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8c5a642 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 | """TR-level HRF regressor and z-score alignment utilities."""
from __future__ import annotations
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any
import nibabel as nib
import numpy as np
import pandas as pd
def _zscore_columns(values: np.ndarray, eps: float = 1e-8) -> np.ndarray:
mean = np.mean(values, axis=0, keepdims=True)
std = np.std(values, axis=0, keepdims=True)
std[std < eps] = 1.0
return ((values - mean) / std).astype(np.float32)
def _trim_time_axis(values: np.ndarray, trim_start_tr: int, trim_end_tr: int) -> np.ndarray:
if trim_start_tr < 0 or trim_end_tr < 0:
raise ValueError("trim_start_tr and trim_end_tr must be non-negative")
start = int(trim_start_tr)
stop = values.shape[0] - int(trim_end_tr)
if start >= stop:
raise ValueError(
"Trimming removed all time points: "
f"n_tr={values.shape[0]}, trim_start={trim_start_tr}, trim_end={trim_end_tr}"
)
return values[start:stop]
def _format_tr_for_filename(tr_seconds: float) -> str:
return f"{tr_seconds:.4f}".replace(".", "p")
def _assert_same_grid(img: nib.Nifti1Image, reference_img: nib.Nifti1Image, atol: float = 1e-5) -> None:
if img.shape[:3] != reference_img.shape:
raise ValueError(
"Image shape mismatch against analysis mask grid: "
f"image={img.shape[:3]}, reference={reference_img.shape}"
)
if not np.allclose(img.affine, reference_img.affine, atol=atol):
raise ValueError("Image affine mismatch against analysis mask grid")
def _extract_masked_bold_2d(
bold_path: Path,
analysis_mask_bool: np.ndarray,
analysis_mask_img: nib.Nifti1Image,
) -> np.ndarray:
img = nib.load(str(bold_path))
if len(img.shape) != 4:
raise ValueError(f"Expected 4D BOLD image, got shape={img.shape} for {bold_path}")
_assert_same_grid(img=img, reference_img=analysis_mask_img)
data = np.asanyarray(img.dataobj)
# Convert from (x,y,z,t) to (t,voxels)
bold_2d = data[analysis_mask_bool, :].T.astype(np.float32)
return bold_2d
def compute_hrf_regressor_matrix(
word_features: np.ndarray,
onsets_s: np.ndarray,
offsets_s: np.ndarray,
tr_seconds: float,
n_volumes: int,
hrf_model: str,
) -> np.ndarray:
"""Build TR-level HRF-convolved regressors from word-level features."""
from nilearn.glm.first_level import compute_regressor
if word_features.ndim != 2:
raise ValueError("word_features must be 2D: (n_words, hidden_dim)")
if onsets_s.shape[0] != word_features.shape[0] or offsets_s.shape[0] != word_features.shape[0]:
raise ValueError("onset/offset lengths must match n_words in word_features")
durations = offsets_s - onsets_s
frame_times = np.arange(n_volumes, dtype=np.float64) * float(tr_seconds) + 0.5 * float(tr_seconds)
regressors = np.zeros((int(n_volumes), int(word_features.shape[1])), dtype=np.float32)
for feature_index in range(word_features.shape[1]):
amplitudes = word_features[:, feature_index].astype(np.float64)
exp_condition = np.array([onsets_s, durations, amplitudes], dtype=np.float64)
signal, _ = compute_regressor(exp_condition, hrf_model, frame_times)
regressors[:, feature_index] = signal[:, 0].astype(np.float32)
return regressors
@dataclass(frozen=True)
class BoldAlignmentRecord:
"""Summary for one subject-run BOLD alignment artifact."""
subject: str
run: int
tr_seconds: float
n_volumes_raw: int
n_tr_aligned: int
n_voxels: int
trim_start_tr: int
trim_end_tr: int
bold_z_path: str
@dataclass(frozen=True)
class RegressorAlignmentRecord:
"""Summary for one model-run-layer regressor artifact."""
model_id: str
model_slug: str
run: int
tr_seconds: float
n_volumes_raw: int
n_tr_aligned: int
layer_idx: int
n_features: int
trim_start_tr: int
trim_end_tr: int
hrf_model: str
regressor_z_path: str
def build_and_cache_alignment_inputs(
manifest_df: pd.DataFrame,
run_events_df: pd.DataFrame,
feature_summary_df: pd.DataFrame,
analysis_mask_path: Path,
output_dir: Path,
trim_start_tr: int,
trim_end_tr: int,
hrf_model: str,
overwrite: bool,
alignment_subjects: list[str] | None = None,
) -> tuple[pd.DataFrame, pd.DataFrame, dict[str, Any]]:
"""Cache z-scored BOLD targets and HRF-convolved z-scored regressors."""
if manifest_df.empty:
raise ValueError("Manifest is empty; cannot build alignment inputs")
if run_events_df.empty:
raise ValueError("run_events_df is empty; cannot build regressors")
if feature_summary_df.empty:
raise ValueError("feature_summary_df is empty; feature extraction must run first")
required_manifest_columns = {"subject", "run", "tr_seconds", "n_volumes", "derivatives_bold_path"}
missing_manifest = required_manifest_columns.difference(manifest_df.columns)
if missing_manifest:
raise ValueError(f"manifest_df missing required columns: {sorted(missing_manifest)}")
required_feature_columns = {"model_id", "model_slug", "run", "features_npz_path"}
missing_feature = required_feature_columns.difference(feature_summary_df.columns)
if missing_feature:
raise ValueError(f"feature_summary_df missing required columns: {sorted(missing_feature)}")
analysis_mask_img = nib.load(str(analysis_mask_path))
analysis_mask_bool = analysis_mask_img.get_fdata() > 0.5
manifest_alignment_df = manifest_df
requested_subjects = [value for value in (alignment_subjects or []) if str(value).strip()]
if requested_subjects:
subject_set = {str(value) for value in requested_subjects}
manifest_alignment_df = manifest_df[manifest_df["subject"].isin(subject_set)].copy()
if manifest_alignment_df.empty:
raise ValueError(
"Alignment subject filter produced no rows. "
f"Requested subjects={sorted(subject_set)}"
)
output_dir = output_dir.resolve()
bold_output_dir = output_dir / "bold_z"
regressor_output_dir = output_dir / "regressors"
bold_output_dir.mkdir(parents=True, exist_ok=True)
regressor_output_dir.mkdir(parents=True, exist_ok=True)
bold_rows: list[BoldAlignmentRecord] = []
for row in manifest_alignment_df.itertuples(index=False):
subject = str(getattr(row, "subject"))
run = int(getattr(row, "run"))
tr_seconds = float(getattr(row, "tr_seconds"))
n_volumes = int(getattr(row, "n_volumes"))
bold_path = Path(str(getattr(row, "derivatives_bold_path")))
bold_z_path = bold_output_dir / f"{subject}_run-{run:02d}_bold_z.npy"
if bold_z_path.exists() and not overwrite:
aligned_bold = np.load(bold_z_path)
else:
bold_2d = _extract_masked_bold_2d(
bold_path=bold_path,
analysis_mask_bool=analysis_mask_bool,
analysis_mask_img=analysis_mask_img,
)
if bold_2d.shape[0] != n_volumes:
raise ValueError(
f"BOLD volume mismatch for {subject} run {run}: "
f"manifest={n_volumes}, loaded={bold_2d.shape[0]}"
)
aligned_bold = _trim_time_axis(
values=bold_2d,
trim_start_tr=trim_start_tr,
trim_end_tr=trim_end_tr,
)
aligned_bold = _zscore_columns(aligned_bold)
np.save(bold_z_path, aligned_bold)
bold_rows.append(
BoldAlignmentRecord(
subject=subject,
run=run,
tr_seconds=tr_seconds,
n_volumes_raw=n_volumes,
n_tr_aligned=int(aligned_bold.shape[0]),
n_voxels=int(aligned_bold.shape[1]),
trim_start_tr=int(trim_start_tr),
trim_end_tr=int(trim_end_tr),
bold_z_path=str(bold_z_path),
)
)
regressor_rows: list[RegressorAlignmentRecord] = []
# Compute run configuration keys from manifest; regressors depend on run + TR + n_volumes.
run_config_map: dict[int, list[tuple[float, int]]] = {}
for run, group_df in manifest_alignment_df.groupby("run"):
unique_pairs = sorted(
{(float(tr), int(n_vol)) for tr, n_vol in zip(group_df["tr_seconds"], group_df["n_volumes"])},
key=lambda pair: (pair[0], pair[1]),
)
run_config_map[int(run)] = unique_pairs
for feature_row in feature_summary_df.itertuples(index=False):
model_id = str(getattr(feature_row, "model_id"))
model_slug = str(getattr(feature_row, "model_slug"))
run = int(getattr(feature_row, "run"))
features_npz_path = Path(str(getattr(feature_row, "features_npz_path")))
if run not in run_config_map:
continue
run_events = run_events_df[run_events_df["run"] == run].sort_values("word_index")
onsets_s = run_events["onset_s"].to_numpy(dtype=np.float64)
offsets_s = run_events["offset_s"].to_numpy(dtype=np.float64)
feature_bundle = np.load(features_npz_path)
layer_keys = sorted(
[key for key in feature_bundle.files if key.startswith("layer_")],
key=lambda value: int(value.split("_")[1]),
)
for tr_seconds, n_volumes in run_config_map[run]:
tr_tag = _format_tr_for_filename(tr_seconds)
model_regressor_dir = regressor_output_dir / model_slug
model_regressor_dir.mkdir(parents=True, exist_ok=True)
for layer_key in layer_keys:
layer_idx = int(layer_key.split("_")[1])
layer_features = np.asarray(feature_bundle[layer_key], dtype=np.float32)
if layer_features.shape[0] != onsets_s.shape[0]:
raise ValueError(
f"Word count mismatch for model={model_id}, run={run}, layer={layer_idx}: "
f"features={layer_features.shape[0]}, events={onsets_s.shape[0]}"
)
regressor_z_path = (
model_regressor_dir
/ f"run-{run:02d}_tr-{tr_tag}_nvol-{n_volumes}_layer-{layer_idx:03d}_regressor_z.npy"
)
if regressor_z_path.exists() and not overwrite:
regressors_aligned = np.load(regressor_z_path)
else:
regressors = compute_hrf_regressor_matrix(
word_features=layer_features,
onsets_s=onsets_s,
offsets_s=offsets_s,
tr_seconds=tr_seconds,
n_volumes=n_volumes,
hrf_model=hrf_model,
)
regressors_aligned = _trim_time_axis(
values=regressors,
trim_start_tr=trim_start_tr,
trim_end_tr=trim_end_tr,
)
regressors_aligned = _zscore_columns(regressors_aligned)
np.save(regressor_z_path, regressors_aligned)
regressor_rows.append(
RegressorAlignmentRecord(
model_id=model_id,
model_slug=model_slug,
run=run,
tr_seconds=tr_seconds,
n_volumes_raw=n_volumes,
n_tr_aligned=int(regressors_aligned.shape[0]),
layer_idx=layer_idx,
n_features=int(regressors_aligned.shape[1]),
trim_start_tr=int(trim_start_tr),
trim_end_tr=int(trim_end_tr),
hrf_model=hrf_model,
regressor_z_path=str(regressor_z_path),
)
)
bold_summary_df = pd.DataFrame([asdict(row) for row in bold_rows])
if not bold_summary_df.empty:
bold_summary_df = bold_summary_df.sort_values(["subject", "run"]).reset_index(drop=True)
regressor_summary_df = pd.DataFrame([asdict(row) for row in regressor_rows])
if not regressor_summary_df.empty:
regressor_summary_df = regressor_summary_df.sort_values(
["model_slug", "run", "layer_idx", "tr_seconds", "n_volumes_raw"]
).reset_index(drop=True)
alignment_qc: dict[str, Any] = {
"analysis_mask_path": str(analysis_mask_path),
"hrf_model": hrf_model,
"trim_start_tr": int(trim_start_tr),
"trim_end_tr": int(trim_end_tr),
"alignment_subjects": sorted({str(value) for value in manifest_alignment_df["subject"].tolist()}),
"n_bold_rows": int(len(bold_summary_df)),
"n_regressor_rows": int(len(regressor_summary_df)),
}
if not bold_summary_df.empty:
alignment_qc["bold_n_tr_min"] = int(bold_summary_df["n_tr_aligned"].min())
alignment_qc["bold_n_tr_max"] = int(bold_summary_df["n_tr_aligned"].max())
alignment_qc["bold_n_voxels_min"] = int(bold_summary_df["n_voxels"].min())
alignment_qc["bold_n_voxels_max"] = int(bold_summary_df["n_voxels"].max())
if not regressor_summary_df.empty:
alignment_qc["regressor_n_tr_min"] = int(regressor_summary_df["n_tr_aligned"].min())
alignment_qc["regressor_n_tr_max"] = int(regressor_summary_df["n_tr_aligned"].max())
alignment_qc["regressor_n_features_min"] = int(regressor_summary_df["n_features"].min())
alignment_qc["regressor_n_features_max"] = int(regressor_summary_df["n_features"].max())
return bold_summary_df, regressor_summary_df, alignment_qc
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