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Dynamics of aesthetic experience are reflected in the default-mode network.

ABSTRACT: Neuroaesthetics is a rapidly developing interdisciplinary field of research that aims to understand the neural substrates of aesthetic experience: While understanding aesthetic experience has been an objective of philosophers for centuries, it has only more recently been embraced by neuroscientists. Recent work in neuroaesthetics has revealed that aesthetic experience with static visual art engages visual, reward and default-mode networks. Very little is known about the temporal dynamics of these networks during aesthetic appreciation. Previous behavioral and brain imaging research suggests that critical aspects of aesthetic experience have slow dynamics, taking more than a few seconds, making them amenable to study with fMRI. Here, we identified key aspects of the dynamics of aesthetic experience while viewing art for various durations. In the first few seconds following image onset, activity in the DMN (and high-level visual and reward regions) was greater for very pleasing images; in the DMN this activity counteracted a suppressive effect that grew longer and deeper with increasing image duration. In addition, for very pleasing art, the DMN response returned to baseline in a manner time-locked to image offset. Conversely, for non-pleasing art, the timing of this return to baseline was inconsistent. This differential response in the DMN may therefore reflect the internal dynamics of the participant's state: The participant disengages from art-related processing and returns to stimulus-independent thought. These dynamics suggest that the DMN tracks the internal state of a participant during aesthetic experience.

SUBMITTER: Belfi AM

PROVIDER: S-EPMC8493917 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionDynamic functional network connectivity (dFNC) analysis of resting state functional magnetic resonance imaging data has yielded insights into many neurological and neuropsychiatric disorders. A common dFNC analysis approach uses hard clustering methods like k-means clustering to assign samples to states that summarize network dynamics. However, hard clustering methods obscure network dynamics by assuming (1) that all samples within a cluster are equally like their assigned centroids and (2) that samples closer to one another in the data space than to their centroids are well-represented by their centroids. In addition, it can be hard to compare subjects, as in some cases an individual may not manifest a state strongly enough to enter a hard cluster. Approaches that allow a dimensional approach to connectivity patterns (e.g., fuzzy clustering) can mitigate these issues. In this study, we present an explainable fuzzy clustering framework by combining fuzzy c-means clustering with several explainability metrics and novel summary features.MethodsWe apply our framework for schizophrenia (SZ) default mode network analysis. Namely, we extract dFNC from individuals with SZ and controls, identify 5 dFNC states, and characterize the dFNC features most crucial to those states with a new perturbation-based clustering explainability approach. We then extract several features typically used in hard clustering and further present a variety of unique features specially designed for use with fuzzy clustering to quantify state dynamics. We examine differences in those features between individuals with SZ and controls and further search for relationships between those features and SZ symptom severity.ResultsImportantly, we find that individuals with SZ spend more time in states of moderate anticorrelation between the anterior and posterior cingulate cortices and strong anticorrelation between the precuneus and anterior cingulate cortex. We further find that individuals with SZ tend to transition more rapidly than controls between low-magnitude and high-magnitude dFNC states.ConclusionWe present a novel dFNC analysis framework and use it to identify effects of SZ upon network dynamics. Given the ease of implementing our framework and its enhanced insight into network dynamics, it has great potential for use in future dFNC studies.

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Dynamics of aesthetic experience are reflected in the default-mode network.

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