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Neural Oscillations Track Subjective and Pupillary Arousal During Naturalistic Movie Viewing.

Code ↔ Paper

7 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 7 matches · 6 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › EEG Power Extraction and Correlation With Arousal Measures ↔ Code/compute_brain_arousal_covariance.m, the whole file · a weak match · score 0.98 · Gaussian smoothing, Hann window, dot product, mathematically equivalent, Log Transformation, arousal trend
  2. [2] § Methods › Statistical Analysis ↔ Code/cluster_permutation_test_oneSample.m, the whole file · a weak match · score 0.91 · empirical cluster mass, maximum cluster mass, adjacent channels, Spatial clusters, spatial adjacency, flipped
  3. [3] § Methods › Statistical Analysis ↔ Code/cluster_perm_RSvsMV.py, lines 41–120 · score 0.85 · empirical cluster mass, randomly flipped, error rate, maximum cluster mass, FWER, family
  4. [4] § Methods › Source Localization ↔ Code/compute_brain_arousal_covariance.m, the whole file · a weak match · score 0.79 · Desikan Killiany atlas, anatomical parcel, anatomical regions, brain, EEG, signal
  5. [5] § Methods › ISC ↔ Code/compute_isc_permutation.m, the whole file · a weak match · score 0.78 · shifting permutation, Pearson correlation, autocorrelation, circular, pairwise, ISC
  6. [6] § Methods › EEG Power Extraction and Correlation With Arousal Measures ↔ Code/compute_isc_permutation.m, the whole file · a weak match · score 0.59 · standard deviation, Pearson correlation, variance, temporal, vectors, transformation
  7. [7] § Methods › Spectral Decomposition and Parametrization › Derived Spectral Metrics ↔ Code/cluster_permutation_test_oneSample.m, the whole file · a weak match · score 0.56 · cluster statistic, uncorrected, permuting, permutation, sum, absolute

Paper

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The authors' code

MATLAB · 146 lines · 6.2 KB · no license · 2 matches

  1. function wCov = compute_brain_arousal_covariance(brain_data, arousal_ts, subject_id, IAF_tab, numEpochs, Fs)
  2. % Written by Magdalena Camenzind, PhD
  3. % Inquiries to [email hidden]
  4. %
  5. % Site: University of North Carolina at Chapel Hill
  6. % Lab: Frohlich Lab, www.frohlichlab.org
  8. % COMPUTE_BRAIN_AROUSAL_COVARIANCE
  9. % Computes the covariance (Pearson's r) between continuous neural spectral power
  10. % and an external arousal timeseries (e.g., pupil dilation or subjective ratings).
  11. %
  12. % This function is agnostic to the spatial domain: the input can be sensor-level
  13. % EEG channels or source-level anatomical regions (e.g., from the Desikan-Killiany atlas).
  14. % It segments the data, computes the Power Spectral Density (PSD) for each epoch,
  15. % extracts low-frequency bands (delta, theta, and individualized low-alpha),
  16. % and calculates the temporal covariance with the arousal signal.
  17. %
  18. % =========================================================================
  19. % INPUTS:
  20. % brain_data - 2D numeric matrix [n_regions x time_samples] for one participant.
  21. % (e.g., 61 channels or 68 cortical parcels).
  22. % arousal_ts - 1D numeric array containing the continuous arousal signal.
  23. % (Must be the same length as numEpochs, e.g., 1200).
  24. % subject_id - Numeric ID of the participant (to look up in IAF_tab).
  25. % IAF_tab - Table loaded from 'foofed_IAF_data2.xlsx' containing columns
  26. % for 'ID' and 'IAF'.
  27. % numEpochs - Integer defining the number of epochs to split the data into (e.g., 1200).
  28. % Fs - Sampling frequency of the neural data in Hz (e.g., 200).
  29. %
  30. % OUTPUTS:
  31. % wCov - A struct containing the [n_regions x 1] covariance matrices
  32. % for the requested frequency bands: .delta, .theta, .lalpha.
  33. % *Note: Because both timeseries are ultimately z-scored, this
  34. % covariance is mathematically equivalent to Pearson's correlation (r).
  35. % =========================================================================
  37. %% 1. Preprocess the Arousal Timeseries
  38. % Ensure the arousal signal is a row vector, detrended, and z-scored
  39. arousal_ts = arousal_ts(:)';
  40. if length(arousal_ts) ~= numEpochs
  41. error('The length of arousal_ts (%d) must match numEpochs (%d).', length(arousal_ts), numEpochs);
  42. end
  44. arousal_det = detrend(arousal_ts);
  45. arousal_z = zscore(arousal_det);
  47. %% 2. Look up Individual Alpha Frequency (IAF)
  48. rowIdx = IAF_tab.ID == subject_id;
  49. if ~any(rowIdx)
  50. error('Subject ID %d not found in the provided IAF table.', subject_id);
  51. end
  53. % Extract and round the IAF value to the nearest 0.5 Hz
  54. IAF_val = IAF_tab.IAF(rowIdx);
  55. IAF_val = round(IAF_val * 2) / 2;
  57. % Define ONLY individualized low-alpha bounds
  58. lb_alpha = IAF_val - 1;
  59. lb_alpha_target = [lb_alpha, IAF_val];
  61. %% 3. Initialize Variables
  62. n_regions = size(brain_data, 1);
  64. % Output structures (restricted to low frequencies)
  65. wCov.lalpha = zeros(n_regions, 1);
  66. wCov.theta = zeros(n_regions, 1);
  67. wCov.delta = zeros(n_regions, 1);
  69. % Frequencies of interest for pwelch
  70. foi = 1:0.5:30;
  71. winSize = Fs; % Window size (1 second)
  72. win = hann(winSize); % Hann window
  74. %% 4. Region Loop (Channels or Anatomical Parcels)
  75. for areaIdx = 1:n_regions
  76. roi_Data = brain_data(areaIdx, :);
  78. % --- Epoching Logic ---
  79. epochLength = floor(length(roi_Data) / numEpochs);
  80. remainder = mod(length(roi_Data), numEpochs);
  81. epochs = cell(1, numEpochs);
  83. startIndex = 1;
  84. for i = 1:numEpochs
  85. if i <= remainder
  86. endIndex = startIndex + epochLength;
  87. else
  88. endIndex = startIndex + epochLength - 1;
  89. end
  90. epochs{i} = roi_Data(startIndex:endIndex);
  91. startIndex = endIndex + 1;
  92. end
  94. % --- Preallocate band power arrays ---
  95. lalpha_vals = zeros(1, numEpochs);
  96. theta_vals = zeros(1, numEpochs);
  97. delta_vals = zeros(1, numEpochs);
  99. % --- Compute PSD for each epoch ---
  100. for k = 1:numEpochs
  101. dat_ep = epochs{k};
  103. % Compute Power Spectral Density
  104. [pxx, ~] = pwelch(detrend(dat_ep'), win, [], foi, Fs);
  106. % Extract Frequency Bands
  107. [~, lb_alpha_idx] = min(abs(foi - lb_alpha_target'), [], 2);
  108. lalpha_pw = pxx(lb_alpha_idx(1):lb_alpha_idx(2));
  110. theta_idx = find(foi > 3.5 & foi < 7.5);
  111. theta_pw = pxx(theta_idx);
  113. delta_idx = find(foi > 1.5 & foi < 4.5);
  114. delta_pw = pxx(delta_idx);
  116. % Average power within the band for this epoch
  117. lalpha_vals(k) = mean(lalpha_pw);
  118. theta_vals(k) = mean(theta_pw);
  119. delta_vals(k) = mean(delta_pw);
  120. end
  122. % --- Signal Post-Processing ---
  123. % 1. Log-transform (normalizes the highly skewed power distribution)
  124. log_delta = log10(delta_vals + eps);
  125. log_theta = log10(theta_vals + eps);
  126. log_lalpha = log10(lalpha_vals + eps);
  128. % 2. Gaussian Smoothing (recovers the slow "Arousal Trend" across the narrative)
  129. smooth_log_delta = smoothdata(log_delta, 'gaussian', 40);
  130. smooth_log_theta = smoothdata(log_theta, 'gaussian', 40);
  131. smooth_log_lalpha = smoothdata(log_lalpha, 'gaussian', 40);
  133. % 3. Z-score (standardizes variance to prepare for correlation)
  134. z_delta = zscore(smooth_log_delta);
  135. z_theta = zscore(smooth_log_theta);
  136. z_lalpha = zscore(smooth_log_lalpha);
  138. % --- Calculate Covariance (Pearson's r) ---
  139. % Dividing the dot product of two z-scored vectors by (N-1) yields Pearson's r.
  140. N_minus_1 = numEpochs - 1;
  142. wCov.lalpha(areaIdx) = sum(z_lalpha .* arousal_z) / N_minus_1;
  143. wCov.theta(areaIdx) = sum(z_theta .* arousal_z) / N_minus_1;
  144. wCov.delta(areaIdx) = sum(z_delta .* arousal_z) / N_minus_1;
  145. end
  146. end

compute_brain_arousal_covariance.m, no license · at the source

Overview

Authors: Magdalena Camenzind1,2, Melanni Nanni‐Zepeda1,2, Anna P. Giron3,4, Konrad Dapper5,6,7, Michael Esterman8,9, Flavio Frohlich1,2, Agnieszka Zuberer1,2,9
ORCID iDs: Magdalena Camenzind, Anna P. Giron, Flavio Frohlich
  1. Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
  2. Carolina Center for Neurostimulation University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
  3. Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TüCMH), University of Tübingen Tübingen Germany
  4. German Center for Mental Health (DZPG), partner site Tübingen Tübingen Germany
  5. Department of Biology Technical University Darmstadt Darmstadt Germany
  6. MEG‐Center University of Tübingen Tübingen Germany
  7. Department of Psychology University of Western Ontario London Ontario Canada
  8. National Center for PTSD VA Boston Healthcare System Boston Massachusetts USA
  9. Department of Psychiatry Boston University Chobanian and Avedisian School of Medicine Boston Massachusetts USA
Institutions: University of North Carolina at Chapel Hill (United States); Deutsches Zentrum für Psychische Gesundheit (Germany); University of Tübingen (Germany); Western University (Canada); Technische Universität Darmstadt (Germany); Boston University (United States); VA Boston Healthcare System (United States)
Journal: The European journal of neuroscience, volume 63, issue 9, article e70543
Dates: received 18 July 2025; accepted 29 April 2026; published online 12 May 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/ejn.70543 · PMID 42117513 · PMCID PMC13162759 · OpenAlex W7160928478
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, fMRI & imaging, Physiology & signal measures
Keywords: arousal, electroencephalography (EEG), emotion, naturalistic stimuli, neural oscillations, Pupillometry
MeSH: Arousal*, Pupil*, Adult, Brain Mapping, Electroencephalography, Emotions, Female, Humans, Male, Motion Pictures, Young Adult (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Interdisciplinary Center for Clinical Research IZKF Tübingen
Citations: not cited yet (Europe PMC); 121 references in the paper

Abstract

Movies evoke dynamic emotional experiences that fluctuate moment‐to‐moment. While fMRI has mapped these fluctuations, the real‐time continuous oscillatory dynamics underlying naturalistic viewing remain less understood. In this study, 25 adults watched an emotionally rich short film while EEG, continuous subjective arousal annotations and pupil diameter were recorded. Inter‐subject correlation (ISC) analyses revealed robust synchronization in behavioural and pupillary arousal across the cohort. Leveraging these shared, group‐level arousal trajectories to probe individual‐level cortical processing, we mapped the neural networks correlating with these two arousal signals. Both pupillary and subjective arousal negatively correlated with low‐frequency power in occipitoparietal regions, reflecting bottom‐up sensory gain control and attentional gating. Furthermore, high‐arousal epochs were marked by low‐frequency desynchronization in the precuneus; this cortical activation likely indexes the rapid retrieval of episodic memories required to update the viewer's situational model during plot shifts. Finally, while both measures tracked low‐frequency acoustic features in the auditory cortex, subjective arousal was more prominently associated with extended top‐down semantic networks and central theta activity. These findings highlight that while pupillary arousal heavily reflects bottom‐up sensory intensity, subjective reporting captures active cognitive integration. Together, this demonstrates how emotional arousal acts as a dynamic control signal, orchestrating a complex interplay of sensory gating, memory updating and top‐down evaluation to make sense of the unfolding narrative.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

OSF 75mzh

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (6), Python (1)
Size: 23 files, 7 scripts
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files), Signal Processing Toolbox (1 file), NumPy (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
7 files
At the source: osf.io/75mzh/overview

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

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  • 7 scripts, each with its path and the digest of its content;
  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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

Data Availability Statement

The datasets analysed during the current study are available from the corresponding author on reasonable request. Aggregated data and code are available on OSF: https://osf.io/75mzh/overview.

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

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 6 keywords, 11 MeSH terms, 1 funder, 115 references.

Cite

This paper

Camenzind, M., Nanni‐Zepeda, M., Giron, A. P., Dapper, K., Esterman, M., Frohlich, F., & Zuberer, A. (2026). Neural Oscillations Track Subjective and Pupillary Arousal During Naturalistic Movie Viewing. The European journal of neuroscience, 63(9), e70543. https://doi.org/10.1111/ejn.70543

BibTeX

@article{camenzind2026neural,
author = {Camenzind, Magdalena and Nanni‐Zepeda, Melanni and Giron, Anna P. and Dapper, Konrad and Esterman, Michael and Frohlich, Flavio and Zuberer, Agnieszka},
title = {{Neural Oscillations Track Subjective and Pupillary Arousal During Naturalistic Movie Viewing}},
journal = {The European journal of neuroscience},
year = {2026},
month = may,
volume = {63},
number = {9},
pages = {e70543},
publisher = {Wiley},
issn = {0953-816X},
doi = {10.1111/ejn.70543},
url = {https://doi.org/10.1111/ejn.70543},
pmid = {42117513},
pmcid = {PMC13162759}
}

RIS

TY - JOUR
AU - Camenzind, Magdalena
AU - Nanni‐Zepeda, Melanni
AU - Giron, Anna P.
AU - Dapper, Konrad
AU - Esterman, Michael
AU - Frohlich, Flavio
AU - Zuberer, Agnieszka
TI - Neural Oscillations Track Subjective and Pupillary Arousal During Naturalistic Movie Viewing
T2 - The European journal of neuroscience
J2 - Eur J Neurosci
PY - 2026
DA - 2026/05/01
VL - 63
IS - 9
SP - e70543
SN - 0953-816X
PB - Wiley
DO - 10.1111/ejn.70543
UR - https://doi.org/10.1111/ejn.70543
LA - en
ER -

CSL-JSON

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