Meta-analysis: Face features (FaceNet) models
Contents
Meta-analysis: Face features (FaceNet) models¶
Meta-analysis for the following models: Set of models investigating face perception:
single-predictor model with binary regressor coding for presence of any face (any_faces)
any_faces + speech
any_faces + speech + log of cumulative time the detected face has been on screen (mean across faces, log_mean_face_time_cum). This operationalizes adaptation effects.
import json
from meta import analyze_collection
%matplotlib inline
ERROR:duecredit:Failed to import duecredit due to No module named 'duecredit'
Get model ids from collection and meta-analyze single-predictor models (any_faces only)
json_data = json.load(open('../analyses/models/facenet.json'))
any_faces_imgs = analyze_collection(json_data['any_faces'])
INFO:nimare.utils:Shared path detected: '/home/alejandro/repos/neuroscout-paper/meta/images/anyFaces/'
WARNING:nimare.base:Masking out 7306 additional voxels. The updated masker is available in the Estimator.masker attribute.
meta-analyzing 19 for anyFaces
Meta-analyze model with added speech predictor
faces_1_imgs = analyze_collection(json_data['any_faces+speech'])
INFO:nimare.utils:Shared path detected: '/home/alejandro/repos/neuroscout-paper/meta/images/anyFaces_speech/'
WARNING:nimare.base:Masking out 7294 additional voxels. The updated masker is available in the Estimator.masker attribute.
meta-analyzing 16 for anyFaces
INFO:nimare.utils:Shared path detected: '/home/alejandro/repos/neuroscout-paper/meta/images/anyFaces_speech/'
WARNING:nimare.base:Masking out 7294 additional voxels. The updated masker is available in the Estimator.masker attribute.
meta-analyzing 16 for speech
Meta-analyze model with speech and adaptation predictors
faces_2_imgs = analyze_collection(json_data['any_faces+speech+log_mean_face_time_cum'], force_recreate=True)
INFO:nimare.extract.utils:Dataset found in /home/alejandro/repos/neuroscout-paper/meta/images/anyFaces_speech_logMeanFaceTimeCum
INFO:nimare.utils:Shared path detected: '/home/alejandro/repos/neuroscout-paper/meta/images/anyFaces_speech_logMeanFaceTimeCum/'
INFO:nimare.utils:Shared path detected: '/home/alejandro/repos/neuroscout-paper/meta/images/anyFaces_speech_logMeanFaceTimeCum/'
WARNING:nimare.base:Masking out 7304 additional voxels. The updated masker is available in the Estimator.masker attribute.
meta-analyzing 17 for anyFaces
INFO:nimare.utils:Shared path detected: '/home/alejandro/repos/neuroscout-paper/meta/images/anyFaces_speech_logMeanFaceTimeCum/'
WARNING:nimare.base:Masking out 7304 additional voxels. The updated masker is available in the Estimator.masker attribute.
meta-analyzing 17 for logMeanFaceTimeCum
INFO:nimare.utils:Shared path detected: '/home/alejandro/repos/neuroscout-paper/meta/images/anyFaces_speech_logMeanFaceTimeCum/'
WARNING:nimare.base:Masking out 7304 additional voxels. The updated masker is available in the Estimator.masker attribute.
meta-analyzing 17 for speech
Figure 5¶
In Figure 5, we plot the “any_faces” predictor, in a meta-analysis of models by itself, in models controlling for speech, and in models controlling for speech and face adaptation
from nilearn import plotting as niplt
import matplotlib.pyplot as plt
fig, axes = plt.subplots(3, 1, figsize=(12, 3 * 2.5))
plot_kwargs=dict(vmax=14, threshold=2.58, cut_coords=[-24, 2, 28, 54], display_mode='z')
niplt.plot_stat_map(any_faces_imgs[0], axes=axes[0], title='faces', **plot_kwargs)
niplt.plot_stat_map(faces_1_imgs[0], axes=axes[1], title='+ speech', **plot_kwargs)
niplt.plot_stat_map(faces_2_imgs[0], axes=axes[2], title = '+ adaptation', **plot_kwargs)
<nilearn.plotting.displays.ZSlicer at 0x7f97b46cbcf8>
