A novel research paradigm in cognitive neuroscience is the use of movies as stimulus material to investigate the neural mechanisms of cognitive and mental activities in subjects during watching movies. Previous studies have suggested that there are sex-related differences in the relationship between the visual and auditory design of movies and the cognitive and mental activities of viewers. Sex differences determine aesthetic differences, and aesthetic differences have different effects on different links in the whole process of film communication. Although it is well documented that there are obvious differences in the cognitive and mental activities between males and females when watching the same movie, the underlying neural mechanisms remain largely unknown. It is important to investigate the sex-related differences in functional brain activity during movie watching.
With the development of functional brain imaging technology, brain age prediction is considered to be an important indicator of an individual's level of brain aging. In addition, a large number of studies have shown that the gap between predicted and actual age is closely related to the deterioration of human brain function. Predicted brain age has been shown to be greater than actual age in patients with Alzheimer's disease, patients with traumatic brain injury, and patients with psychiatric disorders, such as schizophrenia and major depressive disorder. Comparing the similarities and differences in brain age prediction between males and females is expected to reveal different brain ageing trajectories. In particular, comparing male and female brain age prediction models based on movie viewing would be expected to reveal similarities and differences in the patterns of brain functional networks reflected by males and females in response to audio-visual stimuli from films.
Accordingly, this study used functional magnetic resonance imaging (fMRI) data from 528 subjects (261 males, aged 18~87 years) to investigate sex differences in the functional brain network while watching the classic psychological suspense short film 'Bang! You're Dead', directed by Alfred Hitchcock. Using these data, we obtained functional brain connectivity information during movie viewing, performed individualized brain age prediction for males and females separately, and explored the similarities and differences in brain connectivity between males and females during movie viewing. Specifically, a whole-brain functional connectivity matrix was constructed based on the functional activity of the subjects while watching the film. A machine learning technique was then used to predict the brain age of male and female viewers, and the differences in the functional connectivity driving the predictions were analyzed. We examined the similarities and differences in the overall process of brain aging between males and females through the correlation coefficients between the predicted age and the true age, and examined the similarities and differences in the functional brain networks driving the prediction of brain age between males and females.
Our results show that brain connectivity during film viewing can predict the brain age of both men and women with high accuracy. Specifically, the model prediction accuracy for brain age based on male functional brain imaging data was = .821 (± .007, .866), MAE = 8.240 (±.123) years, whereas the model prediction accuracy for brain age prediction for female data was = .848 (± .004), MAE = 7.978 (±.101) years. When analyzing the connections that drive the predictions, there are clear sex differences in the connections associated with the default mode network, reflecting differences in the integration of endogenous and exogenous information between the males and the females. Specifically, the contribution of functional connectivity in prediction models based on male data only explains 2.7% of functional connectivity contributing in prediction models based on female data. Overall, the study demonstrates significant differences in functional brain connectivity between male and female viewers during film viewing, providing neuroscientific evidence for the study of the diverse cognitive psychological activities of film viewers.
Key words
brain age /
sex-related difference /
movie viewer /
alfred hitchcock /
movie neuroscience
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References
[1] 王宜文, 王娅姝, 王泉泉, 刘瑾. (2017). 基于脑成像技术的电影视听及美学研究——以《精神病患者》为例. 当代电影, 6, 32-39.
[2] Bluhm R. L., Osuch E. A., Lanius R. A., Boksman K., Neufeld R. W., Théberge J., & Williamson P. (2008). Default mode network connectivity: Effects of age, sex, and analytic approach. Neuroreport, 19(8), 887-891.
[3] Buckner R. L., Andrews-Hanna J. R., & Schacter D. L. (2008). The brain's default network: Anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences, 1124, 1-38.
[4] Chen X. H., Yuan H., Zheng T. T., Chang Y. C., & Luo Y. (2018). Females are more sensitive to Opponent's emotional feedback: Evidence from event-related potentials. Frontiers in Human Neuroscience, 12, Article 275.
[5] Chung Y., Addington J., Bearden C. E., Cadenhead K., Cornblatt B., Mathalon D. H., & Cannon T. D. (2018). Use of machine learning to determine deviance in neuroanatomical maturity associated with future psychosis in youths at clinically high risk. Jama Psychiatry, 75(9), 960-968.
[6] Codispoti M., Surcinelli P., & Baldaro B. (2008). Watching emotional movies: Affective reactions and gender differences. International Journal of Psychophysiology, 69(2), 90-95.
[7] Cole, J. H., & Franke, K. (2017). Predicting age using neuroimaging: Innovative brain ageing biomarkers. Trends in Neurosciences, 40(12), 681-690.
[8] Cole J. H., Leech R., & Sharp D. J. (2015). Prediction of brain age suggests accelerated atrophy after traumatic brain injury. Annals of Neurology, 77(4), 571-581.
[9] Eickhoff S. B., Milham M., & Vanderwal T. (2020). Towards clinical applications of movie fMRI. Neuroimage, 217, Article 116860.
[10] Ficek-Tani B., Horien C., Ju S., Li N., Lacadie C., Shen X., & Fredericks C. (2023). Sex differences in default mode network connectivity in healthy aging adults. Cerebral Cortex, 33(10), 6139-6151.
[11] Franke, K., & Gaser, C. (2012). Longitudinal changes in individual BrainAGE in healthy aging, mild cognitive impairment, and Alzheimer's disease. GeroPsych, 25(4), 235-245.
[12] Hasson U., Nir Y., Levy I., Fuhrmann G., & Malach R. (2004). Intersubject synchronization of cortical activity during natural vision. Science, 303(5664), 1634-1640.
[13] Hu X. T., Guo L., Han J. W., & Liu T. M. (2017). Decoding power-spectral profiles from FMRI brain activities during naturalistic auditory experience. Brain Imaging and Behavior, 11(1), 253-263.
[14] Jamadar S. D., Sforazzini F., Raniga P., Ferris N. J., Paton B., Bailey M. J., & Egan G. F. (2019) Sexual dimorphism of resting-state network connectivity in healthy ageing. The Journals of Gerontology: Series B-Psychological Sciences and Social Sciences, 74(7), 1121-1131.
[15] Lambrecht L., Kreifelts B., & Wildgruber D. (2014). Gender differences in emotion recognition: Impact of sensory modality and emotional category. Cognition and Emotion, 28(3), 452-469.
[16] Iidaka T., Okada T., Murata T., Omori M., Kosaka H., Sadato N., & Yonekura Y. (2002). Age-related differences in the medial temporal lobe responses to emotional faces as revealed by fMRI. Hippocampus, 12(3), 352-362.
[17] Power J. D., Cohen A. L., Nelson S. M., Wig G. S., Barnes K. A., Church J. A., & Petersen S. E. (2011). Functional network organization of the human brain. Neuron, 72(4), 665-678.
[18] Smitha K. A., Raja K. A., Arun K. M., Rajesh P. G., Thomas B., Kapilamoorthy T. R., & Kesavadas C. (2017). Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks. The Neuroradiology Journal, 30(4), 305-317.
[19] Taylor J. R., Williams N., Cusack R., Auer T., Shafto M. A., Dixon M., & Henson R. N. (2017). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. NeuroImage, 144, 262-269.
[20] Tian L. X., Ye M. T., Chen C., Cao X. Y., & Shen T. H. (2021). Consistency of functional connectivity across different movies. NeuroImage, 233, Article 117926.
[21] Vanderwal T., Eilbott J., & Castellanos F. X. (2019) Movies in the magnet: Naturalistic paradigms in developmental functional neuroimaging. Developmental Cognitive Neuroscience, 36, Article 100600.
[22] Yeshurun Y., Nguyen M., & Hasson U. (2021). The default mode network: Where the idiosyncratic self meets the shared social world. Nature Reviews Neuroscience, 22(3), 181-192.
[23] Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B (Statistical Methodology), 67(2), 301-320.
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