Salience network connectivity in the insula is associated with individual differences in interoceptive accuracy

Abstract

The insula and the anterior cingulate cortex are core brain regions that anchor the salience network, one of several large-scale intrinsic functional connectivity networks that have been derived consistently using resting-state functional magnetic resonance imaging (fMRI). While several studies have shown that the insula and anterior cingulate cortex play important roles in interoceptive awareness, no study to date has examined the association between intrinsic salience network connectivity and interoceptive awareness. In this study, we sought to test this idea in 26 healthy young participants who underwent a resting-state fMRI scan and a heartbeat counting task outside the scanner in the same session. Greater salience network connectivity in the posterior insula (but not the anterior cingulate cortex) using independent component analysis correlated with higher accuracy in the heartbeat counting task. Furthermore, using seed-based approach, greater interoceptive accuracy was associated with greater intrinsic connectivity of all insular functional subdivisions to salience network regions, including the anterior insula, orbitofrontal cortex, ventral striatum and midbrain. These associations remained after correcting for voxel-wise grey matter volumes. The findings underscore the critical role of insular salience network intrinsic connectivity in interoceptive awareness and pave the way for future investigations into how salience network dysconnectivity affects interoceptive awareness in brain disorders.

Neural correlates of interoception: Effects of interoceptive focus and relationship to dimensional measures of body awareness

“…However, we did find that greater Factor 1 scores were associated with reduced errors when monitoring heartbeats at trend level. Similar effects have been reported by others, with greater accuracy on heartbeat detection tasks associated with increased scores on the attentional control subscale of the MAIA [Cali et al, 2015] and the Body Awareness Questionnaire [Chong et al, 2016]. The fact that IA in our task was related to Factor 1 scores and not the other factor scores raises the interesting possibility that certain dimensional aspects of IS may be more related to interoceptive accuracy than others.…”

“…However, for completeness we do report on accuracy measures and their relationship with MAIA factor scores. For the HB condition, accuracy calculations utilized a formula frequently used for heartbeat tracking tasks [Cali et al, 2015;Chong et al, 2016;Dunn et al, 2012;Fustos et al, 2013;Garfinkel et al, 2015;Grabauskaite et al, 2017;Herbert et al, 2007;Mirams et al, 2012;Pollatos et al, 2005Pollatos et al, , 2007aWerner et al, 2009Werner et al, , 2014: 1-(|number of actual heartbeats 2 number of reported heartbeats|/number of actual heartbeats). This formula yields a number between 0 and 1, with 1 representing perfect interoceptive accuracy and any values 0.85 thought to reflect high IA [Herbert et al, 2007;Pollatos et al, 2005Pollatos et al, , 2007aWerner et al, 2009Werner et al, , 2014.…”

“…Neuroimaging studies have linked greater IA (as measured by heartbeat detection accuracy) with increased activation of anterior insula, somatosensory cortex, precentral gyrus, and IFG when attending to body sensations [Caseras et al, ; Critchley et al, ; Kuehn et al, ; Pollatos et al, ], suggesting that enhanced cortical processing may confer an advantage for the accurate detection of body signals. Recent functional connectivity studies have also shown that connectivity of the insula at rest [Chong et al, ] and during task (Kuehn et al, ] is related to IA measured outside of the scanner. Interestingly, objective measures of IA are not always correlated with subjective measures of IS [Ceunen et al, ; Forkmann et al, ; Garfinkel et al, ; Khalsa et al, ; Meessen et al, ; Mehling, ; Whitehead et al, ].…”

Brain connection pattern under interoceptive attention state predict interoceptive intensity and subjective anxiety feeling

“…Multivariate pattern analysis (MVPA) is an emerging method for using multivariable information to predict specific targets in neuroscience; compared with the traditional single-variable method, it has the advantage of focusing on "patterns" rather than local features and is suitable for exploring the relative importance of multiple variables (Haxby, 2012;Woo, Chang, Lindquist, & Wager, 2017). Based on previous studies (Baur, Hanggi, Langer, & Jancke, 2013;Chong, Ng, Lee, & Zhou, 2017;Coste & Kleinschmidt, 2016;Duval et al, 2015;Hermans et al, 2011;Sylvester et al, 2012;Uddin, 2015), we hypothesized that the network modules that could make the greatest predictive contributions might be the modules related to the COTC, the SSM, or the SN for the intensity of interoception and modules related to the VAN, the SN or the DMN for anxiety. An interoceptive attention task was selected to explore the specific roles that different networks play in the intensity of interoception and feelings of anxiety (Avery et al, 2014;Critchley et al, 2004;Pollatos, Schandry, Auer, & Kaufmann, 2007).…”

Higher levels of trait emotional awareness are associated with more efficient global information integration throughout the brain: a graph-theoretic analysis of resting state functional connectivity

“…When subsequently exploring this metric for individual nodes, we also observed a large number of ROIs that contribute to several large-scale neural networks, which also showed the same relationship with tEA. This included: (i) Salience and Sensorimotor network regions (perhaps suggesting more efficient broadcasting/use of information about affective bodily states in those with higher tEA; Smith et al , 2009, 2017a; Yeo et al , 2011; Chong et al , 2017); (ii) Auditory and Language Network regions (perhaps suggesting more efficient broadcasting/use of verbal information in those with higher tEA; Fedorenko and Thompson-Schill, 2014); (iii) Primary Visual, Precuneus and DMN regions (perhaps suggesting more efficient broadcasting/use of emotion-related information in episodic/semantic memory in those with higher tEA, including their use in internal simulation processes such as those involved in imagining feelings in the LEAS’s hypothetical scenarios; Barrett and Satpute, 2013; Eustache et al , 2016); (iv) Visuospatial (Dorsal Attention) and ECN regions (perhaps reflecting more efficient top-down control processes in those with higher tEA; Seeley et al , 2007; Corbetta et al , 2008; Yeo et al , 2011) and (v) Basal Ganglia Network regions (perhaps reflecting more efficient cognitive/behavioral reward-/habit-learning processes, and more efficient broadcasting of affective value information, in those with higher tEA; Hazy et al , 2007; Dolan and Dayan, 2013). However, none of these ROI-specific results remained significant after controlling for network density (i.e.…”