OSCR

Medial temporal lobe tau-neurodegeneration mismatch from structural imaging and plasma biomarkers.

Code ↔ Paper

6 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 6 matches
  1. [1] § Materials and methods › Assigning clinical patients to ADNI-derived T-N phenotypes ↔ src/mismatch/AssignGroup.py, lines 8 to 137 · score 0.71 · cluster centroid, binarized residual, SD, ATM, Euclidean, threshold · Report a mismatch
  2. [2] § Materials and methods › T-N mismatch modelling ↔ src/mismatch/TNMismatchClustering copy.py, lines 424 to 543 · score 0.63 · hierarchical clustering, Ward, dendrogram, bisquare, SD, regression · Report a mismatch
  3. [3] § Materials and methods › T-N mismatch modelling ↔ src/mismatch/TNMismatchClustering.py, lines 465 to 584 · score 0.63 · hierarchical clustering, Ward, dendrogram, bisquare, SD, regression · Report a mismatch
  4. [4] § Materials and methods › Image acquisition and processing › MTL super-points parcellation ↔ src/mismatch/__main__.py, lines 17 to 130 · score 0.61 · spatially contiguous, super point, partitioning, template, graph, clustering · Report a mismatch
  5. [5] § Materials and methods › Image acquisition and processing › MTL super-points parcellation ↔ src/mismatch/MTLSuperPoints.py, lines 123 to 219 · score 0.54 · super point, PyMetis, partitioning, graph, adjacency, template · Report a mismatch
  6. [6] § Materials and methods › Assigning clinical patients to ADNI-derived T-N phenotypes ↔ src/mismatch/TNMismatchClustering copy.py, lines 424 to 543 · score 0.52 · binarized residual, SD, matched, threshold, cluster, regression · Report a mismatch

Paper

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The authors' code

Python · 543 lines · 23 KB · MIT · 2 matches

  1. # tn_mismatch_clustering.py
  3. import pandas as pd
  4. import numpy as np
  5. import statsmodels.api as sm
  6. from sklearn.preprocessing import StandardScaler
  7. from scipy.cluster.hierarchy import linkage, dendrogram, fcluster
  8. from scipy.stats import pearsonr
  9. from scipy.spatial.distance import pdist
  10. import matplotlib.pyplot as plt
  11. import seaborn as sns
  12. import argparse
  13. import os
  14. import nibabel as nib
  16. DKT_LABEL_MAP = {
  17. 1002: 'lh.caudalanteriorcingulate', 1003: 'lh.caudalmiddlefrontal', 1005: 'lh.cuneus',
  18. 1006: 'lh.entorhinal', 1007: 'lh.fusiform', 1008: 'lh.inferiorparietal', 1009: 'lh.inferiortemporal',
  19. 1010: 'lh.isthmuscingulate', 1011: 'lh.lateraloccipital', 1012: 'lh.lateralorbitofrontal',
  20. 1013: 'lh.lingual', 1014: 'lh.medialorbitofrontal', 1015: 'lh.middletemporal', 1016: 'lh.parahippocampal',
  21. 1017: 'lh.paracentral', 1018: 'lh.parsopercularis', 1019: 'lh.parsorbitalis', 1020: 'lh.parstriangularis',
  22. 1021: 'lh.pericalcarine', 1022: 'lh.postcentral', 1023: 'lh.posteriorcingulate', 1024: 'lh.precentral',
  23. 1025: 'lh.precuneus', 1026: 'lh.rostralanteriorcingulate', 1027: 'lh.rostralmiddlefrontal',
  24. 1028: 'lh.superiorfrontal', 1029: 'lh.superiorparietal', 1030: 'lh.superiortemporal',
  25. 1031: 'lh.supramarginal', 1034: 'lh.transversetemporal', 1035: 'lh.insula',
  26. 2002: 'rh.caudalanteriorcingulate', 2003: 'rh.caudalmiddlefrontal', 2005: 'rh.cuneus',
  27. 2006: 'rh.entorhinal', 2007: 'rh.fusiform', 2008: 'rh.inferiorparietal', 2009: 'rh.inferiortemporal',
  28. 2010: 'rh.isthmuscingulate', 2011: 'rh.lateraloccipital', 2012: 'rh.lateralorbitofrontal',
  29. 2013: 'rh.lingual', 2014: 'rh.medialorbitofrontal', 2015: 'rh.middletemporal',
  30. 2016: 'rh.parahippocampal', 2017: 'rh.paracentral', 2018: 'rh.parsopercularis',
  31. 2019: 'rh.parsorbitalis', 2020: 'rh.parstriangularis', 2021: 'rh.pericalcarine',
  32. 2022: 'rh.postcentral', 2023: 'rh.posteriorcingulate', 2024: 'rh.precentral',
  33. 2025: 'rh.precuneus', 2026: 'rh.rostralanteriorcingulate', 2027: 'rh.rostralmiddlefrontal',
  34. 2028: 'rh.superiorfrontal', 2029: 'rh.superiorparietal', 2030: 'rh.superiortemporal',
  35. 2031: 'rh.supramarginal', 2034: 'rh.transversetemporal', 2035: 'rh.insula'
  36. }
  40. BrainCOLOR_LABEL_MAP = {
  41. 100: 'Right.ACgG.anterior.cingulate.gyrus',
  42. 101: 'Left.ACgG.anterior.cingulate.gyrus',
  43. 102: 'Right.AIns.anterior.insula',
  44. 103: 'Left.AIns.anterior.insula',
  45. 104: 'Right.AOrG.anterior.orbital.gyrus',
  46. 105: 'Left.AOrG.anterior.orbital.gyrus',
  47. 106: 'Right.AnG.angular.gyrus',
  48. 107: 'Left.AnG.angular.gyrus',
  49. 108: 'Right.Calc.calcarine.cortex',
  50. 109: 'Left.Calc.calcarine.cortex',
  51. 112: 'Right.CO.central.operculum',
  52. 113: 'Left.CO.central.operculum',
  53. 114: 'Right.Cun.cuneus',
  54. 115: 'Left.Cun.cuneus',
  55. 116: 'Right.Ent.entorhinal.area',
  56. 117: 'Left.Ent.entorhinal.area',
  57. 118: 'Right.FO.frontal.operculum',
  58. 119: 'Left.FO.frontal.operculum',
  59. 120: 'Right.FRP.frontal.pole',
  60. 121: 'Left.FRP.frontal.pole',
  61. 122: 'Right.FuG.fusiform.gyrus',
  62. 123: 'Left.FuG.fusiform.gyrus',
  63. 124: 'Right.GRe.gyrus.rectus',
  64. 125: 'Left.GRe.gyrus.rectus',
  65. 128: 'Right.IOG.inferior.occipital.gyrus',
  66. 129: 'Left.IOG.inferior.occipital.gyrus',
  67. 132: 'Right.ITG.inferior.temporal.gyrus',
  68. 133: 'Left.ITG.inferior.temporal.gyrus',
  69. 134: 'Right.LiG.lingual.gyrus',
  70. 135: 'Left.LiG.lingual.gyrus',
  71. 136: 'Right.LOrG.lateral.orbital.gyrus',
  72. 137: 'Left.LOrG.lateral.orbital.gyrus',
  73. 138: 'Right.MCgG.middle.cingulate.gyrus',
  74. 139: 'Left.MCgG.middle.cingulate.gyrus',
  75. 140: 'Right.MFC.medial.frontal.cortex',
  76. 141: 'Left.MFC.medial.frontal.cortex',
  77. 142: 'Right.MFG.middle.frontal.gyrus',
  78. 143: 'Left.MFG.middle.frontal.gyrus',
  79. 144: 'Right.MOG.middle.occipital.gyrus',
  80. 145: 'Left.MOG.middle.occipital.gyrus',
  81. 146: 'Right.MOrG.medial.orbital.gyrus',
  82. 147: 'Left.MOrG.medial.orbital.gyrus',
  83. 148: 'Right.MPoG.postcentral.gyrus.medial.segment',
  84. 149: 'Left.MPoG.postcentral.gyrus.medial.segment',
  85. 150: 'Right.MPrG.precentral.gyrus.medial.segment',
  86. 151: 'Left.MPrG.precentral.gyrus.medial.segment',
  87. 152: 'Right.MSFG.superior.frontal.gyrus.medial.segment',
  88. 153: 'Left.MSFG.superior.frontal.gyrus.medial.segment',
  89. 154: 'Right.MTG.middle.temporal.gyrus',
  90. 155: 'Left.MTG.middle.temporal.gyrus',
  91. 156: 'Right.OCP.occipital.pole',
  92. 157: 'Left.OCP.occipital.pole',
  93. 160: 'Right.OFuG.occipital.fusiform.gyrus',
  94. 161: 'Left.OFuG.occipital.fusiform.gyrus',
  95. 162: 'Right.OpIFG.opercular.part.of.IFG',
  96. 163: 'Left.OpIFG.opercular.part.of.IFG',
  97. 164: 'Right.OrIFG.orbital.part.of.IFG',
  98. 165: 'Left.OrIFG.orbital.part.of.IFG',
  99. 166: 'Right.PCgG.posterior.cingulate.gyrus',
  100. 167: 'Left.PCgG.posterior.cingulate.gyrus',
  101. 168: 'Right.PCu.precuneus',
  102. 169: 'Left.PCu.precuneus',
  103. 170: 'Right.PHG.parahippocampal.gyrus',
  104. 171: 'Left.PHG.parahippocampal.gyrus',
  105. 172: 'Right.PIns.posterior.insula',
  106. 173: 'Left.PIns.posterior.insula',
  107. 174: 'Right.PO.parietal.operculum',
  108. 175: 'Left.PO.parietal.operculum',
  109. 176: 'Right.PoG.postcentral.gyrus',
  110. 177: 'Left.PoG.postcentral.gyrus',
  111. 178: 'Right.POrG.posterior.orbital.gyrus',
  112. 179: 'Left.POrG.posterior.orbital.gyrus',
  113. 180: 'Right.PP.planum.polare',
  114. 181: 'Left.PP.planum.polare',
  115. 182: 'Right.PrG.precentral.gyrus',
  116. 183: 'Left.PrG.precentral.gyrus',
  117. 184: 'Right.PT.planum.temporale',
  118. 185: 'Left.PT.planum.temporale',
  119. 186: 'Right.SCA.subcallosal.area',
  120. 187: 'Left.SCA.subcallosal.area',
  121. 190: 'Right.SFG.superior.frontal.gyrus',
  122. 191: 'Left.SFG.superior.frontal.gyrus',
  123. 192: 'Right.SMC.supplementary.motor.cortex',
  124. 193: 'Left.SMC.supplementary.motor.cortex',
  125. 194: 'Right.SMG.supramarginal.gyrus',
  126. 195: 'Left.SMG.supramarginal.gyrus',
  127. 196: 'Right.SOG.superior.occipital.gyrus',
  128. 197: 'Left.SOG.superior.occipital.gyrus',
  129. 198: 'Right.SPL.superior.parietal.lobule',
  130. 199: 'Left.SPL.superior.parietal.lobule',
  131. 200: 'Right.STG.superior.temporal.gyrus',
  132. 201: 'Left.STG.superior.temporal.gyrus',
  133. 202: 'Right.TMP.temporal.pole',
  134. 203: 'Left.TMP.temporal.pole',
  135. 204: 'Right.TrIFG.triangular.part.of.IFG',
  136. 205: 'Left.TrIFG.triangular.part.of.IFG',
  137. 206: 'Right.TTG.transverse.temporal.gyrus',
  138. 207: 'Left.TTG.transverse.temporal.gyrus',
  139. 31: 'Right.Amygdala', 32: 'Left.Amygdala', 47: 'Right.Hippocampus', 48: 'Left.Hippocampus',
  140. 35: 'Brain.Stem', 36: 'Right.Caudate', 37: 'Left.Caudate',
  141. 44: 'Right.Cerebral.White.Matter', 45: 'Left.Cerebral.White.Matter'
  142. }
  144. ATLAS_CONFIG = {
  145. "DKT": {
  146. "path": "/project/wolk/xyl/tn_mismatch/tn_mismatch/preprocess/tpl-ADNINormalAgingANTs_res-01_atlas-DKT_desc-31_dseg.nii.gz",
  147. "label_map": DKT_LABEL_MAP
  148. },
  149. "BrainCOLOR": {
  150. "path": "/project/wolk/xyl/tn_mismatch/tn_mismatch/preprocess/antsMalfLabeling.nii.gz",
  151. "label_map": BrainCOLOR_LABEL_MAP
  152. }
  153. }
  156. class TNMismatchClustering:
  157. def __init__(self, atlas=None, custom_atlas_path=None, custom_label_csv=None):
  158. self.atlas = atlas
  159. self.custom_atlas_path = custom_atlas_path
  160. self.custom_label_csv = custom_label_csv
  161. self.seg_path = None
  162. self.label_map = None
  163. self.allowed_labels = None
  164. self._load_atlas()
  166. def _load_atlas(self):
  167. if self.custom_atlas_path and self.custom_label_csv:
  168. self.seg_path = self.custom_atlas_path
  169. self.label_map = self.load_custom_label_csv(self.custom_label_csv)
  170. self.allowed_labels = set(self.label_map.keys())
  171. elif self.atlas:
  172. atlas_info = ATLAS_CONFIG.get(self.atlas)
  173. self.seg_path = atlas_info["path"]
  174. self.label_map = atlas_info["label_map"]
  175. self.allowed_labels = set(self.label_map.keys())
  177. def load_custom_label_csv(self, label_csv):
  178. df = pd.read_csv(label_csv)
  179. colnames = [c.lower() for c in df.columns]
  180. if "index" in colnames and "name" in colnames:
  181. index_col = df.columns[colnames.index("index")]
  182. name_col = df.columns[colnames.index("name")]
  183. elif "labelid" in colnames and "labelname" in colnames:
  184. index_col = df.columns[colnames.index("labelid")]
  185. name_col = df.columns[colnames.index("labelname")]
  186. else:
  187. raise ValueError("CSV must contain either 'index,name' or 'LabelID,LabelName' columns")
  188. return {int(row[index_col]): row[name_col] for _, row in df.iterrows()}
  190. def extract_matched_tau_thickness(self, df, independent, predict):
  191. tau_cols = [c for c in df.columns if c.endswith(f"_{independent}")]
  192. matched = []
  193. unmatched = []
  194. for c in tau_cols:
  195. base = c.replace(f"_{independent}", "")
  196. target = f"{base}_{predict}"
  197. if target in df.columns:
  198. matched.append((c, target))
  199. else:
  200. unmatched.append(base)
  201. if unmatched:
  202. raise ValueError(f"Missing {predict} for {', '.join(unmatched)}")
  203. tau_cols_final = [pair[0] for pair in matched]
  204. thickness_cols_final = [pair[1] for pair in matched]
  205. rois = [c.replace(f"_{independent}", "") for c in tau_cols_final]
  206. return df[tau_cols_final], df[thickness_cols_final], rois
  208. def compute_bisquare_residuals(self, tau_df, thickness_df, log_transform=True, cov_df=None, covariates=None):
  209. n_subjects, n_rois = tau_df.shape
  210. residuals = np.zeros((n_subjects, n_rois))
  212. roi_cov_usage = {} # track ROI-specific covariate usage: {cov: set(roi_idx)}
  213. global_cov_used = set() # track used global covariates
  214. missing_globals = set() # track skipped global covariates
  215. all_roi_specific_cov = set() # track all specified ROI-specific covariates (e.g., "amyloid")
  217. for i in range(n_rois):
  218. tau = tau_df.iloc[:, i]
  219. if log_transform:
  220. tau = np.log10(tau)
  222. thick = thickness_df.iloc[:, i]
  223. mask = tau.notna() & thick.notna()
  225. # Base design matrix with tau
  226. X = pd.DataFrame({'tau': tau})
  228. if covariates and cov_df is not None:
  229. for cov in covariates:
  230. if cov.startswith("*"):
  231. base = cov[1:]
  232. all_roi_specific_cov.add(base)
  233. roi_col = tau_df.columns[i].replace("_tau", f"_{base}")
  234. if roi_col in cov_df.columns:
  235. X[cov] = cov_df[roi_col]
  236. roi_cov_usage.setdefault(base, set()).add(i)
  237. else:
  238. if cov in cov_df.columns:
  239. X[cov] = cov_df[cov]
  240. global_cov_used.add(cov)
  241. else:
  242. missing_globals.add(cov)
  244. # Ensure numeric
  245. non_numeric_cols = X.select_dtypes(exclude=["number"]).columns
  246. if len(non_numeric_cols) > 0:
  247. raise TypeError(f"❌ Non-numeric covariates detected in model for ROI {tau_df.columns[i]}: {', '.join(non_numeric_cols)}")
  249. try:
  250. X_masked = sm.add_constant(X[mask])
  251. y = thick[mask]
  252. rlm_model = sm.RLM(y, X_masked, M=sm.robust.norms.TukeyBiweight()).fit()
  253. pred = rlm_model.predict(sm.add_constant(X))
  254. residuals[:, i] = thick - pred
  255. except Exception as e:
  256. print(f"⚠️ RLM failed for ROI {tau_df.columns[i]}: {e}")
  257. residuals[:, i] = np.nan
  259. # === Summary messages ===
  260. if global_cov_used:
  261. print(f" - Global covariates used: {', '.join(sorted(global_cov_used))}")
  262. if missing_globals:
  263. print(f" - ⚠️ Global covariates NOT found and skipped: {', '.join(sorted(missing_globals))}")
  265. for base in sorted(all_roi_specific_cov):
  266. if base not in roi_cov_usage:
  267. print(f" - ⚠️ ROI-specific covariate NOT found in any ROI: {base}")
  268. elif len(roi_cov_usage[base]) == n_rois:
  269. print(f" - All ROIs used ROI-specific covariate: {base}")
  270. else:
  271. print(f" - ROI-specific covariate used in {len(roi_cov_usage[base])} out of {n_rois} ROIs: {base}")
  273. return pd.DataFrame(residuals, columns=thickness_df.columns)
  275. #def binarize_residuals(self, res_df, sd_thresh):
  276. # if sd_thresh == 0:
  277. # return res_df
  278. # bin_df = pd.DataFrame(index=res_df.index, columns=res_df.columns)
  279. # for col in res_df.columns:
  280. # sd = np.nanstd(res_df[col])
  281. # bin_df[col] = res_df[col].apply(lambda x: -1 if x < -sd_thresh * sd else (1 if x > sd_thresh * sd else 0))
  282. # return bin_df.fillna(0)
  284. def binarize_residuals(self, res_df, sd_thresh, feature_weights=None):
  285. if sd_thresh == 0:
  286. return res_df
  288. bin_df = pd.DataFrame(index=res_df.index, columns=res_df.columns)
  289. for col in res_df.columns:
  290. sd = np.nanstd(res_df[col])
  291. bin_df[col] = res_df[col].apply(
  292. lambda x: -1 if x < -sd_thresh * sd else (1 if x > sd_thresh * sd else 0)
  293. )
  295. bin_df = bin_df.fillna(0)
  297. if feature_weights:
  298. for col in bin_df.columns:
  299. base = col.split("_")[0] # Extract '1002' from '1002_thickness'
  300. weight = feature_weights.get(base)
  301. if weight is not None:
  302. bin_df[col] = bin_df[col].astype(float) * weight
  304. unmatched = set(feature_weights.keys()) - set([col.split("_")[0] for col in bin_df.columns])
  305. if unmatched:
  306. print(f"⚠️ Warning: These features in --feature_weights were not matched to any residual columns: {', '.join(unmatched)}")
  312. return bin_df
  315. def run_hierarchical_clustering(self, bin_df, n_clusters=None):
  316. distance_matrix = pdist(bin_df.values, metric="euclidean")
  317. linkage_matrix = linkage(distance_matrix, method="ward")
  318. if n_clusters is None:
  319. distances = linkage_matrix[:, 2]
  320. diff = np.diff(distances[::-1])
  321. elbow_index = np.argmax(diff[:9]) + 1
  322. n_clusters = elbow_index + 1
  323. print("\n🔎 Auto-selecting number of clusters based on elbow method...")
  324. print(f"📐 Optimal number of clusters estimated: {n_clusters}")
  325. cluster_labels = fcluster(linkage_matrix, n_clusters, criterion="maxclust")
  326. return cluster_labels, linkage_matrix
  328. def determine_cluster_type_all_clusters(self, cluster_means, cluster_sizes):
  329. canonical_id = min(
  330. cluster_means.keys(),
  331. key=lambda cid: (np.nanmean(np.abs(cluster_means[cid])), -cluster_sizes[cid])
  332. )
  333. canonical_profile = cluster_means[canonical_id]
  334. cluster_types = {}
  335. for cid, mean_profile in cluster_means.items():
  336. if cid == canonical_id:
  337. cluster_types[cid] = "Likely Canonical"
  338. else:
  339. mean_residual = np.nanmean(mean_profile)
  340. canonical_mean_residual = np.nanmean(canonical_profile)
  341. if mean_residual < canonical_mean_residual:
  342. cluster_types[cid] = "Likely Vulnerable"
  343. else:
  344. cluster_types[cid] = "Likely Resilient"
  345. return cluster_types
  347. def map_residuals_to_nifti(self, cluster_id, mean_resid, rois, out_path):
  348. seg_img = nib.load(self.seg_path)
  349. seg_data = seg_img.get_fdata()
  350. out_data = np.zeros_like(seg_data, dtype=np.float32)
  351. roi_map = {int(r): mean_resid[i] for i, r in enumerate(rois)}
  352. for label, value in roi_map.items():
  353. out_data[seg_data == label] = value
  354. out_img = nib.Nifti1Image(out_data, affine=seg_img.affine, header=seg_img.header)
  355. nib.save(out_img, out_path)
  357. def plot_heatmap(self, df, out_path, rois=None, title=None):
  358. plt.figure(figsize=(max(20, df.shape[1] * 0.3), max(5, df.shape[0] * 0.3)))
  359. sns.heatmap(df, cmap="vlag", center=0, cbar_kws={"label": "Residual"},
  360. xticklabels=rois if rois else True, yticklabels=False)
  361. plt.xticks(rotation=45, ha='right', fontsize=6)
  362. plt.title(title or "Residual Heatmap")
  363. plt.tight_layout()
  364. plt.savefig(out_path)
  365. plt.close()
  367. def print_centered(self, text):
  368. try:
  369. width = os.get_terminal_size().columns
  370. except OSError:
  371. width = 100
  372. print(text.center(width))
  374. @staticmethod
  375. def report_pairwise_correlations(tau_df, thickness_df, rois):
  376. sig_count = 0
  377. trend_count = 0
  378. total = len(rois)
  380. for i in range(total):
  381. tau = tau_df.iloc[:, i]
  382. thick = thickness_df.iloc[:, i]
  383. mask = tau.notna() & thick.notna()
  384. if mask.sum() < 3:
  385. continue
  386. _, pval = pearsonr(tau[mask], thick[mask])
  387. if pd.notna(pval):
  388. if pval < 0.05:
  389. sig_count += 1
  390. elif pval < 0.1:
  391. trend_count += 1
  393. print(f"\n Pearson correlation summary between independent and dependent variables:")
  395. print(f" - {sig_count} out of {total} ROIs show significant correlation (p < 0.05)")
  396. print(f" - {trend_count} out of {total} ROIs show a trend (0.05 < p < 0.1)")
  398. if (sig_count + trend_count) < (0.3 * total):
  399. print(" ⚠️ Caution: Looks like the correlation between your variables is on the low side.")
  400. print(" Keep in mind - Mismatch modeling works best when there's at least some relationship!")
  408. def plot_dendrogram(self, linkage_matrix, labels, out_path):
  409. plt.figure(figsize=(12, 6))
  410. dendrogram(
  411. linkage_matrix,
  412. labels=labels,
  413. leaf_rotation=90,
  414. leaf_font_size=8,
  415. color_threshold=linkage_matrix[-(len(set(labels))-1), 2] if len(set(labels)) > 1 else 0
  416. )
  417. plt.title("Hierarchical Clustering Dendrogram")
  418. plt.xlabel("Subject Index")
  419. plt.tight_layout()
  420. plt.savefig(out_path)
  421. plt.close()
  422. print(f"🧬 Dendrogram saved to: {out_path}")
  424. def run_pipeline(self, input_csv, independent, predict, sd_thresh=1.5, n_clusters=None,
  425. output_prefix="output", log_transform=True, generate_maps=True, covariates=None, feature_weights=None):
  427. self.print_centered("🌟 Welcome to the Mismatch Universe! 🌟")
  428. self.print_centered("🧠 PATCH Lab warmly welcomes you to explore brain heterogenity together!! 😊")
  430. df = pd.read_csv(input_csv)
  431. ids = df["ID"]
  432. tau_df, thickness_df, rois = self.extract_matched_tau_thickness(df, independent, predict)
  434. print(f"🗺️ Using predefined atlas: {self.atlas}")
  435. print(f"👤 Number of subjects: {df.shape[0]}")
  436. print(f"🧠 Number of ROIs: {len(rois)}")
  438. self.report_pairwise_correlations(tau_df, thickness_df, rois)
  442. print("📈 Running robust bisquare regression...")
  443. residuals = self.compute_bisquare_residuals(tau_df, thickness_df, log_transform=log_transform,
  444. cov_df=df, covariates=covariates)
  445. residuals.insert(0, "ID", ids)
  447. print(f"📊 Using residuals for clustering (SD={sd_thresh})...")
  448. binary_df = self.binarize_residuals(residuals.drop(columns="ID"), sd_thresh, feature_weights=feature_weights)
  451. print("🔗 Running Ward.D2 clustering...")
  452. labels, linkage_matrix = self.run_hierarchical_clustering(binary_df, n_clusters)
  453. residuals["T_N_cluster"] = labels
  455. global_mismatch_scores = residuals.drop(columns=["ID", "T_N_cluster"]).sum(axis=1) * -1
  456. result = pd.DataFrame({"ID": ids, "T_N_cluster": labels,
  457. "Global_Mismatch_Score": global_mismatch_scores})
  459. print("\n📊 Cluster Membership Counts:")
  460. for cid, count in result["T_N_cluster"].value_counts().sort_index().items():
  461. print(f" Cluster {cid}: {count} subjects")
  463. heatmap_dir = f"{output_prefix}_cluster_heatmaps"
  464. os.makedirs(heatmap_dir, exist_ok=True)
  466. cluster_means = {}
  467. cluster_sizes = {}
  468. cluster_heatmap_paths = []
  469. cluster_nii_paths = []
  474. roi_names = [self.label_map.get(int(r), r) for r in rois] if self.label_map else rois
  475. #roi_names = [self.label_map.get(r, r) for r in rois] if self.label_map else rois
  478. for cid in sorted(set(labels)):
  479. cluster_df = residuals[residuals["T_N_cluster"] == cid]
  480. cluster_means[cid] = cluster_df.drop(columns=["ID", "T_N_cluster"]).mean()
  481. cluster_sizes[cid] = len(cluster_df)
  482. heatmap_path = os.path.join(heatmap_dir, f"cluster_{cid}_residuals.png")
  483. cluster_heatmap_paths.append((cid, heatmap_path))
  485. self.plot_heatmap(
  486. cluster_df.drop(columns=["ID", "T_N_cluster"]),
  487. heatmap_path,
  488. roi_names,
  489. f"Residual Heatmap - Cluster {cid}"
  490. )
  491. print(f"🖼️ Residual heatmap saved for Cluster {cid} at: {heatmap_path}")
  493. if generate_maps and self.seg_path:
  494. mean_resid = cluster_means[cid].values
  495. nii_path = os.path.join(heatmap_dir, f"cluster_{cid}_residual_map.nii.gz")
  496. self.map_residuals_to_nifti(cid, mean_resid, rois, nii_path)
  497. print(f"🧠 Residual map for cluster {cid} saved to: {nii_path}")
  498. cluster_nii_paths.append((cid, nii_path))
  500. residuals_path = f"{output_prefix}_residuals.csv"
  501. residuals.to_csv(residuals_path, index=False)
  502. print(f"📁 Residuals saved to: {residuals_path}")
  504. cluster_path = f"{output_prefix}_clusters.csv"
  505. result.to_csv(cluster_path, index=False)
  506. print(f"📁 Cluster labels saved to: {cluster_path}")
  508. print(f"🗺️ Using predefined atlas: {self.atlas}")
  509. cluster_types = self.determine_cluster_type_all_clusters(cluster_means, cluster_sizes)
  510. for cid, ctype in cluster_types.items():
  511. print(f"✅ Cluster {cid} is {ctype}")
  513. name_df = pd.DataFrame([(cid, cluster_types[cid]) for cid in cluster_types],
  514. columns=["T_N_cluster", "Cluster_Label"])
  515. final_result = result.merge(name_df, on="T_N_cluster")
  516. result_path = f"{output_prefix}_clusters_with_names.csv"
  517. final_result.to_csv(result_path, index=False)
  518. print(f"📁 Cluster labels with names saved to: {result_path}")
  520. full_heatmap = f"{output_prefix}_residual_heatmap_all.png"
  521. self.plot_heatmap(
  522. residuals.drop(columns=["ID", "T_N_cluster"]),
  523. full_heatmap,
  524. roi_names,
  525. "Residual Heatmap - All Subjects"
  526. )
  527. print(f"🖼️ Full residual heatmap saved to: {full_heatmap}")
  529. dendro_path = f"{output_prefix}_dendrogram.png"
  530. self.plot_dendrogram(linkage_matrix, labels=[str(i) for i in ids], out_path=dendro_path)
  532. return {
  533. "residual_df": residuals,
  534. "cluster_df": result,
  535. "final_df": final_result,
  536. "residual_csv": residuals_path,
  537. "cluster_csv": cluster_path,
  538. "final_csv": result_path,
  539. "cluster_heatmaps": cluster_heatmap_paths,
  540. "cluster_nifti_maps": cluster_nii_paths,
  541. "all_subjects_heatmap": full_heatmap,
  542. "dendrogram_path": dendro_path
  543. }

TNMismatchClustering copy.py at commit 4d3ec8b, under MIT · at the source

Overview

Authors: Xueying Lyu1, Nidhi S Mundada1, Christopher A Brown2, Niyousha Sadeghpour3, Emily McGrew3, Long Xie3, Yue Li3, Anika Wuestefeld4, Michael Tran Duong3, Philip Cook3, James Gee3, Ilya M Nasrallah3, Alice S Chen-Plotkin2, Leslie M Shaw1, Dawn Mechanic-Hamilton2, Laura E M Wisse4, Paul A Yushkevich3, Sandhitsu R Das2, David A Wolk2
ORCID iDs: Xueying Lyu, Christopher A Brown, Anika Wuestefeld, Michael Tran Duong, Paul A Yushkevich
  1. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
  2. Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
  3. Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
  4. Department of Clinical Sciences Lund, Lund University, Lund SE-221 00, Sweden
Institutions: University of Pennsylvania (United States); Lund University (Sweden)
Journal: Brain : a journal of neurology, volume 149, issue 11, pages 3890-3904
Dates: received 21 August 2025; accepted 14 February 2026; published online 28 April 2026; in print November 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/brain/awag105 · PMID 42046390 · PMCID PMC13480308 · OpenAlex W7156620307
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: structural MRI / diffusion (modality), other (modality), human (organism), other condition (population), Alzheimer's / dementia (population), clinical / translational (subfield)
Methods: Statistics, Machine learning, Preprocessing, fMRI & imaging
Keywords: Alzheimer’s disease, medial temporal lobe, LATE-NC, resilience, plasma ptau217, mismatch
MeSH: Alzheimer Disease*, tau Proteins*, Temporal Lobe*, Aged, Aged, 80 and over, Atrophy, Biomarkers, Female, Humans, Magnetic Resonance Imaging, Male (* major topic)
Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: national institutes of health (U19 AG024904); Pennsylvania Department of Health (2019NF4100087335); Lund University; Bente Rexed Gerstedt Foundation; Swedish Research Council (2022-00900); Alzheimer's Association; Fred and Barbara Erb Family Foundation (AACSF-23-1152241); DoD Alzheimer’s Disease Neuroimaging Initiative; National Institute on Aging; Northern California Institute for Research and Education; National Institute of Biomedical Imaging and Bioengineering; Gouvernement du Canada | Canadian Institutes of Health Research (IRSC); Foundation for the National Institutes of Health; AbbVie; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; Roche; Genentech; Fujirebio; GE Healthcare; IXICO Ltd; Janssen Alzheimer Immunotherapy Research And Development; Johnson and Johnson Pharmaceutical Research and Development; Lumosity; H. Lundbeck A/S; Merck; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer; Piramal Imaging; Servier; Takeda Pharmaceutical Company; Transition Therapeutics
Citations: cited by 1 paper (Europe PMC); 64 references in the paper

Abstract

Although tau pathology is closely associated with neurodegeneration in Alzheimer’s disease, our prior work using multi-modality imaging revealed that mismatch between tau (T) and neurodegeneration (N) might reflect contributions from non-Alzheimer’s disease processes. The medial temporal lobe (MTL), an early site of Alzheimer’s disease pathology, is also a common target of co-pathologies, such as limbic-predominant age-related TDP-43 encephalopathy neuropathological change (LATE-NC), often following an anterior-to-posterior atrophy gradient. Given the susceptibility of MTL to co-pathologies, here we explored T-N mismatch specifically within MTL using plasma ptau217 and MTL morphometry for identifying vulnerabilities and resilience in cognitively impaired or unimpaired patients with Alzheimer’s disease.

We parcellated the MTL into 100 spatially contiguous segments and calculated their T-N mismatch using plasma ptau217 as a measure for T and thickness as a marker of N. Based on these mismatch profiles, we clustered 447 amyloid-positive individuals from the Alzheimer’s Disease Neuroimaging Initiative cohort into data-driven T-N phenotypes. We characterized the T-N phenotypes by examining their cross-sectional and longitudinal atrophy both within the MTL and across the whole brain, in addition to cognitive trajectories. This framework was replicated in an independent cohort and, finally, translated to a real-world clinical sample of 50 patients undergoing anti-amyloid therapy.

Clustering identified three T-N phenotypes with different MTL T-N mismatch profiles, atrophy patterns and cognitive outcomes, despite comparable Alzheimer’s disease severity. The ‘canonical’ group, characterized by low T-N residuals (N ∼ T), showed Alzheimer’s disease-like neurodegeneration patterns. The ‘vulnerable’ group, characterized by disproportionately greater neurodegeneration than tau (N > T), showed atrophy primarily in the anterior MTL that extended into temporal-limbic regions, in both cross-sectional and longitudinal analyses. This group also exhibited neurodegeneration that preceded estimated tau onset and experienced faster cognitive decline across multiple domains, aligning with the typical characteristics of mixed LATE-NC with Alzheimer’s disease. In contrast, the ‘resilient’ group (N < T) showed minimal atrophy and preserved cognitive function. These phenotypes were reproducible in an independent research cohort. Importantly, in a feasibility study applying the model developed from the Alzheimer’s Disease Neuroimaging Initiative to a clinical cohort of patients receiving lecanemab, we identified vulnerable individuals with LATE-like atrophy patterns. This highlights its potential utility for identifying individuals with co-pathology in clinical settings.

Our findings demonstrate that T-N mismatch within MTL using MRI and plasma biomarkers can reveal Alzheimer’s disease groups with varying vulnerability/resilience, with the vulnerable group displaying structural and cognitive outcomes suggestive of LATE-NC. This approach offers a cost-effective strategy for clinical trial stratification and precision medicine for Alzheimer’s disease therapeutics.

Reproduced under the paper's license (CC BY), from the paper cited above.

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xueying-lyu/mismatch

License: MIT
State: the link answers, verified on 4 November 2026
Evidence: files inventoried
Commit: 4d3ec8b02134232871c0e7b73c67aed41f7bb992, 19 August 2025
Languages: Python (9), Jupyter (1)
Size: 62 files, 10 scripts
Software Heritage: not archived
Found in: the text, “Conclusion”
Holds: README, license file, environment (pyproject.toml, setup.cfg), tests, continuous integration, 1 notebook
Not found: CITATION.cff, documentation
Tools: NumPy (7 files), pandas (7 files), NiBabel (3 files), Matplotlib (2 files), scikit-learn (2 files), SciPy (2 files), seaborn (2 files), statsmodels (2 files), Nilearn (1 file)
Availability: 1 check, the latest on 4 November 2026: the link answers
  • 4 November 2026: the link answers
12 files
At the source: github.com/xueying-lyu/mismatch

pyushkevich/cmrep

License: GPL-3.0
State: the link answers, verified on 28 October 2026
Evidence: files inventoried
Commit: c0760b45a8fcb1358619e46fc5c09b1f6e34e7b8, 3 February 2026
Languages: C/C++ (75), MATLAB (17), C (5), C++ (2), Python (1), Jupyter (1), Shell (1)
Size: 314 files, 102 scripts
Software Heritage: archived
Found in: the text, “MTL surface vertex-wise thickness comparisons”
Holds: README, license file, environment (Dockerfile), documentation, 1 notebook
Not found: CITATION.cff, tests, continuous integration
Tools: Image Processing Toolbox (4 files), NumPy (1 file), PyTorch (1 file)
Availability: 2 checks, the latest on 28 October 2026: the link answers
  • 28 October 2026: the link answers
  • 27 September 2026: the link answers
104 files
At the source: github.com/pyushkevich/cmrep

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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Data

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Data availability

Data from ADNI used in this study are publicly available at adni.loni.usc.edu. Data from the Penn ADRC and ATM cohorts are available from the corresponding author upon reasonable request.

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Version 1, 4 November 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 19 authors, 6 keywords, 11 MeSH terms, 44 funders, 0 references.

Cite

This paper

Lyu, X., Mundada, N. S., Brown, C. A., Sadeghpour, N., McGrew, E., Xie, L., Li, Y., Wuestefeld, A., Duong, M. T., Cook, P., Gee, J., Nasrallah, I. M., Chen-Plotkin, A. S., Shaw, L. M., Mechanic-Hamilton, D., Wisse, L. E. M., Yushkevich, P. A., Das, S. R., & Wolk, D. A. (2026). Medial temporal lobe tau-neurodegeneration mismatch from structural imaging and plasma biomarkers. Brain : a journal of neurology, 149(11), 3890-3904. https://doi.org/10.1093/brain/awag105

BibTeX

@article{lyu2026medial,
author = {Lyu, Xueying and Mundada, Nidhi S and Brown, Christopher A and Sadeghpour, Niyousha and McGrew, Emily and Xie, Long and Li, Yue and Wuestefeld, Anika and Duong, Michael Tran and Cook, Philip and Gee, James and Nasrallah, Ilya M and Chen-Plotkin, Alice S and Shaw, Leslie M and Mechanic-Hamilton, Dawn and Wisse, Laura E M and Yushkevich, Paul A and Das, Sandhitsu R and Wolk, David A},
title = {{Medial temporal lobe tau-neurodegeneration mismatch from structural imaging and plasma biomarkers}},
journal = {Brain : a journal of neurology},
year = {2026},
month = nov,
volume = {149},
number = {11},
pages = {3890--3904},
publisher = {Oxford University Press},
issn = {0006-8950},
doi = {10.1093/brain/awag105},
url = {https://doi.org/10.1093/brain/awag105},
pmid = {42046390},
pmcid = {PMC13480308}
}

RIS

TY - JOUR
AU - Lyu, Xueying
AU - Mundada, Nidhi S
AU - Brown, Christopher A
AU - Sadeghpour, Niyousha
AU - McGrew, Emily
AU - Xie, Long
AU - Li, Yue
AU - Wuestefeld, Anika
AU - Duong, Michael Tran
AU - Cook, Philip
AU - Gee, James
AU - Nasrallah, Ilya M
AU - Chen-Plotkin, Alice S
AU - Shaw, Leslie M
AU - Mechanic-Hamilton, Dawn
AU - Wisse, Laura E M
AU - Yushkevich, Paul A
AU - Das, Sandhitsu R
AU - Wolk, David A
TI - Medial temporal lobe tau-neurodegeneration mismatch from structural imaging and plasma biomarkers
T2 - Brain : a journal of neurology
J2 - Brain
PY - 2026
DA - 2026/11/01
VL - 149
IS - 11
SP - 3890
EP - 3904
SN - 0006-8950
PB - Oxford University Press
DO - 10.1093/brain/awag105
UR - https://doi.org/10.1093/brain/awag105
LA - en
ER -

CSL-JSON

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"type": "article-journal",
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"container-title": "Brain : a journal of neurology",
"author": [
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"family": "Lyu",
"given": "Xueying"
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{
"family": "Mundada",
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{
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{
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{
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{
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"DOI": "10.1093/brain/awag105",
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