t\t0.050\t1.000\t1\t1.000\t0.057\t1.490\t404.58\t 3.16\t0.001\t-52 -10  38 \t\n\t\t0.050\t1.000\t1\t1.000\t0.057\t1.489\t402.23\t 3.16\t0.001\t 36  18   0 \t\n\t\t0.050\t1.000\t1\t1.000\t0.056\t1.480\t396.27\t 3.15\t0.001\t-34 -34  66 \t\n\t\t0.050\t1.000\t3\t1.000\t0.056\t1.480\t396.24\t 3.15\t0.001\t 50 -48 -16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.056\t1.480\t394.61\t 3.15\t0.001\t 42 -12  60 \t\n\t\t0.050\t1.000\t1\t1.000\t0.056\t1.480\t393.80\t 3.15\t0.001\t 46  48   4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.056\t1.480\t390.71\t 3.15\t0.001\t 28 -78  44 \t\n\t\t0.050\t1.000\t1\t1.000\t0.056\t1.480\t390.17\t 3.15\t0.001\t 26 -40 -36 \t\n\t\t0.050\t1.000\t1\t1.000\t0.056\t1.480\t389.64\t 3.15\t0.001\t-42 -62  14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.055\t1.480\t386.39\t 3.15\t0.001\t-22 -24   6 \t\n\t\t0.050\t1.000\t3\t1.000\t0.055\t1.470\t378.96\t 3.14\t0.001\t 40   6  26 \t\n\t\t0.050\t1.000\t4\t1.000\t0.054\t1.462\t373.27\t 3.14\t0.001\t 24 -60 -22 \t\n\t\t0.050\t1.000\t1\t1.000\t0.054\t1.447\t364.58\t 3.13\t0.001\t-38 -42 -16 \t\n\t\t0.050\t1.000\t2\t1.000\t0.054\t1.445\t362.34\t 3.13\t0.001\t-34 -60  44 \t\n\t\t0.050\t1.000\t1\t1.000\t0.053\t1.445\t362.14\t 3.13\t0.001\t-30 -10 -10 \t\n\t\t0.050\t1.000\t2\t1.000\t0.053\t1.445\t359.07\t 3.13\t0.001\t 28 -56 -26 \t\n\t\t0.050\t1.000\t1\t1.000\t0.053\t1.445\t357.78\t 3.12\t0.001\t 32 -38  68 \t\n\t\t0.050\t1.000\t2\t1.000\t0.053\t1.445\t356.56\t 3.12\t0.001\t-20 -86  -2 \t\n\t\t0.050\t1.000\t6\t1.000\t0.052\t1.436\t348.40\t 3.12\t0.001\t 38   2  20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.052\t1.436\t347.69\t 3.12\t0.001\t-20 -50 -26 \t\n\t\t0.050\t1.000\t2\t1.000\t0.052\t1.436\t345.79\t 3.11\t0.001\t-48 -52 -28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.052\t1.436\t345.30\t 3.11\t0.001\t  4 -30 -38 \t\n\t\t0.050\t1.000\t1\t1.000\t0.052\t1.433\t340.58\t 3.11\t0.001\t 36 -20 -22 \t\n\t\t0.050\t1.000\t2\t1.000\t0.052\t1.431\t336.22\t 3.11\t0.001\t-48   2  30 \t\n\t\t0.050\t1.000\t1\t1.000\t0.051\t1.430\t334.10\t 3.10\t0.001\t -4  50 -16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.051\t1.430\t333.93\t 3.10\t0.001\t -4  20  46 \t\n\t\t0.050\t1.000\t1\t1.000\t0.051\t1.430\t331.80\t 3.10\t0.001\t 28 -68 -18 \t\n\t\t0.050\t1.000\t2\t1.000\t0.051\t1.416\t322.52\t 3.09\t0.001\t-44 -66  -6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.051\t1.416\t321.66\t 3.09\t0.001\t-38 -64 -30 \t\n\t\t0.050\t1.000\t4\t1.000\t0.051\t1.416\t321.38\t 3.09\t0.001\t 30 -92   6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.050\t1.416\t319.14\t 3.09\t0.001\t-34  36  -8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.050\t1.416\t318.97\t 3.09\t0.001\t-18  -2 -10 \t\n\t\t0.050\t1.000\t6\t1.000\t0.050\t1.416\t318.24\t 3.09\t0.001\t 42 -68  -2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.050\t1.416\t313.92\t 3.09\t0.001\t 46  44  -2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.050\t1.416\t308.62\t 3.08\t0.001\t-34 -12   2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.050\t1.416\t308.16\t 3.08\t0.001\t-42 -10  58 \t\n\t\t0.050\t1.000\t4\t1.000\t0.050\t1.416\t307.63\t 3.08\t0.001\t 26 -28  32 \t\n\t\t0.050\t1.000\t4\t1.000\t0.050\t1.416\t306.97\t 3.08\t0.001\t 50 -72 -10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t304.91\t 3.08\t0.001\t-50  20   4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t304.72\t 3.08\t0.001\t 14 -28  54 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t303.51\t 3.08\t0.001\t 30 -12 -22 \t\n\t\t0.050\t1.000\t3\t1.000\t0.049\t1.416\t303.46\t 3.08\t0.001\t 58 -24  46 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t300.31\t 3.07\t0.001\t 46   8   6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t299.95\t 3.07\t0.001\t-30 -90   4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t299.41\t 3.07\t0.001\t 36 -54  46 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t299.19\t 3.07\t0.001\t 20  -8 -20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t295.55\t 3.07\t0.001\t 28 -78  28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t290.20\t 3.06\t0.001\t-34 -12 -26 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t287.10\t 3.06\t0.001\t 30 -68  18 \t\n\t\t0.050\t1.000\t2\t1.000\t0.048\t1.416\t286.25\t 3.06\t0.001\t-46 -60 -14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t285.35\t 3.06\t0.001\t-56 -14  42 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t284.47\t 3.06\t0.001\t-18  36  30 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t283.18\t 3.06\t0.001\t-28   2 -14 \t\n\t\t0.050\t1.000\t3\t1.000\t0.048\t1.416\t277.45\t 3.05\t0.001\t-26 -12  -6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t277.02\t 3.05\t0.001\t  6 -78 -12 \t\n\t\t0.050\t1.000\t3\t1.000\t0.047\t1.416\t274.22\t 3.05\t0.001\t-28 -24  70 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t272.39\t 3.04\t0.001\t 24 -36  38 \t\n\t\t0.050\t1.000\t2\t1.000\t0.047\t1.416\t272.35\t 3.04\t0.001\t-34 -26  26 \t\n\t\t0.050\t1.000\t2\t1.000\t0.047\t1.416\t268.55\t 3.04\t0.001\t-40 -52 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t267.86\t 3.04\t0.001\t-56  -4  20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t267.66\t 3.04\t0.001\t  4   0  26 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t266.90\t 3.04\t0.001\t 52 -14  50 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t265.16\t 3.04\t0.001\t 48 -16  44 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t263.36\t 3.03\t0.001\t-32 -60 -12 \t\n\t\t0.050\t1.000\t2\t1.000\t0.047\t1.416\t263.00\t 3.03\t0.001\t  6  -2 -22 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t262.32\t 3.03\t0.001\t 40  20  14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t260.80\t 3.03\t0.001\t 48  20  38 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t259.42\t 3.03\t0.001\t 64 -12   6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t258.46\t 3.03\t0.001\t-38 -80 -12 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t256.39\t 3.03\t0.001\t-22 -68 -34 \t\n\t\t0.050\t1.000\t2\t1.000\t0.046\t1.416\t255.42\t 3.02\t0.001\t-32 -86 -12 \t\n\t\t0.050\t1.000\t2\t1.000\t0.046\t1.416\t255.36\t 3.02\t0.001\t 28 -10  44 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t254.48\t 3.02\t0.001\t-28 -72 -28 \t\n\t\t0.050\t1.000\t2\t1.000\t0.046\t1.416\t254.43\t 3.02\t0.001\t 42 -50  52 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t253.45\t 3.02\t0.001\t-42 -44 -22 \t\n\t\t0.050\t1.000\t3\t1.000\t0.046\t1.416\t253.42\t 3.02\t0.001\t-38 -46  44 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t252.86\t 3.02\t0.001\t -8   2  68 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t252.27\t 3.02\t0.001\t-32 -86   2 \t\n\t\t0.050\t1.000\t2\t1.000\t0.046\t1.416\t252.14\t 3.02\t0.001\t-40 -78  -8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t252.11\t 3.02\t0.001\t-22 -26 -14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t251.83\t 3.02\t0.001\t-46  -2  22 \t\n\t\t0.050\t1.000\t3\t1.000\t0.046\t1.416\t251.46\t 3.02\t0.001\t-36 -72   4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t251.12\t 3.02\t0.001\t-16  60 -14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t243.15\t 3.01\t0.001\t  8 -36  46 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t242.62\t 3.01\t0.001\t-36 -50  42 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t242.22\t 3.01\t0.001\t  6  26  54 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t242.14\t 3.01\t0.001\t 44 -70 -12 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t239.87\t 3.01\t0.001\t 48 -62 -20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t239.01\t 3.00\t0.001\t-62 -28  36 \t\n\t\t0.050\t1.000\t2\t1.000\t0.045\t1.416\t238.37\t 3.00\t0.001\t-40 -66 -12 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t236.92\t 3.00\t0.001\t 56 -26  -4 \t\n\t\t0.050\t1.000\t2\t1.000\t0.045\t1.416\t235.99\t 3.00\t0.001\t-18  20  62 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t235.19\t 3.00\t0.001\t-24 -64 -34 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t235.13\t 3.00\t0.001\t -6  72  16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t234.22\t 3.00\t0.001\t-40  42  -8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t233.44\t 3.00\t0.001\t-44 -64 -34 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t233.43\t 3.00\t0.001\t-54  10  34 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t233.30\t 3.00\t0.001\t-44 -16  18 \t\n\t\t0.050\t1.000\t2\t1.000\t0.045\t1.416\t233.17\t 3.00\t0.001\t -8 -88 -12 \t\n\t\t0.050\t1.000\t2\t1.000\t0.045\t1.416\t229.84\t 2.99\t0.001\t  6 -80 -16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t228.59\t 2.99\t0.001\t 22  12  64 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t227.89\t 2.99\t0.001\t-38  -6  50 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t227.81\t 2.99\t0.001\t-48 -44  46 \t\n\t\t0.050\t1.000\t2\t1.000\t0.045\t1.416\t226.96\t 2.99\t0.001\t-30 -78  16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t226.86\t 2.99\t0.001\t-26 -12  26 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t225.47\t 2.99\t0.001\t-18 -38  44 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t223.12\t 2.98\t0.001\t-28   4  -6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t221.36\t 2.98\t0.001\t-30 -66 -20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t220.37\t 2.98\t0.001\t 54 -16  -4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t219.05\t 2.98\t0.001\t 50  14  24 \t\n\t\t0.050\t1.000\t3\t1.000\t0.044\t1.416\t218.44\t 2.98\t0.001\t 40 -50 -16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t215.80\t 2.97\t0.001\t-32 -66  -6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t215.61\t 2.97\t0.001\t 42 -78   2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t209.87\t 2.96\t0.002\t 24 -66  22 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t208.94\t 2.96\t0.002\t 36  36  -4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t208.30\t 2.96\t0.002\t 52  -6  24 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t206.47\t 2.96\t0.002\t 20 -38 -24 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t205.81\t 2.96\t0.002\t 12 -20  40 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t204.58\t 2.96\t0.002\t 20  12  52 \t\n\t\t0.050\t1.000\t2\t1.000\t0.044\t1.416\t202.64\t 2.95\t0.002\t 52  38  20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t201.88\t 2.95\t0.002\t 22 -30  -6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t199.95\t 2.95\t0.002\t-22 -88 -12 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t199.86\t 2.95\t0.002\t-52 -10  34 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t199.61\t 2.95\t0.002\t 40 -34  60 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t198.91\t 2.95\t0.002\t-44  18 -10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t198.74\t 2.95\t0.002\t-44   4 -42 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t198.23\t 2.95\t0.002\t 30 -58  40 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t197.21\t 2.95\t0.002\t-10 -24  20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t196.79\t 2.94\t0.002\t -2 -10  64 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t196.59\t 2.94\t0.002\t 38 -60  56 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t196.45\t 2.94\t0.002\t 56  -8  10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t195.24\t 2.94\t0.002\t 32  26 -22 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t195.10\t 2.94\t0.002\t-44  24  24 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t193.65\t 2.94\t0.002\t 30 -74  14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t193.13\t 2.94\t0.002\t 36   8  18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t192.15\t 2.94\t0.002\t 28 -76  16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t191.22\t 2.94\t0.002\t 12 -14  22 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t190.58\t 2.93\t0.002\t-30  28  14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t188.11\t 2.93\t0.002\t-42  24  -6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t188.05\t 2.93\t0.002\t-22 -20  28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t187.57\t 2.93\t0.002\t-34 -50  38 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t187.46\t 2.93\t0.002\t-20  22  56 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t185.60\t 2.93\t0.002\t-20  22  52 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t184.62\t 2.92\t0.002\t  6   4  22 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t182.98\t 2.92\t0.002\t-20 -82  -4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t182.45\t 2.92\t0.002\t-32 -64 -28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t181.98\t 2.92\t0.002\t 50 -22  30 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t180.39\t 2.92\t0.002\t-48 -32  54 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t179.27\t 2.92\t0.002\t-12  -4  72 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t178.95\t 2.92\t0.002\t-30 -32  66 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t178.71\t 2.91\t0.002\t-10  24  40 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t178.28\t 2.91\t0.002\t 34 -66   2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t178.04\t 2.91\t0.002\t-24 -20   8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t175.91\t 2.91\t0.002\t-34 -70  10 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t175.32\t 2.91\t0.002\t-32 -64 -22 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t174.99\t 2.91\t0.002\t 10 -80 -20 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t174.73\t 2.91\t0.002\t-42 -70 -30 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t173.47\t 2.91\t0.002\t 22 -42 -20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t173.25\t 2.91\t0.002\t 40 -76  16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t172.76\t 2.90\t0.002\t-46 -16  42 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t171.64\t 2.90\t0.002\t 26 -62 -12 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t171.63\t 2.90\t0.002\t  6  -8  34 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t171.55\t 2.90\t0.002\t-22 -92 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t170.64\t 2.90\t0.002\t-22 -52 -14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t170.13\t 2.90\t0.002\t 42  26  -2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t168.77\t 2.90\t0.002\t 32 -82  16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t167.92\t 2.90\t0.002\t-48 -12  34 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t167.29\t 2.89\t0.002\t-46   0  26 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t167.00\t 2.89\t0.002\t-40 -76 -12 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t166.38\t 2.89\t0.002\t-30  -2  34 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t166.31\t 2.89\t0.002\t-18  30 -12 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t163.67\t 2.89\t0.002\t 16 -78  54 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t163.65\t 2.89\t0.002\t -6 -56   6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t163.27\t 2.89\t0.002\t-38 -52  60 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t163.19\t 2.89\t0.002\t-28 -46  30 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t161.97\t 2.88\t0.002\t-22 -68  60 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t161.15\t 2.88\t0.002\t-44 -36  60 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t159.22\t 2.88\t0.002\t 18 -34  -6 \t\n\t\t0.050\t1.000\t2\t1.000\t0.042\t1.416\t159.06\t 2.88\t0.002\t-30 -48 -26 \t\n\t\t0.050\t1.000\t2\t1.000\t0.042\t1.416\t158.98\t 2.88\t0.002\t-50 -30  52 \t\n\t\t0.050\t1.000\t1\t1.000\t0.042\t1.416\t158.39\t 2.88\t0.002\t 38 -34   0 \t\n\t\t0.050\t1.000\t1\t1.000\t0.042\t1.416\t158.31\t 2.88\t0.002\t 26 -82  36 \t\n\t\t0.050\t1.000\t1\t1.000\t0.042\t1.416\t158.03\t 2.88\t0.002\t 30 -80   4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.042\t1.416\t157.67\t 2.88\t0.002\t-24  26   4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.042\t1.416\t157.16\t 2.87\t0.002\t-26 -26 -12 \t\n\t\t0.050\t1.000\t2\t1.000\t0.042\t1.416\t156.38\t 2.87\t0.002\t-36 -84  -8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.042\t1.416\t155.28\t 2.87\t0.002\t  6  12  36 \t\n\t\t0.050\t1.000\t2\t1.000\t0.042\t1.416\t154.90\t 2.87\t0.002\t-20 -34  -2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.042\t1.416\t153.49\t 2.87\t0.002\t-28  28  10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.042\t1.416\t153.14\t 2.87\t0.002\t 20 -38 -30 \t\n\t\t0.050\t1.000\t2\t1.000\t0.042\t1.416\t152.65\t 2.87\t0.002\t-18 -22  40 \t\n\t\t0.050\t1.000\t1\t1.000\t0.042\t1.416\t151.96\t 2.86\t0.002\t-44 -54 -16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.042\t1.416\t151.58\t 2.86\t0.002\t 54   0 -14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.042\t1.416\t151.27\t 2.86\t0.002\t-20 -30  72 \t\n\t\t0.050\t1.000\t2\t1.000\t0.042\t1.416\t150.39\t 2.86\t0.002\t 48  12  28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.042\t1.416\t149.39\t 2.86\t0.002\t-30  22  54 \t\n\t\t0.050\t1.000\t1\t1.000\t0.042\t1.416\t149.07\t 2.86\t0.002\t-36 -50 -20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.042\t1.416\t148.80\t 2.86\t0.002\t 18 -88  -8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.042\t1.416\t148.23\t 2.86\t0.002\t 38 -56  50 \t\n\t\t0.050\t1.000\t1\t1.000\t0.042\t1.416\t148.19\t 2.86\t0.002\t-24 -56 -10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.042\t1.416\t146.69\t 2.85\t0.002\t-44  -6  38 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t146.43\t 2.85\t0.002\t-14 -92  -2 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t144.99\t 2.85\t0.002\t-26 -34  54 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t144.16\t 2.85\t0.002\t 24 -28 -22 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t143.81\t 2.85\t0.002\t-16 -58  32 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t143.43\t 2.85\t0.002\t-32 -80 -24 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t143.41\t 2.85\t0.002\t 44 -52 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t143.23\t 2.85\t0.002\t-46  26  -2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t142.72\t 2.84\t0.002\t-54 -16  46 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t142.45\t 2.84\t0.002\t-24 -56  -6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t142.45\t 2.84\t0.002\t 62 -30  44 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t142.22\t 2.84\t0.002\t-34 -10  26 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t142.08\t 2.84\t0.002\t-16 -40 -20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t142.04\t 2.84\t0.002\t-40 -72  -2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t141.99\t 2.84\t0.002\t  2  -2  28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t141.87\t 2.84\t0.002\t-44 -16  34 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t141.81\t 2.84\t0.002\t-38 -68   0 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t141.72\t 2.84\t0.002\t-24  -2  -6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t140.76\t 2.84\t0.002\t 16  12  40 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t140.49\t 2.84\t0.002\t 54 -26  46 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t140.36\t 2.84\t0.002\t-30  62   0 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t139.68\t 2.84\t0.002\t 54 -54 -20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t139.24\t 2.84\t0.002\t-10 -10  24 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t139.07\t 2.84\t0.002\t-44 -60 -32 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t138.84\t 2.84\t0.002\t-10 -78 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t138.47\t 2.83\t0.002\t-40  30   0 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t137.97\t 2.83\t0.002\t -6  62  10 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t136.92\t 2.83\t0.002\t-10   8  56 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t136.41\t 2.83\t0.002\t-20 -18  56 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t136.13\t 2.83\t0.002\t-46  10  24 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t135.72\t 2.83\t0.002\t-36 -14 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t135.52\t 2.83\t0.002\t-24 -24  70 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t135.07\t 2.83\t0.002\t-54   8  26 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t134.00\t 2.82\t0.002\t 22 -62  58 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t133.96\t 2.82\t0.002\t 24 -72  48 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t132.44\t 2.82\t0.002\t-36 -56  60 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t132.44\t 2.82\t0.002\t-58  10  28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t131.76\t 2.82\t0.002\t-52  12  38 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t130.57\t 2.82\t0.002\t-40 -72 -16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t129.00\t 2.81\t0.002\t-54 -14  32 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t128.95\t 2.81\t0.002\t 32 -74 -20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t128.91\t 2.81\t0.002\t-50  10  14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t128.86\t 2.81\t0.002\t 34  32 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t128.75\t 2.81\t0.002\t-24 -94   2 \t\n\t\t0.050\t1.000\t3\t1.000\t0.043\t1.416\t128.28\t 2.81\t0.002\t-44 -14  38 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t128.15\t 2.81\t0.002\t-34 -22  66 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t127.46\t 2.81\t0.002\t-34 -68 -26 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t126.84\t 2.81\t0.003\t 60 -20  28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t126.76\t 2.81\t0.003\t-48 -36  32 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t126.56\t 2.81\t0.003\t 46 -14  56 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t126.45\t 2.81\t0.003\t -8 -82  42 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t124.82\t 2.80\t0.003\t 42  32 -16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t124.73\t 2.80\t0.003\t-48 -68  18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t124.58\t 2.80\t0.003\t-20  22 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t123.18\t 2.80\t0.003\t-18 -90 -20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t123.05\t 2.80\t0.003\t -8  16  32 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t122.98\t 2.80\t0.003\t-42 -56 -18 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t122.70\t 2.80\t0.003\t 24 -30 -28 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t122.70\t 2.80\t0.003\t-40   0  30 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t121.51\t 2.79\t0.003\t-32 -20  20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t120.75\t 2.79\t0.003\t-30  28  20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t120.24\t 2.79\t0.003\t-44 -20 -14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t119.71\t 2.79\t0.003\t-36 -48 -14 \t\n\t\t0.050\t1.000\t3\t1.000\t0.043\t1.416\t119.43\t 2.79\t0.003\t-42 -72 -10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t119.20\t 2.79\t0.003\t 12 -42  70 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t118.95\t 2.79\t0.003\t-42  10  20 \t\n\t\t0.050\t1.000\t2\t1.000\t0.043\t1.416\t118.81\t 2.79\t0.003\t -6 -88  -4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t117.68\t 2.78\t0.003\t-10  70   6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t117.01\t 2.78\t0.003\t 46  18  32 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t116.81\t 2.78\t0.003\t-30 -18   4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t116.75\t 2.78\t0.003\t 30  34 -14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.043\t1.416\t116.59\t 2.78\t0.003\t  8  20  52 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t116.16\t 2.78\t0.003\t 28  -8 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t116.05\t 2.78\t0.003\t 36   6 -42 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t115.02\t 2.77\t0.003\t 62 -16  24 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t114.99\t 2.77\t0.003\t-26 -18  -6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t114.62\t 2.77\t0.003\t 20 -80 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t114.33\t 2.77\t0.003\t 26 -24  24 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t113.71\t 2.77\t0.003\t-14 -60  54 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t113.55\t 2.77\t0.003\t 18 -60 -20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t112.77\t 2.77\t0.003\t 30 -86 -18 \t\n\t\t0.050\t1.000\t2\t1.000\t0.044\t1.416\t112.64\t 2.77\t0.003\t 44 -66 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t111.90\t 2.77\t0.003\t -2  -2  26 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t110.94\t 2.76\t0.003\t-44 -26  48 \t\n\t\t0.050\t1.000\t2\t1.000\t0.044\t1.416\t110.62\t 2.76\t0.003\t-46  -6  50 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t110.32\t 2.76\t0.003\t 30 -50 -28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t109.98\t 2.76\t0.003\t-24 -74  24 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t109.84\t 2.76\t0.003\t-34 -60 -16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t109.51\t 2.76\t0.003\t-36  24  16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t109.47\t 2.76\t0.003\t-24  38  12 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t109.17\t 2.76\t0.003\t 26 -70 -14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t108.96\t 2.76\t0.003\t-46 -72 -10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t108.76\t 2.76\t0.003\t-26 -60 -22 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t108.52\t 2.76\t0.003\t-20 -78 -28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t108.37\t 2.76\t0.003\t 12  20  22 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t107.78\t 2.75\t0.003\t-32 -80  -4 \t\n\t\t0.050\t1.000\t2\t1.000\t0.044\t1.416\t107.48\t 2.75\t0.003\t-44  32  28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t107.48\t 2.75\t0.003\t -8  -8  64 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t107.13\t 2.75\t0.003\t-28 -32  60 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t106.74\t 2.75\t0.003\t-28 -30  40 \t\n\t\t0.050\t1.000\t3\t1.000\t0.044\t1.416\t106.28\t 2.75\t0.003\t 28  -4 -12 \t\n\t\t0.050\t1.000\t2\t1.000\t0.044\t1.416\t106.21\t 2.75\t0.003\t-30 -16  -2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t106.20\t 2.75\t0.003\t 24 -28  -4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t106.16\t 2.75\t0.003\t 32 -72  -2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t106.04\t 2.75\t0.003\t 48  18  42 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t105.96\t 2.75\t0.003\t-50 -12  42 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t105.87\t 2.75\t0.003\t-16 -62  58 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t105.79\t 2.75\t0.003\t 44 -52  58 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t105.38\t 2.75\t0.003\t 46 -32  52 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t105.28\t 2.75\t0.003\t 38 -26 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t105.08\t 2.75\t0.003\t  2 -30  62 \t\n\t\t0.050\t1.000\t1\t1.000\t0.044\t1.416\t104.87\t 2.74\t0.003\t-14 -22  28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t104.57\t 2.74\t0.003\t-40 -60 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t103.89\t 2.74\t0.003\t-26 -88 -22 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t103.52\t 2.74\t0.003\t-16 -82  32 \t\n\t\t0.050\t1.000\t2\t1.000\t0.045\t1.416\t103.34\t 2.74\t0.003\t 60   2   4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t103.11\t 2.74\t0.003\t -2 -78 -24 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t102.99\t 2.74\t0.003\t-14 -16 -10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t102.93\t 2.74\t0.003\t 16 -32  40 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t102.88\t 2.74\t0.003\t 18  16  36 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t102.76\t 2.74\t0.003\t 56  -2  44 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t102.71\t 2.74\t0.003\t-38 -36 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t102.37\t 2.74\t0.003\t 18  10   2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t102.22\t 2.74\t0.003\t 14  68   0 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t101.82\t 2.73\t0.003\t 24  34 -16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t101.78\t 2.73\t0.003\t -4  -2  70 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t101.57\t 2.73\t0.003\t 46 -76  -6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t 99.41\t 2.73\t0.003\t-56  -2  40 \t\n\t\t0.050\t1.000\t2\t1.000\t0.045\t1.416\t 99.35\t 2.73\t0.003\t 28 -80  20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t 99.10\t 2.73\t0.003\t 14  32  -4 \t\n\t\t0.050\t1.000\t2\t1.000\t0.045\t1.416\t 98.97\t 2.73\t0.003\t 26 -34 -30 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t 98.84\t 2.72\t0.003\t  0   0  60 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t 98.01\t 2.72\t0.003\t-50  34  18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t 97.45\t 2.72\t0.003\t 22 -76  52 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t 97.30\t 2.72\t0.003\t 18 -32  44 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t 97.30\t 2.72\t0.003\t-28  32  14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t 97.02\t 2.72\t0.003\t 28 -30 -26 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t 96.56\t 2.72\t0.003\t-26 -84  -2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.045\t1.416\t 96.37\t 2.72\t0.003\t 22  32 -12 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 96.11\t 2.72\t0.003\t 44   6  28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 95.90\t 2.72\t0.003\t-40  34 -20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 95.79\t 2.71\t0.003\t-20   4 -10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 95.65\t 2.71\t0.003\t 20 -44 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 95.42\t 2.71\t0.003\t-56 -18  48 \t\n\t\t0.050\t1.000\t2\t1.000\t0.046\t1.416\t 95.28\t 2.71\t0.003\t-10 -36  62 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 95.00\t 2.71\t0.003\t-16 -14  -8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 94.74\t 2.71\t0.003\t 28 -68  24 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 94.68\t 2.71\t0.003\t 26 -14 -10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 94.48\t 2.71\t0.003\t 52 -52 -26 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 94.09\t 2.71\t0.003\t 18 -46 -16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 93.96\t 2.71\t0.003\t-12  66   0 \t\n\t\t0.050\t1.000\t2\t1.000\t0.046\t1.416\t 93.42\t 2.71\t0.003\t-22 -84 -14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 93.36\t 2.71\t0.003\t-48 -48 -14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 93.31\t 2.71\t0.003\t-48  -8  52 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 93.20\t 2.71\t0.003\t 38 -52  44 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 93.13\t 2.71\t0.003\t 20  70  18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 93.07\t 2.71\t0.003\t 36 -66  -2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 92.83\t 2.70\t0.003\t -6 -14  68 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 92.11\t 2.70\t0.003\t  8 -30  60 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 91.95\t 2.70\t0.003\t 16  12  34 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 91.59\t 2.70\t0.003\t-48  -6  14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 91.50\t 2.70\t0.003\t-20 -78  28 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 91.28\t 2.70\t0.003\t 22  -4  10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 91.07\t 2.70\t0.003\t-50  32   8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 90.75\t 2.70\t0.004\t 36 -20  64 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 90.51\t 2.70\t0.004\t-28 -88   0 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 90.19\t 2.69\t0.004\t 32   0 -24 \t\n\t\t0.050\t1.000\t1\t1.000\t0.046\t1.416\t 90.03\t 2.69\t0.004\t-18 -46  74 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 89.77\t 2.69\t0.004\t-34 -58  48 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 89.72\t 2.69\t0.004\t-10  72  10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 89.66\t 2.69\t0.004\t -8  -6  60 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 89.19\t 2.69\t0.004\t-44 -68 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 89.12\t 2.69\t0.004\t -8 -10  68 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 88.89\t 2.69\t0.004\t 32 -82 -10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 88.63\t 2.69\t0.004\t 14 -26  78 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 88.38\t 2.69\t0.004\t 44   0  24 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 88.17\t 2.69\t0.004\t 32  -2  50 \t\n\t\t0.050\t1.000\t2\t1.000\t0.047\t1.416\t 88.09\t 2.69\t0.004\t 30  30  14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 87.95\t 2.69\t0.004\t-56 -10  10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 87.71\t 2.69\t0.004\t-16 -86  -8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 87.69\t 2.69\t0.004\t-32  -8   8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 87.54\t 2.68\t0.004\t 28 -26 -16 \t\n\t\t0.050\t1.000\t2\t1.000\t0.047\t1.416\t 87.47\t 2.68\t0.004\t 22 -40 -36 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 87.33\t 2.68\t0.004\t  6  16  16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 86.99\t 2.68\t0.004\t -8 -82 -18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 86.84\t 2.68\t0.004\t-32 -70 -22 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 86.82\t 2.68\t0.004\t-30 -90  16 \t\n\t\t0.050\t1.000\t2\t1.000\t0.047\t1.416\t 86.77\t 2.68\t0.004\t 48 -58 -24 \t\n\t\t0.050\t1.000\t2\t1.000\t0.047\t1.416\t 86.70\t 2.68\t0.004\t 12  70   2 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 86.59\t 2.68\t0.004\t-32  30   4 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 86.45\t 2.68\t0.004\t 32  -2 -40 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 86.34\t 2.68\t0.004\t-28 -22  54 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 86.02\t 2.68\t0.004\t-40  38  -8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 85.82\t 2.68\t0.004\t-40 -70  -6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 85.50\t 2.68\t0.004\t 38  -2  26 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 85.38\t 2.68\t0.004\t 48 -22  52 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 85.35\t 2.68\t0.004\t-22   6 -12 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 85.11\t 2.68\t0.004\t -6  -2  58 \t\n\t\t0.050\t1.000\t1\t1.000\t0.047\t1.416\t 84.52\t 2.67\t0.004\t-34 -78 -12 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 84.37\t 2.67\t0.004\t-28  16  54 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 84.14\t 2.67\t0.004\t-18  26  54 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 84.12\t 2.67\t0.004\t-46 -48  48 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 83.37\t 2.67\t0.004\t 28 -70  30 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 83.19\t 2.67\t0.004\t 12 -88  -6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 82.96\t 2.67\t0.004\t-56 -40 -24 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 82.90\t 2.67\t0.004\t 30 -52 -32 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 82.86\t 2.67\t0.004\t 20 -50 -16 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 82.77\t 2.67\t0.004\t-26 -92   0 \t\n\t\t0.050\t1.000\t2\t1.000\t0.048\t1.416\t 82.10\t 2.66\t0.004\t 36 -80 -14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 82.04\t 2.66\t0.004\t 26 -64  46 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 81.75\t 2.66\t0.004\t 62 -12  18 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 81.38\t 2.66\t0.004\t-26 -32  70 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 81.22\t 2.66\t0.004\t-22 -92   0 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 81.15\t 2.66\t0.004\t-24 -70  20 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 81.07\t 2.66\t0.004\t-32 -74 -30 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 81.05\t 2.66\t0.004\t-26 -16 -38 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 80.94\t 2.66\t0.004\t 20  -6  14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 80.45\t 2.66\t0.004\t-34 -54  52 \t\n\t\t0.050\t1.000\t1\t1.000\t0.048\t1.416\t 80.25\t 2.66\t0.004\t 34 -68   8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t 79.75\t 2.65\t0.004\t 30 -62 -10 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t 79.41\t 2.65\t0.004\t-24  40  -8 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t 79.29\t 2.65\t0.004\t 12 -42  62 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t 79.27\t 2.65\t0.004\t-34 -54  48 \t\n\t\t0.050\t1.000\t2\t1.000\t0.049\t1.416\t 79.20\t 2.65\t0.004\t 40 -74   0 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t 78.53\t 2.65\t0.004\t-32  26 -22 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t 78.48\t 2.65\t0.004\t 26 -28  26 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t 78.38\t 2.65\t0.004\t 48 -66 -12 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t 78.16\t 2.65\t0.004\t 50 -16  34 \t\n\t\t0.050\t1.000\t2\t1.000\t0.049\t1.416\t 78.05\t 2.65\t0.004\t-28 -62  34 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t 77.83\t 2.65\t0.004\t-26 -78  -6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t 77.64\t 2.64\t0.004\t-14 -30 -34 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t 77.48\t 2.64\t0.004\t-54 -32  36 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t 77.13\t 2.64\t0.004\t  6  72  12 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t 77.02\t 2.64\t0.004\t-40 -14  50 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t 76.99\t 2.64\t0.004\t 30 -42  58 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t 76.95\t 2.64\t0.004\t  4 -12  68 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t 76.79\t 2.64\t0.004\t 48 -38 -14 \t\n\t\t0.050\t1.000\t1\t1.000\t0.049\t1.416\t 76.28\t 2.64\t0.004\t 38 -54 -38 \t\n\t\t0.050\t1.000\t1\t1.000\t0.050\t1.416\t 76.04\t 2.64\t0.004\t-34  34   6 \t\n\t\t0.050\t1.000\t1\t1.000\t0.050\t1.416\t 75.02\t 2.63\t0.004\t 40  48  24 \t\n\t\t0.050\t1.000\t1\t1.000\t0.050\t1.416\t 75.00\t 2.63\t0.004\t 18   4  70 \t\n\ntable shows 3 local maxima more than 8.0mm apart\n--------------------------------------------------------------------------------\nHeight threshold: T = 74.36, p = 0.004 (0.050)\nExtent threshold: k = 0 voxels\nExpected voxels per cluster, <k> = 1627672756121055472386048.000\nExpected number of clusters, <c> = 0.05\nFWEp: 74.358, FDRp: Inf, FWEc: Inf, FDRc: Inf\nDegrees of freedom = [1.0, 1.0]\nFWHM = 9022.3 8849.2 8460.1 mm mm mm; 4511.2 4424.6 4230.1 {voxels}\nVolume: 1446728 = 180841 voxels = 0.0 resels\nVoxel size: 2.0 2.0 2.0 mm mm mm; (resel = 84432248537.45 voxels)\n================================================================================\n\n24-Mar-2021 23:42:30 - Done    'Results Report'\n24-Mar-2021 23:42:30 - Done\n\n","name":"stdout"}]},{"cell_type":"code","source":"% Display group positive contrast\n\ncon_fn = spm_vol(fullfile(results_dir, 'con_0001.nii'));\ncontrast = spm_read_vols(con_fn);\n[Ni, Nj, Nk] = size(contrast);\nimagesc(rot90(squeeze(contrast(:,:,round(Nk/2))))); colormap jet; colorbar; axis image; set(gca,'xtick',[],'ytick',[]);","metadata":{},"execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Image 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"The code contained in this notebook is based on Stephan Heunis [tutorial](https://github.com/jsheunis/spm12-octave-jupyter).","metadata":{}}]}