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Regional activity and interregional functional connectivity uniquely contribute to social cognitive judgments during movie-watching.

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  1. [1] § ANALYTIC APPROACH › Individual-Level Analysis: Linear Model Construction ↔ level1/stepwise_ver/ets_sub_linreg_prewhiten_stepwise.py, lines 79–128 · score 0.75 · fit autoregressive models, statsmodels, residuals, squares, prewhitening, ij

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The authors' code

Python · 293 lines · 7.6 KB · no license · 1 match

  1. import sys
  2. import time
  3. import numpy as np
  4. import scipy as sp
  5. import statsmodels.api as sm
  6. import statsmodels.formula.api as smf
  7. import pandas as pd
  8. from statsmodels.tsa.ar_model import AutoReg
  9. import pickle
  11. # %%
  13. # points to data-directory where subject data are- split by subject
  14. bdir = sys.argv[1]
  16. subj = sys.argv[2] # subject ID
  17. beh = sys.argv[3] # behavior label
  19. # subj = 'sub-1807'
  20. # beh = 'awk'
  22. indices = np.load('../data/indices_full.npy')
  24. # %%
  25. debug = True
  27. if debug:
  28. start_time = time.time()
  30. shop_ds_len = 249
  31. zoo_ds_len = 233
  33. # %% read in subject nodal timeseries
  35. zoo_nodal = np.load(f'{bdir}/{subj}/{subj}_pettingzoo_interpolated_ts_custom_parc.npy')
  36. shop_nodal = np.load(f'{bdir}/{subj}/{subj}_souvenirshop_interpolated_ts_custom_parc.npy')
  37. zoo_nodal = zoo_nodal[:zoo_ds_len, :]
  38. shop_nodal = shop_nodal[:shop_ds_len, :]
  39. combo_nodal = np.append(zoo_nodal, shop_nodal, axis=0)
  41. y = np.load(f'../../regs/hrf/{beh}_hrf.npy') # behavior (dependent variable)
  42. y = np.append(y[:zoo_ds_len], y[zoo_ds_len:zoo_ds_len + shop_ds_len])
  43. y = np.reshape(y, (len(y), 1))
  44. dm = np.array([1 for t in range(zoo_ds_len)] + [0 for t in range(shop_ds_len)]) # dummy coded run variable
  47. #%%
  49. combo_i = []
  50. combo_j = []
  51. combo_ij = []
  53. for edge in range(indices.shape[0]):
  54. # get indices for nodes
  55. r, c = indices[edge].split('-')
  56. i = combo_nodal[:, int(r)] # node i timeseries
  57. j = combo_nodal[:, int(c)] # node j timeseries
  59. # standardize timeseries
  60. i_std = (i - i.mean()) / i.std()
  61. j_std = (j - j.mean()) / j.std()
  63. # edge timeseries
  64. ij = i_std * j_std
  66. # append to larger matrix timeseries for later
  67. combo_i.append(i_std)
  68. combo_j.append(j_std)
  69. combo_ij.append(ij)
  71. combo_i = np.array(combo_i).T
  72. combo_j = np.array(combo_j).T
  73. combo_ij = np.array(combo_ij).T
  77. # %%
  79. def prewhiten(fullX, y, p=25, nsample=100):
  81. e = np.zeros(fullX[-1].shape)
  83. diag_idx = []
  84. for edge in range(combo_ij.shape[1]):
  85. r, c = indices[edge].split('-')
  86. if r!=c:
  88. X = np.array([i[:, edge] for i in fullX] + [dm, np.ones(fullX[-1].shape[0])]).T
  89. # beta weights
  90. B = np.linalg.inv(X.T @ X) @ X.T @ y
  91. # get residuals
  92. e[:, edge] = np.squeeze(y - (X @ B))
  93. else:
  94. diag_idx.append(edge)
  95. e[:, edge] = np.nan
  96. # fit autoregressive model
  98. # NOTE: AutoReg model in statsmodels here uses a conditional Max. Likelihood Estimator while MatLab AR model uses
  99. # Least-Squares Estimator
  101. P = []
  102. valid_e = list(set(i for i in range(e.shape[1])) - set(diag_idx))
  103. for edge in np.random.choice(valid_e, nsample, replace=False):
  104. res = AutoReg(e[:, edge], p).fit()
  105. P.append(res.params)
  107. P = np.array(P)
  108. mu = np.nanmean(P, 0)
  110. # construct V inverse to get whitening matrix
  112. Amat = np.zeros((e.shape[0], e.shape[0]))
  114. i = 1
  115. for m in mu:
  116. vec = np.ones(e.shape[0] - i)
  117. d = np.diag(-m * vec, -i)
  118. Amat += d
  119. i += 1
  121. Vinv = Amat @ Amat.T
  123. U, S, _ = np.linalg.svd(Vinv)
  125. # Compute whitening matrix
  126. W = U @ sp.linalg.sqrtm(np.diag(S)) @ U.T
  128. return W, mu
  130. def edge_reg_prewhitened(fullX, y, W, name, save_full=True):
  131. #
  132. fullWX = [W[p:-p, p:-p] @ i[p:-p, :] for i in fullX]
  134. Wy = W[p:-p, p:-p] @ y[p:-p]
  135. Wdm = W[p:-p, p:-p] @ dm[p:-p]
  137. # set up structures to hold results
  138. fullres = [] # full fit OLS model
  139. fval = [] # fvalue of model
  140. fprob = [] # p value of model
  141. beta = [] # beta weights for each variable (nodal activity + ets)
  142. sig = [] # p values for each variable (nodal activity + ets)
  143. r2 = [] # model r2 to assess fit
  144. r2_adj = []
  145. AIC = [] # assess fit
  146. BIC = [] # assess fit
  149. # rerun regression with prewhitened data
  150. for edge in range(fullWX[-1].shape[1]):
  151. r, c = indices[edge].split('-')
  152. if r != c:
  153. X = np.array([Wi[:, edge] for Wi in fullWX] + [Wdm]).T
  156. #smf formula style
  157. df = pd.DataFrame(X, columns=name+['run'])
  158. df[beh] = Wy
  160. res = smf.ols(f'{beh} ~ {" + ".join(name)} + run', data=df).fit()
  163. # X = sm.add_constant(X)
  164. # res = sm.OLS(Wy, X).fit()
  166. betas = {}
  167. sigs = {}
  169. fullres.append(res)
  170. fval.append(res.fvalue)
  171. fprob.append(res.f_pvalue)
  172. for i in range(len(name)):
  173. betas[name[i]] = res.params[name[i]]
  174. sigs[name[i]] = res.pvalues[name[i]]
  176. beta.append(betas)
  177. sig.append(sigs)
  179. r2_adj.append(res.rsquared_adj)
  180. r2.append(res.rsquared)
  181. AIC.append(res.aic)
  182. BIC.append(res.bic)
  183. else:
  184. betas = {}
  185. sigs = {}
  187. fullres.append("NULL")
  188. fval.append(np.nan)
  189. fprob.append(np.nan)
  190. for i in range(len(name)):
  191. betas[name[i]] = np.nan
  192. sigs[name[i]] = np.nan
  194. beta.append(betas)
  195. sig.append(sigs)
  197. r2_adj.append(np.nan)
  198. r2.append(np.nan)
  199. AIC.append(np.nan)
  200. BIC.append(np.nan)
  202. fit_d = {
  203. 'fstat': fval,
  204. 'fprob': fprob,
  205. 'betas': beta,
  206. 'pvalues': sig,
  207. 'adj_rsq': r2_adj,
  208. 'rsq': r2,
  209. 'aic': AIC,
  210. 'bic': BIC,
  211. 'autocorr_params': mu,
  212. }
  214. if save_full:
  215. with open(f'../data/sub_level_edge_model_stepwise_{beh}/{subj}_{"-".join(name)}_full_model.pkl', 'wb') as f:
  216. pickle.dump(fullres, f)
  218. return fit_d, fullres
  220. def model_fit_change(m1, m2):
  222. # compare model fit across edges
  223. params = ['fstat', 'fprob', 'adj_rsq', 'aic', 'bic']
  224. fit_change = {}
  226. for param in params:
  227. fit_change[param] = np.array(m1[param]) - np.array(m2[param])
  229. return fit_change
  232. # %% multiply whitening matrix by all variables
  233. p = 25 # order for ar models
  234. nsample = 100 # number of random edge samples to pull to fit models
  236. #%% step 1
  237. fullX = [combo_i]
  238. name = ['i']
  240. W, mu = prewhiten(fullX, y, p=p, nsample=nsample)
  242. fit_d_s1, res_s1 = edge_reg_prewhitened(fullX, y, W, name, save_full=False)
  244. #%% step 2
  245. fullX = [combo_i, combo_j]
  246. name = ['i', 'j']
  248. W, mu = prewhiten(fullX, y, p=p, nsample=nsample)
  250. fit_d_s2, res_s2 = edge_reg_prewhitened(fullX, y, W, name, save_full=False)
  251. fit_change_1_2 = model_fit_change(fit_d_s1, fit_d_s2)
  253. #%% step 3
  254. fullX = [combo_i, combo_j, combo_ij]
  255. name = ['i', 'j', 'ets']
  257. W, mu = prewhiten(fullX, y, p=p, nsample=nsample)
  259. fit_d_s3, res_s3 = edge_reg_prewhitened(fullX, y, W, name, save_full=True)
  260. fit_change_2_3 = model_fit_change(fit_d_s2, fit_d_s3)
  262. #%%
  264. deltas = {'i_min_i+j': fit_change_1_2, 'i+j_min_i+j+ij': fit_change_2_3}
  266. #%% anova lm
  268. edge_modfit_123 = []
  270. for mod1, mod2, mod3 in zip(res_s1, res_s2, res_s3):
  271. if mod1 == "NULL":
  272. edge_modfit_123.append("NULL")
  273. else:
  274. edge_modfit_123.append(sm.stats.anova_lm(mod1, mod2, mod3))
  276. deltas['sig_r2_anova'] = edge_modfit_123
  278. #%%
  280. # get final runtime for single subject
  281. if debug:
  282. end_time = time.time()
  283. runtime = end_time - start_time
  284. print(runtime)
  286. with open(f'../data/sub_level_edge_model_stepwise_{beh}/{subj}_node_only_fit.pkl', 'wb') as f:
  287. pickle.dump(fit_d_s1, f)
  289. with open(f'../data/sub_level_edge_model_stepwise_{beh}/{subj}_fit.pkl', 'wb') as f:
  290. pickle.dump(fit_d_s3, f)
  292. with open(f'../data/sub_level_edge_model_stepwise_{beh}/{subj}_delta.pkl', 'wb') as f:
  293. pickle.dump(deltas, f)

ets_sub_linreg_prewhiten_stepwise.py at commit cbb342d, no license · at the source

Overview

Authors: Roberto C French1, Haily Merritt2, Colleen Hughes1, Richard Betzel3,4, Anne C Krendl1
ORCID iDs: Roberto C French, Haily Merritt, Richard Betzel, Anne C Krendl
  1. Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
  2. Department of Informatics & Program in Cognitive Science, Indiana University, Bloomington, IN, USA
  3. Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
  4. Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
Institutions: Indiana University Bloomington (United States); Indiana University (United States); University of Minnesota (United States)
Journal: Network neuroscience (Cambridge, Mass.), volume 10, issue 3, pages 738-760
Dates: received 22 July 2025; accepted 12 March 2026; published online 27 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/netn.a.571 · PMID 42529782 · PMCID PMC13418520 · OpenAlex W7155178502
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality)
Methods: Connectivity, fMRI & imaging, Statistics
Keywords: fMRI, Social cognition, Dynamic functional connectivity, Brain-behavior relationship, Movie watching
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIA NIH HHS (R01 AG075044, R01 AG070931)
Citations: not cited yet (Europe PMC); 130 references in the paper
Research resources: RRID:SCR_001362, 2011 RRID:SCR_002502, 2019 RRID:SCR_016216

Abstract

Understanding how the brain gives rise to social cognition has been a key goal of neuroimaging research. Both changes in regional activation as well as functional connectivity have been implicated as potential mechanisms underlying social cognition, but the two have rarely been examined concurrently. Moreover, because the neural processes underlying social cognition are dynamic, developing approaches to capture dynamic changes in regional activity and functional connectivity are critical. Here, we describe a novel analysis approach that captures both regional activity and dynamic functional connectivity simultaneously during a naturalistic, socially focused movie-watching task. We found that both regional activation and functional connectivity were uniquely related to awkwardness, a judgment associated with social faux pas detection and theory of mind. Regional activation within sensorimotor networks was positively associated with awkwardness, whereas activation in the default network was negatively associated. Models including functional connectivity accounted for unique variance beyond models with activity alone. Specifically, dynamic functional connectivity between networks, primarily the frontoparietal control network, was positively associated with awkwardness. Together, these findings suggest that both dynamic regional brain activity and functional connectivity each uniquely contribute to complex and dynamic social judgments.

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

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Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

OSF 8bvu9

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 2 files
Software Heritage: not checked
Found in: the text, “Group-Level Analysis: Network-Level Significance”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: osf.io/8bvu9/

rcf004/edge-timeseries-linear-interaction

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: cbb342dd19c1b76b63f294a3823a30a65ec06234, 21 July 2025
Languages: Python (16), Shell (6)
Size: 37 files, 22 scripts
Software Heritage: not archived
Found in: the text, “Group-Level Analysis: Network-Level Significance”
Holds: README, environment (conda_env.yml)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (16 files), Matplotlib (12 files), SciPy (8 files), pandas (5 files), Nilearn (4 files), seaborn (4 files), statsmodels (4 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
23 files
At the source: github.com/rcf004/edge-timeseries-linear-interaction

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 1 funder, 128 references, 3 RRIDs.

Cite

This paper

French, R. C., Merritt, H., Hughes, C., Betzel, R., & Krendl, A. C. (2026). Regional activity and interregional functional connectivity uniquely contribute to social cognitive judgments during movie-watching. Network neuroscience (Cambridge, Mass.), 10(3), 738-760. https://doi.org/10.1162/netn.a.571

BibTeX

@article{french2026regional,
author = {French, Roberto C and Merritt, Haily and Hughes, Colleen and Betzel, Richard and Krendl, Anne C},
title = {{Regional activity and interregional functional connectivity uniquely contribute to social cognitive judgments during movie-watching}},
journal = {Network neuroscience (Cambridge, Mass.)},
year = {2026},
month = jul,
volume = {10},
number = {3},
pages = {738--760},
publisher = {MIT Press},
issn = {2472-1751},
doi = {10.1162/netn.a.571},
url = {https://doi.org/10.1162/netn.a.571},
pmid = {42529782},
pmcid = {PMC13418520}
}

RIS

TY - JOUR
AU - French, Roberto C
AU - Merritt, Haily
AU - Hughes, Colleen
AU - Betzel, Richard
AU - Krendl, Anne C
TI - Regional activity and interregional functional connectivity uniquely contribute to social cognitive judgments during movie-watching
T2 - Network neuroscience (Cambridge, Mass.)
J2 - Netw Neurosci
PY - 2026
DA - 2026/07/27
VL - 10
IS - 3
SP - 738
EP - 760
SN - 2472-1751
PB - MIT Press
DO - 10.1162/netn.a.571
UR - https://doi.org/10.1162/netn.a.571
LA - en
ER -

CSL-JSON

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