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本页译自 MNE-Python 官方文档:
查看英文原文 ↗。
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说明
转到末尾下载完整示例代码。
绘制皮层分区#
本示例下载 HCP-MMP1.0 分区 [1] 并在 fsaverage 上显示;同时下载 [2] 中定制的 448 标签 aparc 分区。
说明
HCP-MMP 数据集有使用限制的许可条款,其中特别相关的是:
“当公开发表任何受益于 WU-Minn HCP 数据或其衍生数据的结果或算法时,我将声明使用了 WU-Minn HCP 数据及由其衍生的数据。”
# Author: Eric Larson <larson.eric.d@gmail.com>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import mne
Brain = mne.viz.get_brain_class()
subjects_dir = mne.datasets.sample.data_path() / "subjects"
mne.datasets.fetch_hcp_mmp_parcellation(subjects_dir=subjects_dir, verbose=True)
mne.datasets.fetch_aparc_sub_parcellation(subjects_dir=subjects_dir, verbose=True)
labels = mne.read_labels_from_annot(
"fsaverage", "HCPMMP1", "lh", subjects_dir=subjects_dir
)
brain = Brain(
"fsaverage",
"lh",
"inflated",
subjects_dir=subjects_dir,
cortex="low_contrast",
background="white",
size=(800, 600),
)
brain.add_annotation("HCPMMP1")
aud_label = [label for label in labels if label.name == "L_A1_ROI-lh"][0]
brain.add_label(aud_label, borders=False)

Reading labels from parcellation...
read 181 labels from /home/circleci/mne_data/MNE-sample-data/subjects/fsaverage/label/lh.HCPMMP1.annot
Reading labels from parcellation...
read 181 labels from /home/circleci/mne_data/MNE-sample-data/subjects/fsaverage/label/lh.HCPMMP1.annot
也可以绘制合并后的标签集(每个半球 23 个)。
brain = Brain(
"fsaverage",
"lh",
"inflated",
subjects_dir=subjects_dir,
cortex="low_contrast",
background="white",
size=(800, 600),
)
brain.add_annotation("HCPMMP1_combined")

Reading labels from parcellation...
read 23 labels from /home/circleci/mne_data/MNE-sample-data/subjects/fsaverage/label/lh.HCPMMP1_combined.annot
还可以添加另一种自定义分区
brain = Brain(
"fsaverage",
"lh",
"inflated",
subjects_dir=subjects_dir,
cortex="low_contrast",
background="white",
size=(800, 600),
)
brain.add_annotation("aparc_sub")

Reading labels from parcellation...
read 226 labels from /home/circleci/mne_data/MNE-sample-data/subjects/fsaverage/label/lh.aparc_sub.annot
参考文献#
脚本总运行时间:(0 分 5.884 秒)