Movie fMRI has been increasingly used in investigations of human brain function. Inter-subject functional correlation (ISFC), which evaluates stimulus-dependent inter-regional synchrony between brains exposed to the same stimulus, is emerging as an influencing measure for movie fMRI data analyses. Before the wide application of ISFC analyses, it will be useful to investigate the degree to which they are similar and different across different movies. Based on the four movie fMRI runs of 178 subjects included in the "human connectome project (HCP) S1200 Release", we evaluated ISFCs throughout the brain and analyzed their consistency across different movies using intra-class correlation (ICC). We also investigated the generalizability of ISFC-based predictive models, which is closely related to their consistency, with sex classification and grip strength prediction used as test cases. The results showed that the intensity of ISFCs was generally weak (0.047). Except a few within-network ones (e.g., ICC of ISFC in the PON was 0.402), ISFCs throughout the brain exhibited low consistency, as indicated by a mean ICC of 0.130. The accuracies for inter-run predictions (60.7-72.8% for sex classification, and R = 0.122-0.275 for grip strength prediction) were much lower than those for intra-run predictions (73.2-83.0% for sex classification, and R = 0.325-0.403 for grip strength prediction), and this indicates poor generalizability of predictive models based on ISFCs. According to these findings, ISFC analyses capture aspects of brain function that are specific to each individual movie, and this specificity should be taken into account (in some cases might be especially useful) in future naturalistic studies.
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http://dx.doi.org/10.1007/s11682-022-00740-8 | DOI Listing |
Neurophotonics
January 2025
Washington University School of Medicine, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States.
Significance: Decoding naturalistic content from brain activity has important neuroscience and clinical implications. Information about visual scenes and intelligible speech has been decoded from cortical activity using functional magnetic resonance imaging (fMRI) and electrocorticography, but widespread applications are limited by the logistics of these technologies.
Aim: High-density diffuse optical tomography (HD-DOT) offers image quality approaching that of fMRI but with the silent, open scanning environment afforded by optical methods, thus opening the door to more naturalistic research and applications.
Nat Commun
January 2025
Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea.
Our naturalistic experiences are organized into memories through multiple processes, including novelty encoding, memory formation, and retrieval. However, the neural mechanisms coordinating these processes remain elusive. Using fMRI data acquired during movie viewing and subsequent narrative recall, we examine hippocampal neural subspaces associated with distinct memory processes and characterized their relationships.
View Article and Find Full Text PDFElife
January 2025
Department of Psychology, Queens University, Kingston, Canada.
Movie-watching is a central aspect of our lives and an important paradigm for understanding the brain mechanisms behind cognition as it occurs in daily life. Contemporary views of ongoing thought argue that the ability to make sense of events in the 'here and now' depend on the neural processing of incoming sensory information by auditory and visual cortex, which are kept in check by systems in association cortex. However, we currently lack an understanding of how patterns of ongoing thoughts map onto the different brain systems when we watch a film, partly because methods of sampling experience disrupt the dynamics of brain activity and the experience of movie-watching.
View Article and Find Full Text PDFbioRxiv
December 2024
Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, USA.
In sensory and mid-level regions of the brain, stimulus information is often topographically organized; functional responses are arranged in maps according to features such as retinal coordinates, auditory pitch, and object animacy or size. However, such organization is typically measured during stimulus input, e.g.
View Article and Find Full Text PDFNatl Sci Rev
January 2025
School of Systems Science, Beijing Normal University, Beijing 100875, China.
Quantifying emergence and modeling emergent dynamics in a data-driven manner for complex dynamical systems is challenging due to the fact that emergent behaviors cannot be directly captured by micro-level observational data. Thus, it is crucial to develop a framework to identify emergent phenomena and capture emergent dynamics at the macro-level using available data. Inspired by the theory of causal emergence (CE), this paper introduces a machine learning framework to learn macro-dynamics in an emergent latent space and quantify the degree of CE.
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