Stop interpolating edges.

xcp_d/utils/utils.py

@@ -463,6 +463,24 @@ def denoise_with_nilearn(
         sample_mask=sample_mask,
         t_r=TR,
     )
+    # Replace any high-motion volumes at the beginning or end of the run with the closest
+    # low-motion volume's data.
+    # From https://stackoverflow.com/a/48106843/2589328
+    nums = sorted(set(np.where(sample_mask)[0]))
+    gaps = [[s, e] for s, e in zip(nums, nums[1:]) if s + 1 < e]
+    edges = iter(nums[:1] + sum(gaps, []) + nums[-1:])
+    consecutive_outliers_idx = list(zip(edges, edges))
+    if consecutive_outliers_idx[0][0] == 0:
+        for i_vol in range(consecutive_outliers_idx[0][0], consecutive_outliers_idx[0][1] + 1):
+            interpolated_unfiltered_bold[i_vol, :] = interpolated_unfiltered_bold[
+                consecutive_outliers_idx[0][1] + 1, :
+            ]
+
+    if consecutive_outliers_idx[-1][1] == n_volumes:
+        for i_vol in range(consecutive_outliers_idx[-1][0], consecutive_outliers_idx[0][1] + 1):
+            interpolated_unfiltered_bold[i_vol, :] = interpolated_unfiltered_bold[
+                consecutive_outliers_idx[-1][0] - 1, :
+            ]
 
     # Now apply the bandpass filter to the interpolated, denoised data
     if low_pass is not None and high_pass is not None: