OSCR

Multi-level static and dynamic graph-theoretical analyses of resting-state functional networks in a Chinese cohort of preterm neonates.

Code ↔ Paper

5 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 5 matches
  1. [1] § STAR★Methods › Quantification and statistical analysis ↔ risk_factor.ipynb, lines 94–109 · score 0.97 · fetal distress, thyroid dysfunction, gestational diabetes, maternal age, delivery mode, multiple pregnancy
  2. [2] § STAR★Methods › Method details › Clinical and perinatal data collection ↔ risk_factor.ipynb, lines 94–109 · score 0.97 · fetal distress, thyroid dysfunction, gestational diabetes, maternal age, delivery mode, multiple pregnancy
  3. [3] § Results › Group differences in static graph-theoretical metrics between term and overall preterm groups and across term and preterm subgroups ↔ results_Demo.m, lines 191–278 · score 0.71 · nodal clustering coefficient, nodal local efficiency, NCp, NLe, Dc, Bc
  4. [4] § Results › Group differences in static graph-theoretical metrics between term and overall preterm groups and across term and preterm subgroups ↔ results_Demo.m, lines 191–278 · score 0.63 · nodal clustering coefficient, nodal local efficiency, NCp, NLe, Dc, Ne
  5. [5] § Results › Group differences in static graph-theoretical metrics between term and overall preterm groups and across term and preterm subgroups ↔ correlation.R, lines 48–112 · score 0.58 · clustering coefficient, global efficiency, local efficiency, AUC, Eloc, Lp

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Jupyter notebook · 434 lines · 17 KB · no license · 2 matches

  1. # %%
  2. # ============================================
  3. # Group LASSO + Cross-validation + OLS + FDR correction
  4. # ============================================
  5. import pandas as pd
  6. import numpy as np
  7. from sklearn.preprocessing import StandardScaler, OneHotEncoder
  8. from sklearn.compose import ColumnTransformer
  9. from group_lasso import GroupLasso
  10. from sklearn.model_selection import KFold
  11. import statsmodels.api as sm
  12. import matplotlib.pyplot as plt
  13. import seaborn as sns
  14. from tqdm import tqdm
  15. from scipy import stats
  16. from datetime import datetime
  17. from statsmodels.stats.multitest import multipletests
  19. # Set plotting style
  20. plt.rcParams['font.sans-serif'] = ['Arial', 'DejaVu Sans']
  21. plt.rcParams['axes.unicode_minus'] = False
  23. def calculate_score(y_true, y_pred):
  24. """Robust scoring function (Pearson correlation)"""
  25. if len(np.unique(y_pred)) < 2: # avoid constant predictions
  26. return 0
  27. try:
  28. corr = np.corrcoef(y_true, y_pred)[0, 1]
  29. return corr if not np.isnan(corr) else 0
  30. except:
  31. return 0
  33. def plot_significant_features(coef_df, outcome, p_values=None, q_values=None):
  34. """Improved visualization with FDR-corrected p-values"""
  35. if len(coef_df) == 0:
  36. print(f"⚠️ No significant variables to visualize for {outcome}")
  37. return
  39. plt.figure(figsize=(12, max(6, len(coef_df) * 0.5)))
  41. # Color based on significance level
  42. colors = []
  43. for i in range(len(coef_df)):
  44. if q_values is not None and q_values[i] < 0.05:
  45. colors.append('red') # FDR significant
  46. elif p_values is not None and p_values[i] < 0.05:
  47. colors.append('orange') # raw p-value significant
  48. else:
  49. colors.append('lightblue') # not significant
  51. y_pos = np.arange(len(coef_df))
  52. bars = plt.barh(y_pos, coef_df['coef'], color=colors, alpha=0.7)
  53. plt.yticks(y_pos, coef_df['variable'])
  54. plt.axvline(0, color='gray', linestyle='--', alpha=0.7)
  55. plt.xlabel('Coefficient magnitude', fontsize=12)
  57. # Legend
  58. if q_values is not None:
  59. plt.title(f'Predictors for {outcome}\n(red: FDR q<0.05, orange: p<0.05, light blue: not significant)', fontsize=14)
  60. else:
  61. plt.title(f'Predictors for {outcome}\n(orange: p<0.05, light blue: not significant)', fontsize=14)
  63. # Add coefficient values and significance markers
  64. for i, (bar, coef) in enumerate(zip(bars, coef_df['coef'])):
  65. text_x = coef + (0.01 if coef >= 0 else -0.01)
  66. ha = 'left' if coef >= 0 else 'right'
  68. # Coefficient value
  69. plt.text(text_x, i, f'{coef:.3f}', va='center', ha=ha, fontsize=10)
  71. # Significance marker
  72. sig_marker = ""
  73. if q_values is not None and q_values[i] < 0.05:
  74. sig_marker = "**" # FDR significant
  75. elif p_values is not None and p_values[i] < 0.05:
  76. sig_marker = "*" # raw p-value significant
  78. if sig_marker:
  79. plt.text(text_x + (0.02 if coef >= 0 else -0.02), i, sig_marker,
  80. va='center', ha=ha, fontsize=12, fontweight='bold')
  82. plt.tight_layout()
  83. plt.show()
  85. # 1️⃣ Load data
  86. print("📁 Loading data...")
  87. df = pd.read_excel("clinical_data.xlsx") # Put your file in current directory
  89. # Remove irrelevant columns
  90. for c in ["ID", "PMA", "BW"]: # adjust column names as needed
  91. if c in df.columns:
  92. df = df.drop(columns=c)
  94. # 2️⃣ Define variables
  95. outcomes = ["Sigma", "Cp", "Lp", "Eg", "Eloc"]
  96. covariates = ["GA", "PNA", "sex", "mean_FD"] # not penalized
  97. predictors = [
  98. "maternal_age", "delivery_mode", "hypertension", "gestational_diabetes",
  99. "high_risk_IUI", "antenatal_steroids", "antenatal_magnesium", "multiple_pregnancy",
  100. "meconium_stained_fluid", "fetal_distress", "thyroid_dysfunction", "anemia"
  101. ]
  102. # Categorical variables (will be one-hot encoded)
  103. cat_vars = [
  104. "sex", "delivery_mode", "hypertension", "gestational_diabetes", "high_risk_IUI",
  105. "antenatal_steroids", "antenatal_magnesium", "multiple_pregnancy", "meconium_stained_fluid",
  106. "fetal_distress", "thyroid_dysfunction", "anemia"
  107. ]
  109. summary_results = []
  111. # 3️⃣ Loop over each graph metric
  112. for outcome in outcomes:
  113. print(f"\n{'='*50}")
  114. print(f"📊 Analyzing {outcome}")
  115. print(f"{'='*50}")
  117. cols = [outcome] + covariates + predictors
  118. data = df[cols].dropna()
  120. if len(data) < 30:
  121. print(f"⚠️ Insufficient sample size (n={len(data)}), skipping")
  122. continue
  124. print(f"✅ Effective sample size: {len(data)}")
  126. y = data[outcome].values
  127. X = data[covariates + predictors]
  129. # Convert categorical variables to string for consistent encoding
  130. for c in cat_vars:
  131. if c in X.columns:
  132. X[c] = X[c].astype(str)
  134. # One-hot + scaling
  135. numeric_features = [c for c in X.columns if c not in cat_vars]
  136. categorical_features = [c for c in X.columns if c in cat_vars]
  138. preprocessor = ColumnTransformer(
  139. transformers=[
  140. ("num", StandardScaler(), numeric_features),
  141. ("cat", OneHotEncoder(drop="first", sparse_output=False), categorical_features)
  142. ]
  143. )
  145. X_processed = preprocessor.fit_transform(X)
  146. X_processed = np.asarray(X_processed)
  147. feature_names = (
  148. numeric_features +
  149. list(preprocessor.named_transformers_['cat'].get_feature_names_out(categorical_features))
  150. )
  152. # Standardize y
  153. y_scaled = (y - np.mean(y)) / np.std(y)
  155. print(f"📐 Processed feature dimension: {X_processed.shape}")
  156. print(f"🔢 Number of features: {len(feature_names)}")
  158. # Check for missing values
  159. if np.isnan(X_processed).any() or np.isnan(y_scaled).any():
  160. print("⚠️ Warning: Missing values detected in preprocessed data")
  162. # ===============================
  163. # Step 1️⃣ Automatic λ search via cross-validation (group_reg)
  164. # ===============================
  166. # Define groups: -1 for unpenalized covariates, positive integers for predictor groups
  167. groups = np.ones(len(feature_names)) # default group 1
  168. for i, name in enumerate(feature_names):
  169. # Check if this feature belongs to a covariate (unpenalized)
  170. is_covariate = False
  171. for cov in covariates:
  172. if name.startswith(cov) or name == cov:
  173. is_covariate = True
  174. break
  175. if is_covariate:
  176. groups[i] = -1 # unpenalized group
  177. else:
  178. # Assign proper group index for predictors
  179. for j, pred in enumerate(predictors):
  180. if name.startswith(pred) or name == pred:
  181. groups[i] = j
  182. break
  184. lambda_grid = np.logspace(-3, -1, 6) # λ from 0.001 to 0.1
  185. cv = KFold(n_splits=5, shuffle=True, random_state=42)
  186. cv_scores = []
  188. for lam in tqdm(lambda_grid, desc=f"CV λ for {outcome}"):
  189. scores = []
  190. for train_idx, test_idx in cv.split(X_processed):
  191. try:
  192. gl = GroupLasso(
  193. groups=groups,
  194. group_reg=lam,
  195. l1_reg=0,
  196. n_iter=5000,
  197. tol=1e-4,
  198. scale_reg="group_size",
  199. random_state=42,
  200. supress_warning=True
  201. )
  202. gl.fit(X_processed[train_idx], y_scaled[train_idx])
  203. y_pred = gl.predict(X_processed[test_idx])
  204. score = calculate_score(y_scaled[test_idx], y_pred)
  205. scores.append(score)
  206. except Exception as e:
  207. scores.append(0)
  209. cv_scores.append(np.nanmean(scores))
  211. best_lambda = lambda_grid[np.nanargmax(cv_scores)]
  212. best_score = np.nanmax(cv_scores)
  213. print(f"✅ Optimal λ (group_reg) = {best_lambda:.4f}, CV score = {best_score:.4f}")
  215. # ===============================
  216. # Step 2️⃣ Fit Group LASSO with optimal λ
  217. # ===============================
  218. gl_final = GroupLasso(
  219. groups=groups,
  220. group_reg=best_lambda,
  221. l1_reg=0,
  222. n_iter=5000,
  223. tol=1e-4,
  224. scale_reg="group_size",
  225. random_state=42,
  226. supress_warning=True
  227. )
  228. gl_final.fit(X_processed, y_scaled)
  229. coef = gl_final.coef_
  231. nonzero_idx = np.where(coef != 0)[0]
  232. selected_features = [feature_names[i] for i in nonzero_idx]
  233. print(f"🎯 Group LASSO selected {len(selected_features)} variables")
  234. if selected_features:
  235. print(f"Selected: {selected_features}")
  237. # ===============================
  238. # Step 3️⃣ OLS significance test + FDR correction
  239. # ===============================
  240. if len(selected_features) > 0:
  241. X_selected = X_processed[:, nonzero_idx]
  243. # Remove constant columns
  244. non_constant_idx = [i for i in range(X_selected.shape[1])
  245. if np.std(X_selected[:, i]) > 1e-8]
  246. X_selected = X_selected[:, non_constant_idx]
  247. selected_features_clean = [selected_features[i] for i in non_constant_idx]
  249. if len(selected_features_clean) > 0:
  250. X_ols = sm.add_constant(pd.DataFrame(X_selected,
  251. columns=selected_features_clean))
  253. # Check multicollinearity
  254. cond_number = np.linalg.cond(X_ols)
  255. if cond_number < 1e10:
  256. ols_model = sm.OLS(y_scaled, X_ols).fit()
  258. # Model diagnostics
  259. r2 = ols_model.rsquared
  260. adj_r2 = ols_model.rsquared_adj
  261. aic = ols_model.aic
  262. bic = ols_model.bic
  264. print(f"📈 Model diagnostics - R²: {r2:.3f}, adj.R²: {adj_r2:.3f}")
  265. print(f"📊 Model diagnostics - AIC: {aic:.2f}, BIC: {bic:.2f}")
  266. print(f"🔍 Condition number: {cond_number:.2f}")
  268. # Residual normality test
  269. residuals = ols_model.resid
  270. if len(residuals) > 7:
  271. _, p_normality = stats.normaltest(residuals)
  272. print(f"📏 Residual normality p-value: {p_normality:.3f}")
  274. # Collect p-values for all selected variables
  275. p_values = []
  276. for i, feature in enumerate(selected_features_clean):
  277. p_val = ols_model.pvalues[i+1] # skip intercept
  278. p_values.append(p_val)
  280. # FDR correction (Benjamini-Hochberg)
  281. reject_fdr, pvals_corrected_fdr, _, _ = multipletests(
  282. p_values, alpha=0.05, method='fdr_bh'
  283. )
  285. # Identify significant features
  286. sig_features_raw = [] # raw p < 0.05
  287. sig_features_fdr = [] # FDR q < 0.05
  289. for i, feature in enumerate(selected_features_clean):
  290. if p_values[i] < 0.05:
  291. sig_features_raw.append(feature)
  292. if pvals_corrected_fdr[i] < 0.05:
  293. sig_features_fdr.append(feature)
  295. print(f"🔬 Raw significant variables (p<0.05): {len(sig_features_raw)}")
  296. print(f"🎯 FDR significant variables (q<0.05): {len(sig_features_fdr)}")
  298. # Store results
  299. result_info = {
  300. "outcome": outcome,
  301. "sample_size": len(data),
  302. "best_lambda": best_lambda,
  303. "cv_score": best_score,
  304. "selected_features_count": len(selected_features_clean),
  305. "selected_features": selected_features_clean,
  306. "significant_features_raw": sig_features_raw,
  307. "significant_features_fdr": sig_features_fdr,
  308. "r_squared": r2,
  309. "adj_r_squared": adj_r2,
  310. "aic": aic,
  311. "bic": bic,
  312. "condition_number": cond_number
  313. }
  315. # Add per-feature details
  316. for i, feature in enumerate(selected_features_clean):
  317. result_info[f"coef_{feature}"] = ols_model.params[i+1]
  318. result_info[f"pvalue_{feature}"] = p_values[i]
  319. result_info[f"qvalue_fdr_{feature}"] = pvals_corrected_fdr[i]
  321. summary_results.append(result_info)
  323. # Visualize all selected variables with FDR coloring
  324. coef_df = pd.DataFrame({
  325. "variable": selected_features_clean,
  326. "coef": ols_model.params.values[1:],
  327. "p_value": p_values,
  328. "q_value_fdr": pvals_corrected_fdr
  329. })
  331. plot_significant_features(
  332. coef_df, outcome,
  333. p_values=p_values,
  334. q_values=pvals_corrected_fdr
  335. )
  337. # Print details of FDR-significant variables
  338. if sig_features_fdr:
  339. print("🎯 FDR-significant variables details:")
  340. for feature in sig_features_fdr:
  341. idx = selected_features_clean.index(feature)
  342. print(f" {feature}: coef={ols_model.params[idx+1]:.3f}, "
  343. f"p={p_values[idx]:.4f}, q(FDR)={pvals_corrected_fdr[idx]:.4f}")
  344. else:
  345. print(f"⚠️ High multicollinearity (condition number: {cond_number:.2f}), skipping OLS")
  346. else:
  347. print("⚠️ All selected variables are constant, skipping OLS")
  348. else:
  349. print("⚠️ Group LASSO selected no variables")
  351. # ===============================
  352. # Step 4️⃣ Save aggregated results
  353. # ===============================
  354. if summary_results:
  355. results_df = pd.DataFrame(summary_results)
  356. results_df['analysis_time'] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
  358. # Create output directory if not exists
  359. import os
  360. os.makedirs("results", exist_ok=True)
  362. results_df.to_csv("results/group_lasso_ols_results.csv", index=False, encoding="utf-8-sig")
  364. # Save FDR-significant features
  365. sig_results_fdr = []
  366. for result in summary_results:
  367. for feature in result['significant_features_fdr']:
  368. sig_results_fdr.append({
  369. 'outcome': result['outcome'],
  370. 'significant_feature': feature,
  371. 'lambda': result['best_lambda'],
  372. 'sample_size': result['sample_size'],
  373. 'coefficient': result.get(f'coef_{feature}', 'NA'),
  374. 'p_value': result.get(f'pvalue_{feature}', 'NA'),
  375. 'q_value_fdr': result.get(f'qvalue_fdr_{feature}', 'NA')
  376. })
  378. if sig_results_fdr:
  379. pd.DataFrame(sig_results_fdr).to_csv(
  380. "results/significant_features_fdr.csv", index=False, encoding="utf-8-sig"
  381. )
  383. # Save raw-significant features
  384. sig_results_raw = []
  385. for result in summary_results:
  386. for feature in result['significant_features_raw']:
  387. sig_results_raw.append({
  388. 'outcome': result['outcome'],
  389. 'significant_feature': feature,
  390. 'lambda': result['best_lambda'],
  391. 'sample_size': result['sample_size'],
  392. 'coefficient': result.get(f'coef_{feature}', 'NA'),
  393. 'p_value': result.get(f'pvalue_{feature}', 'NA'),
  394. 'q_value_fdr': result.get(f'qvalue_fdr_{feature}', 'NA')
  395. })
  397. if sig_results_raw:
  398. pd.DataFrame(sig_results_raw).to_csv(
  399. "results/significant_features_raw.csv", index=False, encoding="utf-8-sig"
  400. )
  402. print(f"\n✅ All results saved in './results/' directory.")
  403. print(f"📊 Detailed results: group_lasso_ols_results.csv")
  404. print(f"🎯 FDR-significant features: significant_features_fdr.csv")
  405. print(f"🔬 Raw-significant features: significant_features_raw.csv")
  407. # Summary statistics
  408. total_analyses = len(summary_results)
  409. total_fdr_sig = sum(len(r['significant_features_fdr']) for r in summary_results)
  410. total_raw_sig = sum(len(r['significant_features_raw']) for r in summary_results)
  412. print(f"\n📈 Summary:")
  413. print(f" Analyses completed: {total_analyses}")
  414. print(f" Total FDR-significant variables (q<0.05): {total_fdr_sig}")
  415. print(f" Total raw-significant variables (p<0.05): {total_raw_sig}")
  417. if total_fdr_sig > 0:
  418. print(f"\n🎯 Overview of FDR-significant findings:")
  419. for result in summary_results:
  420. if result['significant_features_fdr']:
  421. print(f" {result['outcome']}: {len(result['significant_features_fdr'])} variable(s)")
  422. for feature in result['significant_features_fdr']:
  423. q_val = result.get(f'qvalue_fdr_{feature}', 'NA')
  424. p_val = result.get(f'pvalue_{feature}', 'NA')
  425. # Handle float formatting safely
  426. p_str = f"{p_val:.4f}" if isinstance(p_val, (int, float)) else str(p_val)
  427. q_str = f"{q_val:.4f}" if isinstance(q_val, (int, float)) else str(q_val)
  428. print(f" - {feature} (p={p_str}, q={q_str})")
  429. else:
  430. print("\n⚠️ No significant variables found.")
  432. print("\n📝 Notes:")
  433. print(" FDR correction uses Benjamini-Hochberg method to control false discovery rate.")
  434. print(" * indicates raw p < 0.05, ** indicates FDR q < 0.05")

risk_factor.ipynb at commit 13ab2c3, no license · at the source

Overview

Authors: Ting Peng1, Suhua Xu2, Ying Lin3, Jiaqi Li1, Chunjie Jiang1, Xin Xu4, Miaoshuang Liu4, Lin Zhang5, Mingwen Yang5, Zuozhen Lan5, Juan Yue6, Han Zhang6, Jungang Liu5, Wenhao Zhou1,7,8,9, Guoqiang Cheng1
ORCID iDs: Ting Peng
  1. Department of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai 201102, China
  2. Department of Neonatology, Shanghai Children’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 201102, China
  3. Fujian Key Laboratory of Neonatal Diseases, Children’s Hospital of Fudan University (Xiamen Branch), Xiamen Children’s Hospital, Xiamen 361006, China
  4. Department of Neonatology, Children’s Hospital of Fudan University (Xiamen Branch), Xiamen Children’s Hospital, Xiamen 361006, China
  5. Department of Radiology, Children’s Hospital of Fudan University (Xiamen Branch), Xiamen Children’s Hospital, Xiamen 361006, China
  6. School of Biomedical Engineering, ShanghaiTech University, Shanghai 201102, China
  7. Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510005, China
  8. Center for Molecular Medicine, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai 201102, China
  9. Shanghai Key Laboratory of Birth Defects, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai 201102, China
Institutions: Children's Hospital of Fudan University (China); Shanghai Jiao Tong University (China); Shanghai Children's Hospital (China); Children’s Hospital of Fudan University Xiamen Branch (China); ShanghaiTech University (China); Fudan University (China); Guangzhou Women and Children Medical Center (China); Guangzhou Medical University (China)
Journal: iScience, volume 29, issue 8, article 117012
Dates: received 16 March 2026; accepted 15 July 2026; published online 30 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.isci.2026.117012 · PMID 42571414 · PMCID PMC13452229 · OpenAlex W7171874328
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), other condition (population), developmental (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: graph theory, functional connectivity, preterm neonates, brain development, dynamic network
Topic: Infant Development and Preterm Care (Pediatrics, Perinatology and Child Health, Medicine), according to OpenAlex
Funding: National Major Science and Technology Projects of China
Citations: not cited yet (Europe PMC); 70 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

tpengfdu-eng/neonate_GT

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 13ab2c3afbe9b5016cc00e1ca41d479e958f297b, 29 April 2026
Languages: MATLAB (5), Jupyter (3), R (2)
Size: 10 files, 10 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: 3 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (3 files), pandas (3 files), ggplot2 (2 files), Matplotlib (2 files), SciPy (2 files), seaborn (2 files), statsmodels (2 files), patchwork (1 file), reshape2 (1 file), scikit-learn (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
10 files
At the source: github.com/tpengfdu-eng/neonate_GT

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 10 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to the authors' code: tpengfdu-eng/neonate_GT
  • it says that the data are available on request
  • it says that the code is available on request

Read it in the paper: doi.org/10.1016/j.isci.2026.117012.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Authors: added Ting Peng (0000-0001-6155-0995); removed Ting Peng

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 5 keywords, 1 funder, 70 references.

Cite

This paper

Peng, T., Xu, S., Lin, Y., Li, J., Jiang, C., Xu, X., Liu, M., Zhang, L., Yang, M., Lan, Z., Yue, J., Zhang, H., Liu, J., Zhou, W., & Cheng, G. (2026). Multi-level static and dynamic graph-theoretical analyses of resting-state functional networks in a Chinese cohort of preterm neonates. iScience, 29(8), 117012. https://doi.org/10.1016/j.isci.2026.117012

BibTeX

@article{peng2026multi,
author = {Peng, Ting and Xu, Suhua and Lin, Ying and Li, Jiaqi and Jiang, Chunjie and Xu, Xin and Liu, Miaoshuang and Zhang, Lin and Yang, Mingwen and Lan, Zuozhen and Yue, Juan and Zhang, Han and Liu, Jungang and Zhou, Wenhao and Cheng, Guoqiang},
title = {{Multi-level static and dynamic graph-theoretical analyses of resting-state functional networks in a Chinese cohort of preterm neonates}},
journal = {iScience},
year = {2026},
month = jul,
volume = {29},
number = {8},
pages = {117012},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.117012},
url = {https://doi.org/10.1016/j.isci.2026.117012},
pmid = {42571414},
pmcid = {PMC13452229}
}

RIS

TY - JOUR
AU - Peng, Ting
AU - Xu, Suhua
AU - Lin, Ying
AU - Li, Jiaqi
AU - Jiang, Chunjie
AU - Xu, Xin
AU - Liu, Miaoshuang
AU - Zhang, Lin
AU - Yang, Mingwen
AU - Lan, Zuozhen
AU - Yue, Juan
AU - Zhang, Han
AU - Liu, Jungang
AU - Zhou, Wenhao
AU - Cheng, Guoqiang
TI - Multi-level static and dynamic graph-theoretical analyses of resting-state functional networks in a Chinese cohort of preterm neonates
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/07/30
VL - 29
IS - 8
SP - 117012
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.117012
UR - https://doi.org/10.1016/j.isci.2026.117012
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.isci.2026.117012",
"type": "article-journal",
"title": "Multi-level static and dynamic graph-theoretical analyses of resting-state functional networks in a Chinese cohort of preterm neonates",
"container-title": "iScience",
"author": [
{
"family": "Peng",
"given": "Ting"
},
{
"family": "Xu",
"given": "Suhua"
},
{
"family": "Lin",
"given": "Ying"
},
{
"family": "Li",
"given": "Jiaqi"
},
{
"family": "Jiang",
"given": "Chunjie"
},
{
"family": "Xu",
"given": "Xin"
},
{
"family": "Liu",
"given": "Miaoshuang"
},
{
"family": "Zhang",
"given": "Lin"
},
{
"family": "Yang",
"given": "Mingwen"
},
{
"family": "Lan",
"given": "Zuozhen"
},
{
"family": "Yue",
"given": "Juan"
},
{
"family": "Zhang",
"given": "Han"
},
{
"family": "Liu",
"given": "Jungang"
},
{
"family": "Zhou",
"given": "Wenhao"
},
{
"family": "Cheng",
"given": "Guoqiang"
}
],
"container-title-short": "iScience",
"volume": "29",
"issue": "8",
"page": "117012",
"DOI": "10.1016/j.isci.2026.117012",
"PMID": "42571414",
"PMCID": "PMC13452229",
"ISSN": "2589-0042",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.isci.2026.117012",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
30
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1162/imag.a.1347 [code]
Neural and behavioural correlates of theory of mind reasoning in five-year-old children born preterm.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: reshape2, patchwork, ggplot2, 6 other tools, fMRI, other condition, 2 references
[2] doi:10.1016/j.nicl.2026.104012 [code]
Structural-functional multilayer brain network properties and outcome of combined repetitive transcranial magnetic stimulation and psychotherapy for obsessive-compulsive disorder.
Journal: NeuroImage. Clinical
In common: reshape2, statsmodels, ggplot2, 7 other tools, fMRI, other condition, 2 references
[3] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: reshape2, patchwork, statsmodels, 8 other tools
[4] doi:10.1038/s41467-026-76676-0 [code]
Determinants of functional burden pleiotropy and gene dosage responses across human traits.
Journal: Nature communications
In common: reshape2, patchwork, statsmodels, 8 other tools
[5] doi:10.1016/j.isci.2026.116439 [code]
Decoding the role of transcriptomic clocks in the human prefrontal cortex.
Journal: iScience
In common: reshape2, patchwork, statsmodels, 8 other tools
[6] doi:10.1038/s41586-026-10629-x [code]
Whole-genome duplication shaped cell-type evolution in the vertebrate brain.
Journal: Nature
In common: reshape2, patchwork, statsmodels, 8 other tools
[7] doi:10.1371/journal.pbio.3003960 [code]
Brachyury expression levels predict lineage potential and axis-forming ability of in vitro-derived neuromesodermal progenitors.
Journal: PLoS biology
In common: reshape2, patchwork, statsmodels, 8 other tools
[8] doi:10.1038/s41467-026-76939-w [code]
HIPPIE: a generative model for electrophysiological analysis across species, technologies, and modalities.
Journal: Nature communications
In common: reshape2, patchwork, statsmodels, 8 other tools
[9] doi:10.1038/s41514-026-00391-9 [code]
Region-specific transcriptional signatures of brain aging in the absence of neuropathology at the single-cell level.
Journal: npj aging
In common: reshape2, patchwork, statsmodels, 8 other tools
[10] doi:10.1038/s41467-026-71415-x [code]
Regional BOLD variability reflects microstructural maturation and neuronal ensheathment in the preterm infant cortex.
Journal: Nature communications
In common: reshape2, ggplot2, tidyverse, developmental, fMRI, other condition, 3 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.