处理fMRI数据：使用Nilearn加载和预处理NIfTI文件

pip install nilearn

from nilearn.image import load_img

# 替换为你的NIfTI文件路径
nifti_image = load_img("path/to/your/nifti_file.nii")

data = nifti_image.get_fdata()
print(data.shape) # 打印数据的维度

from nilearn.image import load_img
import numpy as np

# 替换为你的NIfTI文件路径
file_path = "path/to/your/nifti_file.nii"

try:
    nifti_image = load_img(file_path)
    data = nifti_image.get_fdata()
    print(f"Successfully loaded image with shape: {data.shape}")

    # 这里可以进行后续的数据处理，例如：
    # 计算平均激活值
    mean_activation = np.mean(data)
    print(f"Mean activation: {mean_activation}")

except FileNotFoundError:
    print(f"Error: File not found at {file_path}")
except Exception as e:
    print(f"An error occurred: {e}")

pip install joblib

from nilearn.image import load_img
import numpy as np
from joblib import Parallel, delayed
import os

def process_nifti(file_path):
    try:
        nifti_image = load_img(file_path)
        data = nifti_image.get_fdata()
        print(f"Successfully loaded image with shape: {data.shape} from {file_path}")
        # 进行数据处理
        mean_activation = np.mean(data)
        print(f"Mean activation: {mean_activation} from {file_path}")
        return mean_activation
    except FileNotFoundError:
        print(f"Error: File not found at {file_path}")
        return None
    except Exception as e:
        print(f"An error occurred: {e}")
        return None

def main():
    # 替换为你的NIfTI文件目录
    data_dir = "path/to/your/nifti/files"
    file_paths = [os.path.join(data_dir, f) for f in os.listdir(data_dir) if f.endswith(".nii")] # 假设所有文件都是.nii格式

    # 使用多进程处理
    num_cores = os.cpu_count()  # 获取CPU核心数
    results = Parallel(n_jobs=num_cores)(delayed(process_nifti)(file_path) for file_path in file_paths)

    # 处理结果
    print("All files processed.")
    valid_results = [r for r in results if r is not None]
    if valid_results:
        print(f"Average mean activation across all valid files: {np.mean(valid_results)}")

if __name__ == "__main__":
    main()