Subjective well-being is associated with the functional connectivity network of the dorsal anterior insula

Journal Pre-proof Subjective well-being is associated with the functional connectivity network of the dorsal anterior insula Rui Li, Xinyi Zhu, Zhiwei Zheng, Pengyun Wang, Juan Li PII:

S0028-3932(20)30065-8

DOI:

https://doi.org/10.1016/j.neuropsychologia.2020.107393

Reference:

NSY 107393

To appear in:

Neuropsychologia

Received Date: 5 August 2019 Revised Date:

7 February 2020

Accepted Date: 10 February 2020

Please cite this article as: Li, R., Zhu, X., Zheng, Z., Wang, P., Li, J., Subjective well-being is associated with the functional connectivity network of the dorsal anterior insula, Neuropsychologia (2020), doi: https://doi.org/10.1016/j.neuropsychologia.2020.107393. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

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Title:

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Subjective Well-Being is Associated with the Functional Connectivity Network of the Dorsal

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Anterior Insula

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Authors:

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Rui Li a,b, Xinyi Zhu a,b, Zhiwei Zheng a,b, Pengyun Wang a,b, Juan Li a,b,c,d,*

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Affiliations:

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a

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Chinese Academy of Sciences, Beijing, China

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b

Department of Psychology, University of Chinese Academy of Sciences, Beijing, China

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c

State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy

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of Sciences, Beijing, China

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d

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Sciences, Beijing, China

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*

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Address: CAS Key Laboratory of Mental Health, Institute of Psychology, 16 Lincui Road,

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Chaoyang District, Beijing 100101, China; Phone: 86-10-64861622, FAX: 86-10-64872070,

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Email: [email protected]

Center on Aging Psychology, CAS Key Laboratory of Mental Health, Institute of Psychology,

Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of

Correspondence: Juan Li

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Abstract:

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The feeling of happiness is beneficial for both mental and physical health. Based on the findings

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of previous studies that reported that the insular cortex is a crucial region for subjective feelings,

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including happiness, in this study, we further identified the subregion of the insula and its

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functional connectivity associated with subjective well-being (SWB). Using an iterative

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seed-target-seed approach, we labelled the posterior, dorsal, and ventral anterior insular regions of

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interest (ROIs) and evaluated the association between functional connectivity of each of these

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insular ROIs and the self-reported SWB in a group of 75 healthy elderly adults. We demonstrated

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that the functional connectivity of the dorsal anterior insula (dAI) was significantly correlated with

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SWB. This relationship was negative and unique for the functional connectivity of left dAI with

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specific regions from the default-mode network, including the anterior medial prefrontal cortex

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and inferior parietal lobe. Our result suggested a functional connectivity network of the left dAI

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with specific DMN brain regions, suggesting the neural basis of SWB.

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Key Words: functional connectivity; happiness; insula; subjective well-being

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1. Introduction

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Subjective well-being (SWB), mostly measured as self-reported levels of life satisfaction and

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happiness by standardized survey questions, has been a topic of great interest in social and

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psychological sciences (E. Diener, 2000; Kahneman & Krueger, 2006). A broad range of factors

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related with SWB, including demographic events, social networking, economic status, cultural

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differences, and physical and mental health have been extensively studied (E. Diener, Oishi, &

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Lucas, 2003; Ed Diener & Ryan, 2009; Dolan, Peasgood, & White, 2008; Pinquart & Sorensen,

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2000). In addition, biological studies on the molecular and neural correlates of SWB have recently

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emerged (Kong, Hu, Wang, Song, & Liu, 2015; Rietveld, et al., 2013; Rutledge, Skandali, Dayan,

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& Dolan, 2014; Urry, et al., 2004).

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Functional and anatomical neuroimaging studies have suggested an afferent neural system

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as a common neural foundation for feelings (Craig, 2002). The insular cortex within this system is

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substantiated as an integrative region that is essential for processing subjective feelings, including

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that of well-being (Craig, 2002; Damasio & Carvalho, 2013; Duquette, 2017; K. C. R. Fox, et al.,

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2018; Funahashi, 2011; Harrison, Gray, Gianaros, & Critchley, 2010; Reeve & Lee, 2018).

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Previous studies have linked the insula to distinct but intercorrelated mental well-beings including

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subjective (Kraus, et al., 2007; Nardo, et al., 2011; Rutledge, et al., 2014), psychological (Lewis,

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Kanai, Rees, & Bates, 2014), and social well-being (Kong, Xue, & Wang, 2016). For instance,

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activation of the anterior insula (AI) was found to positively correlate with ratings of momentary

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SWB in a probabilistic reward task (Rutledge, et al., 2014), whereas increased regional cerebral

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blood flow in the posterior insula (PI) was correlated with decreased SWB scores in subjects with

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post-traumatic stress disorder (Nardo, et al., 2011). In addition, it has been shown that the

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amplitude of spontaneous fluctuations in the insular cortex positively predicts social well-being

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(Kong, et al., 2016) and larger insular cortex volume correlates with higher psychological

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well-being (Lewis, et al., 2014). A recent review demonstrated that the insula is one of the most

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frequently reported regions to exhibit activation when remembering happy events (Suardi, Sotgiu,

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Costa, Cauda, & Rusconi, 2016), further suggesting the critical role of the insula in individual

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well-being.

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The insular cortex, however, is heterogeneous in both anatomy and function (Ghaziri, et al.,

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2018; Morel, Gallay, Baechler, Nyss, & Gallay, 2013; Nomi, Schettini, Broce, Dick, & Uddin,

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2018). Although neuroimaging studies have associated the feeling of well-being with the insula,

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the functional association of the different subregions of the insular cortex with SWB is not yet

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completely understood. Functional parcellation of the insula performed using connectivity (Chang,

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Yarkoni, Khaw, & Sanfey, 2013), cluster analysis (Deen, Pitskel, & Pelphrey, 2011), and

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meta-analysis of neuroimaging data (Kurth, Zilles, Fox, Laird, & Eickhoff, 2010) have

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consistently identified 3 major insular subdivisions including the PI, dorsal AI (dAI), and ventral

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AI (vAI). The PI is usually coactivated with the somatosensory cortex to mediate sensorimotor

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processes, and induce interoceptive signals (Craig, 2002; Harrison, et al., 2010) and emotional

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states, such as pain and disgust (Henderson, Rubin, & Macefield, 2011; Segerdahl, Mezue, Okell,

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Farrar, & Tracey, 2015; Uddin, 2015; Wager, et al., 2004). On the other hand, the activation of the

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AI is associated with the perception and integration of signals from emotional and affective

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processes (Uddin, 2015; Uddin, Kinnison, Pessoa, & Anderson, 2014). The AI is critical for

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understanding the feelings of both others and ourselves (Singer, 2006), and is considered to 4

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constitute a unique neural basis for subjective feelings (Craig, 2002; Immordino-Yang & Yang,

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2017; Namkung, Kim, & Sawa, 2017). Moreover, the AI is involved in multiple cognitive

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processes, including attention, inhibition, memory, and decision making (Menon & Uddin, 2010;

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Uddin, 2015; Uddin, et al., 2014), and further functional parcellation of the AI has presented the

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cognitive dAI subdivision and affective vAI subdivision perspective (Kurth, et al., 2010;

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Odriozola, et al., 2016; Touroutoglou, Hollenbeck, Dickerson, & Barrett, 2012). However,

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unsupported evidences for this simplified cognitive-affective dissociation in AI subregions have

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also been reported. In particular, the inconsistency was that the dAI showed functional diversity to

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participate in widespread task domains of both cognition and emotion in two meta analyses (Kurth,

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et al., 2010; Uddin, et al., 2014), whereas it showed functional centrality connecting with a large

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number of regions in the coactivation network (Uddin, et al., 2014). Large-scale brain network

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investigations further reported that the dAI is a crucial region in the salience network and plays a

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central role in mediating internal self-related activity in the default-mode network (DMN) and

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external stimulus-induced activity in the central-executive network (Li, et al., 2018; Uddin, et al.,

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2014). These multifaceted evidences jointly imply that the dAI plays a hub role in the brain to

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control information from multiple sources including subjective feelings (Namkung, et al., 2017;

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Uddin, et al., 2014).

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The SWB is generally considered a multidimensional construct, composed of both cognitive

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assessment of life satisfaction and affective evaluation of happiness (E. Diener, 2000; T. G. Jones,

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Rapport, Hanks, Lichtenberg, & Telmet, 2003). Based on the fundamental role of the insula in

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feelings and the central hub position of the dAI in cognitive and affective processes, we

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hypothesized that the dAI may constitute a critical neural basis for SWB. In this study, we used 5

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resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) to correlate

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functional connectivity of distinct insular subregions with SWB measured using the Index of

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Well-Being (IWB) scale (Campbell, Converse, & Rodgers, 1976) in a group of 75 healthy elderly

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adults. Our aim was to determine whether the SWB is significantly associated with the functional

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connectivity of the dAI. An iterative seed-target-seed approach (Bickart, Hollenbeck, Barrett, &

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Dickerson, 2012) that depends on a priori knowledge was used to functionally parcellate the

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insular cortex to the PI, dAI, and vAI. Recent functional connectivity studies of the insula using

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cluster analysis (Deen, et al., 2011) and surface-based (Nelson, et al., 2010) and seed-based

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(Touroutoglou, et al., 2012) functional connectivity analysis have consistently reported that each

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of the 3 functional subdivisions of the insula is strongly connected with a different area in the

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cingulate cortex (CC), as follows: the PI with the middle CC (MCC), the dAI with the dorsal

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anterior CC (dACC), and the vAI with the pregenual anterior CC (pACC). Using these intrinsic

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connectivity variations of insular subdivisions as a priori knowledge, we defined regions of

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interest (ROIs) for the dAI, vAI and PI, and examined our hypothesis on the relationship between

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dAI connectivity and SWB.

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2. Materials and Methods

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2.1 Participants and image acquisition

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Data were collected from 75 healthy elderly adults (age: 70.6 ± 5.5 years, range: 60-80 years; 35

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males, and 40 females). All participants met the following inclusion criteria: 1) score ≥ 21 in the

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Montreal Cognitive Assessment Beijing Version (J. Yu, Li, & Huang, 2012); 2) score < 16 on the

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Center for Epidemiological Survey Depression Scale (Roberts & Vernon, 1983); 3) a score ≤ 16 in 6

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the Activities of Daily Living test (Lawton & Brody, 1969); 4) the absence of neurological deficits

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and traumatic brain injury; 5) not exhibiting dementia, depression, or any other known

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neurological or psychiatric disease; 6) right-handed. The SWB of each participant was measured

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using the IWB (Campbell, et al., 1976). The IWB scores of all participants ranged between 7.6

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and 14.0 points (mean ± SD, 11.0 ± 1.8). Besides, cognitive function of each participant was

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evaluated using the psychometric questionnaires of the digit span test, category fluency test, trail

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making test and the paired associative learning test. The IWB did not correlated with MoCA or

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any of these cognitive measurements (all p values > 0.09) in the present sample. Note that the

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IWB negatively correlated with CES-D (r = -0.41, p = 0.0001).

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Imaging data from each participant were acquired using a 3.0-Tesla Siemens Trio scanner

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(Erlangen, Germany) at the Beijing MRI Center for Brain Research. T2*-weighted resting-state

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functional images were collected using an echo-planar image sequence with the following

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parameters: repetition time (TR) = 2000 ms; echo time (TE) = 30 ms; flip angle = 90°; field of

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view = 200 mm × 200 mm; acquisition matrix = 64 × 64; in-plane resolution = 3.125 mm × 3.125

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mm; thickness = 3.0 mm; gap = 0.6 mm; 33 slices, and 200 volumes. T1-weighted high-resolution

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anatomical images were collected using a magnetization-prepared rapid gradient echo sequence

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with the following parameters: TR = 1900 ms; TE = 2.2 ms; flip angle = 9°; matrix = 256 × 256;

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voxel size = 1 mm ×1 mm ×1 mm, and 176 slices.

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This study was approved by the Ethics Committee of the Institute of Psychology, Chinese

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Academy of Sciences. Each participant provided written informed consent before taking part in

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our experiments according to institutional guidelines.

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2.2 Image Preprocessing

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Imaging data were preprocessed by the Statistical Parametric Mapping program (SPM12;

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http://www.fil.ion.ucl.ac.uk/spm) and the toolbox for Data Processing & Analysis for Brain

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Imaging (DPABI V3.1; http://rfmri.org/dpabi). The preprocessing included removal of the first 5

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volumes; corrections for the intra-volume acquisition time differences and the inter-volume

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geometrical displacement; normalization to the Montreal Neurological Institute (MNI) space

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(resampling size = 3 mm × 3 mm × 3 mm); and spatial smoothing with a 4-mm full-width at a half

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maximum Gaussian kernel. Images were further denoised by de-trending to reduce the effect of

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low-frequency drifts, temporal band-pass filtering (0.01-0.08 Hz) to reduce the effect of

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high-frequency physiological noise, regression of the head motion using the Friston 24-parameter

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model with scrubbing (Friston, Williams, Howard, Frackowiak, & Turner, 1996; Power, et al.,

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2014; Yan, et al., 2013), and a regression of the white matter and cerebrospinal fluid signals. The

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data from all 75 participants passed a quality control step to ensure good quality of raw images,

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good coregistration accuracy, and a head movement of less than 2.0 mm translation and 2.0°

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rotation during scanning.

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2.3 Defining connectional subregions of the insula

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An iterative seed-target-seed approach (Bickart, et al., 2012) that depends on a priori knowledge

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was used to define the dAI, vAI, and PI seed ROIs in the insula. The detailed processing

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procedures were as follows: 1) we defined the functional connectivity of the whole insula. The

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whole insula was designated as the left and right total insula in the Anatomical Automatic

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Labeling (AAL) atlas toolbox (Tzourio-Mazoyer, et al., 2002). We performed a linear correlation 8

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analysis by calculating the Pearson correlation coefficient between the averaged time course of

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voxels in the whole insula and the time series of each voxel across the whole brain. To improve

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normality, Fisher’s r-to-z transformation was performed to convert these resultant correlation

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maps to z maps. The individual z maps were entered into a one-sample t test to produce a

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group-level statistical significance functional connectivity map of the whole insula at a

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significance level of p < 5e-15, corrected by false discovery rate (FDR). 2) We identified three

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target ROIs, i.e., the dACC, pACC and MCC, by searching the insula functional connectivity map

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to select MNI coordinates of voxels at peak significance within each target region. Each target

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ROI was defined as a sphere centered on the peak voxel with a radius of 3 mm. According to a

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priori knowledge, each of the 3 target ROIs was hypothesized to strongly connect with one of the

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subregions in the insula (i.e., dACC with dAI, pACC with vAI, and MCC with PI). 3) We

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computed the Pearson correlation coefficient between the average time course of voxels in each

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target ROI and the time series of each voxel in the AAL-defined whole insula, and converted these

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correlation maps to z maps through Fisher’s r-to-z transformation. 4) Finally we performed

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contrast analyses on the z maps from each target ROI, including dACC over pACC and MCC,

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pACC over dACC and MCC, MCC over dACC and pACC, to produce the dAI, vAI, and PI seed

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ROIs in the insula, respectively (paired t-test, p < 0.05, corrected by FDR).

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2.4 Relationship between insula connectivity and well-being

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To determine the insular subregion and the functionally connected regions that are related to

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individual well-being, we performed Pearson correlation analyses between the IWB score and

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functional connectivity of the dAI, vAI, and PI seed ROIs. The age, sex, and education level were

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considered as covariates. Considering the proposed functional asymmetry of the insula (Craig,

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2005; Uddin, Nomi, Hebert-Seropian, Ghaziri, & Boucher, 2017), we correlated the functional

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connectivity of bilateral insular ROIs separately with the IWB scores (Gaussian random field

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[GRF] correction, voxel-level p < 0.01 and cluster-level p < 0.05, two-tailed). The functional

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connectivity of the insular subregions was calculated as the Fisher’s r-to-z transformed z maps of

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Pearson correlations between averaged time course of all voxels for each insular subregion seed

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ROI, as defined above, and the time series of each voxel across the whole brain.

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2.5 Methodological considerations

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First, global signal was retained in the preprocessed imaging data due to the well-known

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controversy (M. D. Fox, Zhang, Snyder, & Raichle, 2009; Gotts, Saad, et al., 2013; Saad, et al.,

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2013). Recent studies suggest that removing global signal may help decrease the influence of

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motion and other artifactual variances (Power, et al., 2014; Yan, et al., 2013). To exclude the

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possible influence of global signal on the findings, we examined the results using data with global

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signal further removed in the preprocessing step.

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Second, although a Friston 24-parameter higher-order regression model and a motion

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scrubbing method were used during the individual-level preprocessing step, the influence of head

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motion on the results may still exist. To further control for motion confounds in the preprocessed

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data, we calculated the mean frame-to-frame root mean square (RMS) (Van Dijk, Sabuncu, &

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Buckner, 2012) of head motion in the DPABI, and used the RMS as an additional covariate in the

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group-level statistical analysis.

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Third, given that the participants in this study were elderly adults who often present gray 10

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matter atrophy, the relationship between insular connectivity and SWB may be influenced by

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individual variations in gray matter integrity. To rule out this possibility, we performed a

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voxel-based morphometry analysis on the T1 structural images using the new segment and

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DARTEL algorithm with default parameters in the DPABI. Individual voxel-wise gray matter

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volume was derived and insular gray matter volume was extracted as a covariate in the

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group-level statistical analysis for the relationship between insular connectivity and SWB.

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Fourth, we used a small ROI size with a radius of 3 mm to extract the signals of target ROIs

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to expectedly better differentiate their functional connectivity profiles with the insula. Considering

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the effect of ROI size may show effect on stability of the connectivity result (Almgren, et al.,

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2018), we reanalyzed the data using a larger ROI size with a radius of 6 mm to evaluate the

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stability of the present result.

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Finally, although the parcellation of the posterior, dorsal and ventral anterior insula was

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mostly used for investigation of the insular function, we note that novel atlases that divided the

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insula into fine distinctions have recently emerged. For instance, Faillenot et al., (2017)

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subdivided the insula into 6 regions (Faillenot, Heekemann, Frot, & Hammers, 2017). To compare

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with this new atlas, we additionally calculated the functional connectivity of the insular subregions

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from Hammers_mith atlases (http://www.brain-development.org), and investigated their functional

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correlations with the IWB as a supplement to the present study.

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3. Results

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3.1 Connectional subregions of the insula

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Based on the iterative seed-target-seed approach, we first produced the functional connectivity

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map of the AAL-defined total insula (Figure 1). Three local clusters (peak coordinates are in the

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space of MNI) outside the insula were identified as the target ROIs including the dACC (9, 30, 30),

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pACC (6, 42, 0), and MCC (15, -39, 51) by searching the insular functional connectivity map

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(height threshold t = 10.5, p < 5e-15, corrected by FDR, extent threshold k = 10 voxels). We note

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here that the three target ROIs were visually selected by referring to the AAL atlas and locations

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reported in previous parcellation of the cingulate cortex (Heilbronner & Hayden, 2016;

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Palomero-Gallagher, Mohlberg, Zilles, & Vogt, 2008; C. Yu, et al., 2011). The pACC and dACC

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ROIs lie respectively rostral and dorsal to the genu of the corpus callosum (Heilbronner & Hayden,

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2016; Palomero-Gallagher, et al., 2008). The overlay of the insular connectivity map on AAL of

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the ACC and MCC was demonstrated in supplementary material (Supp-Figure 1). We, next,

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calculated the functional connectivity maps for each of the three 3-mm spherical target ROIs with

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voxels in the insula, and conducted contrast analyses on these maps (Figure 2A). As predicted, 3

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clusters from the dACC over pACC and MCC, pACC over dACC and MCC, MCC over dACC

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and pACC comparisons (p < 0.05, corrected by FDR, extent threshold k = 10 voxels) were

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identified as the dAI, vAI, and PI seed ROIs in the insula, respectively (Figure 2B). The 3

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connectionally defined subregion seed ROIs of the insula are visually consistent with previous

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subdivisions of the insula produced by cluster analysis (Deen, et al., 2011).

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3.2 Insular connectivity and well-being

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Voxel-wise exploration of the correlation between the IWB score and functional connectivity of

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each insular subregion ROI was performed to determine the subregion and the functionally

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connected regions that are related to SWB (GRF correction with voxel-level p < 0.01 and

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cluster-level p < 0.05, two-tailed). As shown in Figure 3, the functional connectivity of the left

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dAI (ldAI) with anterior medial prefrontal cortex (aMPFC; MNI coordinate: 15, 57, 27) and right

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inferior parietal lobe (rIPL; MNI coordinate: 60, -45, 39) was negatively and significantly

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correlated with the IWB (peak r = -0.43, p = 0.002; and peak r = -0.48, p < 0.0001, respectively).

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After controlling for the CES-D, the negative correlation between IWB and functional

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connectivity of the ldAI with MPFC and IPL was not influenced (r = -0.26, p = 0.02 for

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ldAl-aMPFC, and r = -0.46, p < 0.0001 for ldAI-right IPL). By refereeing to the 7-network

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template of Yeo et al. (Yeo, et al., 2011), we found that the two clusters of the aMPFC and rIPL

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were both from the DMN (see supplementary material Supp-Figure 2 for the overlay of the two

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regions on DMN template). Functional connectivity of the right dAI, bilateral vAI or bilateral PI

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did not show significant correlation with the IWB score at the significance level of voxel p < 0.01

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and cluster p < 0.05 with GRF correction.

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3.3 Methodological considerations

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After regression of the global signal, the functional connectivity of ldAI with MPFC and IPL was

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still negatively and significantly correlated with the IWB score (r = -0.26, p = 0.02 for

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ldAl-aMPFC, and r = -0.30, p = 0.01 for ldAI-right IPL). Furthermore, head motion (mean RMS)

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did not correlate with the connectivity of ldAI with aMPFC and rIPL (all p values > 0.83). When

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taking the mean RMS as an additional covariate, the functional connectivity of ldAI with aMPFC

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and rIPL were still significantly correlated with IWB score (all p values < 0.002). To exclude the

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possibility that the gray matter volume of the ldAI correlated with the IWB score and influenced

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the correlations of the IWB score with insular connections, we extracted the mean gray matter

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volume of voxels in the ldAI seed ROI, and did not find any correlation between the gray matter

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volume of the ldAI seed ROI and the IWB score (r = -0.02, p = 0.86). When the gray matter

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volume of the ldAI ROI was taken as a covariate, none of the significant correlations between

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ldAI connectivity and IWB score was significantly influenced (all p values < 0.001). Using a

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larger ROI size with a radius of 6 mm, we recalculated the functional connectivity of the target

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ROIs with the insular cortex, and also successfully defined the dAI, vAI and PI ROIs in the insula.

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Examination of the relationship between the functional connectivity of the insular ROIs and

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individual IWB scores (voxel p < 0.01 and cluster p < 0.05, GRF correction) replicated the result

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that the IWB was negatively and uniquely related to the functional connectivity of the ldAI with

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the aMPFC and rIPL in the DMN (see supplementary material Supp-Figure 3).

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additionally used 6 left and 6 right insular ROIs from the Hammers_mith 6-partition atlas to

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explore the relationship between IWB and voxel-wise functional connectivity of each insular ROI.

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However, no significant correlation was found at the significance level of voxel p < 0.01 and

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cluster p < 0.05 with GRF correction. The dAI ROI we defined from the iterative seed-target-seed

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approach was located at the junction of the anterior short gyrus and middle short gyrus of the

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insula in the Hammers_mith atlas (see supplementary material Supp-Figure 4). To analyze

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ROI-level functional connectivity of the left anterior/middle short gyrus of the insula with the two

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regions of aMPFC and rIPL, we found a significant and negative correlation between the IWB and

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functional connectivity of the left insular middle short gyrus with rIPL (r = -0.32, p = 0.006), and

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a trend of negative correlation between the IWB and functional connectivity of the left insular

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anterior short gyrus with rIPL (r = -0.22, p = 0.058). 14

Finally, we

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4. Discussion

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We defined distinct insular subregional ROIs with distinguishable connectivity profiles using an

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iterative seed-target-seed approach and investigated their relationship to SWB. We found that the

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functional connectivity between the dorsal section of the AI and brain regions including the

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aMPFC and rIPL was significantly correlated with SWB. Interestingly, these relationships were

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negative and specific for functional connectivity between the ldAI and region from the DMN.

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An advantage of the seed-target-seed approach that we adopted to define the insular

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subregional ROIs is that it may enable the accurate and specific localization of ROIs in the studied

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population. Using distinct connectivity profiles of insular subregions with the CC (Deen, et al.,

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2011; Nelson, et al., 2010; Touroutoglou, et al., 2012) we successfully identified the PI, dAI, and

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vAI ROIs that were located in consistency with the corresponding parcellations of the insular

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cortex reported in previous studies (Deen, et al., 2011; Kurth, et al., 2010). We conducted analyses

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for the relationship between insular subregions and IWB score and observed that the functional

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connectivity of the ldAI significantly predicts individual SWB. This result first suggests that the

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traditional cognitive-affective differentiation perspective for dorsal-ventral AI (Kurth, et al., 2010;

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Odriozola, et al., 2016) may be indeed oversimplified. Apart from being involved in cognitive

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processes (Chang, et al., 2013), the dAI has been previously found to be activated in interoceptive

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awareness (Critchley, Wiens, Rotshtein, Ohman, & Dolan, 2004), and emotional experience (Zaki,

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Davis, & Ochsner, 2012). Thus, these results are consistent with the increasing evidence

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supporting that the AI, especially its dorsal section, plays a central role in integrating various

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information from cognitive, emotional, and affective processes (Namkung, et al., 2017; Uddin,

15

312

2015; Uddin, et al., 2014). This integration is achieved due to its strong functional connections in

313

the brain (Uddin, et al., 2014) and its mediation ability in controlling dynamics of various

314

functional networks (Li, et al., 2018; Menon, 2011; Menon & Uddin, 2010). Moreover, our results

315

deepened the recognition of insula’s role in human feelings (Craig, 2002). Although previous

316

studies have found that the insula was functionally (Kong, et al., 2016), anatomically (Lewis, et al.,

317

2014), and physiologically (Nardo, et al., 2011) related to the feeling of well-being, and was

318

activated when remembering happy events (Funahashi, 2011; Suardi, et al., 2016), we are the first

319

to directly and explicitly demonstrate the involvement of the dorsal section of the AI in SWB.

320

An interesting finding is that the functional connectivity of the ldAI with DMN regions

321

including the aMPFC and rIPL negatively predicts individual SWB. This relationship was

322

similarly demonstrated in a previous study by Luo et al (Luo, Kong, Qi, You, & Huang, 2016), in

323

which they reported that the DMN regional connectivity of aMPFC, IPL and posterior CC is also

324

negatively correlated with happiness. The MPFC and AI are major regions participating in

325

emotional and subjective experience appraisal (Etkin, Egner, & Kalisch, 2011; Fossati, et al., 2003;

326

Kalisch, Wiech, Critchley, & Dolan, 2006; Pavuluri & May, 2015). The aMPFC is a core hub in

327

the DMN specifically involved in self-relevant and affective thoughts (Andrews-Hanna, Reidler,

328

Sepulcre, Poulin, & Buckner, 2010; D'Argembeau, et al., 2005). Hyper-activation of the aMPFC

329

has been strongly linked to rumination (N. P. Jones, Fournier, & Stone, 2017; Luo, et al., 2016),

330

which is usually associated with negative mood, stress, anxiety, and depression (Nolen-Hoeksema,

331

Wisco, & Lyubomirsky, 2008). A longitudinal study of post-traumatic stress disorder patients

332

demonstrated that the SWB correlated negatively with rumination; happier people are less

333

vulnerable to rumination and anxiety (Zanon, Hutz, Reppold, & Zenger, 2016). Consistent with 16

334

this notion, we observed the negative relationship between ldAI-DMN connectivity and individual

335

SWB. Besides, neuroimaging studies of depressed patients have reported that the aMPFC

336

consistently showed greater coactivation during ruminative thoughts (Cooney, Joormann, Eugene,

337

Dennis, & Gotlib, 2010), and decreased coactivation after receiving transcutaneous vagus nerve

338

stimulations (Fang, et al., 2016). Furthermore, a review on neuroimaging studies of

339

autobiographical memories suggests that remembering happy events activated brain regions

340

primarily in the insula, prefrontal and ACC, and many other associated cortical regions and limbic

341

structures (Suardi, et al., 2016). Hence, the inhibited connectivity between the dAI and DMN

342

regions particularly the aMPFC was speculated as a significant mechanism underlying the neural

343

network of subjective happiness.

344

The result also suggests that the involvement of the dAI to SWB is left lateralized,

345

supporting a previous hypothesis that the neural representations of subjective feelings and

346

emotions in the brain are asymmetrical (Craig, 2005). Previous task neuroimaging studies reported

347

that the left AI was selectively activated when participants were experiencing joy (Takahashi, et al.,

348

2008) and recalling autobiographical happy events (Cerqueira, et al., 2008). It is consistent with

349

the finding from a quantitative meta-analytic study that demonstrates hemispheric dominance of

350

the left insula in emotion (Duerden, Arsalidou, Lee, & Taylor, 2013). Left lateralization of the AI

351

has also been found in association with social affect (Caria, Sitaram, Veit, Begliomini, &

352

Birbaumer, 2010; Craig, 2005; Uddin, et al., 2017), and top-down cognitive control processes that

353

are important for behavioral adaptations (Gotts, Jo, et al., 2013; Spaeti, et al., 2014), suggesting a

354

crucial role of the left AI in both cognitive and affective processing.

17

355

This study presents the following limitations. First, although the enrolled participants were

356

healthy elderly, we did not intend to investigate the relationship between aging and SWB, or

357

conclude that the present finding is specific to the elderly population. The central purpose of this

358

study was to illuminate the functional role of insular subregions in SWB. It would be interesting to

359

compare SWB and the related brain network mechanism between young and older adults in future

360

studies. Second, other mental well-beings such as the psychological well-being and social

361

well-being have been previously found to be related with the insular cortex (Kong, et al., 2016;

362

Lewis, et al., 2014). Based on this, it would be interesting to examine whether the dAI is involved

363

in these, or if each mental well-being is associated with a different dAI connectivity network.

364

These investigations would forward our understanding on the neural mechanism of well-beings.

365

Third, The SWA was a broad concept that refers to multidimensional evaluation of

366

individual lives (E. Diener, 2000; T. G. Jones, Rapport, Hanks, Lichtenberg, & Telmet, 2003).

367

Due to the limited behavioral data in the present sample, however, the relationship of IWB

368

with more traditional measures of affect or emotion remains to be addressed. Finally, as an

369

integral region, the insula also functions in various cognitive processes through influential

370

connections with large-scale networks in the brain (Li, et al., 2018; Menon, 2011; Menon & Uddin,

371

2010; Uddin, et al., 2014). Especially given the emerging of new fine parcellation atlases such as

372

the Hammers_mith atlas (Faillenot, et al., 2017), further studies are required to differentiate the

373

connectivity patterns of the insula participating in cognitive and affective processes.

374

5. Conclusion

375

Using a seed-target-seed approach we identified significant association between the functional 18

376

connectivity of the dorsal section of anterior insula and self-reported SWB. This relationship was

377

negative and unique for the functional connectivity of left dAI with regions from the DMN

378

including the aMPFC and rIPL. Our results suggested a connectivity network of ldAI with specific

379

DMN brain regions that constitutes a neural basis of subjective happiness.

380

Conflict of interest

381

The authors declare no competing financial interests

382

Acknowledgements

383

This work was supported by the National Natural Science Foundation of China (31671157,

384

31711530157, 61673374, 31470998, 31861133011), the Beijing Municipal Science & Technology

385

Commission (Z171100000117006, Z171100008217006), and the National Key Research and

386

Development Program of China (2018YFC2001701, 2018YFC2000303, 2016YFC1305900,

387

2017YFB1401203).

388

References

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Almgren, H., de Steen, F. V., Kuehn, S., Razi, A., Friston, K., & Marinazzo, D. (2018). Variability and reliability of effective connectivity within the core default mode network: A multi-site longitudinal spectral DCM study. Neuroimage, 183, 757-768. Andrews-Hanna, J. R., Reidler, J. S., Sepulcre, J., Poulin, R., & Buckner, R. L. (2010). Functional-Anatomic Fractionation of the Brain's Default Network. Neuron, 65, 550-562. Bickart, K. C., Hollenbeck, M. C., Barrett, L. F., & Dickerson, B. C. (2012). Intrinsic Amygdala-Cortical Functional Connectivity Predicts Social Network Size in Humans. Journal of Neuroscience, 32, 14729-14741. Campbell, A., Converse, P. E., & Rodgers, W. L. (1976). The quality of American life: Perceptions, evaluations, and satisfactions. New York: Russell Sage Foundation. Caria, A., Sitaram, R., Veit, R., Begliomini, C., & Birbaumer, N. (2010). Volitional Control of Anterior Insula Activity Modulates the Response to Aversive Stimuli. A Real-Time Functional Magnetic Resonance Imaging Study. Biological Psychiatry, 68, 425-432. 19

402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445

Cerqueira, C. T., Almeida, J. R. C., Gorenstein, C., Gentil, V., Leite, C. C., Sato, J. R., Amaro, E., Jr., & Busatto, G. F. (2008). Engagement of multifocal neural circuits during recall of autobiographical happy events. Brazilian Journal of Medical and Biological Research, 41, 1076-1085. Chang, L. J., Yarkoni, T., Khaw, M. W., & Sanfey, A. G. (2013). Decoding the Role of the Insula in Human Cognition: Functional Parcellation and Large-Scale Reverse Inference. Cerebral Cortex, 23, 739-749. Cooney, R. E., Joormann, J., Eugene, F., Dennis, E. L., & Gotlib, I. H. (2010). Neural correlates of rumination in depression. Cognitive Affective & Behavioral Neuroscience, 10, 470-478. Craig, A. D. (2002). How do you feel? Interoception: the sense of the physiological condition of the body. Nature Reviews Neuroscience, 3, 655-666. Craig, A. D. (2005). Forebrain emotional asymmetry: a neuroanatomical basis? Trends in Cognitive Sciences, 9, 566-571. Critchley, H. D., Wiens, S., Rotshtein, P., Ohman, A., & Dolan, R. J. (2004). Neural systems supporting interoceptive awareness. Nature Neuroscience, 7, 189-195. D'Argembeau, A., Collette, F., Van der Linden, M., Laureys, S., Del Fiore, G., Degueldre, C., Luxen, A., & Salmon, E. (2005). Self-referential reflective activity and its relationship with rest: a PET study. Neuroimage, 25, 616-624. Damasio, A., & Carvalho, G. B. (2013). OPINION The nature of feelings: evolutionary and neurobiological origins. Nature Reviews Neuroscience, 14, 143-152. Deen, B., Pitskel, N. B., & Pelphrey, K. A. (2011). Three Systems of Insular Functional Connectivity Identified with Cluster Analysis. Cerebral Cortex, 21, 1498-1506. Diener, E. (2000). Subjective well-being - The science of happiness and a proposal for a national index. American Psychologist, 55, 34-43. Diener, E., Oishi, S., & Lucas, R. E. (2003). Personality, culture, and subjective well-being: Emotional and cognitive evaluations of life. Annual Review Of Psychology, 54, 403-425. Diener, E., & Ryan, K. (2009). Subjective well-being: a general overview. South African Journal Of Psychology, 39, 391-406. Dolan, P., Peasgood, T., & White, M. (2008). Do we really know what makes us happy? A review of the economic literature on the factors associated with subjective well-being. Journal Of Economic Psychology, 29, 94-122. Duerden, E. G., Arsalidou, M., Lee, M., & Taylor, M. J. (2013). Lateralization of affective processing in the insula. Neuroimage, 78, 159-175. Duquette, P. (2017). Increasing Our Insular World View: Interoception and Psychopathology for Psychotherapists. Frontiers in neuroscience, 11. Etkin, A., Egner, T., & Kalisch, R. (2011). Emotional processing in anterior cingulate and medial prefrontal cortex. Trends in Cognitive Sciences, 15, 85-93. Faillenot, I., Heekemann, R. A., Frot, M., & Hammers, A. (2017). Macroanatomy and 3D probabilistic atlas of the human insula. Neuroimage, 150, 88-98. Fang, J., Rong, P., Hong, Y., Fan, Y., Liu, J., Wang, H., Zhang, G., Chen, X., Shi, S., Wang, L., Liu, R., Hwang, J., Li, Z., Tao, J., Wang, Y., Zhu, B., & Kong, J. (2016). Transcutaneous Vagus Nerve Stimulation Modulates Default Mode Network in Major Depressive Disorder. Biological Psychiatry, 79, 266-273. Fossati, P., Hevenor, S. J., Graham, S. J., Grady, C., Keightley, M. L., Craik, F., & Mayberg, H. (2003). 20

446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489

In search of the emotional self: An fMRI study using positive and negative emotional words. American Journal of Psychiatry, 160, 1938-1945. Fox, K. C. R., Andrews-Hanna, J. R., Mills, C., Dixon, M. L., Markovic, J., Thompson, E., & Christoff, K. (2018). Affective neuroscience of self-generated thought. Annals Of the New York Academy Of Sciences, 1426, 25-51. Fox, M. D., Zhang, D., Snyder, A. Z., & Raichle, M. E. (2009). The Global Signal and Observed Anticorrelated Resting State Brain Networks. Journal Of Neurophysiology, 101, 3270-3283. Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S. J., & Turner, R. (1996). Movement-related effects in fMRI time-series. Magnetic Resonance in Medicine, 35, 346-355. Funahashi, S. (2011). BRAIN MECHANISMS OF HAPPINESS. Psychologia, 54, 222-233. Ghaziri, J., Tucholka, A., Girard, G., Boucher, O., Houde, J.-C., Descoteaux, M., Obaid, S., Gilbert, G., Rouleau, I., & Nguyen, D. K. (2018). Subcortical structural connectivity of insular subregions. Scientific Reports, 8. Gotts, S. J., Jo, H. J., Wallace, G. L., Saad, Z. S., Cox, R. W., & Martin, A. (2013). Two distinct forms of functional lateralization in the human brain. Proceedings of the National Academy of Sciences of the United States of America, 110, E3435-E3444. Gotts, S. J., Saad, Z. S., Jo, H. J., Wallace, G. L., Cox, R. W., & Martin, A. (2013). The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders. Frontiers In Human Neuroscience, 7. Harrison, N. A., Gray, M. A., Gianaros, P. J., & Critchley, H. D. (2010). The Embodiment of Emotional Feelings in the Brain. Journal of Neuroscience, 30, 12878-12884. Heilbronner, S. R., & Hayden, B. Y. (2016). Dorsal Anterior Cingulate Cortex: A Bottom-Up View. In S. E. Hyman (Ed.), Annual Review of Neuroscience, Vol 39 (Vol. 39, pp. 149-170). Henderson, L. A., Rubin, T. K., & Macefield, V. G. (2011). Within-Limb Somatotopic Representation of Acute Muscle Pain in the Human Contralateral Dorsal Posterior Insula. Human Brain Mapping, 32, 1592-1601. Immordino-Yang, M. H., & Yang, X.-F. (2017). Cultural differences in the neural correlates of social-emotional feelings: an interdisciplinary, developmental perspective. Current opinion in psychology, 17, 34-40. Jones, N. P., Fournier, J. C., & Stone, L. B. (2017). Neural correlates of autobiographical problem-solving deficits associated with rumination in depression. Journal Of Affective Disorders, 218, 210-216. Jones, T. G., Rapport, L. J., Hanks, R. A., Lichtenberg, P. A., & Telmet, K. (2003). Cognitive and psychosocial predictors of subjective well-being in urban older adults. Clinical Neuropsychologist, 17, 3-18. Kahneman, D., & Krueger, A. B. (2006). Developments in the measurement of subjective well-being. Journal Of Economic Perspectives, 20, 3-24. Kalisch, R., Wiech, K., Critchley, H. D., & Dolan, R. J. (2006). Levels of appraisal: A medial prefrontal role in high-level appraisal of emotional material. Neuroimage, 30, 1458-1466. Kong, F., Hu, S., Wang, X., Song, Y., & Liu, J. (2015). Neural correlates of the happy life: The amplitude of spontaneous low frequency fluctuations predicts subjective well-being. Neuroimage, 107, 136-145. Kong, F., Xue, S., & Wang, X. (2016). Amplitude of low frequency fluctuations during resting state predicts social well-being. Biological Psychology, 118, 161-168. 21

490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533

Kraus, T., Hoesl, K., Kiess, O., Schanze, A., Kornhuber, J., & Forster, C. (2007). BOLD fMRI deactivation of limbic and temporal brain structures and mood enhancing effect by transcutaneous vagus nerve stimulation. Journal Of Neural Transmission, 114, 1485-1493. Kurth, F., Zilles, K., Fox, P. T., Laird, A. R., & Eickhoff, S. B. (2010). A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis. Brain Structure & Function, 214, 519-534. Lawton, M. P., & Brody, E. M. (1969). Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist, 9, 179-186. Lewis, G. J., Kanai, R., Rees, G., & Bates, T. C. (2014). Neural correlates of the 'good life': eudaimonic well-being is associated with insular cortex volume. Social Cognitive And Affective Neuroscience, 9, 615-618. Li, R., Zhang, S., Yin, S., Ren, W., He, R., & Li, J. (2018). The fronto-insular cortex causally mediates the default-mode and central-executive networks to contribute to individual cognitive performance in healthy elderly. Human Brain Mapping, 39, 4302-4311. Luo, Y., Kong, F., Qi, S., You, X., & Huang, X. (2016). Resting-state functional connectivity of the default mode network associated with happiness. Social Cognitive And Affective Neuroscience, 11, 516-524. Menon, V. (2011). Large-scale brain networks and psychopathology: a unifying triple network model. Trends in Cognitive Sciences, 15, 483-506. Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: a network model of insula function. Brain Structure & Function, 214, 655-667. Morel, A., Gallay, M. N., Baechler, A., Nyss, M., & Gallay, D. S. (2013). THE HUMAN INSULA: ARCHITECTONIC ORGANIZATION AND POSTMORTEM MRI REGISTRATION. Neuroscience, 236, 117-135. Namkung, H., Kim, S.-H., & Sawa, A. (2017). The Insula: An Underestimated Brain Area in Clinical Neuroscience, Psychiatry, and Neurology. Trends In Neurosciences, 40, 200-207. Nardo, D., Hogberg, G., Flumeri, F., Jacobsson, H., Larsson, S. A., Hallstrom, T., & Pagani, M. (2011). Self-rating scales assessing subjective well-being and distress correlate with rCBF in PTSD-sensitive regions. Psychological Medicine, 41, 2549-2561. Nelson, S. M., Dosenbach, N. U. F., Cohen, A. L., Wheeler, M. E., Schlaggar, B. L., & Petersen, S. E. (2010). Role of the anterior insula in task-level control and focal attention. Brain Structure & Function, 214, 669-680. Nolen-Hoeksema, S., Wisco, B. E., & Lyubomirsky, S. (2008). Rethinking Rumination. Perspectives on Psychological Science, 3, 400-424. Nomi, J. S., Schettini, E., Broce, I., Dick, A. S., & Uddin, L. Q. (2018). Structural Connections of Functionally Defined Human Insular Subdivisions. Cerebral Cortex, 28, 3445-3456. Odriozola, P., Uddin, L. Q., Lynch, C. J., Kochalka, J., Chen, T., & Menon, V. (2016). Insula response and connectivity during social and non-social attention in children with autism. Social Cognitive And Affective Neuroscience, 11, 433-444. Palomero-Gallagher, N., Mohlberg, H., Zilles, K., & Vogt, B. (2008). Cytology and receptor architecture of human anterior cingulate cortex. Journal of Comparative Neurology, 508, 906-926. Pavuluri, M., & May, A. (2015). I feel, therefore, I am: the insula and its role in human emotion, cognition and the sensory-motor system. AIMS Neuroscience, 2, 18-27. 22

534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577

Pinquart, M., & Sorensen, S. (2000). Influences of socioeconomic status, social network, and competence on subjective well-being in later life: A meta-analysis. Psychology and Aging, 15, 187-224. Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage, 84, 320-341. Reeve, J., & Lee, W. (2018). A neuroscientific perspective on basic psychological needs. Journal of personality. Rietveld, C. A., Cesarini, D., Benjamin, D. J., Koellinger, P. D., De Neve, J.-E., Tiemeier, H., Johannesson, M., Magnusson, P. K. E., Pedersen, N. L., Krueger, R. F., & Bartels, M. (2013). Molecular genetics and subjective well-being. Proceedings of the National Academy of Sciences of the United States of America, 110, 9692-9697. Roberts, R. E., & Vernon, S. W. (1983). The Center for Epidemiologic Studies Depression Scale: its use in a community sample. American Journal of Psychiatry, 140, 41-46. Rutledge, R. B., Skandali, N., Dayan, P., & Dolan, R. J. (2014). A computational and neural model of momentary subjective well-being. Proceedings of the National Academy of Sciences of the United States of America, 111, 12252-12257. Saad, Z. S., Reynolds, R. C., Jo, H. J., Gotts, S. J., Chen, G., Martin, A., & Cox, R. W. (2013). Correcting brain-wide correlation differences in resting-state FMRI. Brain connectivity, 3, 339-352. Segerdahl, A. R., Mezue, M., Okell, T. W., Farrar, J. T., & Tracey, I. (2015). The dorsal posterior insula subserves a fundamental role in human pain. Nature Neuroscience, 18, 499-+. Singer, T. (2006). The neuronal basis and ontogeny of empathy and mind reading: Review of literature and implications for future research. Neuroscience and Biobehavioral Reviews, 30, 855-863. Spaeti, J., Chumbley, J., Brakowski, J., Doerig, N., Holtforth, M. G., Seifritz, E., & Spinelli, S. (2014). Functional Lateralization of the Anterior Insula During Feedback Processing. Human Brain Mapping, 35, 4428-4439. Suardi, A., Sotgiu, I., Costa, T., Cauda, F., & Rusconi, M. (2016). The neural correlates of happiness: A review of PET and fMRI studies using autobiographical recall methods. Cognitive Affective & Behavioral Neuroscience, 16, 383-392. Takahashi, H., Matsuura, M., Koeda, M., Yahata, N., Suhara, T., Kato, M., & Okubo, Y. (2008). Brain activations during judgments of positive self-conscious emotion and positive basic emotion: Pride and joy. Cerebral Cortex, 18, 898-903. Touroutoglou, A., Hollenbeck, M., Dickerson, B. C., & Barrett, L. F. (2012). Dissociable large-scale networks anchored in the right anterior insula subserve affective experience and attention. Neuroimage, 60, 1947-1958. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15, 273-289. Uddin, L. Q. (2015). Salience processing and insular cortical function and dysfunction. Nature Reviews Neuroscience, 16, 55-61. Uddin, L. Q., Kinnison, J., Pessoa, L., & Anderson, M. L. (2014). Beyond the Tripartite Cognition-Emotion-Interoception Model of the Human Insular Cortex. Journal of Cognitive 23

578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605

Neuroscience, 26, 16-27. Uddin, L. Q., Nomi, J. S., Hebert-Seropian, B., Ghaziri, J., & Boucher, O. (2017). Structure and Function of the Human Insula. Journal of Clinical Neurophysiology, 34, 300-306. Urry, H. L., Nitschke, J. B., Dolski, I., Jackson, D. C., Dalton, K. M., Mueller, C. J., Rosenkranz, M. A., Ryff, C. D., Singer, B. H., & Davidson, R. J. (2004). Making a life worth living - Neural correlates of well-being. Psychological Science, 15, 367-372. Van Dijk, K. R. A., Sabuncu, M. R., & Buckner, R. L. (2012). The influence of head motion on intrinsic functional connectivity MRI. Neuroimage, 59, 431-438. Wager, T. D., Rilling, J. K., Smith, E. E., Sokolik, A., Casey, K. L., Davidson, R. J., Kosslyn, S. M., Rose, R. M., & Cohen, J. D. (2004). Placebo-induced changes in fMRI in the anticipation and experience of pain. Science, 303, 1162-1167. Yan, C. G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R. C., Di Martino, A., Li, Q. Y., Zuo, X. N., Castellanos, F. X., & Milham, M. P. (2013). A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage, 76, 183-201. Yeo, B. T. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zoeller, L., Polimeni, J. R., Fischl, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106, 1125-1165. Yu, C., Zhou, Y., Liu, Y., Jiang, T., Dong, H., Zhang, Y., & Walter, M. (2011). Functional segregation of the human cingulate cortex is confirmed by functional connectivity based neuroanatomical parcellation. Neuroimage, 54, 2571-2581. Yu, J., Li, J., & Huang, X. (2012). The Beijing version of the montreal cognitive assessment as a brief screening tool for mild cognitive impairment: a community-based study. Bmc Psychiatry, 12. Zaki, J., Davis, J. I., & Ochsner, K. N. (2012). Overlapping activity in anterior insula during interoception and emotional experience. Neuroimage, 62, 493-499. Zanon, C., Hutz, C. S., Reppold, C. T., & Zenger, M. (2016). Are happier people less vulnerable to rumination, anxiety, and post-traumatic stress? Evidence from a large scale disaster. Psicologia-Reflexao E Critica, 29.

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Figure Legends

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Figure 1. Functional connectivity map of the insular cortex. The map shows the regions that are

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significantly correlated with the insula, as defined by the AAL (one sample t-test, corrected by

610

FDR with p < 5e-15 and extent threshold k = 10 voxels). The target ROIs including the dACC,

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pACC, and MCC for differentiating the insular subregion connectivity were denoted on the map.

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Figure 2. Three connectionally defined subregional ROIs in the insula. A, contrast analyses on

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functional connectivity of dACC, pACC, and MCC with the total insula (left, dACC over pACC

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and MCC; middle, pACC over dACC and MCC; right, MCC over dACC and pACC) derived from

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paired t-test with p < 0.05, corrected by FDR and extent threshold k = 10 voxels. B, three

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connectionally defined subregion seed ROIs in the insula. The dAI (red), vAI (blue), and the PI

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(green) seed ROIs were defined respectively as the intersection of the above contrast maps.

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Figure 3. Relationship between the SWB and functional connectivity of the ldAI with aMPFC (A)

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and rIPL (B). Each dot represents data from one participant.

25

Highlights:


A seed-target-seed approach was used to derive subregional ROIs in the insula.




The dorsal anterior insula (dAI) was found to relate to subjective well-being.




This relation was negative and unique for connectivity of left dAI with DMN areas.




The ldAI-DMN connection constitutes a neural basis of subjective happiness.

CRediT authorship contribution statement Rui Li: Conceptualization, Methodology, Formal analysis, Investigation, Data Curation, Writing Original Draft, Writing - Review & Editing, Visualization, Funding acquisition. Xinyi Zhu: Investigation, Resources, Data Curation. Zhiwei Zheng: Investigation, Resources, Data Curation. Pengyun Wang: Investigation, Resources, Data Curation. Juan Li: Conceptualization, Investigation, Resources, Data Curation, Writing - Review & Editing, Supervision, Project administration, Funding acquisition.