Can Replaying a Movie Clip Reduce Variability in Single-Subject Resting-State fMRI?
Poster No:
1398
Submission Type:
Abstract Submission
Authors:
Céline Provins1, Alexandre Cionca2, Élodie Savary1, Hélène Lajous3, Benedetta Franceschiello4, Yasser Alemán-Gómez5, Patric Hagmann6, Oscar Esteban1
Institutions:
1Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Vaud, 2EPFL, Lausanne, Vaud, 3Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Vaud, 4Institute of Systems Engineering, School of Engineering, HES-SO Valais-Wallis, Sion, Valais, 5Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Vaud, 6Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Vaud
First Author:
Céline Provins
Department of Radiology, Lausanne University Hospital and University of Lausanne
Lausanne, Vaud
Department of Radiology, Lausanne University Hospital and University of Lausanne
Lausanne, Vaud
Co-Author(s):
Élodie Savary
Department of Radiology, Lausanne University Hospital and University of Lausanne
Lausanne, Vaud
Department of Radiology, Lausanne University Hospital and University of Lausanne
Lausanne, Vaud
Benedetta Franceschiello
Institute of Systems Engineering, School of Engineering, HES-SO Valais-Wallis
Sion, Valais
Institute of Systems Engineering, School of Engineering, HES-SO Valais-Wallis
Sion, Valais
Yasser Alemán-Gómez
Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Vaud
Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Vaud
Patric Hagmann
Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Vaud
Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL)
Lausanne, Vaud
Oscar Esteban
Department of Radiology, Lausanne University Hospital and University of Lausanne
Lausanne, Vaud
Department of Radiology, Lausanne University Hospital and University of Lausanne
Lausanne, Vaud
Introduction:
Resting-state functional MRI (rs-fMRI) is influenced by ongoing cognition, which is neither measured nor controlled (Finn, 2021). This variability complicates signal interpretation and undermines the reliability of edge-level connectivity estimates (Nakuci et al., 2023; Noble et al., 2017). Naturalistic paradigms using real-world stimuli, like movies, help maintain wakefulness and synchronize brain activity across sessions and individuals through time-locked stimuli (Hasson et al., 2004).
In this project, we tested stimulus habituation by repeatedly playing a naturalistic, mute movie clip while acquiring fMRI on a single individual. We quantified variability as residual variance after modeling functional connectivity (FC) with principal component analysis (PCA).
In this project, we tested stimulus habituation by repeatedly playing a naturalistic, mute movie clip while acquiring fMRI on a single individual. We quantified variability as residual variance after modeling functional connectivity (FC) with principal component analysis (PCA).
Methods:
Data. One 40yo. male underwent 36 MRI sessions over the span of four weeks (described in Provins et al., (2023)). The rs-fMRI was acquired in a 3T Siemens scanner using a multi-echo EPI BOLD sequence, whose parameters are in the supplementary material.
Naturalistic movie. rs-fMRI was acquired while watching a silent 20-minute naturalistic scene of a calm sunset landscape. The footage was carefully extracted from a 4-hour static recording (Figure 1) to minimize distractions, such as moving boats and cars, and cut out the night scene, which could induce sleep. Aside from occasionally passing cars in the village and one passing boat, no sudden stimuli are present. Other visible movements include waves, flickering lights, and drifting clouds.
Preprocessing. Data were preprocessed with fMRIPrep (Esteban et al., 2019) and further denoised by regressing out the six motion parameters, the WM and CSF mean signal, censoring frames with a framewise displacement above 0.4mm and smoothed to an estimated 4mm Gaussian kernel. Nodes in FC matrices were defined by the 128 regions of the DiFuMo atlas (Dadi et al. 2020). FC was estimated as the correlation of the region-wise-averaged timeseries.
Residual variance evolution with movie repetition. Individual FC matrices were aggregated in a single PCA, expressing each FC as a linear combination of principal components and residuals (Bari et al., 2019). The elbow plot indicated to keep two principal components (Figure S1). Residual variance across edges was plotted against session index to visually assess habituation, with linear and quadratic fits evaluated using R². To identify which predictors of no interest (among time of day, day of week, and phase encoding direction) influenced the variance decrease, fits were plotted separately for bins of factor value (Figures S2-4).
Habituation significance. The significance of the habituation was tested using ordinary least squares implemented in statsmodels to determine whether the slope differed significantly from zero. The p-values were corrected for multiple comparisons using Bonferroni method.
Data & code availability. The dataset and fMRIPrep derivatives will be publicly released upon Stage 2 completion of the registered report. The naturalistic movie and supplementary material can be found at https://osf.io/4rp69. The analysis is openly available at https://github.com/TheAxonLab/hcph-sops.
Naturalistic movie. rs-fMRI was acquired while watching a silent 20-minute naturalistic scene of a calm sunset landscape. The footage was carefully extracted from a 4-hour static recording (Figure 1) to minimize distractions, such as moving boats and cars, and cut out the night scene, which could induce sleep. Aside from occasionally passing cars in the village and one passing boat, no sudden stimuli are present. Other visible movements include waves, flickering lights, and drifting clouds.
Preprocessing. Data were preprocessed with fMRIPrep (Esteban et al., 2019) and further denoised by regressing out the six motion parameters, the WM and CSF mean signal, censoring frames with a framewise displacement above 0.4mm and smoothed to an estimated 4mm Gaussian kernel. Nodes in FC matrices were defined by the 128 regions of the DiFuMo atlas (Dadi et al. 2020). FC was estimated as the correlation of the region-wise-averaged timeseries.
Residual variance evolution with movie repetition. Individual FC matrices were aggregated in a single PCA, expressing each FC as a linear combination of principal components and residuals (Bari et al., 2019). The elbow plot indicated to keep two principal components (Figure S1). Residual variance across edges was plotted against session index to visually assess habituation, with linear and quadratic fits evaluated using R². To identify which predictors of no interest (among time of day, day of week, and phase encoding direction) influenced the variance decrease, fits were plotted separately for bins of factor value (Figures S2-4).
Habituation significance. The significance of the habituation was tested using ordinary least squares implemented in statsmodels to determine whether the slope differed significantly from zero. The p-values were corrected for multiple comparisons using Bonferroni method.
Data & code availability. The dataset and fMRIPrep derivatives will be publicly released upon Stage 2 completion of the registered report. The naturalistic movie and supplementary material can be found at https://osf.io/4rp69. The analysis is openly available at https://github.com/TheAxonLab/hcph-sops.
Results:
The residual variance decreases across movie repetitions. The slope of the linear regression across all sessions is not significantly negative (β=-0.0002, p=0.94; Figure 2). However, removing the outlier session 30 yields significance (β=-0.0006, p=0.038). Session 30 shows lower correlations with other sessions compared to the rest (Figure S5).
Limitations. Future work will examine whether factors like head motion, coffee intake, sleepiness, and image quality explain the FC differences in session 30 to determine whether its exclusion from the analysis is justified. We will also assess whether certain functional networks are more sensitive to stimulus habituation.
Limitations. Future work will examine whether factors like head motion, coffee intake, sleepiness, and image quality explain the FC differences in session 30 to determine whether its exclusion from the analysis is justified. We will also assess whether certain functional networks are more sensitive to stimulus habituation.
Conclusions:
Demonstrating reduced residual variance due to stimulus habituation is a first step toward more reliable edge-level FC estimation.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
FUNCTIONAL MRI
MRI
Other - Naturalistic Paradigms; Functional Connectivity; Precision Neuroimaging; Variability; Reliability; Dense-Sampling Dataset; Repeated-measures; Habituation
1|2Indicates the priority used for review
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Provide references using APA citation style.
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Finn, E. S. (2021). Is it time to put rest to rest? Trends in Cognitive Sciences, 25(12), 1021–1032. https://doi.org/10.1016/j.tics.2021.09.005
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Noble, S., Spann, M. N., Tokoglu, F., Shen, X., Constable, R. T., & Scheinost, D. (2017). Influences on the Test–Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility. Cerebral Cortex, 27(11), 5415–5429. https://doi.org/10.1093/cercor/bhx230
Provins, C., Lajous, H., Savary, E., Fornari, E., Franceschiello, B., Alemán-Gómez, Y., Thompson, W. H., Jelescu, I., Hagmann, P., & Esteban, O. (2023). Reliability characterization of MRI measurements for analyses of brain networks on a human phantom. https://doi.org/10.6084/m9.figshare.19579873.v1