included low-res spatiam data

Author: bthirion

ibc_data/all_contrasts.tsv

@@ -357,6 +357,12 @@ lyon-mveb	2_letters_different-same	Maintaining two letters versus one	maintain 1
 lyon-mveb	4_letters_different-same	Maintaining four letters versus one	maintain 1 letter	maintain 4 letters	['visual_attention', 'visual_working_memory', 'string_maintenance', 'working_memory']
 lyon-mveb	6_letters_different-same	Maintaining six letters versus one	maintain 1 letter	maintain 6 letters	['visual_attention', 'visual_working_memory', 'string_maintenance', 'working_memory', 'task_difficulty']
 lyon-mveb	6_letters_different-2_letters_different	Maintaining six letters versus two	maintain 2 letters	maintain 6 letters	['visual_attention', 'visual_working_memory', 'string_maintenance', 'working_memory', 'task_difficulty']
+lyon-mveb	2_letters_different	Maintaining two letters	fixation	maintain 2 letters	[' ']
+lyon-mveb	4_letters_different	Maintaining four letters	fixation	maintain 4 letters	[' ']
+lyon-mveb	6_letters_different	Maintaining six letters	fixation	maintain 6 letters	[' ']
+lyon-mveb	2_letters_same	Maintaining one letter	fixation	maintain 1 letter	[' ']
+lyon-mveb	4_letters_same	Maintaining one letter	fixation	maintain 1 letter	[' ']
+lyon-mveb	6_letters_same	Maintaining one letter	fixation	maintain 1 letter	[' ']
 lyon-mvis	response	press a button	wait	button press	['response_selection', 'response_execution']
 lyon-mvis	2_dots-2_dots_control	maintain position of two dots versus one	maintain 1 position	maintain 2 positions	['visual_attention', 'visual_working_memory', 'working_memory', 'spatial_working_memory']
 lyon-mvis	4_dots-4_dots_control	maintain position of four dots versus one	maintain 1 position	maintain 4 positions	['visual_attention', 'visual_working_memory', 'working_memory', 'spatial_working_memory']
@@ -379,33 +385,33 @@ lyon-mcse	high_salience_left	looking for a salient symbol in left visual field	f
 lyon-mcse	high_salience_right	looking for a salient symbol in right visual field	fixation	high salience right	[' ']
 lyon-mcse	low_salience_left	looking for a symbol in left vs right visual field	fixation	low salience left	[' ']
 lyon-mcse	low_salience_right	looking for a symbol in left vs right visual field	fixation	low salience right	[' ']
-lyon-mcse	high+low_salience	looking for a symbol	fixation	visual search	[' ']
+lyon-mcse	low+high_salience	looking for a symbol	fixation	visual search	[' ']
 audio1	animal-others				[' ']
-audio1	music-others				[' ']
+audio1	music-other				[' ']
 audio1	nature-others				[' ']
 audio1	speech-others				[' ']
 audio1	tool-others				[' ']
 audio1	voice-others				[' ']
 audio1	mean-silence	listening to sounds vs silence	silence	sound	['auditory_perception', 'sounds_perception', 'listening']
-audio1	animal-silence	listen to animals vs silence	silence	animal sound	['auditory_perception', 'sounds_perception', 'listening']
-audio1	music-silence	listen to music vs silence	silence	music	['auditory_perception', 'sounds_perception', 'listening', 'music_perception']
-audio1	nature-silence	listen to nature vs silence	silence	nature sound	['auditory_perception', 'sounds_perception', 'listening']
-audio1	speech-silence	listen to speech vs silence	silence	speech	['auditory_perception', 'sounds_perception', 'voice_perception', 'listening', 'speech_perception', 'language_processing']
-audio1	tool-silence	listen to tool vs silence	silence	tool sound	['auditory_perception', 'sounds_perception', 'listening']
-audio1	voice-silence	listen to voice vs sounds 	silence	voice	['auditory_perception', 'auditory_attention', 'sounds_perception', 'voice_perception', 'listening']
-audio2	animal-silence				[' ']
+audio1	animal	listen to animals vs silence	silence	animal sound	['auditory_perception', 'sounds_perception', 'listening']
+audio1	music	listen to music vs silence	silence	music	['auditory_perception', 'sounds_perception', 'listening', 'music_perception']
+audio1	nature	listen to nature vs silence	silence	nature sound	['auditory_perception', 'sounds_perception', 'listening']
+audio1	speech	listen to speech vs silence	silence	speech	['auditory_perception', 'sounds_perception', 'voice_perception', 'listening', 'speech_perception', 'language_processing']
+audio1	tool	listen to tool vs silence	silence	tool sound	['auditory_perception', 'sounds_perception', 'listening']
+audio1	voice	listen to voice vs sounds 	silence	voice	['auditory_perception', 'auditory_attention', 'sounds_perception', 'voice_perception', 'listening']
+audio2	Animal-others				[' ']
 audio2	music-others				[' ']
 audio2	nature-others				[' ']
 audio2	speech-others				[' ']
 audio2	tool-others				[' ']
 audio2	voice-others				[' ']
 audio2	mean-silence	listening to sounds vs silence	silence	sound	['auditory_perception', 'sounds_perception', 'listening']
-audio2	animal-silence	listen to animals vs silence	silence	animal sound	['auditory_perception', 'sounds_perception', 'listening']
-audio2	music-silence	listen to music vs silence	silence	music	['auditory_perception', 'sounds_perception', 'listening', 'music_perception']
-audio2	nature-silence	listen to nature vs silence	silence	nature sound	['auditory_perception', 'sounds_perception', 'listening']
-audio2	speech-silence	listen to speech vs silence	silence	speech	['auditory_perception', 'sounds_perception', 'voice_perception', 'listening', 'speech_perception', 'language_processing']
-audio2	tool-silence	listen to tool vs silence	silence	tool sound	['auditory_perception', 'sounds_perception', 'listening']
-audio2	voice-silence	listen to voice vs sounds 	silence	voice	['auditory_perception', 'auditory_attention', 'sounds_perception', 'voice_perception', 'listening']
+audio2	animal	listen to animals vs silence	silence	animal sound	['auditory_perception', 'sounds_perception', 'listening']
+audio2	music	listen to music vs silence	silence	music	['auditory_perception', 'sounds_perception', 'listening', 'music_perception']
+audio2	nature	listen to nature vs silence	silence	nature sound	['auditory_perception', 'sounds_perception', 'listening']
+audio2	speech	listen to speech vs silence	silence	speech	['auditory_perception', 'sounds_perception', 'voice_perception', 'listening', 'speech_perception', 'language_processing']
+audio2	tool	listen to tool vs silence	silence	tool sound	['auditory_perception', 'sounds_perception', 'listening']
+audio2	voice	listen to voice vs sounds 	silence	voice	['auditory_perception', 'auditory_attention', 'sounds_perception', 'voice_perception', 'listening']
 attention	spatial_cue-double_cue				[' ']
 attention	spatial_cue				[' ']
 attention	incongruent-congruent				[' ']
@@ -441,7 +447,7 @@ stroop	incongruent-congruent				[' ']
 columbia-cards	num_loss_cards				[' ']
 columbia-cards	loss				[' ']
 columbia-cards	gain				[' ']
-dot-patterns	'cue'				[' ']
+dot-patterns	cue				[' ']
 dot-patterns	correct_cue_correct_probe				[' ']
 dot-patterns	correct_cue_incorrect_probe				[' ']
 dot-patterns	incorrect_cue_correct_probe				[' ']

ibc_public/utils_data.py

@@ -402,7 +402,7 @@ def copy_db(df, write_dir, filename='result_db.csv'):
 
 
 def make_surf_db(derivatives=DERIVATIVES, conditions=CONDITIONS,
-                subject_list=SUBJECTS, task_list=False):
+                 subject_list=SUBJECTS, task_list=False, lowres=False):
     """ Create a database for surface data (gifti files)
 
     derivatives: string,
@@ -456,10 +456,13 @@ def make_surf_db(derivatives=DERIVATIVES, conditions=CONDITIONS,
             # some renaming
             if ((contrast == 'probe') & (task_name == 'rsvp_language')):
                     contrast = 'language_probe'
+            dir_ = 'res_surf_%s_ffx' % task
+            if lowres:
+                dir_ = 'res_fsaverage5_%s_ffx' % task
             for side in ['lh', 'rh']:
                 wc = os.path.join(
-                    derivatives, subject, '*/res_surf_%s_ffx/stat_surf/%s_%s.gii'
-                    % (task, contrast, side))
+                    derivatives, subject, '*', dir_, 'stat_surf',
+                    '%s_%s.gii' % (contrast, side))
                 imgs_ = glob.glob(wc)
                 imgs_.sort()
                 for img in imgs_:

ibc_public/utils_pipeline.py

@@ -519,7 +519,7 @@ def fixed_effects_analysis(subject_dic, surface=False, mask_img=None,
                             subject_dic['output_dir'],
                             np.unique(session_paradigm),
                             contrast,
-                            data_available=True, side=side)
+                            data_available=True, side=side, lowres=lowres)
                     if not data_available:
                         raise ValueError('Missing texture stats files for '
                                          'fixed effects computations')

processing/surface_based_analysis.py

@@ -143,10 +143,10 @@ def project_volume(work_dir, subject, sessions, do_bbr=True):
 protocols = ['preference', 'mtt1', 'mtt2', 'clips4', 'tom', 'self']
 protocols = ['lyon1', 'lyon2', 'audio1', 'audio2', 'stanford1',
              'stanford2', 'stanford3']
-
+protocols = ['clips1', 'clips2', 'clips3', 'clips4']
 subject_sessions = sorted(get_subject_session(protocols))
 
-Parallel(n_jobs=4)(
+Parallel(n_jobs=2)(
     delayed(project_volume)(work_dir, subject_session[0], [subject_session[1]],
                             do_bbr=True)
     for subject_session in subject_sessions)

processing/surface_glm_only.py

@@ -84,22 +84,27 @@ def run_subject_surface_glm(jobfile, subject, session, protocol, lowres=False):
         if len(subject['session_id']) > 0:
             print(len(subject['session_id']))
         if len(subject['session_id']) > 0:
-            if protocol == 'retino':
-                first_level(subject, compcorr=True,
-                            additional_regressors=RETINO_REG,
-                            smooth=None, surface=True)
-            else:
-                first_level(subject, compcorr=True, smooth=None, surface=True)
-                fixed_effects_analysis(subject, surface=True, lowres=True)
+            #if protocol == 'retino':
+            #    first_level(subject, compcorr=True,
+            #                additional_regressors=RETINO_REG,
+            #                smooth=None, surface=True)
+            #else:
+            first_level(subject, compcorr=True, smooth=None, surface=True)
+            fixed_effects_analysis(subject, surface=True, lowres=True)
 
 
 if __name__ == '__main__':
-    for protocol in ['archi', 'screening', 'rsvp-language', 'hcp1', 'hcp2']:
+    protocols = ['preference', 'mtt1', 'mtt2', 'tom', 'self']
+    protocols = ['retino']
+    protocols = ['lyon1', 'lyon2', 'audio1', 'audio2', 'stanford1',
+                 'stanford2', 'stanford3']
+    protocols = ['archi', 'screening', 'rsvp-language', 'hcp1', 'hcp2']
+    for protocol in protocols:
         jobfile = 'ini_files/IBC_preproc_%s.ini' % protocol
         acquisition = protocol  # 'clips4' #
         lowres = True
         subject_session = sorted(get_subject_session(acquisition))
-        Parallel(n_jobs=1)(
+        Parallel(n_jobs=4)(
             delayed(run_subject_surface_glm)(
                 jobfile, subject, session, protocol, lowres=lowres)
             for (subject, session) in subject_session)