diff --git a/notebooks/atlas_viewer.ipynb b/notebooks/atlas_viewer.ipynb new file mode 100644 index 0000000..685c2bc --- /dev/null +++ b/notebooks/atlas_viewer.ipynb @@ -0,0 +1,112 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Atlas viewer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This atlas viewer reads out the atlas information at a given MNI coordinate, from the following five atlases:\n", + "1. SPM12's **Anatomical Automatic Labeling (AAL)** atlas\n", + "2. FreeSurfer's **Desikan-Killiany** atlas (based on subject `cvs_avg35_inMNI152`)\n", + "3. FreeSurfer's **Destrieux** atlas (based on subject `cvs_avg35_inMNI152`)\n", + "4. FSL's **Harvard-Oxford** cortical and subcortical probability atlas\n", + "5. FSL's **Juelich** probability atlas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To run the atlas viewer, simply chose your x, y, z coordinate via the sliders and than click either on the *Show Segmentation Information* or on the *Show Location Figure* button." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Segmentation information at [5.0, 1.0, 61.0]:\n", + "aal Supp_Motor_Area_R\n", + "freesurfer_desikan-killiany ctx-rh-superiorfrontal\n", + "freesurfer_destrieux ctx_rh_G_front_sup\n", + "HarvardOxford 85% Juxtapositional_Lobule_Cortex_(formerly_Supplementary_Motor_Cortex)\n", + "HarvardOxford 2% Precentral_Gyrus\n", + "Juelich 84% GM_Premotor_cortex_BA6_R\n", + "\n", + "\n" + ] + }, + { + "data": { + "image/png": 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Fbp5k7ZGgUyMOh0MULJ3a47kqFArSFIwpGABz3lO6V4OVSOj0WSAQwI4dO/DA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\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "from nipy.labs import viz\n", + "import ipywidgets\n", + "from IPython.display import display, clear_output\n", + "\n", + "x_value = ipywidgets.IntSlider(min=-75, max=75, value=5, description='x_value')\n", + "y_value = ipywidgets.IntSlider(min=-110, max=75, value=1, description='y_value')\n", + "z_value = ipywidgets.IntSlider(min=-72, max=85, value=61, description='z_value')\n", + "\n", + "def showInfo(_):\n", + " clear_output()\n", + " coord = [x_value.value, y_value.value, z_value.value]\n", + " coordStr = ','.join([str(coord[0]),str(coord[1]),str(coord[2])])\n", + " %run ../scripts/atlas_reader.py all $coordStr 0 1\n", + "\n", + " template = '../scripts/templates/MNI152_T1_1mm_brain.nii.gz'\n", + " anatimg = nb.load(template)\n", + " anatdata, anataff = anatimg.get_data(), anatimg.get_affine()\n", + " anatdata = anatdata.astype(np.float)\n", + " anatdata[anatdata < 10.] = np.nan\n", + " fig=viz.plot_anat(anatdata, anataff, coord, slicer='ortho')\n", + "\n", + "showAtlasInfo = ipywidgets.Button(description='Show Atlas Information')\n", + "showAtlasInfo.on_click(showInfo)\n", + "display(x_value, y_value, z_value, showAtlasInfo)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/scripts/atlas_reader.py b/scripts/atlas_reader.py new file mode 100644 index 0000000..d4f5a15 --- /dev/null +++ b/scripts/atlas_reader.py @@ -0,0 +1,119 @@ +import sys +import numpy as np +import nibabel as nb + + +def getVoxCoord(affine, coord): + """computes voxel ID from MNI coordinate""" + inverse = np.linalg.inv(affine) + voxCoord = np.dot(inverse, np.hstack((coord, 1)))[:3] + return voxCoord.round().astype('int').tolist() + + +def getLabel(atlastype, labelID): + """reads out the name of a specific label""" + if 'freesurfer' in atlastype: + atlastype = 'freesurfer' + labels = np.recfromcsv('../scripts/atlases/labels_%s.csv' % atlastype) + labelIdx = labels.index == labelID + if labelIdx.sum() == 0: + label = 'No_label' + else: + label = labels[labelIdx][0][1] + return label + + +def readAtlas(atlastype, coordinate, probThresh=5): + """ + Reads specific atlas and returns segment/probability information. + It is possible to threshold a given probability atlas [in percentage]. + """ + + if atlastype in ['aal', + 'freesurfer_desikan-killiany', + 'freesurfer_destrieux']: + atlas = nb.load('../scripts/atlases/atlas_%s.mgz' % atlastype) + probAtlas = False + else: + atlas = nb.load('../scripts/atlases/atlas_%s.nii.gz' % atlastype) + probAtlas = True + + # Get atlas data and affine matrix + data = atlas.get_data() + affine = atlas.get_affine() + + # Get voxel index + voxID = getVoxCoord(affine, coordinate) + + # Get Label information + if probAtlas: + probs = data[voxID[0], voxID[1], voxID[2]] + probs[probs < probThresh] = 0 + idx = np.where(probs)[0] + + # sort list by probability + idx = idx[np.argsort(probs[idx])][::-1] + + # get probability and label names + probLabel = [] + for i in idx: + label = getLabel(atlastype, i) + probLabel.append([probs[i], label]) + + # If no labels found + if probLabel == []: + probLabel = [[0, 'No_label']] + + return probLabel + + else: + labelID = int(data[voxID[0], voxID[1], voxID[2]]) + label = getLabel(atlastype, labelID) + return label + + +def writeOutputToScreen(atlasinfo, coord): + """Writes output to the sceen""" + print "Segmentation information at {0}:".format(coord) + + for ainfo in atlasinfo: + if ainfo[0] in ['aal', + 'freesurfer_desikan-killiany', + 'freesurfer_destrieux']: + print "{0:<36}{1}".format(ainfo[0], ainfo[1]) + else: + for s in ainfo[1]: + print "{0:<30}{1:>4}% {2}".format(ainfo[0], s[0], s[1]) + print "\n" + + +def getAtlasinfo(coord, atlastype='all', probThresh=5, writeToScreen=True): + + atlasinfo = [] + if atlastype != 'all': + segment = readAtlas(atlastype, coord, probThresh) + atlasinfo.append([atlastype, segment]) + else: + for atypes in ['aal', + 'freesurfer_desikan-killiany', + 'freesurfer_destrieux', + 'HarvardOxford', + 'Juelich']: + segment = readAtlas(atypes, coord, probThresh) + atlasinfo.append([atypes, segment]) + + # Write output to screen + if writeToScreen: + writeOutputToScreen(atlasinfo, coord) + + return atlasinfo + + +if __name__ == "__main__": + + atlastype = str(sys.argv[1]) + coord = [float(x) for x in str(sys.argv[2]).split(',')] + probThresh = int(sys.argv[3]) + writeToScreen = bool(sys.argv[4]) + + getAtlasinfo(coord, atlastype, probThresh, writeToScreen) diff --git a/scripts/atlases/atlas_HarvardOxford.nii.gz b/scripts/atlases/atlas_HarvardOxford.nii.gz new file mode 100644 index 0000000..17f17b6 Binary files /dev/null and b/scripts/atlases/atlas_HarvardOxford.nii.gz differ diff --git a/scripts/atlases/atlas_Juelich.nii.gz b/scripts/atlases/atlas_Juelich.nii.gz new file mode 100644 index 0000000..0a6c70c Binary files /dev/null and b/scripts/atlases/atlas_Juelich.nii.gz differ diff --git a/scripts/atlases/atlas_aal.mgz b/scripts/atlases/atlas_aal.mgz new file mode 100644 index 0000000..6788f74 Binary files /dev/null and b/scripts/atlases/atlas_aal.mgz differ diff --git a/scripts/atlases/atlas_freesurfer_desikan-killiany.mgz b/scripts/atlases/atlas_freesurfer_desikan-killiany.mgz new file mode 100644 index 0000000..a1bcc8b Binary files /dev/null and b/scripts/atlases/atlas_freesurfer_desikan-killiany.mgz differ diff --git a/scripts/atlases/atlas_freesurfer_destrieux.mgz b/scripts/atlases/atlas_freesurfer_destrieux.mgz new file mode 100644 index 0000000..2a7eeea Binary files /dev/null and b/scripts/atlases/atlas_freesurfer_destrieux.mgz differ diff --git a/scripts/atlases/description_HarvardOxford.rst b/scripts/atlases/description_HarvardOxford.rst new file mode 100644 index 0000000..dc3f6fc --- /dev/null +++ b/scripts/atlases/description_HarvardOxford.rst @@ -0,0 +1,38 @@ +Harvard-Oxford atlas +-------------------- + +The Harvard-Oxford cortical and subcortical structural atlases are a copy of +FSL's (Version 5.0) under `$FSL_DIR/data/atlases/HarvardOxford/`: + - HarvardOxford-cortl-prob-1mm.nii.gz' + - HarvardOxford-sub-prob-1mm.nii.gz' +For further information see http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases. + +The following 4 areas were deleted from the subcortical structural atlas, as +they are already coverd in the cortical structural atlas: + - 'Left Cerebral White Matter' + - 'Left Cerebral Cortex ' + - 'Right Cerebral White Matter' + - 'Right Cerebral Cortex ' + +Afterwards, the cortical and subcortical structural atlases were merged with +the following python code: + + import nibabel as nb + cort = nb.load('HarvardOxford-cort-prob-1mm.nii.gz') + sub = nb.load('HarvardOxford-sub-prob-1mm.nii.gz') + newsubdata = sub.get_data()[:, :, :, range(2, 11) + range(13, 21)] + data = np.concatenate((cort.get_data(), newsubdata), axis=3) + affine = cort.get_affine() + nb.Nifti1Image(data.astype('uint8'), affine).to_filename('bladi.nii.gz') + + +References +---------- + +Makris N, Goldstein JM, Kennedy D, Hodge SM, Caviness VS, Faraone SV, Tsuang MT, Seidman LJ. Decreased volume of left and total anterior insular lobule in schizophrenia. Schizophr Res. 2006 Apr;83(2-3):155-71 + +Frazier JA, Chiu S, Breeze JL, Makris N, Lange N, Kennedy DN, Herbert MR, Bent EK, Koneru VK, Dieterich ME, Hodge SM, Rauch SL, Grant PE, Cohen BM, Seidman LJ, Caviness VS, Biederman J. Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder. Am J Psychiatry. 2005 Jul;162(7):1256-65 + +Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006 Jul 1;31(3):968-80. + +Goldstein JM, Seidman LJ, Makris N, Ahern T, O'Brien LM, Caviness VS Jr, Kennedy DN, Faraone SV, Tsuang MT. Hypothalamic abnormalities in schizophrenia: sex effects and genetic vulnerability. Biol Psychiatry. 2007 Apr 15;61(8):935-45 diff --git a/scripts/atlases/description_Juelich.txt b/scripts/atlases/description_Juelich.txt new file mode 100644 index 0000000..fe3cf9f --- /dev/null +++ b/scripts/atlases/description_Juelich.txt @@ -0,0 +1,16 @@ +Juelich atlas +-------------- + +The Juelich atlas is a direct copy of FSL's (Version 5.0) Juelich atlas under +`$FSL_DIR/data/atlases/Juelich/Juelich-prob-1mm.nii.gz`. +For further information see http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases. + + +References +---------- + +Eickhoff, S. B., Stephan, K. E., Mohlberg, H., Grefkes, C., Fink, G. R., Amunts, K., & Zilles, K. (2005). A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage, 25(4), 1325-1335. + +Eickhoff, S. B., Heim, S., Zilles, K., & Amunts, K. (2006). Testing anatomically specified hypotheses in functional imaging using cytoarchitectonic maps. Neuroimage, 32(2), 570-582. + +Eickhoff, S. B., Paus, T., Caspers, S., Grosbras, M. H., Evans, A. C., Zilles, K., & Amunts, K. (2007). Assignment of functional activations to probabilistic cytoarchitectonic areas revisited. Neuroimage, 36(3), 511-521. \ No newline at end of file diff --git a/scripts/atlases/description_aal.txt b/scripts/atlases/description_aal.txt new file mode 100644 index 0000000..81461f8 --- /dev/null +++ b/scripts/atlases/description_aal.txt @@ -0,0 +1,15 @@ +AAL atlas +--------- + +The Anatomical Automatic Labeling (AAL) atlas is a copy of SPM12's AAL atlas +(from 25/08/2015) that can be found here: http://www.gin.cnrs.fr/spip-php-article217 + +The NIfTI file was converted into MGZ-file format with the following command: + + mri_convert aal_SPM12/AAL.nii aal.mgz + + +References +---------- + +Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., ... & Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1), 273-289. diff --git a/scripts/atlases/description_freesurfer.txt b/scripts/atlases/description_freesurfer.txt new file mode 100644 index 0000000..64519ee --- /dev/null +++ b/scripts/atlases/description_freesurfer.txt @@ -0,0 +1,19 @@ +FreeSurfer atlas +---------------- + +The following two atlases are a copy from FreeSurfer's (v5.3.) subject folder +`cvs_avg35_inMNI152` under `$FREESURFER_HOME/subjects/cvs_avg35_inMNI152/mri`: + - Desikan-Killiany atlas was renamed from aparc+aseg.mgz to + atlas_freesurfer_desikan-killiany.mgz + - Destrieux atlas was renamed from aparc.a2009s+aseg.mgz to + atlas_freesurfer_destrieux.mgz + + +References +---------- + +Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Ségonne, F., Salat, D. H., ... & Caviness, V. (2004). Automatically parcellating the human cerebral cortex. Cerebral cortex, 14(1), 11-22. + +Destrieux, C., Fischl, B., Dale, A. M., & Halgren, E. (2009). A sulcal depth-based anatomical parcellation of the cerebral cortex. NeuroImage, 47, S151. + +Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., ... & Albert, M. S. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 31(3), 968-980. diff --git a/scripts/atlases/labels_HarvardOxford.csv b/scripts/atlases/labels_HarvardOxford.csv new file mode 100644 index 0000000..1ad29bf --- /dev/null +++ b/scripts/atlases/labels_HarvardOxford.csv @@ -0,0 +1,66 @@ +index,name +0,Frontal_Pole +1,Insular_Cortex +2,Superior_Frontal_Gyrus +3,Middle_Frontal_Gyrus +4,Inferior_Frontal_Gyrus_pars_triangularis +5,Inferior_Frontal_Gyrus_pars_opercularis +6,Precentral_Gyrus +7,Temporal_Pole +8,Superior_Temporal_Gyrus_anterior_division +9,Superior_Temporal_Gyrus_posterior_division +10,Middle_Temporal_Gyrus_anterior_division +11,Middle_Temporal_Gyrus_posterior_division +12,Middle_Temporal_Gyrus_temporooccipital_part +13,Inferior_Temporal_Gyrus_anterior_division +14,Inferior_Temporal_Gyrus_posterior_division +15,Inferior_Temporal_Gyrus_temporooccipital_part +16,Postcentral_Gyrus +17,Superior_Parietal_Lobule +18,Supramarginal_Gyrus_anterior_division +19,Supramarginal_Gyrus_posterior_division +20,Angular_Gyrus +21,Lateral_Occipital_Cortex_superior_division +22,Lateral_Occipital_Cortex_inferior_division +23,Intracalcarine_Cortex +24,Frontal_Medial_Cortex +25,Juxtapositional_Lobule_Cortex_(formerly_Supplementary_Motor_Cortex) +26,Subcallosal_Cortex +27,Paracingulate_Gyrus +28,Cingulate_Gyrus_anterior_division +29,Cingulate_Gyrus_posterior_division +30,Precuneous_Cortex +31,Cuneal_Cortex +32,Frontal_Orbital_Cortex +33,Parahippocampal_Gyrus_anterior_division +34,Parahippocampal_Gyrus_posterior_division +35,Lingual_Gyrus +36,Temporal_Fusiform_Cortex_anterior_division +37,Temporal_Fusiform_Cortex_posterior_division +38,Temporal_Occipital_Fusiform_Cortex +39,Occipital_Fusiform_Gyrus +40,Frontal_Operculum_Cortex +41,Central_Opercular_Cortex +42,Parietal_Operculum_Cortex +43,Planum_Polare +44,Heschl's_Gyrus_(includes_H1_and_H2) +45,Planum_Temporale +46,Supracalcarine_Cortex +47,Occipital_Pole +48,Left_Lateral_Ventrical +49,Left_Thalamus +50,Left_Caudate +51,Left_Putamen +52,Left_Pallidum +53,Brain-Stem +54,Left_Hippocampus +55,Left_Amygdala +56,Left_Accumbens +57,Right_Lateral_Ventricle +58,Right_Thalamus +59,Right_Caudate +60,Right_Putamen +61,Right_Pallidum +62,Right_Hippocampus +63,Right_Amygdala +64,Right_Accumbens \ No newline at end of file diff --git a/scripts/atlases/labels_Juelich.csv b/scripts/atlases/labels_Juelich.csv new file mode 100644 index 0000000..4032670 --- /dev/null +++ b/scripts/atlases/labels_Juelich.csv @@ -0,0 +1,122 @@ +index,name +0,GM_Anterior_intra-parietal_sulcus_hIP1_L +1,GM_Anterior_intra-parietal_sulcus_hIP1_R +2,GM_Anterior_intra-parietal_sulcus_hIP2_L +3,GM_Anterior_intra-parietal_sulcus_hIP2_R +4,GM_Anterior_intra-parietal_sulcus_hIP3_L +5,GM_Anterior_intra-parietal_sulcus_hIP3_R +6,GM_Amygdala_centromedial_group_L +7,GM_Amygdala_centromedial_group_R +8,GM_Amygdala_laterobasal_group_L +9,GM_Amygdala_laterobasal_group_R +10,GM_Amygdala_superficial_group_L +11,GM_Amygdala_superficial_group_R +12,GM_Broca's_area_BA44_L +13,GM_Broca's_area_BA44_R +14,GM_Broca's_area_BA45_L +15,GM_Broca's_area_BA45_R +16,GM_Hippocampus_cornu_ammonis_L +17,GM_Hippocampus_cornu_ammonis_R +18,GM_Hippocampus_entorhinal_cortex_L +19,GM_Hippocampus_entorhinal_cortex_R +20,GM_Hippocampus_dentate_gyrus_L +21,GM_Hippocampus_dentate_gyrus_R +22,GM_Hippocampus_hippocampal-amygdaloid_transition_area_L +23,GM_Hippocampus_hippocampal-amygdaloid_transition_area_R +24,GM_Hippocampus_subiculum_L +25,GM_Hippocampus_subiculum_R +26,GM_Inferior_parietal_lobule_PF_L +27,GM_Inferior_parietal_lobule_PF_R +28,GM_Inferior_parietal_lobule_PFcm_L +29,GM_Inferior_parietal_lobule_PFcm_R +30,GM_Inferior_parietal_lobule_PFm_L +31,GM_Inferior_parietal_lobule_PFm_R +32,GM_Inferior_parietal_lobule_PFop_L +33,GM_Inferior_parietal_lobule_PFop_R +34,GM_Inferior_parietal_lobule_PFt_L +35,GM_Inferior_parietal_lobule_PFt_R +36,GM_Inferior_parietal_lobule_Pga_L +37,GM_Inferior_parietal_lobule_Pga_R +38,GM_Inferior_parietal_lobule_PGp_L +39,GM_Inferior_parietal_lobule_PGp_R +40,GM_Primary_auditory_cortex_TE1.0_L +41,GM_Primary_auditory_cortex_TE1.0_R +42,GM_Primary_auditory_cortex_TE1.1_L +43,GM_Primary_auditory_cortex_TE1.1_R +44,GM_Primary_auditory_cortex_TE1.2_L +45,GM_Primary_auditory_cortex_TE1.2_R +46,GM_Primary_motor_cortex_BA4a_L +47,GM_Primary_motor_cortex_BA4a_R +48,GM_Primary_motor_cortex_BA4p_L +49,GM_Primary_motor_cortex_BA4p_R +50,GM_Primary_somatosensory_cortex_BA1_L +51,GM_Primary_somatosensory_cortex_BA1_R +52,GM_Primary_somatosensory_cortex_BA2_L +53,GM_Primary_somatosensory_cortex_BA2_R +54,GM_Primary_somatosensory_cortex_BA3a_L +55,GM_Primary_somatosensory_cortex_BA3a_R +56,GM_Primary_somatosensory_cortex_BA3b_L +57,GM_Primary_somatosensory_cortex_BA3b_R +58,GM_Secondary_somatosensory_cortex_/_Parietal_operculum_OP1_L +59,GM_Secondary_somatosensory_cortex_/_Parietal_operculum_OP1_R +60,GM_Secondary_somatosensory_cortex_/_Parietal_operculum_OP2_L +61,GM_Secondary_somatosensory_cortex_/_Parietal_operculum_OP2_R +62,GM_Secondary_somatosensory_cortex_/_Parietal_operculum_OP3_L +63,GM_Secondary_somatosensory_cortex_/_Parietal_operculum_OP3_R +64,GM_Secondary_somatosensory_cortex_/_Parietal_operculum_OP4_L +65,GM_Secondary_somatosensory_cortex_/_Parietal_operculum_OP4_R +66,GM_Superior_parietal_lobule_5Ci_L +67,GM_Superior_parietal_lobule_5Ci_R +68,GM_Superior_parietal_lobule_5L_L +69,GM_Superior_parietal_lobule_5L_R +70,GM_Superior_parietal_lobule_5M_L +71,GM_Superior_parietal_lobule_5M_R +72,GM_Superior_parietal_lobule_7A_L +73,GM_Superior_parietal_lobule_7A_R +74,GM_Superior_parietal_lobule_7M_L +75,GM_Superior_parietal_lobule_7M_R +76,GM_Superior_parietal_lobule_7PC_L +77,GM_Superior_parietal_lobule_7PC_R +78,GM_Superior_parietal_lobule_7P_L +79,GM_Superior_parietal_lobule_7P_R +80,GM_Visual_cortex_V1_BA17_L +81,GM_Visual_cortex_V1_BA17_R +82,GM_Visual_cortex_V2_BA18_L +83,GM_Visual_cortex_V2_BA18_R +84,GM_Visual_cortex_V3V_L +85,GM_Visual_cortex_V3V_R +86,GM_Visual_cortex_V4_L +87,GM_Visual_cortex_V4_R +88,GM_Visual_cortex_V5_L +89,GM_Visual_cortex_V5_R +90,GM_Premotor_cortex_BA6_L +91,GM_Premotor_cortex_BA6_R +92,WM_Acoustic_radiation_R +93,WM_Acoustic_radiation_L +94,WM_Callosal_body +95,WM_Cingulum_R +96,WM_Cingulum_L +97,WM_Corticospinal_tract_R +98,WM_Corticospinal_tract_L +99,WM_Fornix +100,WM_Inferior_occipito-frontal_fascicle_R +101,WM_Inferior_occipito-frontal_fascicle_L +102,GM_Lateral_geniculate_body_R +103,GM_Lateral_geniculate_body_L +104,GM_Mamillary_body +105,GM_Medial_geniculate_body_R +106,GM_Medial_geniculate_body_L +107,WM_Optic_radiation_R +108,WM_Optic_radiation_L +109,WM_Superior_longitudinal_fascicle_R +110,WM_Superior_longitudinal_fascicle_L +111,WM_Superior_occipito-frontal_fascicle_R +112,WM_Superior_occipito-frontal_fascicle_L +113,WM_Uncinate_fascicle_R +114,WM_Uncinate_fascicle_L +115,GM_Insula_Id1_L +116,GM_Insula_Id1_R +117,GM_Insula_Ig1_L +118,GM_Insula_Ig1_R +119,GM_Insula_Ig2_L +120,GM_Insula_Ig2_R \ No newline at end of file diff --git a/scripts/atlases/labels_aal.csv b/scripts/atlases/labels_aal.csv new file mode 100644 index 0000000..1f62bd0 --- /dev/null +++ b/scripts/atlases/labels_aal.csv @@ -0,0 +1,117 @@ +index,name +2001,Precentral_L +2002,Precentral_R +2101,Frontal_Sup_L +2102,Frontal_Sup_R +2111,Frontal_Sup_Orb_L +2112,Frontal_Sup_Orb_R +2201,Frontal_Mid_L +2202,Frontal_Mid_R +2211,Frontal_Mid_Orb_L +2212,Frontal_Mid_Orb_R +2301,Frontal_Inf_Oper_L +2302,Frontal_Inf_Oper_R +2311,Frontal_Inf_Tri_L +2312,Frontal_Inf_Tri_R +2321,Frontal_Inf_Orb_L +2322,Frontal_Inf_Orb_R +2331,Rolandic_Oper_L +2332,Rolandic_Oper_R +2401,Supp_Motor_Area_L +2402,Supp_Motor_Area_R +2501,Olfactory_L +2502,Olfactory_R +2601,Frontal_Sup_Medial_L +2602,Frontal_Sup_Medial_R +2611,Frontal_Med_Orb_L +2612,Frontal_Med_Orb_R +2701,Rectus_L +2702,Rectus_R +3001,Insula_L +3002,Insula_R +4001,Cingulum_Ant_L +4002,Cingulum_Ant_R +4011,Cingulum_Mid_L +4012,Cingulum_Mid_R +4021,Cingulum_Post_L +4022,Cingulum_Post_R +4101,Hippocampus_L +4102,Hippocampus_R +4111,ParaHippocampal_L +4112,ParaHippocampal_R +4201,Amygdala_L +4202,Amygdala_R +5001,Calcarine_L +5002,Calcarine_R +5011,Cuneus_L +5012,Cuneus_R +5021,Lingual_L +5022,Lingual_R +5101,Occipital_Sup_L +5102,Occipital_Sup_R +5201,Occipital_Mid_L +5202,Occipital_Mid_R +5301,Occipital_Inf_L +5302,Occipital_Inf_R +5401,Fusiform_L +5402,Fusiform_R +6001,Postcentral_L +6002,Postcentral_R +6101,Parietal_Sup_L +6102,Parietal_Sup_R +6201,Parietal_Inf_L +6202,Parietal_Inf_R +6211,SupraMarginal_L +6212,SupraMarginal_R +6221,Angular_L +6222,Angular_R +6301,Precuneus_L +6302,Precuneus_R +6401,Paracentral_Lobule_L +6402,Paracentral_Lobule_R +7001,Caudate_L +7002,Caudate_R +7011,Putamen_L +7012,Putamen_R +7021,Pallidum_L +7022,Pallidum_R +7101,Thalamus_L +7102,Thalamus_R +8101,Heschl_L +8102,Heschl_R +8111,Temporal_Sup_L +8112,Temporal_Sup_R +8121,Temporal_Pole_Sup_L +8122,Temporal_Pole_Sup_R +8201,Temporal_Mid_L +8202,Temporal_Mid_R +8211,Temporal_Pole_Mid_L +8212,Temporal_Pole_Mid_R +8301,Temporal_Inf_L +8302,Temporal_Inf_R +9001,Cerebelum_Crus1_L +9002,Cerebelum_Crus1_R +9011,Cerebelum_Crus2_L +9012,Cerebelum_Crus2_R +9021,Cerebelum_3_L +9022,Cerebelum_3_R +9031,Cerebelum_4_5_L +9032,Cerebelum_4_5_R +9041,Cerebelum_6_L +9042,Cerebelum_6_R +9051,Cerebelum_7b_L +9052,Cerebelum_7b_R +9061,Cerebelum_8_L +9062,Cerebelum_8_R +9071,Cerebelum_9_L +9072,Cerebelum_9_R +9081,Cerebelum_10_L +9082,Cerebelum_10_R +9100,Vermis_1_2 +9110,Vermis_3 +9120,Vermis_4_5 +9130,Vermis_6 +9140,Vermis_7 +9150,Vermis_8 +9160,Vermis_9 +9170,Vermis_10 \ No newline at end of file diff --git a/scripts/atlases/labels_freesurfer.csv b/scripts/atlases/labels_freesurfer.csv new file mode 100644 index 0000000..3cb4ec3 --- /dev/null +++ b/scripts/atlases/labels_freesurfer.csv @@ -0,0 +1,264 @@ +index,name +0,Unknown +2,Left-Cerebral-White-Matter +4,Left-Lateral-Ventricle +5,Left-Inf-Lat-Vent +7,Left-Cerebellum-White-Matter +8,Left-Cerebellum-Cortex +10,Left-Thalamus-Proper +11,Left-Caudate +12,Left-Putamen +13,Left-Pallidum +14,3rd-Ventricle +15,4th-Ventricle +16,Brain-Stem +17,Left-Hippocampus +18,Left-Amygdala +24,CSF +26,Left-Accumbens-area +28,Left-VentralDC +30,Left-vessel +31,Left-choroid-plexus +41,Right-Cerebral-White-Matter +43,Right-Lateral-Ventricle +44,Right-Inf-Lat-Vent +46,Right-Cerebellum-White-Matter +47,Right-Cerebellum-Cortex +49,Right-Thalamus-Proper +50,Right-Caudate +51,Right-Putamen +52,Right-Pallidum +53,Right-Hippocampus +54,Right-Amygdala +58,Right-Accumbens-area +60,Right-VentralDC +62,Right-vessel +63,Right-choroid-plexus +77,WM-hypointensities +80,non-WM-hypointensities +85,Optic-Chiasm +251,CC_Posterior +252,CC_Mid_Posterior +253,CC_Central +254,CC_Mid_Anterior +255,CC_Anterior +1000,ctx-lh-unknown +1001,ctx-lh-bankssts +1002,ctx-lh-caudalanteriorcingulate +1003,ctx-lh-caudalmiddlefrontal +1005,ctx-lh-cuneus +1006,ctx-lh-entorhinal +1007,ctx-lh-fusiform +1008,ctx-lh-inferiorparietal +1009,ctx-lh-inferiortemporal +1010,ctx-lh-isthmuscingulate +1011,ctx-lh-lateraloccipital +1012,ctx-lh-lateralorbitofrontal +1013,ctx-lh-lingual +1014,ctx-lh-medialorbitofrontal +1015,ctx-lh-middletemporal +1016,ctx-lh-parahippocampal +1017,ctx-lh-paracentral +1018,ctx-lh-parsopercularis +1019,ctx-lh-parsorbitalis +1020,ctx-lh-parstriangularis +1021,ctx-lh-pericalcarine +1022,ctx-lh-postcentral +1023,ctx-lh-posteriorcingulate +1024,ctx-lh-precentral +1025,ctx-lh-precuneus +1026,ctx-lh-rostralanteriorcingulate +1027,ctx-lh-rostralmiddlefrontal +1028,ctx-lh-superiorfrontal +1029,ctx-lh-superiorparietal +1030,ctx-lh-superiortemporal +1031,ctx-lh-supramarginal +1032,ctx-lh-frontalpole +1033,ctx-lh-temporalpole +1034,ctx-lh-transversetemporal +1035,ctx-lh-insula +2000,ctx-rh-unknown +2001,ctx-rh-bankssts +2002,ctx-rh-caudalanteriorcingulate +2003,ctx-rh-caudalmiddlefrontal +2005,ctx-rh-cuneus +2006,ctx-rh-entorhinal +2007,ctx-rh-fusiform 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+11106,ctx_lh_G_and_S_cingul-Ant +11107,ctx_lh_G_and_S_cingul-Mid-Ant +11108,ctx_lh_G_and_S_cingul-Mid-Post +11109,ctx_lh_G_cingul-Post-dorsal +11110,ctx_lh_G_cingul-Post-ventral +11111,ctx_lh_G_cuneus +11112,ctx_lh_G_front_inf-Opercular +11113,ctx_lh_G_front_inf-Orbital +11114,ctx_lh_G_front_inf-Triangul +11115,ctx_lh_G_front_middle +11116,ctx_lh_G_front_sup +11117,ctx_lh_G_Ins_lg_and_S_cent_ins +11118,ctx_lh_G_insular_short +11119,ctx_lh_G_occipital_middle +11120,ctx_lh_G_occipital_sup +11121,ctx_lh_G_oc-temp_lat-fusifor +11122,ctx_lh_G_oc-temp_med-Lingual +11123,ctx_lh_G_oc-temp_med-Parahip +11124,ctx_lh_G_orbital +11125,ctx_lh_G_pariet_inf-Angular +11126,ctx_lh_G_pariet_inf-Supramar +11127,ctx_lh_G_parietal_sup +11128,ctx_lh_G_postcentral +11129,ctx_lh_G_precentral +11130,ctx_lh_G_precuneus +11131,ctx_lh_G_rectus +11132,ctx_lh_G_subcallosal +11133,ctx_lh_G_temp_sup-G_T_transv +11134,ctx_lh_G_temp_sup-Lateral +11135,ctx_lh_G_temp_sup-Plan_polar +11136,ctx_lh_G_temp_sup-Plan_tempo 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+12123,ctx_rh_G_oc-temp_med-Parahip +12124,ctx_rh_G_orbital +12125,ctx_rh_G_pariet_inf-Angular +12126,ctx_rh_G_pariet_inf-Supramar +12127,ctx_rh_G_parietal_sup +12128,ctx_rh_G_postcentral +12129,ctx_rh_G_precentral +12130,ctx_rh_G_precuneus +12131,ctx_rh_G_rectus +12132,ctx_rh_G_subcallosal +12133,ctx_rh_G_temp_sup-G_T_transv +12134,ctx_rh_G_temp_sup-Lateral +12135,ctx_rh_G_temp_sup-Plan_polar +12136,ctx_rh_G_temp_sup-Plan_tempo +12137,ctx_rh_G_temporal_inf +12138,ctx_rh_G_temporal_middle +12139,ctx_rh_Lat_Fis-ant-Horizont +12140,ctx_rh_Lat_Fis-ant-Vertical +12141,ctx_rh_Lat_Fis-post +12143,ctx_rh_Pole_occipital +12144,ctx_rh_Pole_temporal +12145,ctx_rh_S_calcarine +12146,ctx_rh_S_central +12147,ctx_rh_S_cingul-Marginalis +12148,ctx_rh_S_circular_insula_ant +12149,ctx_rh_S_circular_insula_inf +12150,ctx_rh_S_circular_insula_sup +12151,ctx_rh_S_collat_transv_ant +12152,ctx_rh_S_collat_transv_post +12153,ctx_rh_S_front_inf +12154,ctx_rh_S_front_middle +12155,ctx_rh_S_front_sup 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