{"id":19252259,"url":"https://github.com/sccn/std_dipoledensity","last_synced_at":"2025-02-23T16:50:37.013Z","repository":{"id":56738526,"uuid":"327717455","full_name":"sccn/std_dipoleDensity","owner":"sccn","description":null,"archived":false,"fork":false,"pushed_at":"2024-09-18T16:52:54.000Z","size":2551,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-01-05T06:43:03.698Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"MATLAB","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sccn.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-01-07T20:26:21.000Z","updated_at":"2024-09-18T16:52:57.000Z","dependencies_parsed_at":"2024-08-23T21:34:03.618Z","dependency_job_id":"e12e2681-89b8-4fa2-9e39-0096fa932c78","html_url":"https://github.com/sccn/std_dipoleDensity","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sccn%2Fstd_dipoleDensity","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sccn%2Fstd_dipoleDensity/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sccn%2Fstd_dipoleDensity/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sccn%2Fstd_dipoleDensity/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sccn","download_url":"https://codeload.github.com/sccn/std_dipoleDensity/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240347944,"owners_count":19787236,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-09T18:26:10.017Z","updated_at":"2025-02-23T16:50:36.989Z","avatar_url":"https://github.com/sccn.png","language":"MATLAB","funding_links":[],"categories":[],"sub_categories":[],"readme":"![topImage.png](images/topImage.png)\n\n# STUDY dipole density EEGLAB plugin\n\nDipoleDensity, called by the function std_dipplotWithDensity or in the\nEEGLAB gui under Study-\\\u003e\"Plot group dipoles with density\", is a\nvisualization tool for dipole clusters within a study set. This function\nwill present three different visualizations of selected clusters to\nbetter understand their distributions throughout the brain (the clusters\nmust already be computed). \n\nNote that it is possible to plot Dipole Density directly from the EEGLAB Study plot. However, this plugin offers additional functionalities. \n\nThe interface is shown here:\n\n![Dipoledensity_ui.png](images/Dipoledensity_ui.png)\n\nEach row of \"Cluster\"/\"Group\"/\"Color\" can plot a separate cluster and\nany left blank will be ignored. The inputs work as follow:\n\n|                   |                                                                                                                                                                                                                                                                                                                                                                                              |\n|-------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| Cluster           | Select a cluster to be plotted                                                                                                                                                                                                                                                                                                                                                               |\n| Group             | Select a subgroup of the cluster to plot based on study conditions                                                                                                                                                                                                                                                                                                                           |\n| Color             | Select a color to represent the corresponding cluster in the 3D dipole plot (third image in the lower table)                                                                                                                                                                                                                                                                                 |\n| Slice orientation | Select the orientation in which the MRI is sliced for the tiled dipole density plot (first image in the tabel below)                                                                                                                                                                                                                                                                         |\n| Smoothing kernal  | Select the width in mm to blurr each dipole. Default is 20mm, although that is quite heavy. 10mm provides a nice tradeoff between resolution and cluster visability. Trying multiple values here is highly suggested. This affects both the tiled and interactive images (first and second images in the lower table).                                                                       |\n| Slice orientation | Select the orientation in which the MRI is sliced for the tiled dipole density plot                                                                                                                                                                                                                                                                                                          |\n| Color range       | Select the range of values to show in the tiled dipole density plot. To select a useful range of values, it is helpful to first plot the figures with an automatic range and then look at the colorbar. The values are quite small as the dipole density values are normalized such that the voxels of the brain sum to 1. This will affect the Interactive plot as well in a later version. |\n| Plot 1 - Plot 2   | If groups are selected in the dropdown menu, a second version of the tiled dipole density figure showing the the difference in dipole densities between the groups will be created. The difference will be thresholded by using the valuethe given Thresh. The thresholding is done using uncorrected p-valuel.                                                                              |\n| Save plot         | If checked, saves all plots excluding the interactive dipole density figures.                                                                                                                                                                                                                                                                                                                |\n\nThe plots provided are shown below:\n\n\u003ctable\u003e\n\u003ctbody\u003e\n\u003ctr class=\"odd\"\u003e\n\u003ctd\u003e\u003cp\u003eTiled dipole density plot: Each cluster selected will create a tiled image of MRI slices showing the cluster densities. The orientation of the slices is chosen using \"Slice Orientation\" in the original pop-up interface.\u003c/p\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cp\u003e\u003cimg src=\"images/Dipoledensity_dipplottile.png\"\u003e\u003c/p\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr class=\"even\"\u003e\n\u003ctd\u003e\u003cp\u003eInteractive dipole density plot: This figure contains the same information as in the tiled dipole density plot. the interface allows the slice locations to be shifted in real time to allow for a more intuitive egometric understanding of dipole density locations.\u003c/p\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cp\u003e\u003cimg src=\"images/Dipoledensity_dipplotinteractive.png\" width=\"150px\" height=\"300px\"\u003e\u003c/p\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr class=\"odd\"\u003e\n\u003ctd\u003e\u003cp\u003e3D dipplot: All the dipoles from the selected clusters and groups are presented in 3D. Each cluster/group is marked by the color chosed in the user interface.\u003c/p\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cp\u003e\u003cimg src=\"images/Dipoledensity_dipplot3d.png\"\u003e\u003c/p\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\nNote: Another use of this function could be to estimate the number of\nclusters to group dipoles into; although, to do so you must first create\na cluster containing all dipoles. Eventually, this will be integrated as\nan option so that the extra clustering step is not necessary for this\ninitial analysis.\n\nAuthors: Luca Pion-Tonacini and Makoto Miyakoshi. SCCN, INC, UCSD. The function to show tiled dipole plots (mir3dplot) was written by Arnaud Delorme.\n\n# Version history\n\n## Version 1.00 Update (08/23/2024)\nUpdated to be compatible with EEGLAB 2024.1. Currently it is alpha version,\nnot extensively tested yet. Once tested enough, it will be officially registered\nto the EEGLAB extentinon manager.\n\n\n## Version 0.40 Update (10/31/2018)\n\n[Talairach daemon](http://www.talairach.org/index.html) (Lancaster et\nal., 1997; Lancaster et al., 2000) is supported, via Christian Kothe's\nfunction, to obtain anatomical location of the cluster centroid.\nStandard deviation of the cluster centroid (the mean across x, y, and z\nvalues) is used to determine the diameter of the confusion sphere. The\ncursor's initial position is set to the voxel with the maximum dipole\ndensity. Probabilistic dipole density is correctly scaled so that sum of\nall the voxels == 1.\n\nIn using Talairach daemon, I prepared an exclusion criteria to exclude\nnon-EEG-generative areas that are included by default. For the Level3\noutputs, the following list is applied. For the Level5 outputs, only\nBrodmann areas are used. Only these two levels are used. The full list\nof the Talairach daemon output can be found\n[here](http://www.talairach.org/labels.html). The probabilities for\nLevel3 (anatomical names) and Level5 (Brodmann area numbers) are\ncalculated separately, so the sum of probability FOR EACH is 1. If you\nadd up Level3 and Level5 together, they will overlap each other and the\nsum will be 2.\n\n```\n`      % Exclusion list--'x' means to exclude.`\n`        Angular Gyrus`\n`        Anterior Cingulate`\n`      x Caudate`\n`      x Cerebellar Lingual`\n`      x Cerebellar Tonsil`\n`        Cingulate Gyrus`\n`      x Claustrum`\n`      x Culmen`\n`      x Culmen of Vermis`\n`        Cuneus`\n`      x Declive`\n`      x Declive of Vermis`\n`      x Extra-Nuclear`\n`      x Fastigium`\n`      x Fourth Ventricle`\n`        Fusiform Gyrus`\n`        Inferior Frontal Gyrus`\n`        Inferior Occipital Gyrus`\n`        Inferior Parietal Lobule`\n`      x Inferior Semi-Lunar Lobule`\n`        Inferior Temporal Gyrus`\n`        Insula`\n`      x Lateral Ventricle`\n`      x Lentiform Nucleus`\n`        Lingual Gyrus`\n`        Medial Frontal Gyrus`\n`        Middle Frontal Gyrus`\n`        Middle Occipital Gyrus`\n`        Middle Temporal Gyrus`\n`      x Nodule`\n`        Orbital Gyrus`\n`        Paracentral Lobule`\n`      x Parahippocampal Gyrus`\n`        Postcentral Gyrus`\n`        Posterior Cingulate`\n`        Precentral Gyrus`\n`        Precuneus`\n`      x Pyramis`\n`      x Pyramis of Vermis`\n`        Rectal Gyrus`\n`      x Subcallosal Gyrus`\n`      x Sub-Gyral`\n`        Superior Frontal Gyrus`\n`        Superior Occipital Gyrus`\n`        Superior Parietal Lobule`\n`        Superior Temporal Gyrus`\n`        Supramarginal Gyrus`\n`      x Thalamus`\n`      x Third Ventricle`\n`        Transverse Temporal Gyrus`\n`      x Tuber`\n`      x Tuber of Vermis`\n`      x Uncus`\n`      x Uvula`\n`      x Uvula of Vermis`\n```\n\n![Version0_40.png](images/Version0_40.png)\n\nThis is how the probabilistic labels are generated.\n\n1.  You specify one point per IC cluster (the point where peak dipole\n    probability is shown) in inside-brain space in MNI coordinate.\n2.  You expand the point to a sphere with a radius that has the length\n    of standard deviation of dipole locations (in x, y, and z--then the\n    three SD values are averaged).\n3.  The expanded 'sphere' now contains multiple spatial grid points,\n    each of which has anatomical label. Now, let's say the current\n    sphere contains 30 spatial grid points. If 10/30 points are assigned\n    with 'medial PFC', then 10/30 = 33% of the anatomical label is\n    associated with 'medial PFC'. If 7/30 points are assigned with\n    'medial OFC', then 7/30 = 23.3% of the anatomical label is\n    associated with 'medial OFC'. In this way, you list up all the\n    unique anatomical labels, then counts how many points are associated\n    with, then finally calculate the ratio against the total number of\n    the points included.\n\n## Update (05/26/2017)\n\nUsed and bug in calculating dual dipole (now selects the side by\nobtaining the actual x coordinate values) fixed.\n\n## Update (05/22/2017)\n\nA cluster centroid is calculated only using one of the bilateral dipoles\non the side the centoid of the single dipoles exists.\n\n## Update (03/16/2017)\n\nWhen (symmetrical) two dipoles are fit bilaterally (using Caterina's\nfitTwoDipoles plugin, for example), mean, standard deviation, and\nstandard error of dipole clusters are computed only using the one on the\nsame side of the cluster centroid. Visualization still uses two dipoles.\n\n## Update (03/06/2017)\n\nNow crosshair is supported on the interactive browser. Also, input for\nthe 3-D Gaussian kernel size was changed from sigma in Gaussian equation\nto full-width half-maximum (FWHM) which is more standard in\nneuroimaging. FWHM = sigma\\*2.355.\n\n![Crosshair2.png](images/Crosshair2.png)\n\n## Update (09/13/2016)\n\nNow it returns cluster centroid standard deviation and errors as\nfollows.\n*Cluster: 3*\n*Centroid in MNI: \\[-29 -69 8\\]*\n*Standard Deviation: \\[ 8 15 16\\]*\n*Standard Error : \\[ 2 4 5\\]*\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsccn%2Fstd_dipoledensity","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsccn%2Fstd_dipoledensity","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsccn%2Fstd_dipoledensity/lists"}