ORIGINAL ARTICLE
Neuroanatomical predictors of real-time fMRI-based anterior insula regulation. A supervised machine learning study
Andrea Caria,
Alessandro Grecucci,
Corresponding Author
Andrea Caria
Department of Psychology and Cognitive Science, University of Trento, Rovereto, Italy
Correspondence
Andrea Caria, Dipartimento di Psicologia e Scienze Cognitive, Università degli Studi di Trento, Corso Bettini, 31, Rovereto, Trento 38068, Italy.
Email: [email protected]
Contribution: Conceptualization, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review & editing
Search for more papers by this authorAlessandro Grecucci
Department of Psychology and Cognitive Science, University of Trento, Rovereto, Italy
Contribution: Conceptualization, Formal analysis, Investigation, Methodology, Writing - review & editing
Search for more papers by this authorAndrea Caria,
Alessandro Grecucci,
Corresponding Author
Andrea Caria
Department of Psychology and Cognitive Science, University of Trento, Rovereto, Italy
Correspondence
Andrea Caria, Dipartimento di Psicologia e Scienze Cognitive, Università degli Studi di Trento, Corso Bettini, 31, Rovereto, Trento 38068, Italy.
Email: [email protected]
Contribution: Conceptualization, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review & editing
Search for more papers by this authorAlessandro Grecucci
Department of Psychology and Cognitive Science, University of Trento, Rovereto, Italy
Contribution: Conceptualization, Formal analysis, Investigation, Methodology, Writing - review & editing
Search for more papers by this authorAbstract
Increasing evidence showed that learned control of metabolic activity in selected brain regions can support emotion regulation. Notably, a number of studies demonstrated that neurofeedback-based regulation of fMRI activity in several emotion-related areas leads to modifications of emotional behavior along with changes of neural activity in local and distributed networks, in both healthy individuals and individuals with emotional disorders. However, the current understanding of the neural mechanisms underlying self-regulation of the emotional brain, as well as their relationship with other emotion regulation strategies, is still limited. In this study, we attempted to delineate neuroanatomical regions mediating real-time fMRI-based emotion regulation by exploring whole brain GM and WM features predictive of self-regulation of anterior insula (AI) activity, a neuromodulation procedure that can successfully support emotional brain regulation in healthy individuals and patients. To this aim, we employed a multivariate kernel ridge regression model to assess brain volumetric features, at regional and network level, predictive of real-time fMRI-based AI regulation. Our results showed that several GM regions including fronto-occipital and medial temporal areas and the basal ganglia as well as WM regions including the fronto-occipital fasciculus, tapetum and fornix significantly predicted learned AI regulation. Remarkably, we observed a substantial contribution of the cerebellum in relation to both the most effective regulation run and average neurofeedback performance. Overall, our findings highlighted specific neurostructural features contributing to individual differences of AI-guided emotion regulation. Notably, such neuroanatomical topography partially overlaps with the neurofunctional network associated with cognitive emotion regulation strategies, suggesting common neural mechanisms.
CONFLICT OF INTEREST
Authors have no financial and non-financial conflicts of interest to declare.
Open Research
DATA AVAILABILITY STATEMENT
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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