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Differential functional brain network connectivity during visceral interoception as revealed by independent component analysis of fMRI TIME-series.

ABSTRACT: Influential theories of brain-viscera interactions propose a central role for interoception in basic motivational and affective feeling states. Recent neuroimaging studies have underlined the insula, anterior cingulate, and ventral prefrontal cortices as the neural correlates of interoception. However, the relationships between these distributed brain regions remain unclear. In this study, we used spatial independent component analysis (ICA) and functional network connectivity (FNC) approaches to investigate time course correlations across the brain regions during visceral interoception. Functional magnetic resonance imaging (fMRI) was performed in thirteen healthy females who underwent viscerosensory stimulation of bladder as a representative internal organ at different prefill levels, i.e., no prefill, low prefill (100 ml saline), and high prefill (individually adapted to the sensations of persistent strong desire to void), and with different infusion temperatures, i.e., body warm (?37°C) or ice cold (4-8°C) saline solution. During Increased distention pressure on the viscera, the insula, striatum, anterior cingulate, ventromedial prefrontal cortex, amygdalo-hippocampus, thalamus, brainstem, and cerebellar components showed increased activation. A second group of components encompassing the insula and anterior cingulate, dorsolateral prefrontal and posterior parietal cortices and temporal-parietal junction showed increased activity with innocuous temperature stimulation of bladder mucosa. Significant differences in the FNC were found between the insula and amygdalo-hippocampus, the insula and ventromedial prefrontal cortex, and the ventromedial prefrontal cortex and temporal-parietal junction as the distention pressure on the viscera increased. These results provide new insight into the supraspinal processing of visceral interoception originating from an internal organ.

SUBMITTER: Jarrahi B

PROVIDER: S-EPMC6869370 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Differential functional brain network connectivity during visceral interoception as revealed by independent component analysis of fMRI TIME-series.

Jarrahi Behnaz B Mantini Dante D Balsters Joshua Henk JH Michels Lars L Kessler Thomas M TM Mehnert Ulrich U Kollias Spyros S SS

Human brain mapping 20150807 11

Influential theories of brain-viscera interactions propose a central role for interoception in basic motivational and affective feeling states. Recent neuroimaging studies have underlined the insula, anterior cingulate, and ventral prefrontal cortices as the neural correlates of interoception. However, the relationships between these distributed brain regions remain unclear. In this study, we used spatial independent component analysis (ICA) and functional network connectivity (FNC) approaches t ...[more]

PMID: 26249369

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Project description:Functional magnetic resonance imaging (fMRI) is increasingly used to characterize functional connectivity between brain regions. Given the vast number of between-voxel interactions in high-dimensional fMRI data, it is an ongoing challenge to detect stable and generalizable functional connectivity in the brain among groups of subjects. Component models can be used to define subspace representations of functional connectivity that are more interpretable. It is, however, unclear which component model provides the optimal representation of functional networks for multi-subject fMRI datasets. A flexible cross-validation approach that assesses the ability of the models to predict voxel-wise covariance in new data, using three different measures of generalization was proposed. This framework is used to compare a range of component models with varying degrees of flexibility in their representation of functional connectivity, evaluated on both simulated and experimental resting-state fMRI data. It was demonstrated that highly flexible subject-specific component subspaces, as well as very constrained average models, are poor predictors of whole-brain functional connectivity, whereas the best-generalizing models account for subject variability within a common spatial subspace. Within this set of models, spatial Independent Component Analysis (sICA) on concatenated data provides more interpretable brain patterns, whereas a consistent-covariance model that accounts for subject-specific network scaling (PARAFAC2) provides greater stability in functional connectivity relationships between components and their spatial representations. The proposed evaluation framework is a promising quantitative approach to evaluating component models, and reveals important differences between subspace models in terms of predictability, robustness, characterization of subject variability, and interpretability of the model parameters. Hum Brain Mapp 38:882-899, 2017. © 2016 Wiley Periodicals, Inc.

Project description:IntroductionIndependent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non-contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data.ObjectiveHere, we detail a graph building technique that allows these ICNs to be analyzed with graph theory.MethodsFirst, ICA was performed at the single-subject level in 15 healthy volunteers using a 3T MRI scanner. The identification of nine networks was performed by a multiple-template matching procedure and a subsequent component classification based on the network "neuronal" properties. Second, for each of the identified networks, the nodes were defined as 1,015 anatomically parcellated regions. Third, between-node functional connectivity was established by building edge weights for each networks. Group-level graph analysis was finally performed for each network and compared to the classical network.ResultsNetwork graph comparison between the classically constructed network and the nine networks showed significant differences in the auditory and visual medial networks with regard to the average degree and the number of edges, while the visual lateral network showed a significant difference in the small-worldness.ConclusionsThis novel approach permits us to take advantage of the well-recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well-established graph measures to evaluate connectivity differences. Moreover, by providing a graph for each separate network, it can offer the possibility to extract graph measures in a specific way for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength.

Dataset Information

Differential functional brain network connectivity during visceral interoception as revealed by independent component analysis of fMRI TIME-series.

Publications

Differential functional brain network connectivity during visceral interoception as revealed by independent component analysis of fMRI TIME-series.

Similar Datasets

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