CHAPTER 9
Yoed N. Kenett and Anjan Chatterjee
Abstract
Studying the two main components of well-being—hedonia and eudaimonia—can shed insight into its psychological and neural aspects. This chapter begins by highlighting how neuroscience research in two related domains—creativity and meditation—has been useful. Then, the authors review the extant neuroscientific research on hedonic and eudaimonic well-being. Finally, they propose a testable, general framework on how the brain may realize both hedonic and eudaimonic well-being. This approach is inspired by advances in neuroscience research that examines the structure and dynamics of large-scale brain networks. Identifying these neural markers of well-being can elucidate what motivates human flourishing, and how neural mechanisms might be enhanced to facilitate well-being.
Key Words: well-being, network neuroscience, hedonia, eudaimonia, DMN, creativity, mindfulness
Introduction
Living a happy life can be a lifelong pursuit. People are happy when they feel positive emotions, engage in interesting activities, and experience pleasure (Diener & Biswas-Diener, 2011; Lyubomirsky, Sheldon, & Schkade, 2005). Happiness indicates a sense of well-being—a sense that can be framed biologically (some people are genetically predisposed to well-being), contextually (well-being for a political refugee is different that it would be for a campaigning politician), or psychologically (the mental state of being well).
Here, we focus on psychological and their accompanying neural states of well-being (Huta & Waterman, 2014; Ryan & Deci, 2001). Drawing on Aristotle, we distinguish between hedonic well-being (HWB) and eudaimonic well-being (EWB). Aristotle (1925/1998) distinguished hedonia, or pleasure, from eudaimonia, or “virtuous activity of [the] soul” (Aristotle, 1925/1998; p. 18). While hedonia refers to experiencing pleasure, eudaimonia points to the lifelong exercise of developing character virtues (Dolcos, Moore, & Katsumi, 2018). The goals of HWB are short-term and differ from the goals of EWB, which span a longer duration. As HWB and EWB address different aspects human flourishing, identifying how the brain enables hedonic and eudaimonic states can shed light into biological mechanisms recruited by humanist activities that promote well-being. In this chapter we use the terms well-being and flourishing interchangeably.
Contemporary research recognizes HWB and EWB as generally distinct psychological constructs (Disabato, Goodman, Kashdan, Short, & Jarden, 2016; Keyes, Shmotkin, & Ryff, 2002). HWB is characterized by affective and cognitive evaluations of one’s life in relation to pleasure. These characteristics include life satisfaction, as well as frequent positive and infrequent negative emotions (Diener, 2000). EWB is characterized by an individual’s realization of their potential; it is related to a sense of autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance (Ryff, 2018). HWB emphasizes positive affect, and EWB emphasizes purpose and growth as defining features (Diener, 2000; Ryan & Deci, 2001; Ryff, 2018; Urry et al., 2004).
Identifying brain mechanisms related to HWB and EWB could enrich our understanding of well-being (Luo et al., 2017). These notions of well-being are rarely studied in cognitive neuroscience. Neuroscientists have considerable experience using functional imaging techniques to assess brain responses evoked by specific experimental tasks. The history of assessing enduring mental states or personality traits is more limited. Resting-state functional magnetic resonance imaging signals (RS-fMRI; synchronized brain activity at rest) can be used to study natural, time-evolving, brain activation related to behavioral and psychological states (Lurie et al., 2020. By coupling neural activation patterns and specific types of well-being, we have the possibility to better understand and chart biological changes rendered by engaging in positive humanistic activities (Tay, Pawelski, & Keith, 2018). Here, we examine the extant literature on the neural correlates of HWB and EWB and propose a consolidating framework to determine neural markers of well-being.
Network Neuroscience: A Primer
Network science is a useful tool to study neural structures and dynamics (Bassett & Sporns, 2017; Medaglia, Lynall, & Bassett, 2015; Sporns, 2011). Network science, based on mathematical graph theory, is a way to study complex systems as networks (Bassett & Sporns, 2017; Medaglia et al., 2015; Sporns, 2011). A network is composed of nodes that represent the basic units of the system (e.g., brain regions) and links, or edges, that signify relations between the nodes (e.g., functional connectivity across brain regions).
Network neuroscience approaches relevant to well-being consider interactions of three large-scale brain networks: the executive control network (ECN), the default mode network (DMN), and the salience network (SN). The ECN is a set of brain regions that activate during cognitive tasks that require externally directed attention, working memory, relational integration, response inhibition, and task switching (Zabelina & Andrews-Hanna, 2016). The DMN is a set of brain regions that activate in the absence of most external tasks and is associated with mind-wandering and spontaneous thought (Andrews-Hanna, Smallwood, & Spreng, 2014). The SN is a set of brain regions involved in detecting, integrating, and filtering relevant interoceptive, autonomic, and emotional information (Seeley et al., 2007; Uddin, 2015).
Network neuroscience can study processes related to human flourishing, such as aesthetics, mindfulness, and creativity (Tay et al., 2018). Chatterjee and Vartanian (2014) proposed a general framework for neural mechanisms related to aesthetic experiences. This framework involves complex interactions of neural systems similar to those associated with well-being. Providing empirical support for this framework, Belfi et al. (2019) reported that the DMN responds to aesthetically pleasing artwork, potentially tracking the participants’ internal state while they are engaged with these images. The authors suggest that during aesthetic appreciation of visual art, the DMN engages in “top-down” sense-making with “bottom-up” sensory input. This interaction highlights the role of different neural networks that deliver mental states such as aesthetic appreciation. Before we discuss network neuroscience observations related to well-being, we briefly describe two examples of domains related to human flourishing, examples that highlight the potential of this analytical approach.
Example I: Meditation and Mindfulness
The past two decades have seen increasing neuroscientific interest in meditation and mindfulness as ways to enhance well-being (Dolcos et al., 2018; Fox et al., 2014; Tang, Hölzel, & Posner, 2015; Van Dam et al., 2018). Meditation encompasses mental training methods that can improve attentional and emotional self-regulation (Tang et al., 2015). Mindfulness generally refers to the awareness that emerges from purposeful attention to oneself (Kabat‐Zinn, 2003). Neuroscientific studies try to identify underlying brain mechanisms associated with mental improvement produced by meditation and mindfulness (Dolcos et al., 2018).
Despite their popularity, meditation and mindfulness refer to different practices, making the study of their benefits to well-being challenging (Van Dam et al., 2018). Broad claims about lasting effects of these practices on the brain are premature (Fox et al., 2014). Nonetheless, the morphometry (e.g., cortical thickness or density) of a few core brain regions do seem affected by meditation and mindfulness training (Fox et al., 2014; Tang et al., 2015): the frontopolar cortex, related to enhanced meta-awareness following meditation practice; the sensory cortices and insula, areas related to body awareness; the hippocampus, related to memory; the anterior- and mid-cingulate cortex, and the orbitofrontal cortex, areas related to self- and emotion-regulation; and the superior longitudinal fasciculus and corpus callosum, areas related to intra- and inter-hemispherical communication (Dolcos et al., 2018; Fox et al., 2014; Tang et al., 2015).
More recent fMRI studies have investigated the interactions between the ECN, DMN, and SN in mindfulness (Bilevicius, Smith, & Kornelsen, 2018; Doll, Hölzel, Boucard, Wohlschläger, & Sorg, 2015; Kim et al., 2019; Parkinson, Kornelsen, & Smith, 2019). Mindfulness training leads to functional connectivity changes between the DMN and SN (Doll et al., 2015) and between the ECN and SN (Parkinson et al., 2019). Kim et al. (2019) used a mediation analysis in a real-time fMRI study and found that mindfulness scores correlated with coupling of the SN and DMN, which was mediated by the ECN (Kim et al., 2019).
Identifying activation in these brain regions in relation to mindfulness and meditation relates them to neurocognitive processes and advances hypotheses for how the brain realizes a mindful and meditative state. These studies suggest that mindfulness training at the neural level engages one’s ability to control (ECN) spontaneously generated internal thought (DMN) in relation to externally driven (SN) stimuli.
Example II: Creativity
While many acts can be creative, most agree that creativity involves generating something that is novel and useful (Benedek & Fink, 2019). Neuroscience studies of creativity have examined the link between cognitive processes, such as executive functions (working memory, fluid intelligence, task switching), attention, inhibition, and episodic memory, as well as personality traits (openness to experience) related to creativity (Beaty, Benedek, Silvia, & Schacter, 2016; Beaty, Seli, & Schacter, 2019). Creativity plays an important role in flourishing and in our engagement with the arts and humanities (Tay et al., 2018).
Preliminary observations report functional connectivity patterns that predict differences in people’s creative ability (Beaty et al., 2018; Kenett et al., 2020; Li et al., 2017; Sun et al., 2019), interactions during creative thinking (Beaty, Benedek, Kaufman, & Silvia, 2015; Shi, Sun, Xia, et al., 2018; Vartanian et al., 2018), and reveal white matter connectivity patterns that constrain neural dynamics of creative thinking (Kenett et al., 2018; Ryman et al., 2014).
The ECN, DMN, and SN couple together at different stages of the creative process. Beaty, Benedek, Kaufman, and Silvia (2015) found that early stages are characterized by tighter coupling between the DMN and SN, and later stages by tighter coupling between the ECN and DMN (Beaty et al., 2015). Beaty et al. (2018) described a “creative connectome” that links these three brain networks and predicts participants’ performance on a creativity task. RS-fMRI studies report that tighter coupling between the ECN and the DMN in the “resting brain” correlates with better performance in creativity tasks (Beaty et al., 2014; Beaty et al., 2019; Shi, Sun, Xia, et al., 2018). Finally, Fink et al. (2018) reported that training on a creativity task leads to changes in the connectivity patterns of several resting brain networks, further demonstrating a correspondence between RS and task-state brain networks relevant to creative thought. These studies indicate that a creative person may have heightened ability to shift between spontaneous, free, and evaluative, controlled thought that brings forth ideas that are novel and that also make sense.
The Neuroscience of Well-Being
Most studies investigate neural activity related to HWB (Kong, Hu, Wang, Song, & Liu, 2015; Luo et al., 2017; Shi, Sun, Wu, et al., 2018) and EWB (Kong, Liu, et al., 2015; Lewis, Kanai, Rees, & Bates, 2014; Van Reekum et al., 2007) independently of each other.
HWB encompasses an affective component related to positive rather than negative emotional experiences in one’s life and a cognitive component related to a person’s appraisals of his or her life (Diener, 2000; Diener & Biswas-Diener, 2011; Pavot & Diener, 1993). Examining the neural amplitude of low-frequency fluctuations of RS-fMRI in relation to subjective measures of affect and satisfaction with life, Kong, Hu, et al. (2015) found that the affective component correlated positively with the amplitude of low-frequency fluctuations (higher scores relate to higher amplitudes) in the right amygdala (a brain region related to emotion regulation), and the cognitive component correlated positively with the left dorsolateral prefrontal cortex and bilateral orbitofrontal cortex. These two brain regions are associated with cognitive control and emotional regulation through the inhibition of inappropriate emotions and behaviors (Hooker & Knight, 2006).
Shi, Sun, Wu, et al. (2018) found that the strength of functional connectivity between SN and DMN correlated negatively with HWB (higher HWB scores relate to lower SN-DMN functional connectivity). HWB correlates negatively with a neural “state” in which the ECN, DMN, and SN are highly decoupled from each other: weak between network functional connectivity and strong within network connectivity (Shi, Sun, Wu, et al., 2018).
HWB is also directly related to specific cognitive processes (Shi, Sun, Wu, et al., 2018). For example, unhappy people are sensitive to negative feedback and ruminate more frequently, as seen in people with depression (Lyubomirsky, Boehm, Kasri, & Zehm, 2011). Rumination relates to atypical coupling between the hippocampus (a brain region related to memory) and the amygdala (a brain region related to emotion regulation; Cooney, Joormann, Eugène, Dennis, & Gotlib, 2010; Disner, Beevers, Haigh, & Beck, 2011). Luo, Kong, Qi, You, and Huang (2015) found that unhappy people have higher RS functional connectivity within regions of the DMN (anterior medial prefrontal cortex, bilateral posterior cingulate cortex, and the left inferior parietal cortex). Such a connectivity pattern is also related to higher rumination (Luo et al., 2015).
EWB relates to individual fulfillment, such as personal growth, positive relations, and purpose in life (Ryff, 2018). Archontaki, Lewis, and Bates (2013) argued that the neural mechanism underlying EWB exerts control over facets (autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance; Ryff, 2018). They argue that these neural control systems process emotional, reward incentives, and motivational information (Archontaki et al., 2013). For example, EWB relates to greater gray matter volume of the right insula, a key component of the SN in orienting attention to the external world (Lewis et al., 2014). Furthermore, EWB is associated with increased activity of the superior temporal lobe (Luo et al., 2014), and increased or sustained activity of the prefrontal cortex in response to affective stimuli (Heller et al., 2013; Van Reekum et al., 2007).
Using RS-fMRI to examine the neural correlates of EWB, Kong, Lie, et al. (2015) found that EWB was positively correlated with low-frequency fluctuation (higher EWB scores relate to higher low-frequency fluctuations) in the right posterior superior temporal gyrus (pSTG; implicated in self-referential processes and autobiographical memory) and the thalamus (involved in relaying motor and sensory information across the brain), replicating a previous study (Luo et al., 2014). Also, EWB correlated negatively with the strength of the thalamic-insular functional connectivity (higher EWB scores relate to lower thalamic-insular functional connectivity). Such correlations mediated the relation between EWB and personality traits; namely, the pSTG and thalamus mediated the effect of neuroticism as well as extraversion on EWB, whereas the thalamus only mediated the effect of conscientiousness on EWB.
The neural underpinnings of HWB and EWB are typically studied independently. However, recent large-scale genetic studies report that HWB and EWB have strong genetic correlations (r = .78; Baselmans & Bartels, 2018; Baselmans et al., 2019). These studies have demonstrated how the expression of these genes in specific brain regions relate to well-being (Baselmans et al., 2019), which further suggests that there might be similarities in their respective neural correlates.
Investigating HWB and EWB together could shed novel insights on similarity and differences in the neural connectivity patterns and dynamics of these complex constructs (see Berridge & Kringelbach, 2011, for a similar view). Two studies take this approach. One of the earliest such studies looked at differences in frontal EEG patterns as related to HWB and EWB (Urry et al., 2004). The authors found greater alpha waves activity in the left superior prefrontal cortex, related to both HWB and EWB (Urry et al., 2004). In a second study, Luo et al. (2017) compiled a behavioral measure that relates HWB to EWB. This measure, eudaimonic-hedonic balance, quantifies the balance between these two notions of well-being, based on trait measures of HWB and EWB (Luo et al., 2017): a positive score indicates a dominance of EWB, while a negative score indicates a dominance of HWB. The authors found that their measure correlated positively with functional connectivity of the bilateral ventral medial prefrontal cortex and the bilateral precuneus, both regions of the DMN. The hyper-connectivity of these regions was related to dominance of EWB over HWB (Luo et al., 2017).
Conclusions
Establishing the neuroscience of well-being faces challenges. The construct of well-being is imprecise and does not lend itself easily to testable hypotheses and tractable neuroscientific experiments. Functional neuroimaging methods are better developed to examine the brain’s reaction to specific situations and less so to enduring mental states. Nonetheless, recent advances in network neuroscience and RS-fMRI promise to move this field forward (Lurie et al., 2018). Many of the studies cited here use reverse inference in making psychological claims. Reverse inference means that one infers psychological or mental states from patterns of neural activity. Such inferences are better thought of as generating hypotheses rather than confirming them. Based on the studies we reviewed, we generate two hypotheses, make a recommendation, and offer one prediction.
First, we hypothesize that HWB and EWB have similar neural signatures that relate to connectivity patterns within and across ECN, DMN, and SN. This hypothesis is supported by the observation that HWB and EWB share a strong genetic correspondence (Baselmans & Bartels, 2018). Activation in the DMN relates positively with both HWB and EWB, thus potentially playing a role connecting HWB and EWB (Kringelbach & Berridge, 2009). For example, a core region of the DMN, the medial prefrontal cortex, is involved in the experience of pleasure (HWB) as well as introspective self-referential cognition (EWB).
Second, we hypothesize that HWB and EWB are also characterized by distinct neural activation and connectivity patterns. The affective component of HWB is typically transient and is potentially driven by the DMN and SN; its cognitive components may require prolonged coordination between the ECN and DMN. Such coordination converges with long-term goals of flourishing and self-fulfillment, related to EWB. HWB and EWB may represent similar neural activations with different temporal characteristics (short- vs. long-term) rather than about activation of different brain regions.
Our recommendation is that we need a better psychological understanding and means to measure well-being that lend themselves to experimental scrutiny. Such an understanding can lead to experimental designs that make forward inferences to confirm or reject hypotheses. We need to link psychological theory to neural implementation more directly. Our general framework for a neuroscientific approach to well-being suggests that both of aspects of well-being are relevant at different time-scales and that they are implemented by overlapping neural mechanisms. While one may have repeated short-term hedonic experiences, these experiences do not automatically give rise to a long-term eudaimonic life of fulfillment. Similarly, long-term pursuit of flourishing may not generate short-term hedonic experiences. Rather, a person who better capitalizes short-term hedonic experiences to facilitate long-term eudaimonic experiences may flourish (see also Wilkinson & King, this volume, Chapter 7).
Finally, we predict that neural processes and functional connectivity patterns related to well-being are modifiable by humanist interventions, such as engagement with the arts (Tay et al., 2018). The effects of meditation and mindfulness and of creativity training on brain structure and function provide support for this prediction (Kim et al., 2019; Tang et al., 2015). Ultimately, the study of the neuroscience of well-being is motivated to understand how we might enhance human flourishing. A clearer sense of the biology of well-being offers a window into the malleability of this psychological state and tests the role that the arts and humanities play in enhancing human flourishing.
Acknowledgment
This chapter was supported by a gift from the Global Wellness Institute to Anjan Chatterjee.
References
Andrews-Hanna, J. R., Smallwood, J., & Spreng, R. N. (2014). The default network and self-generated thought: Component processes, dynamic control, and clinical relevance. Annals of the New York Academy of Sciences, 1316(1), 29–52.
Archontaki, D., Lewis, G. J., & Bates, T. C. (2013). Genetic influences on psychological well‐being: A nationally representative twin study. Journal of Personality, 81(2), 221–230.
Aristotle. (1925/1998). The Nicomachean ethics (W. D. Ross, Trans.). Oxford, UK: Oxford University Press.
Baselmans, B. M. L., & Bartels, M. (2018). A genetic perspective on the relationship between eudaimonic and hedonic well-being. Scientific Reports, 8(1), 14610.
Baselmans, B. M. L., Jansen, R., Ip, H. F., van Dongen, J., Abdellaoui, A., van de Weijer, M. P., Bao, Y., Smart, M., Kumari, M., Willemsen, G., Hottenga, J.-J., consortium, B., Consortium, S. S. G. A., Boomsma, D. I., de Geus, E. J. C., Nivard, M. G., & Bartels, M. (2019). Multivariate genome-wide analyses of the well-being spectrum. Nature Genetics, 51(3), 445–451.
Bassett, D. S., & Sporns, O. (2017). Network neuroscience. Nature Neuroscience, 20(3), 353–364.
Beaty, R. E., Benedek, M., Kaufman, S. B., & Silvia, P. J. (2015). Default and executive network coupling supports creative idea production. Scientific Reports, 5, 10964.
Beaty, R. E., Benedek, M., Silvia, P. J., & Schacter, D. L. (2016). Creative cognition and brain network dynamics. Trends in Cognitive Sciences, 20(2), 87–95.
Beaty, R. E., Benedek, M., Wilkins, R. W., Jauk, E., Fink, A., Silvia, P. J., Hodges, D. A., Koschutnig, K., & Neubauer, A. C. (2014). Creativity and the default network: A functional connectivity analysis of the creative brain at rest. Neuropsychologia, 64(0), 92-98.
Beaty, R. E., Kenett, Y. N., Christensen, A. P., Rosenberg, M. D., Benedek, M., Chen, Q., Fink, A., Qiu, J., Kwapil, T. R., Kane, M. J., & Silvia, P. J. (2018). Robust prediction of individual creative ability from brain functional connectivity. Proceedings of the National Academy of Sciences, 115(5), 1087–1092.
Beaty, R. E., Seli, P., & Schacter, D. L. (2019). Network neuroscience of creative cognition: mapping cognitive mechanisms and individual differences in the creative brain. Current Opinion in Behavioral Sciences, 27, 22–30.
Belfi, A. M., Vessel, E. A., Brielmann, A., Isik, A. I., Chatterjee, A., Leder, H., Pelli, D. G., & Starr, G. G. (2019). Dynamics of aesthetic experience are reflected in the default-mode network. NeuroImage, 188, 584–597.
Benedek, M., & Fink, A. (2019). Toward a neurocognitive framework of creative cognition: the role of memory, attention, and cognitive control. Current Opinion in Behavioral Sciences, 27, I116–122.
Berridge, K. C., & Kringelbach, M. L. (2011). Building a neuroscience of pleasure and well-being. Psychology of Well-Being: Theory, Research and Practice, 1(1), 3.
Bilevicius, E., Smith, S. D., & Kornelsen, J. (2018). Resting-state network functional connectivity patterns associated with the Mindful Attention Awareness Scale. Brain Connectivity, 8(1), 40–48.
Chatterjee, A., & Vartanian, O. (2014). Neuroaesthetics. Trends in Cognitive Sciences, 18(7), 370–375.
Cooney, R. E., Joormann, J., Eugène, F., Dennis, E. L., & Gotlib, I. H. (2010). Neural correlates of rumination in depression. Cognitive, Affective, & Behavioral Neuroscience, 10(4), 470–478.
Diener, E. (2000). Subjective well-being: The science of happiness and a proposal for a national index. American Psychologist, 55(1), 34–43.
Diener, E., & Biswas-Diener, R. (2011). Happiness: Unlocking the mysteries of psychological wealth. New York, NY: John Wiley & Sons.
Disabato, D. J., Goodman, F. R., Kashdan, T. B., Short, J. L., & Jarden, A. (2016). Different types of well-being? A cross-cultural examination of hedonic and eudaimonic well-being. Psychological Assessment, 28(5), 471–482.
Disner, S. G., Beevers, C. G., Haigh, E. A. P., & Beck, A. T. (2011). Neural mechanisms of the cognitive model of depression. Nature Reviews Neuroscience, 12(8), 467–477.
Dolcos, S., Moore, M., & Katsumi, Y. (2018). Neuroscience and well-being. In E. Diener, S. Oishi, & L. Tay (Eds.), Handbook of well-being (pp. 1–26). Salt Lake City, UT: DEF.
Doll, A., Hölzel, B. K., Boucard, C. C., Wohlschläger, A. M., & Sorg, C. (2015). Mindfulness is associated with intrinsic functional connectivity between default mode and salience networks. Frontiers in Human Neuroscience, 9, 461.
Fink, A., Benedek, M., Koschutnig, K., Papousek, I., Weiss, E. M., Bagga, D., & Schöpf, V. (2018). Modulation of resting-state network connectivity by verbal divergent thinking training. Brain and Cognition, 128, 1–6.
Fox, K. C. R., Nijeboer, S., Dixon, M. L., Floman, J. L., Ellamil, M., Rumak, S. P., Sedlmeier, P., & Christoff, K. (2014). Is meditation associated with altered brain structure? A systematic review and meta-analysis of morphometric neuroimaging in meditation practitioners. Neuroscience & Biobehavioral Reviews, 43, 48–73.
Heller, A. S., van Reekum, C. M., Schaefer, S. M., Lapate, R. C., Radler, B. T., Ryff, C. D., & Davidson, R. J. (2013). Sustained striatal activity predicts eudaimonic well-being and cortisol output. Psychological Science, 24(11), 2191–2200.
Hooker, C. I., & Knight, R. T. (2006). The role of lateral orbitofrontal cortex in the inhibitory control of emotion. The Orbitofrontal Cortex, 307.
Huta, V., & Waterman, A. S. (2014). Eudaimonia and its distinction from hedonia: Developing a classification and terminology for understanding conceptual and operational definitions. Journal of Happiness Studies, 15(6), 1425–1456.
Kabat‐Zinn, J. (2003). Mindfulness‐based interventions in context: Past, present, and future. Clinical Psychology: Science and Practice, 10(2), 144–156.
Kenett, Y. N., Medaglia, J. D., Beaty, R. E., Chen, Q., Betzel, R. F., Thompson-Schill, S. L., & Qiu, J. (2018). Driving the brain towards creativity and intelligence: A network control theory analysis. Neuropsychologia, 118, 79–90.
Kenett, Y. N., Betzel, R. F., & Beaty, R. E. (2020). Community structure of the creative brain at rest. NeuroImage (220), 116578.
Keyes, C. L. M., Shmotkin, D., & Ryff, C. D. (2002). Optimizing well-being: The empirical encounter of two traditions. Journal of Personality and Social Psychology, 82(6), 1007–1022.
Kim, H.-C., Tegethoff, M., Meinlschmidt, G., Stalujanis, E., Belardi, A., Jo, S., Lee, J., Kim, D.-Y., Yoo, S.-S., & Lee, J.-H. (2019). Mediation analysis of triple networks revealed functional feature of mindfulness from real-time fMRI neurofeedback. NeuroImage, 195, 409–432.
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., Liu, L., Wang, X., Hu, S., Song, Y., & Liu, J. (2015). Different neural pathways linking personality traits and eudaimonic well-being: a resting-state functional magnetic resonance imaging study. Cognitive, Affective, & Behavioral Neuroscience, 15(2), 299–309.
Kringelbach, M. L., & Berridge, K. C. (2009). Towards a functional neuroanatomy of pleasure and happiness. Trends in Cognitive Sciences, 13(11), 479–487.
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(5), 615–618.
Li, J., Zhang, D., Liang, A., Liang, B., Wang, Z., Cai, Y., Gao, M., Gao, Z., Chang, S., & Jiao, B. (2017). High transition frequencies of dynamic functional connectivity states in the creative brain. Scientific Reports, 7, 46072.
Luo, Y., Huang, X., Yang, Z., Li, B., Liu, J., & Wei, D. (2014). Regional homogeneity of intrinsic brain activity in happy and unhappy individuals. PloS One, 9(1), e85181.
Luo, Y., Kong, F., Qi, S., You, X., & Huang, X. (2015). Resting-state functional connectivity of the default mode network associated with happiness. Social Cognitive and Affective Neuroscience, 11(3), 516–524.
Luo, Y., Qi, S., Chen, X., You, X., Huang, X., & Yang, Z. (2017). Pleasure attainment or self-realization: The balance between two forms of well-beings are encoded in default mode network. Social Cognitive and Affective Neuroscience, 12(10), 1678–1686.
Lurie, D., Kessler, D., Bassett, D., Betzel, R. F., Breakspear, M., Keilholz, S., Kucyl, A., Liegeois, R., Lindquist, M. A., Mclintosh, A. R., Poldrack, R. A., Shine, J. A., Thompson, W. H., Bielczyk, N. Z., Douw, L., Kraft, D., Miller, R. L., Muthuraman, M., Pasquin, L., Vidaurre, D., Xie, H., & Calhoun, V. D. (2020). Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Network Neuroscience, 4(1), 30–69.
Lyubomirsky, S., Boehm, J. K., Kasri, F., & Zehm, K. (2011). The cognitive and hedonic costs of dwelling on achievement-related negative experiences: Implications for enduring happiness and unhappiness. Emotion, 11(5), 1152–1167.
Lyubomirsky, S., Sheldon, K. M., & Schkade, D. (2005). Pursuing happiness: The architecture of sustainable change. Review of General Psychology, 9(2), 111–131.
Medaglia, J. D., Lynall, M.-E., & Bassett, D. S. (2015). Cognitive network neuroscience. Journal of Cognitive Neuroscience, 27(8), 1471–1491.
Parkinson, T. D., Kornelsen, J., & Smith, S. D. (2019). Trait mindfulness and functional connectivity in cognitive and attentional resting state networks. Frontiers in Human Neuroscience, 13(112).
Pavot, W., & Diener, E. (1993). The affective and cognitive context of self-reported measures of subjective well-being. Social Indicators Research, 28(1), 1–20.
Ryan, R. M., & Deci, E. L. (2001). On happiness and human potentials: A review of research on hedonic and eudaimonic well-being. Annual Review of Psychology, 52(1), 141–166.
Ryff, C. D. (2018). Eudaimonic well-being. In K. Shigemasu, S. Kuwano, T. Sato, & T. Matsuzawa (Eds.), Diversity in harmony - Insights from psychology: Proceedings of the 31st International Congress of Psychology (pp. 375–395). John Wiley & Sons Ltd. https://doi.org/10.1002/9781119362081.ch20.
Ryman, S. G., van den Heuvel, M. P., Yeo, R. A., Caprihan, A., Carrasco, J., Vakhtin, A. A., Flores, R. A., Wertz, C., & Jung, R. E. (2014). Sex differences in the relationship between white matter connectivity and creativity. NeuroImage, 101, 380–389.
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., Reiss, A. L., & Greicius, M. D. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. Journal of Neuroscience, 27(9), 2349–2356.
Shi, L., Sun, J., Wu, X., Wei, D., Chen, Q., Yang, W., Chen, H., & Qiu, J. (2018). Brain networks of happiness: Dynamic functional connectivity among the default, cognitive and salience networks relates to subjective well-being. Social Cognitive and Affective Neuroscience, 13(8), 851–862.
Shi, L., Sun, J., Xia, Y., Ren, Z., Chen, Q., Wei, D., Yang, W., & Qiu, J. (2018). Large-scale brain network connectivity underlying creativity in resting-state and task fMRI: Cooperation between default network and frontal-parietal network. Biological Psychology, 135, 102–111.
Sporns, O. (2011). Networks of the brain. Cambridge, MA: MIT Press.
Sun, J., Liu, Z., Rolls, E. T., Chen, Q., Yao, Y., Yang, W., Wei, D., Zhang, Q., Zhang, J., & Feng, J. (2019). Verbal creativity correlates with the temporal variability of brain networks during the resting state. Cerebral Cortex, 29(3), 1047–1058.
Tang, Y.-Y., Hölzel, B. K., & Posner, M. I. (2015). The neuroscience of mindfulness meditation. Nature Reviews Neuroscience, 16(4), 213–225.
Tay, L., Pawelski, J. O., & Keith, M. G. (2018). The role of the arts and humanities in human flourishing: A conceptual model. The Journal of Positive Psychology, 13(3), 215–225.
Uddin, L. Q. (2015). Salience processing and insular cortical function and dysfunction. Nature Reviews Neuroscience, 16(1), 55–61.
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(6), 367–372.
Van Dam, N. T., van Vugt, M. K., Vago, D. R., Schmalzl, L., Saron, C. D., Olendzki, A., Meissner, T., Lazar, S. W., Kerr, C. E., Gorchov, J., Fox, K. C. R., Field, B. A., Britton, W. B., Brefczynski-Lewis, J. A., & Meyer, D. E. (2018). Mind the hype: A critical evaluation and prescriptive agenda for research on mindfulness and meditation. Perspectives on Psychological Science, 13(1), 36–61.
Van Reekum, C. M., Urry, H. L., Johnstone, T., Thurow, M. E., Frye, C. J., Jackson, C. A., Schaefer, H. S., Alexander, A. L., & Davidson, R. J. (2007). Individual differences in amygdala and ventromedial prefrontal cortex activity are associated with evaluation speed and psychological well-being. Journal of Cognitive Neuroscience, 19(2), 237–248.
Vartanian, O., Beatty, E. L., Smith, I., Blackler, K., Lam, Q., & Forbes, S. (2018). One-way traffic: The inferior frontal gyrus controls brain activation in the middle temporal gyrus and inferior parietal lobule during divergent thinking. Neuropsychologia, 118, 68–78.
Zabelina, D. L., & Andrews-Hanna, J. R. (2016). Dynamic network interactions supporting internally-oriented cognition. Current Opinion in Neurobiology, 40, 86–93.