Abundant evidence highlights the important role of personality traits, age, and social relationships in the experience of subjective well-being. Experiential factors influence well-being, and evidence shows that specific forms of training, such as physical exercise and mindfulness meditation, can produce strong and enduring beneficial effects on well-being. These factors also shape the structure and function of our brains throughout the lifespan, with fascinating implications for our well-being. However, understanding how well-being is created (and changed) by our brains has only recently become the focus of neuroscientific investigations. In this chapter, we review recent neuroscientific evidence revealing how the neurocircuitries underlying personality traits (i.e., optimism, negative bias, self-esteem, extraversion, and neuroticism), successful emotional aging, and social relationships (i.e., love and loneliness) contribute to well-being, and how these circuits and systems are altered in chronic stress, anxiety, and depression. Identifying the neural correlates of well-being can illuminate the processes that cause higher levels of well-being, which, in turn, can inform promising training interventions that can induce neuroplastic changes and help people live happier, healthier, and more successful lives.
Keywords: Affective biases, Neuroplasticity, Prefrontal cortex, Amygdala, Orbitofrontal cortex, Ventral striatum.
Dolcos, S., Moore, M., & Katsumi, Y. (2018). Neuroscience and well-being. In E. Diener, S. Oishi, & L. Tay (Eds.), Handbook of well-being. Salt Lake City, UT: DEF Publishers. DOI:nobascholar.com
The study of well-being has deep philosophical roots dating back to Aristotle, who proposed that well-being has at least two components: hedonia and eudaimonia (Aristotle, 2009; Seligman, Steen, Park, & Peterson, 2005). The term “hedonic” originates in the ancient Greek word “Hedone”, which means pleasure. Hedonic well-being is exemplified in Ed Diener’s concept of subjective well-being, which encompasses a combination of positive affect and life satisfaction. Thus, hedonic well-being highlights positive affect as a defining feature of well-being. Eudaimonic well-being emphasizes aspects of a life experienced as meaningful (with purpose, growth, etc.), and may or may not be accompanied by positive affect. Studying the neural substrates of eudaimonic well-being is more challenging, as it is harder to define life meaning and to link it to specific brain regions or networks. Therefore, in this chapter we will focus mostly on the hedonic component of well-being.
Subjective well-being is influenced by a combination of individual characteristics, such as genetics, personality traits (Lucas & Diener, 2008), and age (Carstensen et al., 2011), by psychosocial factors, such as having a reasonable income (Diener & Oishi, 2000; Luhmann, Schimmack, & Eid, 2011), being married (Diener, Gohm, Suh, & Oishi, 2000), or having meaningful social relationships (Cacioppo & Patrick, 2008), as well as by lifestyle-related factors, such as exercise (Hassmén, Koivula, & Uutela, 2000) and engagement in mindfulness meditation (Carmody & Baer, 2008). Although a large amount of evidence accumulated during the last decade has advanced our understanding of the roles of these factors in the experience of subjective well-being, a number of essential issues remain unclear. Among these, a key issue refers to the brain mechanisms supporting well-being, which could help clarify why some people experience greater well-being than others, what might go wrong in affective disturbances, and how different types of interventions can enhance brain function and well-being. Recent brain imaging advances enabling the investigation of the neural correlates of positive emotions and overall well-being have provided essential contributions to answering these fundamental questions.
The goal of this chapter is to review recent neuroscientific evidence revealing how the different brain regions and systems contribute to well-being, how these are different in affective disturbances, and how this knowledge about the brain can inform interventions aimed at helping people lead happier, healthier, and more successful lives. We start with a general overview of the brain and how it works, while also introducing some of the main brain regions typically involved in general emotion processing and in positive emotions associated with well-being (pleasure, reward). Then, we examine how the structure and function of these regions is different in people experiencing low levels of well-being, such as in stress and affective disorders (anxiety and depression). Next, we review evidence pointing to links between various brain regions and factors that contribute to well-being, including individual differences (i.e., personality- and age-related) and psychosocial factors (i.e., social connectedness). Finally, we conclude with evidence highlighting changes in the brain resulting from interventions focused on increasing well-being.
All of our mental life—our thoughts, emotions, memories, and decisions—is brought about by the physical substance of the brain. To understand how the brain enables well-being, it is necessary to first understand the basics of its organization (or structure), function, and modes of communication. All brain activity depends on the workings of neurons—cells that carry information from one place to another through a combination of electrical and chemical signals. Information is transferred between neurons at synapses, and is typically mediated by chemical signaling molecules, called neurotransmitters. The most relevant neurotransmitters that facilitate the enjoyment of a wide range of experiences and the desire to engage in these experiences are dopamine and opioids (the endogenous brain variety of opiates) (Kringelbach & Berridge, 2010). The outer layer of the brain forms the cerebral cortex, which is responsible for many of the attributes that we consider inherently human, like language, reasoning, and imagination. The cerebral cortex is divided into four lobes. The frontal lobe, the front section of the brain, is involved in the planning, guidance, and evaluation of behavior. The parietal lobe, located in the middle section of the brain, is involved in integrating information from multiple sensory modalities within the body, and information from memory. The temporal lobe, located in the bottom section of the brain, is associated with auditory processing, memory, visual item recognition, and emotion. Finally, the occipital lobe, located at the back of the brain, is mainly involved in processing visual information. Below the cortex, in the center of the brain, are the subcortical structures, which contain many of the core areas involved in emotion and reward processing, and contributing to the experience of well-being. An abundance of neuroscientific evidence indicates that the subjective experience of happiness, pleasure, rewards, and general well-being depends on shared or overlapping networks of interacting subcortical and cortical regions. These networks involved in processing emotions and rewards include subcortical structures such as the amygdala, ventral tegmental area, nucleus accumbens, and ventral pallidum, and portions of the prefrontal cortex regions, including the medial prefrontal cortex, orbitofrontal cortex, anterior cingulate cortex, and insula.
Emotion processing has been associated with a wide range of interacting brain regions (Lindquist, Wager, Kober, Bliss-Moreau, & Barrett, 2012). In this review we focus on regions that have been typically associated with well-being. A region that plays a critical role in the detection of emotional information and in learning the emotional significance of the information is the amygdala (AMY), a small, almond shaped subcortical region located deep in the brain, in the temporal lobe. Traditionally, the AMY was linked exclusively to processing negative emotions, such as fear and anger. However, current views also highlight the AMY’s role in processing positive emotions, and more generally in detecting salient stimuli (Adolphs, 2008, 2009; Dolcos, Iordan, & Dolcos, 2011; Whalen, 2007). Positive experiences increase the release of dopamine, the most important neurotransmitter for the experience of reward or pleasure, which makes the AMY react stronger to positive events and experiences, and send signals to the nearby hippocampus (HC) to remember such experiences.
The main cortical areas involved in emotion processing are located in the frontal lobe, which consists of a number of regions. The most relevant for well-being are the prefrontal cortex regions (especially its medial and orbital parts), the inferior frontal cortex, and the anterior cingulate cortex. The prefrontal cortex (PFC), which comprises the anterior regions of the frontal lobe, takes part in the more complex aspects of planning, organizing, and executing behavior. The medial PFC (mPFC) plays important roles in processing information about the self and others, emotion regulation, and emotional decision making. The orbitofrontal cortex (OFC), the most ventral part of the PFC, is especially involved in the experience of pleasure, happiness, and well-being. Located just above the orbits, this region receives input from the external senses (vision, hearing, touch, smell, and taste), as well as input related to the internal states of the body (via the insula, described below). By comparing the properties of an external stimulus (e.g., sweet, friendly) with the body’s current internal state, the OFC (especially its mid-anterior part) computes the current emotional value of a stimulus at a particular moment, and tracks changes in the subjective pleasure or reward. The inferior frontal cortex (IFC)1 is involved in a diverse range of processes, such as basic processing of emotional and salient information, response inhibition, and emotion regulation. Another frontal lobe structure, the anterior cingulate cortex (ACC) is connected to many brain areas, including the PFC and AMY, which allows this region to have an important integrative role in emotion, self-monitoring, and reward anticipation. The ACC is involved in assessing the value of responses; that is, whether an action is likely to elicit a reward or punishment, which is different from the role of the OFC, which computes whether a stimulus is currently rewarding or punishing. Overall, there is also evidence that the left PFC plays a more specialized role in positive affect and approach-driven behaviors, whereas the right hemisphere is involved in negative affect and withdrawal/avoidance-driven behaviors, but this evidence is not discussed in details here (Davidson, 2004; Eddington, Dolcos, Cabeza, & Strauman, 2007; Eddington et al., 2009; Herrington et al., 2010).
Another brain region involved in emotion processing is the insula, which is an area of cortex tucked between the frontal and temporal lobes. Commonly known for its role in disgust, the insula actually has a wider role in emotional processing, being involved in monitoring the internal states of a body. The more complex whole-body sensations associated with emotional states, like feelings of happiness, sadness, or elation, are processed in the anterior insula, whereas basic visceral sensations like pain, temperature, or fatigue are processed in the posterior insula. Figure 1 highlights these brain regions and their main roles.
Figure 1. Neural circuitry of well-being emphasized in the present review. The areas circumscribed by dotted lines shown in the left panel illustrate brain regions located deep inside the brain. Note that the highlighted regions are meant to give a general idea of the locations in the brain, whereas delineations used for analysis might target specifically gray or white matter and use nomenclature specific to one or the other.
Neuroscientific evidence has shown that positive experiences engage a set of reward brain centers, which paint a “hedonic gloss” on some of our experiences. Before talking about these reward centers, we need to define the concept of reward. Reward consists of three components (Berridge & Kringelbach, 2015). The first component is the one most associated with reward, which is the actual pleasure or liking. The second component is the motivation for reward, or wanting. The third component is learning about future rewards based on past experiences. “Liking” is the core process of hedonic pleasure, whereas “wanting” refers to the motivation to obtain the reward, or the incentive salience. “Liking” is a present-moment, interoceptive feeling of well-being, whereas “wanting” is a future expectation of well-being. In addition to these psychological dissociations, “liking” and “wanting” can be also dissociated at the neuroanatomical and neurochemical levels. “Wanting” is generated by more widely distributed subcortical brain circuits, especially by the mesolimbic dopamine system, which connects the dopamine-rich ventral tegmental area to the nucleus accumbens (see Figure 1). The ventral tegmental area contains many neurons that produce dopamine. The nucleus accumbens (NAcc), a subcortical structure located deep inside the brain, contains many dopamine receptors. Dopamine released from the mesolimbic system into the NAcc regulates the incentive salience of the stimuli. In addition to dopamine, other neurotransmitters, including opioids (natural brain neurochemicals similar to opiate drugs), also contribute to the incentive salience or “wanting” (reviewed in Berridge, 2009). “Liking” is generated by more restricted brain regions than “wanting”, and is also more restricted neurochemically. “Liking” is enhanced by opioid stimulation in opioid hotspots such as the NAcc and the ventral pallidum. Although dopamine was considered by many as playing a role in sensory pleasure, recent evidence suggests that dopamine is only involved in “wanting” something, but not necessarily in “liking” it (Berridge & Kringelbach, 2015). Therefore, “liking” and “wanting” are different aspect of pleasure, and they involve different neurotransmitters. Opioids are the ones causing people to enjoy the experience, and dopamine keeps them coming back for more. While the “liking” component of reward is relatively understudied, the “wanting” part is very well studied, especially in the form of addiction.
In summary, no single brain area is responsible for the experience of emotion, pleasure, and reward. Rather, a number of brain regions interact to produce the different types of emotional experiences that accompany well-being.
As described above, the experience of positive emotions, pleasure, and well-being is supported by the normal functioning of a number of brain regions and their interconnections2. Disturbances in the structure and function of these regions can reduce the ability to experience positive emotions and pleasure, which is commonly seen in chronic stress, anxiety, and depression.
Everyone experiences stress in their life—from minor stressors such as a traffic jam or financial difficulties to major stressors such as divorce or the loss of a loved one. Stressors can cause a wide range of emotional responses, from mild alertness, to a sense of being overwhelmed and literally feeling stressed out. However, not all stress is bad. Acute or moderate stress (eustress), or what we commonly refer to as basic fight-or-flight response, not only helps people get out of danger in unexpected emergency situations, but can also have beneficial effects on people’s health, strengthening the immune and cardiovascular systems. However, chronic stress, that is stress extending over longer periods of time, with no end in sight, can have deleterious effects on brain structure and function, and on people’s health and well-being, leading to increased incidence of disease, such as generalized anxiety disorder, major depressive disorder, and post-traumatic stress disorder (PTSD) (Dohrenwend & Dohrenwend, 1974; Kendler, Gardner, & Prescott, 1999; Monat, Lazarus, & Reevy, 2007). Before describing the effects of stress on the brain, we need to understand how stress works.
Exposure to stressors activates the body’s stress response (the fight-or-flight response), which involves the production of hormones and neurochemicals in the brain. Two key hormones are cortisol, which helps activate our brain and senses to increase alertness to better deal with emergency situations, and adrenaline, which gets the body immediately activated and aroused for action. Cortisol hard-wires the pathways between the AMY, which is important for emotions and for learning about aversive stimuli, and the adjacently located HC, which is critical for episodic memories. The hippocampal cells have the largest number of cortisol receptors in the brain, making the HC highly receptive to any change in cortisol levels. Short exposure to cortisol improves hippocampal activity and memory, but prolonged exposure to high levels of cortisol damages hippocampal cells. Patients with conditions associated with long-term stress exposure, such as depression and PTSD, have significantly shrunken HC (Arnone, McIntosh, Ebmeier, Munafo, & Anderson, 2012; Bonne et al., 2008; Koolschijn, van Haren, Lensvelt-Mulders, Hulshoff Pol, & Kahn, 2009; McEwen, Nasca, & Gray, 2016), suggesting that long-term cortisol exposure has destroyed their hippocampal cells. They also show changes in the size and connectivity of the AMY (Hamilton, Siemer, & Gotlib, 2008; McEwen et al., 2016; Morey et al., 2012). Increased stress in patients with these conditions puts the AMY into overdrive, and the increased AMY activity is accompanied by reduced activity in the PFC (Bremner, 2006).
Anxiety and depression are associated with symptoms of reduced well-being, such as negative affective biases (Cisler, Bacon, & Williams, 2009; Eysenck, Derakshan, Santos, & Calvo, 2007; Gotlib, Krasnoperova, Yue, & Joormann, 2004), and enhanced encoding of negative stimuli (Hamilton & Gotlib, 2008; Russo et al., 2006). In addition, structural and functional neuroimaging evidence indicates that brain regions implicated in emotion and reward processing are altered in anxiety and depression. For example, individuals with anxiety and depression tend to show decreased volume in frontal lobe regions such as the OFC and ACC (Arnone et al., 2012; Koolschijn et al., 2009; Shang et al., 2014; Talati, Pantazatos, Schneier, Weissman, & Hirsch, 2013), as well as in medial temporal regions such as the HC and AMY (Alemany et al., 2013; Arnone et al., 2012; Fisler et al., 2013; Hayano et al., 2009; Irle et al., 2010; Koolschijn et al., 2009; Meng et al., 2013), suggesting that these conditions are linked to structural changes in key regions engaged during processing and integration of emotional and motivational signals.
Furthermore, anxiety and depression have been linked to reduced integrity of the structural connections between these brain regions, including between the frontal lobe and the AMY (Cullen et al., 2010; de Kwaasteniet et al., 2013; Kim & Whalen, 2009; Zhang et al., 2012), supporting the idea that the structure within and between key brain regions involved in emotion and reward processing are altered in anxiety and depression. Moreover, anxiety and depression are associated with aberrant brain activity, including communication between brain regions when the brain is “at rest” (i.e., resting state functional connectivity). For instance, anxiety is characterized by reduced resting state connectivity between PFC and the AMY (Hahn et al., 2011; Kim et al., 2011), and depression by increased resting state connectivity within a network of brain regions implicated in autobiographical memory, and decreased connectivity within a network implicated in cognitive control, relative to healthy individuals (Rayner, Jackson, & Wilson, 2016). These findings suggest that maladaptive cross-talk between brain regions occurs in anxiety and depression even when not engaged in a particular task. During tasks, individuals with anxiety or depression tend to show enhanced response in AMY to negative stimuli compared to control groups (Etkin & Wager, 2007; Groenewold, Opmeer, de Jonge, Aleman, & Costafreda, 2013), consistent with the idea that anxiety and depression influence the mechanisms subserving emotion processing.
Together, these findings identify brain mechanisms underlying chronic stress and reduced mental health, which are key factors in understanding the neuroscience of well-being, and highlight potential targets for preventive and therapeutic interventions aimed at decreasing susceptibility and increasing resilience against affective conditions to support improved well-being.
Important insight into understanding well-being has been offered by research clarifying the neural substrates of individual differences that play a role in promoting enhanced well-being and protecting against symptoms of reduced well-being. Among these, the most studied are individual differences in personality and age, which are discussed here. The role of social relationships is also discussed, with a focus on love and loneliness.
Personality influences many aspects of our lives, including our emotions, thoughts, and ultimately our well-being. Although a breadth of personality characteristics can be considered in the context of well-being, the present discussion focuses on several key factors that emerge from the literature, including optimism, negative bias, self-esteem, and Big 5 traits.
Optimism, a deep-rooted mindset defined as the dispositional tendency for people to hold generalized favorable expectancies about their future (Carver, Scheier, & Segerstrom, 2010), has been shown to be associated with coping behavior (Nes & Segerstrom, 2006), to be beneficial in times of adversity (Carver et al., 2010), and to promote psychological well-being (Andersson, 1996; Carver et al., 2010). Optimism is not just feeling good or thinking positively—it also helps us stay engaged in important tasks with important rewards (Scheier & Carver, 1985). Optimism leads to increased well-being because by expecting to do well, people will increase their engagement and persistence, being therefore more likely to achieve their goals.
Emerging evidence shows that optimism is associated with the structure and function of the brain. Structurally, trait optimism has been associated with greater volume in reward-related (Grabenhorst & Rolls, 2011; Kringelbach & Berridge, 2009; Phelps, Lempert, & Sokol-Hessner, 2014) and approach-oriented (Eddington et al., 2007) processing regions, such as the OFC (Dolcos, Hu, Iordan, Moore, & Dolcos, 2016). Moreover, optimism has been shown to mediate the relation between the OFC and symptoms of reduced well-being, thus demonstrating that increased volume in this brain region can play a protective role against anxiety through increased optimism (Dolcos et al., 2016). In addition to the OFC, trait optimism has also been associated with a region of the ACC (rostral anterior cingulate cortex, rACC) when imagining future positive events vs. future negative events (Sharot, Riccardi, Raio, & Phelps, 2007). This region, known to be involved in self-reflection (Moran, Macrae, Heatherton, Wyland, & Kelley, 2006; Paulus & Frank, 2003) and assessing the salience of emotional and motivational information (Bush, Luu, & Posner, 2000), has also shown increased functional connectivity with the AMY when imagining future positive events compared to imagining negative events (Sharot et al., 2007).
Research examining the brain chemistry associated with optimism (Fox, 2013) posits that the optimistic mindset is rooted in the “sunny brain”, the pleasure center of the brain, whereas the pessimistic mindset is rooted in the “rainy brain”. The sunny brain consists of neurons in a core reward region, the NAcc, that forms links with neurons in particular areas of the PFC, and seems to be left lateralized (Davidson & Irwin, 1999). Converging evidence from healthy and clinical studies support these lateralization findings, suggesting that the two hemispheres are differentially engaged in these fundamental approaches to life (reviewed in Hecht, 2013).
Overall, the reviewed evidence highlights the role of the OFC, rACC, and NAcc in supporting optimism, and points to brain-personality mechanisms promoting enhanced well-being as well as protecting against reduced well-being.
Negative Bias. Another important aspect of understanding the neural correlates of well-being is clarifying the links between individual differences in affective biases that influence well-being. Positive affective biases tend to be observed in optimistic individuals and are associated with indicators of improved well-being (Segerstrom, 2001), whereas negative affective biases tend to be observed in individuals with emotional disorders and are associated with indicators of reduced well-being (Cisler et al., 2009; Eysenck et al., 2007; Gotlib et al., 2004). Negative affective biases are often expressed as enhanced sensitivity to negative or threatening information (Bar-Haim, Lamy, Pergamin, Bakermans-Kranenburg, & van IJzendoorn, 2007), and likely reflect compromised executive control of attention (Cisler et al., 2009) and impairment of the ability to inhibit prepotent responses and to resist interference to task-irrelevant distracters.
Negative attentional biases can be captured by tasks like the Affective Go/No-Go, in which participants are asked to respond to target stimuli and withhold from responding to distracter stimuli, while targets and distracters differ in affective valence. A negative affective bias can be detected by an increased speed and/or accuracy in participant responses when attending to negatively (vs. positively) valenced information (Schulz et al., 2007). This pattern is typically shown by patients with affective disorders, whereas the opposite pattern is typically shown by healthy participants (Erickson et al., 2005). Together, these results support the idea that negative biases in attention can be targeted to clarify potential links between impaired control mechanisms and affective disorders. Structural and functional imaging evidence has linked the IFC with performance in Go/No-Go tasks (Brown et al., 2015; Brown et al., 2012; Liddle, Kiehl, & Smith, 2001; Swick, Ashley, & Turken, 2008, 2011). Structural damage to the IFC is associated with higher error rates (Swick et al., 2008), and greater IFC activity has been seen in no-go trials (Liddle et al., 2001), particularly for negative emotional contexts (Brown et al., 2012), consistent with the idea that IFC helps to inhibit prepotent responses and is differentially engaged by emotional stimuli. Furthermore, the IFC responses to affective stimuli seem to be modulated by trait anxiety (Fales, Becerril, Luking, & Barch, 2010). More specifically, Fales et al. (2010) showed that higher anxiety was associated with greater activation within the left IFC in response to fearful (vs. neutral) targets, while lower anxiety was associated with greater activation to happy (vs. neutral) targets. Extending these previous findings, a recent study (Hu & Dolcos, 2017) showed that trait anxiety was negatively correlated with left IFC volume, and positively correlated with a negative bias in reaction time. Furthermore, trait anxiety mediated the negative association between the IFC volume and the negative bias measure, thus demonstrating that decreased volume in this brain region is linked to negative affective bias through anxiety.
Overall, extant evidence highlights the influence of negative affective bias in attention, and points to the IFC as a region both structurally and functionally associated with executive control. Available evidence shows that negative affective bias tends to be associated with enhanced sensitivity to negative or threatening information, possibly linked to compromised executive control of attention, and subserved by alterations of underlying brain mechanisms. In turn, negative affective bias plays a key role in understanding the neurophysiological substrates of well-being.
Self-Esteem is another important factor that has been shown to greatly predict well-being (Diener, 2009). Self-esteem typically refers to one’s global attitudes about oneself (Rosenberg, 1965). Interestingly, evidence suggests that when happiness decreases, self-esteem decreases as well (Laxer, 1964; Wessman & Ricks, 1966), suggesting that the relation between these factors might be bidirectional (Diener, 2009).
At a neural level, self-esteem has been shown to be associated with both the structure and function of the brain. Structurally, self-esteem has been linked to greater HC volume, in both younger and older adults, suggesting a potential protective role against challenges such as stress (Pruessner et al., 2005). Self-esteem is also associated with the structural connections between regions, such as the white matter integrity between the mPFC and the ventral striatum (Chavez & Heatherton, 2015), consistent with the role of these brain regions in processing information about the self and positive affective processes such as reward, respectively. Functionally, self-esteem has been linked to modulation of brain activity both at rest and during tasks. Greater self-esteem was associated with greater resting state functional connectivity between the ventral mPFC and the HC (and between the cuneus/lingual gyrus and right dorsolateral PFC and ACC) (Pan et al., 2016), consistent with the idea that brain networks supporting self-referential, memory, and cognitive control-related processes support self-esteem. Furthermore, tasks involving social exclusion or social feedback have shown that self-esteem modulates the engagement of ACC and mPFC responses (Onoda et al., 2010; Somerville, Kelley, & Heatherton, 2010). For instance, individuals with lower self-esteem relative to a high self-esteem group reported greater social pain in response to social exclusion, and had both greater response in ACC and greater connectivity between ACC and PFC (Onoda et al., 2010). These findings are consistent with the idea that self-esteem influences brain mechanisms engaged for interpretation of social signals and can lead to differential outcomes relevant to well-being.
Together, extant evidence suggests that self-esteem and the associated neural correlates influence factors of well-being. Self-esteem appears to be greatly linked to indices such as happiness, and is supported by both the structure and function of the brain. Hence, continuing research on these relations will refine and improve understanding and promotion of well-being.
Big 5 Traits. Yet another important aspect of understanding individual differences and the brain regions subserving well-being is the role of general personality factors. The link between the brain and well-being is complex, and might be better understood if individual differences in general personality traits are also taken into account. Personality is commonly studied using the Big 5 model, which includes the dimensions of extraversion, neuroticism, agreeableness, conscientiousness, and openness to experience (Costa & McCrae, 1992). Whereas the Big 5 have been shown to be linked to measures of well-being, most findings focus on extraversion and neuroticism, which are also the focus of this review (for details about agreeableness, conscientiousness, and openness to experience, see DeNeve & Cooper, 1998; Gutierrez, Moreno-Jimenez, Garrosa, & Puente, 2005; Hayes & Joseph, 2003). Extraversion is typically defined as a trait reflecting sensitivity to reward and positive affect, whereas neuroticism generally encompasses sensitivity to punishment and negative affect. Extraversion is linked to indicators of increased well-being, such as positive affect (DeNeve & Cooper, 1998; Gutierrez et al., 2005), enhanced recall of positive memories (Mayo, 1983; Rusting, 1999) and maintaining a positive mood after retrieval of positive personal memories (Denkova, Dolcos, & Dolcos, 2012). In contrast, neuroticism is associated with indicators of decreased well-being, such as negative affect, reduced life satisfaction, reduced happiness, and a tendency to recall negative information (Bradley & Mogg, 1994; DeNeve & Cooper, 1998; Gutierrez et al., 2005).
Available evidence links extraversion and neuroticism to the structure and function of the brain. For example, extraversion is positively associated with the medial OFC (DeYoung et al., 2010). Neuroticism is negatively linked to AMY volume (Hu et al., in press), and positively linked to the volume of regions engaged by sensitivity to punishment, negative emotion, and emotional dysregulation (mid-cingulate gyrus and caudate) (DeYoung et al., 2010). These traits have also been linked to other structural aspects such as cortical thickness (Riccelli, Toschi, Nigro, Terracciano, & Passamonti, 2017), and the structural connections between brain regions (i.e., white matter) (Xu & Potenza, 2012), as well as to the function of the brain. For instance, resting state functional connectivity measures have shown that extraversion and neuroticism are differentially associated with activity in networks of brain regions while at rest (Adelstein et al., 2011; Sampaio, Soares, Coutinho, Sousa, & Goncalves, 2014), consistent with the idea that these traits are linked to regions engaged in processes such as emotion regulation, self-evaluation, and reward (Adelstein et al., 2011; Andrews-Hanna, Reidler, Sepulcre, Poulin, & Buckner, 2010). Moreover, extraversion and neuroticism influence brain responses, especially in the AMY and HC, during tasks that involve emotional learning (Haas & Canli, 2008; Hooker, Verosky, Miyakawa, Knight, & D'Esposito, 2008). Overall, the reviewed evidence highlights the role of general personality factors as possible targets for better understanding susceptibility, and improving resilience, against emotional disturbances.
Healthy aging is generally associated with improved emotional functioning and well-being (Carstensen et al., 2011; Mather, 2012; Scheibe & Carstensen, 2010). Brain imaging findings parallel this behavioral evidence, suggesting that healthy aging is associated with relatively preserved structural integrity in regions typically involved in the processing and valuation of emotional information (AMY, mPFC), whereas it is associated with marked anatomical (Fjell et al., 2009; Raz, Ghisletta, Rodrigue, Kennedy, & Lindenberger, 2010; Raz et al., 2005) and physiological (Fabiani, 2012; Reuter-Lorenz & Park, 2014) losses in specific brain structures typically involved in executive control and emotion regulation (lateral PFC). Of note, given that the mPFC is also involved in some forms of emotion regulation (Roy, Shohamy, & Wager, 2012) and does not seem to show significant age-related cortical volume loss, it is possible that older adults may rely more on this structure in controlling their emotional responses (Mather, 2016).
In the context of overall preserved emotional functioning and well-being in aging, considerable evidence supports the idea of an age-related positivity effect in emotional perception, attention, and memory, by which older adults tend to pay greater attention to and remember more positive information (Charles, Mather, & Carstensen, 2003; Isaacowitz, Wadlinger, Goren, & Wilson, 2006; Knight et al., 2007; Mather & Carstensen, 2003) and show reduced processing of negative information (Grühn, Scheibe, & Baltes, 2007; Wood & Kisley, 2006), compared to younger adults (see also Mather, 2016; Reed & Carstensen, 2012; Reed, Chan, & Mikels, 2014). In order to explain this age-related positivity effect and its impact on well-being, a few models of emotional aging have been proposed. According to the Socioemotional Selectivity Theory (SST; Carstensen, Fung, & Charles, 2003), older adults’ preference for positive over negative information is driven in part by their prioritization of more present-focused motivational goals related to emotional meaning and satisfaction, which in turn enhances their well-being. The SST model interprets the positivity effect as a consequence of enhanced top-down modulation by the PFC/ACC influencing AMY response (Dolcos, Katsumi, & Dixon, 2014; St. Jacques, Dolcos, & Cabeza, 2010), thus emphasizing the role of motivational goals in emotion regulation (Carstensen et al., 2003). Another influential model called the Aging Brain Model (ABM; Cacioppo, Berntson, Bechara, Tranel, & Hawkley, 2011) interprets it as a consequence of impaired bottom-up processing of negative arousing stimuli by the AMY in older adults, considering that its response to positive stimuli may be relatively unimpaired by advancing age (Cacioppo et al., 2011). It is important to note, however, that there is evidence identifying activation of overlapping areas of the AMY between younger and older adults in processing negative stimuli (Dolcos et al., 2014; St. Jacques et al., 2010). Therefore, it is unlikely that the age-related reduction in AMY activity can be explained by impaired functioning of this region, as posited by the ABM.
In addition to reduced processing of negative stimuli, available evidence points to the link between enhanced processing of positive/rewarding stimuli and increased psychological well-being in aging. For instance, among a sample of middle-aged and older adults, those who showed sustained activity in the striatum, which is typically involved in the processing of various types of reward, over time in response to viewing positive pictures also reported overall greater levels of well-being (Heller et al., 2013). Moreover, striatal activation related to the viewing of positive stimuli mediated the relationship between well-being and daily levels of the stress hormone cortisol. In particular, greater levels of well-being were associated with increased activation in the striatum during positive picture processing, which in turn was associated with decreased levels of cortisol (Heller et al., 2013). Overall, these findings suggest that sustained engagement of the striatum in positive emotion processing is a key factor associated with well-being and adaptive regulation of stress necessary for maintaining health (see also Ryff, Heller, Schaefer, Van Reekum, & Davidson, 2016).
Taken together, these findings suggest that healthy aging is associated with an age-related positivity effect in emotion processing, as reflected in reduced processing of negative stimuli and enhanced processing of positive stimuli. This age-related positivity effect appears to be subserved by brain regions involved in basic/bottom-up processing (e.g., AMY, striatum) and those involved in higher-order/top-down processing and control (e.g., PFC, ACC) of emotional information. Successful engagement of these regions may in turn lead to improved subjective well-being in older adults.
Humans are fundamentally a social species, sharing a strong need to interact with others. Social relationships can provide enjoyment and can feel rewarding (Kahneman, Krueger, Schkade, Schwarz, & Stone, 2004). They give people access to the benefits of shared resources, security, and social support (Baumeister & Leary, 1995), and have a protective role, being associated with a variety of positive outcomes ranging from lower rates of mortality (House, Landis, & Umberson, 1988) and increased survival from heart attacks (Seeman, 1996) to physical (Holt-Lunstad, Smith, & Layton, 2010) and mental/subjective well-being across the lifespan. On the flip side, however, the perceived absence of social relationships, or loneliness, makes people feel more unsafe and threatened in social contexts (Cacioppo et al., 2011; Cacioppo & Patrick, 2008), and is associated with increased risk of depression (Cacioppo, Hawkley, & Thisted, 2010; Cacioppo, Hughes, Waite, Hawkley, & Thisted, 2006) and other negative outcomes.
Interactions with others are so critical for our survival that we developed larger brains and complex neural networks to be able to understand other people and to predict their intentions. Interestingly, when at rest, the brain’s pattern of activity looks very similar to when people are thinking about others, oneself, and the relation of oneself to other people (Mars et al., 2012). This has led some scientists to believe that “our brains are built to practice thinking about the social world and our place in it” (Lieberman, 2013). As such, it should not be surprising that a lack of interactions with the social world, and especially perceived loneliness, has deleterious effects on health through its effects on the brain (Cacioppo & Hawkley, 2009). The neural substrates supporting social cognition/behavior consist of many cortical and subcortical regions, each involved in several distinct processes. Most of the structures that we have talked about as being important in processing emotions and rewards are also important for social behavior. These include the AMY, the OFC, and the ventral striatum (especially the NAcc, caudate nucleus, and the putamen). Additional brain regions are also involved in social relationships. The next paragraphs review structural and functional changes in the brain associated with the involvement in dyadic or more extended social relationships (i.e., love and social networks), and the perceived absence of social relationships (i.e., loneliness).
Love is a complex and rewarding mental state involving cognitive and goal-directed behavioral components (Bianchi-Demicheli, Grafton, & Ortigue, 2006). The different kinds of love (e.g., passionate, maternal) are important predictors of positive emotions, happiness, and satisfaction (Myers, 1992). Evidence indicates both common and dissociable circuits for different kinds of love (e.g., romantic vs. maternal). Functional brain imaging evidence (Bartels & Zeki, 2000; Bartels & Zeki, 2004) shows that all types of love engage subcortical dopaminergic reward-related brain systems (involving dopamine and oxytocin receptors), which play an important role in goal-directed motivation, reward, and pair-bonding (Ortigue, Bianchi-Demicheli, Patel, Frum, & Lewis, 2010). However, the different types of love also involve distinct neural networks inside and outside the dopaminergic network (Bartels & Zeki, 2004; Fisher, Aron, & Brown, 2005). For instance, passionate love engages the ventral tegmental area (part of the brain’s reward circuit, associated with the motivation to pursue rewards) and the caudate nucleus (associated with reward detection and expectation). Maternal love recruits similar brain areas, with a specific involvement of the periaqueductal gray matter, an area that receives direct connections from the limbic emotional system, and contains a high density of receptors (vasopressin) that are important in maternal bonding and in pain suppression during intense emotional experiences like childbirth.
Interestingly, the presence of a loved one is not only rewarding, but it may act as a buffer against pain or stress. Showing female participants pictures of a loved male while receiving painful stimulation was associated with reduced reported pain intensity (Master et al., 2009), and such an analgesic effect correlated with activity in the reward-related NAcc (Younger, Aron, Parke, Chatterjee, & Mackey, 2010). Holding a partner’s hand, relative to holding the hand of a stranger or squeezing a ball, produced similar effects (Master et al., 2009). Moreover, love also reduces critical social assessment of others. For instance, when people look at their loved partners, the neural circuits that are normally associated with critical social assessment (AMY, temporo-parietal junction, and mPFC) of other people are suppressed, whereas regions involved in reward and attachment (caudate nucleus, putamen, medial insula, ACC) are activated (Bartels & Zeki, 2000). In sum, love facilitates positive emotions, by connecting the NAcc with the PFC, and reduces negative emotions, such as social judgment, by connecting the NAcc to the AMY.
Loneliness has been linked to a number of psychological states that contribute to morbidity and mortality, including increased depression (Booth, 2000; Cacioppo et al., 2010; VanderWeele, Hawkley, Thisted, & Cacioppo, 2011) and lower subjective well-being (Kong, Zhao, & You, 2013; VanderWeele, Hawkley, & Cacioppo, 2012). Loneliness also influences individuals’ perception and appraisal of the world. There is behavioral and neural evidence that loneliness increases attention to potential threats from the social world (Cacioppo & Hawkley, 2009), and is associated with increased worry about being evaluated negatively, as well as with higher levels of perceived threat in social situations (even when there is no increased likelihood of being rejected; Jones, Freemon, & Goswick, 1981). Lonely people also tend to feel more unsafe, compared to non-lonely people. They tend to appraise other individuals as more threatening, and to isolate themselves when confronted with stressors, rather than actively looking for the help and support of others (Berscheid & Reis, 1998; Cacioppo & Hawkley, 2005).
Loneliness involves multiple brain mechanisms, and produces significant changes in both brain structure and function. Recent structural brain imaging studies have identified relationships between loneliness and the density of gray and white matter in different regions of the brain. Loneliness was negatively correlated with the gray matter density in the left posterior superior temporal sulcus (pSTS), an area involved in processing biological motion and social perception (Kanai et al., 2012); the pSTS size was also related to poorer performance in a social perception task. Loneliness has also been associated with widespread reduction in white matter density in areas related to self- and social-cognition (bilateral inferior parietal lobe, the right anterior insula, and left posterior temporo-parietal junction), as well as areas associated with empathy (the left pSTS and right lateral PFC) and self-efficacy (dorsomedial PFC) (Nakagawa et al., 2015). In addition to these structural changes, neuroimaging evidence also reveals functional changes associated with perceived loneliness in a number of brain regions involved in the detection of threat (AMY, ACC, the ventrolateral PFC, and the insula; Cacioppo et al., 2013; Eisenberger & Lieberman, 2005; Eisenberger, Lieberman, & Williams, 2003), and managing the demands of complex social contexts (OFC, mPFC, STS, temporo-parietal junction; Bickart, Hollenbeck, Barrett, & Dickerson, 2012; Cacioppo, Norris, Decety, Monteleone, & Nusbaum, 2009; Cacioppo et al., 2016; Eisenberger & Cole, 2012; Klumpp, Angstadt, & Phan, 2012).
Overall, the evidence reviewed in this section illustrates the essential role of social relationships for our happiness and well-being. Our brains are wired to be social, and this wiring of our brains motivates us to engage in relationships with others, which in turn, increases positive emotions, enjoyment, and feelings of reward. The fact that by simply enhancing social connections in our everyday lives we can enhance our well-being highlights the need to develop interventions focused on educating the social brain.
The brain is a dynamic system, which responds to environmental demands and generates goals and actions, and hence its anatomical structure and physiology continuously change throughout our life. This ability to physically change and to maintain this change is known as neuroplasticity. Neuroplasticity has fascinating implications for our well-being! A growing body of evidence suggests that neuroplasticity is associated with variations in brain activity, which might come from experience (May, 2011), training/learning (Draganski & May, 2008), clinical status (Cahn et al., 2002), or injury (Sidaros et al., 2009). Plasticity allows our brains incredible flexibility, and evidence shows that our experiences can make real changes in the structure and function of our brains. This means that we have the power to change our brains for the better. Although the mechanisms underlying neuroplasticity are still an area of active research, it may describe a result of Hebbian learning, which posits that repeated patterns of neuronal firing lead to increased synaptic connectivity (Hebb, 1949), and suggests that these alterations might lead to changes in brain structure, such as gray matter volume (Boyke, Driemeyer, Gaser, Buchel, & May, 2008; Draganski et al., 2004; Draganski et al., 2006) and white matter connections between brain regions (Tang, Lu, Fan, Yang, & Posner, 2012), as well as brain function (Dayan & Cohen, 2011). Notably, extant evidence points to the possibility that interventions can be performed to improve well-being and the underlying structure and function of the brain (Davidson & McEwen, 2012). This idea has led to new and emerging research targeting different forms of training interventions to improve well-being and the brain. Areas in this domain include physical training and mindfulness training.
Physical activity has been consistently associated with profound benefits for brain structure and function, and overall well-being. Engaging in physical activity might be enjoyable, and is associated with increased positive mood (Steptoe, Kimbell, & Basford, 1998; Yeung, 1996) and decreased negative mood (McIntyre, Watson, & Cunningham, 1990; Raglin & Wilson, 1996; Steptoe, Kearsley, & Walters, 1993). More important than these transient effects on mood are the more enduring effects of physical exercise, reflected in reduced stress and symptoms of anxiety and depression (Blumenthal et al., 2007; Silveira et al., 2013), prevention of the onset of depression (Schuch et al., 2016), and overall improved well-being (Penedo & Dahn, 2005). How is physical activity relieving the detrimental effects of stress and improving mood and well-being? Physical exercise changes the way the brain processes the information, and even more importantly, it changes the brain, having a positive effect on the structure and function of a number of brain regions (Foster, 2015), including the HC and the PFC. Although the exact mechanisms underlying the beneficial effects of physical activity on the human brain and well-being are not clear, extant research attributes these advantageous effects to a number of factors, including changes in neurotransmitters and neurogenesis – that is, the capability to recruit, in some specific areas of the adult brain, more neurons, showing cellular and synaptic plasticity abilities (Garcia-Segura, 2009). We will review these effects in turn below.
A possible neurobiological mechanism underlying these beneficial effects of exercise is the increased synthesis and release of neurotransmitters and neurotrophins, which could result in neurogenesis and neuroplasticity (Dishman et al., 2006). A critical mechanism involved in depression seems to be the depletion of a category of neurotransmitters called monoamines, including serotonin and norepinephrine (López-Muñoz & Alamo, 2009). Animal models provide evidence that, similar to the effects of antidepressants, regular aerobic exercise increases serotonergic and noradrenergic levels in the brain (Chaouloff, 1989; de Coverley Veale, 1987; Meeusen & De Meirleir, 1995; Van Praag, 1982). For instance, treadmill training and wheel running have been linked to increased levels of norepinephrine in the HC and frontal cortex in rodents (Dishman, 1997; Dunn, Reigle, Youngstedt, Armstrong, & Dishman, 1996). Exercise has also been associated with increases in the synthesis of serotonin (Chaouloff, 1997; Dunn & Dishman, 1991; Meeusen & De Meirleir, 1995; Wilson & Marsden, 1996).
Accumulating evidence shows that physical exercise has a strong influence on inducing neuroplasticity (Voss et al., 2013), increasing the levels of the brain derived neurotrophic factor (BDNF). Research in rodents has shown that 20 days of voluntary wheel running increased BDNF levels in the HC and caudal neocortex (Meeusen & De Meirleir, 1995; Russo-Neustadt, Beard, & Cotman, 1999). The increase in BDNF increases functioning in the serotonergic system and may promote neuronal growth. Research in humans confirms the beneficial effects of physical exercise on neurogenesis. A 3‑month fitness training (Pereira et al., 2007) study in a small group of middle-aged participants showed increases in measures of cerebral blood volume (CBV) in a region of the HC (i.e., the dentate gyrus). The location of the CBV changes in the dentate gyrus is interesting, given that previous animal evidence has shown neurogenesis in these very specific regions.
Physical activity interventions have also been shown to have positive effects in children, producing changes in brain structure and function. Research shows that more active children have greater HC volume (Chaddock, Erickson, Prakash, Kim, et al., 2010), along with greater basal ganglia volume (Chaddock, Erickson, Prakash, VanPatter, et al., 2010), and greater white matter integrity (Schaeffer et al., 2014). In addition, they show increased and more efficient patterns of brain activity (Voss, Nagamatsu, Liu-Ambrose, & Kramer, 2011), and superior executive and cognitive control performance (Chaddock et al., 2012; Davis et al., 2011; Hillman et al., 2014).
Most studies on the effects of exercise in humans have been done in the elderly. These studies have demonstrated that exercise improves older adults’ cognitive abilities in various domains (and the associated neural mechanisms), or more directly, subjective well-being (reviewed in Bamidis et al., 2014; Gajewski & Falkenstein, 2016; Hillman, Erickson, & Kramer, 2008; Kramer & Erickson, 2007). Greater physical activity and cardiorespiratory fitness have been associated with reduced age-related cognitive declines and a lower risk for dementia in older adults (Buchman et al., 2012; Sofi et al., 2011). Furthermore, mounting evidence suggests that aerobic exercise interventions, such as those involving walking or dancing, lead to enhanced cognitive abilities and subjective well-being in older adults (Awick et al., 2017; Awick et al., 2015; Colcombe & Kramer, 2003; Kattenstroth, Kalisch, Holt, Tegenthoff, & Dinse, 2013; Netz, Wu, Becker, & Tenenbaum, 2005). Recent brain imaging studies in this population have shown that physical exercise induces both structural and functional changes in a variety of brain regions. For instance, weekly aerobic walking was associated with increased volume of the anterior HC in older adults, which was in turn positively associated with improvements in spatial memory performance due to exercise (Erickson et al., 2011). Similar beneficial effects of exercise have also been identified with respect to neural functioning. In particular, older adults who had undergone aerobic training for six months showed increased activity in the lateral PFC and parietal cortex and decreased activity in the ACC during a challenging task involving conflict resolution and selective attention; these participants also showed improvements in behavioral performance on the task (Colcombe et al., 2004). Overall, these findings suggest that exercise interventions can enhance the ability of the aging brain to effectively allocate neural resources, which in turn leads to improved performance on a cognitively challenging task (see also Voss et al., 2011).
In summary, exercise has beneficial effects by increasing the production of neurotransmitters such as serotonin and noradrenaline, by stimulating the growth factor and blood flow, and by stimulating adult neurogenesis.
In recent years, there has been growing interest in the use of mindfulness-based training and intervention programs to improve well-being and to identify the underlying brain mechanisms associated with such improvements (Chiesa & Serretti, 2010; Fox et al., 2014; Gotink, Meijboom, Vernooij, Smits, & Hunink, 2016; Lomas, Ivtzan, & Fu, 2015). Mindfulness generally refers to “the awareness that emerges through paying attention on purpose, in the present moment, and non-judgmentally to the unfolding of experience moment by moment” (Kabat-Zinn, 2003). Training programs focusing on mindfulness typically target skills aimed at reducing distressing symptoms, including those associated with psychological disorders and somatic conditions.
Behaviorally, mindfulness training tends to mediate improved indices of well-being, such as decreased stress, rumination, and trait anxiety, as well as increased positive states of mind, psychological well-being, quality of life, and self-compassion (Branstrom, Kvillemo, Brandberg, & Moskowitz, 2010; Carmody & Baer, 2008; Nyklicek & Kuijpers, 2008; Shapiro, Brown, & Biegel, 2007; reviewed in Visted, Vollestad, Nielsen, & Nielsen, 2015). Notably, mindfulness training has been targeted in many different populations. For example, mindfulness interventions have been tested in healthy and clinical populations (Chiesa & Serretti, 2010; Fox et al., 2014; Gotink et al., 2016; Lomas et al., 2015), including groups with psychological conditions such as anxiety (Vollestad, Nielsen, & Nielsen, 2012; Vollestad, Sivertsen, & Nielsen, 2011) and depression (Jain, Walsh, Eisendrath, Christensen, & Cahn, 2015; Kuyken et al., 2016; van der Velden et al., 2015). Mindfulness-based interventions have also been tested in different age groups, such as children and adolescents (Kallapiran, Koo, Kirubakaran, & Hancock, 2015), young and middle-aged adults (Fox et al., 2014), and older adults (Ehrenbrusthoff, Ryan, Schofield, & Martin, 2012). Although there is much heterogeneity of the practices and goals of mindfulness interventions when applied to these various groups, available evidence generally points to improvement in intervention outcomes (Quaglia, Braun, Freeman, McDaniel, & Brown, 2016; Visted et al., 2015).
At a neural level, mindfulness interventions have been shown to induce changes in both the structure and function of the brain. Structural studies have associated mindfulness training with increased gray matter volume or density in the cingulate cortex (Hölzel et al., 2011), insula (Murakami et al., 2012), and HC (Hölzel et al., 2011). Evidence for structural changes in the AMY seems a bit less clear (Hölzel et al., 2010; Murakami et al., 2012; Pickut et al., 2013), and is perhaps influenced by other factors such as sample demographics. However, overall, available evidence appears consistent with the idea that mindfulness engages brain regions that are key for body awareness, memory, and emotion (Fox et al., 2014). Mindfulness and meditation practice have also been associated with modulation of structural connectivity between brain regions. For example, meditation practice has been linked to changes suggesting enhanced connectivity within and between hemispheres of the brain (Luders et al., 2012; Tang et al., 2010), which is consistent with the idea that mindfulness and meditation training can improve the underlying connections in the brain that support attentional self-regulation processes (Luders et al., 2012). At the functional level, mindfulness training was associated with changes in the resting state functional connectivity between the so-called default mode and salience networks (Doll, Hölzel, Boucard, Wohlschläger, & Sorg, 2015), consistent with the idea that interactions between brain networks support the ability to attend to current experience without judgment. Moreover, mindfulness training appears to also influence brain responses when engaged in tasks, such as paradigms that involve responding to, or regulating response to, emotional stimuli. A study by Creswell and colleagues (2007), for example, showed that when labeling emotions, the AMY was negatively associated with lateral PFC activation in more mindful individuals. This evidence seems consistent with the idea that mindfulness training modulates the neural mechanisms involved in emotional processes.
Although how mindfulness interventions alter brain structure is still an open question that is being investigated, a number of possible mechanisms have been proposed (reviewed in Tang, Hölzel, & Posner, 2015). An account referred to as “use-dependent plasticity” (Bütefisch et al., 2000; Kleim et al., 2002; Kleim, Barbay, & Nudo, 1998; Nudo, Milliken, Jenkins, & Merzenich, 1996; for reviews of relevant studies in humans see Draganski & May, 2008; May, 2011) suggests that there is a relation between the structure of the brain and its level of use. This account may describe a result of Hebbian learning as mentioned above (Hebb, 1949), and suggests that these structural changes might lead to alteration in brain structure (Draganski et al., 2006). Consistent with the Hebbian learning model, it is possible that repeated engagement in mindfulness affects brain structure through expansions of existing synapses and dendrites, or creation of new synapses. It is also possible that engaging in mindfulness induces the formation of new neurons or myelin sheaths that help insulate the connections between neurons. Another possibility is that mindfulness influences autonomic and immune activity, which might help preserve or restore neurons. Alterations in these mechanisms at the level of brain cells might contribute to overall changes in the volume of brain regions and the integrity of the connections between them.
In sum, extant evidence on mindfulness training and the brain suggests that mindfulness interventions can enhance well-being, and modulate the structure and function of the brain. Available evidence highlights the benefits of mindfulness training, and further research in this area will continue to refine understanding of the effective practices and underlying neural mechanisms, in order to better promote well-being.
Taken together, the diverse body of neuroscientific evidence reviewed in this chapter reveals that factors known to play critical roles in well-being, such as personality traits, age, and social relationships, are influenced by subtle differences in the volume, function, and connectivity of a network of emotion- and reward-related regions. Our mind is highly plastic, and what we think, feel, and do changes the physical structure of the brain, and the function of different brain circuits. Given that the brain circuits supporting positive emotions and rewards are remarkably malleable, we have the amazing ability to reshape our brains, to change our affective mindsets, and to improve our well-being. One way to enhance well-being is to address the rising tide of chronic stress, anxiety, and depression, conditions that have negative impacts on the life and well-being of an increasing number of people worldwide. Another way is to engage in activities that have positive impacts on our minds and bodies. Being physically active and engaging in mindfulness meditation are known to produce many physiological changes in our body, but they can also produce striking changes in the structure, function, and communication between the brain regions supporting well-being, helping us to lead happier and healthier lives. This suggests that well-being might be enhanced through training. Future research is needed to tap further into the enormous potential benefits of these training activities and to determine whether such training can produce changes with long-lasting consequences.
1It is important to consider the distinction between cortices and gyri when discussing neural correlates. Anatomically, cortex usually refers to gray matter in particular, while gyrus might refer to both gray and white matter. Depending on the goal of a study, the focus might be specifically on the gray matter of a particular region, in which case it is referred to as cortex (e.g., inferior frontal cortex, IFC), or the focus might be on a region of interest that does not distinguish between gray and white matter, and thus it is referred to as gyrus (e.g., inferior frontal gyrus, IFG). Hence, in the text and Figure 1 both cortical and gyral terms appear.
2Current brain imaging techniques allow noninvasive mapping of the anatomical and functional connections among distant brain regions. Structural connectivity refers to the “integrity” of the anatomical connections linking sets of neuronal elements. These connections refer to the white matter fibers linking different areas of the brain, and are often measured using a special magnetic resonance imaging approach called diffusion tensor imaging, or DTI. Functional connectivity evaluates the interconnections between spatially separated brain regions that occur at rest (resting state functional connectivity), or when a subject is performing an explicit task.
Adelstein, J. S., Shehzad, Z., Mennes, M., DeYoung, C. G., Zuo, X. N., Kelly, C., Margulies, D. S., Bloomfield, A., Gray, J. R., Castellanos, F. X., & Milham, M. P. (2011). Personality is reflected in the brain's intrinsic functional architecture. PLoS ONE, 6(11). doi: 10.1371/journal.pone.0027633
Adolphs, R. (2008). Fear, faces, and the human amygdala. Current Opinion in Neurobiology, 18(2), 166-172. doi: 10.1016/j.conb.2008.06.006
Adolphs, R. (2009). The social brain: Neural basis of social knowledge. Annual Review of Psychology, 60, 693-716. doi: 10.1146/annurev.psych.60.110707.163514
Alemany, S., Mas, A., Goldberg, X., Falcon, C., Fatjo-Vilas, M., Arias, B., Bargallo, N., Nenadic, I., Gasto, C., & Fananas, L. (2013). Regional gray matter reductions are associated with genetic liability for anxiety and depression: An MRI twin study. Journal of Affective Disorders, 149(1-3), 175-181. doi: 10.1016/j.jad.2013.01.019
Andersson, G. (1996). The benefits of optimism: A meta-analytic review of the Life Orientation Test. Personality and Individual Differences, 21(5), 719-725. doi: 10.1016/0191-8869(96)00118-3
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(4), 550-562. doi: 10.1016/j.neuron.2010.02.005
Aristotle. (2009). The nicomachean ethics. Oxford, UK: Oxford University Press.
Arnone, D., McIntosh, A. M., Ebmeier, K. P., Munafo, M. R., & Anderson, I. M. (2012). Magnetic resonance imaging studies in unipolar depression: Systematic review and meta-regression analyses. European Neuropsychopharmacology, 22(1), 1-16. doi: 10.1016/j.euroneuro.2011.05.003
Awick, E. A., Ehlers, D. K., Aguiñaga, S., Daugherty, A. M., Kramer, A. F., & McAuley, E. (2017). Effects of a randomized exercise trial on physical activity, psychological distress and quality of life in older adults. General Hospital Psychiatry, 49. doi: 10.1016/j.genhosppsych.2017.06.005
Awick, E. A., Wójcicki, T. R., Olson, E. A., Fanning, J., Chung, H. D., Zuniga, K., Mackenzie, M., Kramer, A. F., & McAuley, E. (2015). Differential exercise effects on quality of life and health-related quality of life in older adults: A randomized controlled trial. Quality of Life Research, 24(2), 455-462. doi: 10.1007/s11136-014-0762-0
Bamidis, P. D., Vivas, A. B., Styliadis, C., Frantzidis, C., Klados, M., Schlee, W., Siountas, A., & Papageorgiou, S. G. (2014). A review of physical and cognitive interventions in aging. Neuroscience & Biobehavioral Reviews, 44, 206-220. doi: 10.1016/j.neubiorev.2014.03.019
Bar-Haim, Y., Lamy, D., Pergamin, L., Bakermans-Kranenburg, M. J., & van IJzendoorn, M. H. (2007). Threat-related attentional bias in anxious and nonanxious individuals: A meta-analytic study. Psychological Bulletin, 133(1), 1-24. doi: 10.1037/0033-2909.133.1.1
Bartels, A., & Zeki, S. (2000). The neural basis of romantic love. NeuroReport, 11(17), 3829 – 3834.
Bartels, A., & Zeki, S. (2004). The neural correlates of maternal and romantic love. NeuroImage, 21(3), 1155-1166. doi: 10.1016/j.neuroimage.2003.11.003
Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117(3), 497-529. doi: 10.1037/0033-2909.117.3.497
Berridge, K. C. (2009). Wanting and liking: Observations from the neuroscience and psychology laboratory. Inquiry, 52(4), 378. doi: 10.1080/00201740903087359
Berridge, K. C., & Kringelbach, M. L. (2015). Pleasure systems in the brain. Neuron, 86(3), 646-664. doi: 10.1016/j.neuron.2015.02.018
Berscheid, E., & Reis, H. T. (1998). Attraction and close relationships. In D. Gilbert, S. Fiske & G. Lindzey (Eds.), The handbook of social psychology. New York: McGraw-Hill.
Bianchi-Demicheli, F., Grafton, S. T., & Ortigue, S. (2006). The power of love on the human brain. Social Neuroscience, 1(2), 90-103. doi: 10.1080/17470910600976547
Bickart, K. C., Hollenbeck, M. C., Barrett, L. F., & Dickerson, B. C. (2012). Intrinsic amygdala-cortical functional connectivity predicts social network size in humans. The Journal of Neuroscience, 32(42), 14729-14741. doi: 10.1523/JNEUROSCI.1599-12.2012
Blumenthal, J. A., Babyak, M. A., Doraiswamy, P. M., Watkins, L., Hoffman, B. M., Barbour, K. A., Herman, S., Craighead, W. E., Brosse, A. L., Waugh, R., Hinderliter, A., & Sherwood, A. (2007). Exercise and pharmacotherapy in the treatment of major depressive disorder. Psychosomatic Medicine, 69(7), 587-596. doi: 10.1097/PSY.0b013e318148c19a
Bonne, O., Vythilingam, M., Inagaki, M., Wood, S., Neumeister, A., Nugent, A. C., Snow, J., Luckenbaugh, D. A., Bain, E. E., Drevets, W. C., & Charney, D. S. (2008). Reduced posterior hippocampal volume in posttraumatic stress disorder. The Journal of Clinical Psychiatry, 69(7), 1087-1091.
Booth, R. (2000). Loneliness as a component of psychiatric disorders. Medscape General Medicine, 2, 1-7.
Boyke, J., Driemeyer, J., Gaser, C., Buchel, C., & May, A. (2008). Training-induced brain structure changes in the elderly. The Journal of Neuroscience, 28(28), 7031-7035. doi: 10.1523/JNEUROSCI.0742-08.2008
Bradley, B. P., & Mogg, K. (1994). Mood and personality in recall of positive and negative information. Behaviour Research and Therapy, 32(1), 137-141. doi: 10.1016/0005-7967(94)90095-7
Branstrom, R., Kvillemo, P., Brandberg, Y., & Moskowitz, J. T. (2010). Self-report mindfulness as a mediator of psychological well-being in a stress reduction intervention for cancer patients—a randomized study. Annals of Behavioral Medicine, 39(2), 151-161. doi: 10.1007/s12160-010-9168-6
Bremner, J. D. (2006). Traumatic stress: Effects on the brain. Dialogues in Clinical Neuroscience, 8(4), 445-461.
Brown, M. R. G., Benoit, J. P. A., Juhas, M., Dametto, E., Tse, T. T., MacKay, M., Sen, B., Carroll, A. M., Hodlevskyy, O., Silverstone, P. H., Dolcos, F., Dursun, S. M., & Greenshaw, A. J. (2015). fMRI investigation of response inhibition, emotion, impulsivity, and clinical high-risk behavior in adolescents. Frontiers in Systems Neuroscience, 9. doi: 10.3389/fnsys.2015.00124
Brown, M. R. G., Lebel, R. M., Dolcos, F., Wilman, A. H., Silverstone, P. H., Pazderka, H., Fujiwara, E., Wild, T. C., Carroll, A. M., Hodlevskyy, O., Zedkova, L., Zwaigenbaum, L., Thompson, A. H., Greenshaw, A. J., & Dursun, S. M. (2012). Effects of emotional context on impulse control. NeuroImage, 63(1), 434-446. doi: 10.1016/j.neuroimage.2012.06.056
Buchman, A. S., Boyle, P. A., Yu, L., Shah, R. C., Wilson, R. S., & Bennett, D. A. (2012). Total daily physical activity and the risk of AD and cognitive decline in older adults. Neurology, 78(17), 1323-1329. doi: 10.1212/WNL.0b013e3182535d35
Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Sciences, 4(6), 215-222. doi: 10.1016/S1364-6613(00)01483-2
Bütefisch, C. M., Davis, B. C., Wise, S. P., Sawaki, L., Kopylev, L., Classen, J., & Cohen, L. G. (2000). Mechanisms of use-dependent plasticity in the human motor cortex. Proceedings of the National Academy of Sciences of the United States of America, 97(7), 3661-3665. doi: 10.1073/pnas.050350297
Cacioppo, J. T., Berntson, G. G., Bechara, A., Tranel, D., & Hawkley, L. C. (2011). Could an aging brain contribute to subjective well-being? The value added by a social neuroscience perspective. In A. Todorov, S. Fiske & D. Prentice (Eds.), Social neuroscience: Toward understanding the underpinnings of the social mind (pp. 249-262). Oxford, New York: Oxford University Press.
Cacioppo, J. T., & Hawkley, L. C. (2005). People thinking about people: The vicious cycle of being a social outcast in one’s own mind. In K. D. Williams, J. P. Forgas & W. von Hippel (Eds.), The social outcast: Ostracism, social exclusion, rejection, and bullying. New York: Psychology Press.
Cacioppo, J. T., & Hawkley, L. C. (2009). Perceived social isolation and cognition. Trends in Cognitive Sciences, 13(10), 447-454. doi: 10.1016/j.tics.2009.06.005
Cacioppo, J. T., Hawkley, L. C., & Thisted, R. A. (2010). Perceived social isolation makes me sad: Five year cross-lagged analyses of loneliness and depressive symptomatology in the chicago health, aging, and social relations study. Psychology and Aging, 25(2), 453-463. doi: 10.1037/a0017216
Cacioppo, J. T., Hughes, M. E., Waite, L. J., Hawkley, L. C., & Thisted, R. A. (2006). Loneliness as a specific risk factor for depressive symptoms: Cross-sectional and longitudinal analyses. Psychology and Aging, 21(1), 140-151. doi: 10.1037/0882-79184.108.40.206
Cacioppo, J. T., Norris, C. J., Decety, J., Monteleone, G., & Nusbaum, H. (2009). In the eye of the beholder: Individual differences in perceived social isolation predict regional brain activation to social stimuli. Journal of Cognitive Neuroscience, 21(1), 83-92. doi: 10.1162/jocn.2009.21007
Cacioppo, J. T., & Patrick, B. (2008). Loneliness: Human nature and the need for social connection. New York, NY: W. W. Norton & Company.
Cacioppo, S., Bangee, M., Balogh, S., Cardenas-Iniguez, C., Qualter, P., & Cacioppo, J. T. (2016). Loneliness and implicit attention to social threat: A high-performance electrical neuroimaging study. Cognitive Neuroscience, 7(1-4), 138-159. doi: 10.1080/17588928.2015.1070136
Cacioppo, S., Frum, C., Asp, E., Weiss, R. M., Lewis, J. W., & Cacioppo, J. T. (2013). A quantitative meta-analysis of functional imaging studies of social rejection. Scientific Reports 3:2027, 1-3. doi: 10.1038/srep02027
Cahn, W., Pol, H. E. H., Lems, E. B. T. E., van Haren, N. E. M., Schnack, H. G., van der Linden, J. A., Schothorst, P. F., van Engeland, H., & Kahn, R. S. (2002). Brain volume changes in first-episode schizophrenia: A 1-year follow-up study. Archives of General Psychiatry, 59(11), 1002-1010. doi: 10.1001/archpsyc.59.11.1002
Carmody, J., & Baer, R. A. (2008). Relationships between mindfulness practice and levels of mindfulness, medical and psychological symptoms and well-being in a mindfulness-based stress reduction program. Journal of Behavioral Medicine, 31(1), 23-33. doi: 10.1007/s10865-007-9130-7
Carstensen, L. L., Fung, H. H., & Charles, S. T. (2003). Socioemotional selectivity theory and the regulation of emotion in the second half of life. Motivation and Emotion, 27, 103-123. doi: 10.1023/A:1024569803230
Carstensen, L. L., Turan, B., Scheibe, S., Ram, N., Ersner-Hershfield, H., Samanez-Larkin, G. R., Brooks, K. P., & Nesselroade, J. R. (2011). Emotional experience improves with age: Evidence based on over 10 years of experience sampling. Psychology and Aging, 26(1), 21-33. doi: 10.1037/a0021285
Carver, C. S., Scheier, M. F., & Segerstrom, S. C. (2010). Optimism. Clinical Psychology Review, 30(7), 879-889. doi: 10.1016/J.Cpr.2010.01.006
Chaddock, L., Erickson, K. I., Prakash, R. S., Kim, J. S., Voss, M. W., VanPatter, M., Pontifex, M. B., Raine, L. B., Konkel, A., Hillman, C. H., Cohen, N. J., & Kramer, A. F. (2010). A neuroimaging investigation of the association between aerobic fitness, hippocampal volume, and memory performance in preadolescent children. Brain Research, 1358, 172-183. doi: 10.1016/j.brainres.2010.08.049
Chaddock, L., Erickson, K. I., Prakash, R. S., VanPatter, M., Voss, M. W., Pontifex, M. B., Raine, L. B., Hillman, C. H., & Kramer, A. F. (2010). Basal ganglia volume is associated with aerobic fitness in preadolescent children. Developmental Neuroscience, 32(3), 249-256. doi: 10.1159/000316648
Chaddock, L., Hillman, C. H., Pontifex, M. B., Johnson, C. R., Raine, L. B., & Kramer, A. F. (2012). Childhood aerobic fitness predicts cognitive performance one year later. Journal of Sports Sciences, 30(5), 421-430. doi: 10.1080/02640414.2011.647706
Chaouloff, F. (1989). Physical exercise and brain monoamines: A review. Acta Physiologica Scandinavica, 137(1), 1-13. doi: 10.1111/j.1748-1716.1989.tb08715.x
Chaouloff, F. (1997). Effects of acute physical exercise on central serotonergic systems. Acta Physiologica Scandinavica, 137(1), 1-13.
Charles, S. T., Mather, M., & Carstensen, L. L. (2003). Aging and emotional memory: The forgettable nature of negative images for older adults. Journal of Experimental Psychology: General, 132(2), 310-324. doi: 10.1037/0096-34220.127.116.110
Chavez, R. S., & Heatherton, T. F. (2015). Multimodal frontostriatal connectivity underlies individual differences in self-esteem. Social Cognitive and Affective Neuroscience, 10(3), 364-370. doi: 10.1093/scan/nsu063
Chiesa, A., & Serretti, A. (2010). A systematic review of neurobiological and clinical features of mindfulness meditations. Psychological Medicine, 40(8), 1239-1252. doi: 10.1017/S0033291709991747
Cisler, J. M., Bacon, A. K., & Williams, N. L. (2009). Phenomenological characteristics of attentional biases towards threat: A critical review. Cognitive Therapy and Research, 33(2), 221-234. doi: 10.1007/s10608-007-9161-y
Colcombe, S., & Kramer, A. F. (2003). Fitness effects on the cognitive function of older adults. Psychological Science, 14(2), 125-130. doi: 10.1111/1467-9280.t01-1-01430
Colcombe, S. J., Kramer, A. F., Erickson, K. I., Scalf, P., McAuley, E., Cohen, N. J., Webb, A., Jerome, G. J., Marquez, D. X., & Elavsky, S. (2004). Cardiovascular fitness, cortical plasticity, and aging. Proceedings of the National Academy of Sciences of the United States of America, 101(9), 3316-3321. doi: 10.1073/pnas.0400266101
Costa, P. T., & McCrae, R. R. (1992). Revised NEO personality inventory and NEO five factor inventory: Professional manual. Odessa, FL: Psychological Assessment.
Creswell, J. D., Way, B. M., Eisenberger, N. I., & Lieberman, M. D. (2007). Neural correlates of dispositional mindfulness during affect labeling. Psychosomatic Medicine, 69(6), 560-565. doi: 10.1097/PSY.0b013e3180f6171f
Cullen, K. R., Klimes-Dougan, B., Muetzel, R., Mueller, B. A., Camchong, J., Houri, A., Kurma, S., & Lim, K. O. (2010). Altered white matter microstructure in adolescents with major depression: A preliminary study. Journal of the American Academy of Child and Adolescent Psychiatry, 49(2), 173-183 e171. doi: 10.1016/j.jaac.2009.11.005
Davidson, R. J. (2004). What does the prefrontal cortex “do” in affect: Perspectives on frontal EEG asymmetry research. Biological Psychology, 67(1), 219-234. doi: 10.1016/j.biopsycho.2004.03.008
Davidson, R. J., & Irwin, W. (1999). The functional neuroanatomy of emotion and affective style. Trends in Cognitive Sciences, 3(1), 11-21. doi: 10.1016/S1364-6613(98)01265-0
Davidson, R. J., & McEwen, B. S. (2012). Social influences on neuroplasticity: Stress and interventions to promote well-being. Nature Neuroscience, 15(5), 689-695. doi: 10.1038/nn.3093
Davis, C. L., Tomporowski, P. D., McDowell, J. E., Austin, B. P., Miller, P. H., Yanasak, N. E., Allison, J. D., & Naglieri, J. A. (2011). Exercise improves executive function and achievement and alters brain activation in overweight children: A randomized controlled trial. Health Psychology, 30(1), 91-98. doi: 10.1037/a0021766
Dayan, E., & Cohen, L. G. (2011). Neuroplasticity subserving motor skill learning. Neuron, 72(3), 443-454. doi: 10.1016/j.neuron.2011.10.008
de Coverley Veale, D. M. W. (1987). Exercise and mental health. Acta Psychiatrica Scandinavica, 76(2), 113-120. doi: 10.1111/j.1600-0447.1987.tb02872.x
de Kwaasteniet, B., Ruhe, E., Caan, M., Rive, M., Olabarriaga, S., Groefsema, M., Heesink, L., van Wingen, G., & Denys, D. (2013). Relation between structural and functional connectivity in major depressive disorder. Biological Psychiatry, 74(1), 40-47. doi: 10.1016/j.biopsych.2012.12.024
DeNeve, K. M., & Cooper, H. (1998). The happy personality: A meta-analysis of 137 personality traits and subjective well-being. Psychological Bulletin, 124(2), 197-229. doi: 10.1037/0033-2909.124.2.197
Denkova, E., Dolcos, S., & Dolcos, F. (2012). Reliving emotional personal memories: Affective biases linked to personality and sex-related differences. Emotion, 12(3), 515-528. doi: 10.1037/a0026809
DeYoung, C. G., Hirsh, J. B., Shane, M. S., Papademetris, X., Rajeevan, N., & Gray, J. R. (2010). Testing predictions from personality neuroscience: Brain structure and the big five. Psychological Science, 21(6), 820-828. doi: 10.1177/0956797610370159
Diener, E. (2009). Subjective well-being. The science of well-being (pp. 11-58). Springer.
Diener, E., Gohm, C. L., Suh, E., & Oishi, S. (2000). Similarity of the relations between marital status and subjective well-being across cultures. Journal of Cross-Cultural Psychology, 31(4), 419-436. doi: 10.1177/0022022100031004001
Diener, E., & Oishi, S. (2000). Money and happiness: Income and subjective well-being across nations. In E. Diener & E. M. Suh (Eds.), Culture and subjective well-being (pp. 185-218). Cambridge, MA: MIT Press.
Dishman, R. K. (1997). Brain monoamines, exercise, and behavioral stress: Animal models. Medicine and Science in Sports and Exercise, 29(1), 63-74. doi: 10.1097/00005768-199701000-00010
Dishman, R. K., Berthoud, H.-R., Booth, F. W., Cotman, C. W., Edgerton, V. R., Fleshner, M. R., Gandevia, S. C., Gomez-Pinilla, F., Greenwood, B. N., Hillman, C. H., Kramer, A. F., Levin, B. E., Moran, T. H., Russo-Neustadt, A. A., Salamone, J. D., van Hoomissen, J. D., Wade, C. E., York, D. A., & Zigmond, M. J. (2006). Neurobiology of exercise. Obesity, 14(3), 345-356. doi: 10.1038/oby.2006.46
Dohrenwend, B. S., & Dohrenwend, B. P. (1974). Stressful life events: Their nature and effect. New York: Wiley.
Dolcos, F., Iordan, A. D., & Dolcos, S. (2011). Neural correlates of emotion-cognition interactions: A review of evidence from brain imaging investigations. Journal of Cognitive Psychology, 23(6), 669-694. doi: 10.1080/20445911.2011.594433
Dolcos, S., Hu, Y., Iordan, A. D., Moore, M., & Dolcos, F. (2016). Optimism and the brain: Trait optimism mediates the protective role of the orbitofrontal cortex gray matter volume against anxiety. Social Cognitive and Affective Neuroscience, 11(2), 263-271. doi: 10.1093/scan/nsv106
Dolcos, S., Katsumi, Y., & Dixon, R. A. (2014). The role of arousal in the spontaneous regulation of emotions in healthy aging: A fMRI investigation. Frontiers in Psychology, 5, 681. doi: 10.3389/fpsyg.2014.00681
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. doi: 10.3389/fnhum.2015.00461
Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., & May, A. (2004). Neuroplasticity: Changes in grey matter induced by training. Nature, 427(6972), 311-312. doi: 10.1038/427311a
Draganski, B., Gaser, C., Kempermann, G., Kuhn, H. G., Winkler, J., Buchel, C., & May, A. (2006). Temporal and spatial dynamics of brain structure changes during extensive learning. The Journal of Neuroscience, 26(23), 6314-6317. doi: 10.1523/JNEUROSCI.4628-05.2006
Draganski, B., & May, A. (2008). Training-induced structural changes in the adult human brain. Behavioural Brain Research, 192(1), 137-142. doi: 10.1016/j.bbr.2008.02.015
Dunn, A. L., & Dishman, R. K. (1991). Exercise and the neurobiology of depression. Exercise and Sport Sciences Reviews, 19, 41-98.
Dunn, A. L., Reigle, T. G., Youngstedt, S. D., Armstrong, R. B., & Dishman, R. K. (1996). Brain norepinephrine and metabolites after treadmill training and wheel running in rats. Medicine and Science in Sports and Exercise, 28(2), 204-209. doi: 10.1097/00005768-199602000-00008
Eddington, K. M., Dolcos, F., Cabeza, R., & Strauman, T. J. (2007). Neural correlates of promotion and prevention goal activation: An fMRI study using an idiographic approach. Journal of Cognitive Neuroscience, 19(7), 1152-1162 doi: 10.1162/jocn.2007.19.7.1152
Eddington, K. M., Dolcos, F., McLean, A. N., Krishnan, K. R., Cabeza, R., & Strauman, T. J. (2009). Neural correlates of idiographic goal priming in depression: Goal-specific dysfunctions in the orbitofrontal cortex. Social Cognitive and Affective Neuroscience, 4(3), 238-246. doi: 10.1093/scan/nsp016
Ehrenbrusthoff, K., Ryan, C. G., Schofield, P. A., & Martin, D. J. (2012). Physical therapy management of older adults with chronic low back pain: A systematic review. Journal of Pain Management, 5(4), 317.
Eisenberger, N. I., & Cole, S. W. (2012). Social neuroscience and health: Neurophysiological mechanisms linking social ties with physical health. Nature Neuroscience, 15(5), 669-674. doi: 10.1038/nn.3086
Eisenberger, N. I., & Lieberman, M. D. (2005). Why it hurts to be left out: The neurocognitive overlap between physical and social pain. In K. D. Williams, J. P. Forgas & W. von Hippel (Eds.), Sydney symposium of social psychology series. The social outcast: Ostracism, social exclusion, rejection, and bullying (pp. 109-127).
Eisenberger, N. I., Lieberman, M. D., & Williams, K. D. (2003). Does rejection hurt? An fMRI study of social exclusion. Science, 302(5643), 290-292. doi: 10.1126/science.1089134
Erickson, K., Drevets, W. C., Clark, L., Cannon, D. M., Bain, E. E., Zarate, C. A., Charney, D. S., & Sahakian, B. J. (2005). Mood-congruent bias in affective go/no-go performance of unmedicated patients with major depressive disorder. American Journal of Psychiatry, 162(11), 2171-2173. doi: 10.1176/appi.ajp.162.11.2171
Erickson, K. I., Voss, M. W., Prakash, R. S., Basak, C., Szabo, A., Chaddock, L., Kim, J. S., Heo, S., Alves, H., & White, S. M. (2011). Exercise training increases size of hippocampus and improves memory. Proceedings of the National Academy of Sciences of the United States of America, 108(7), 3017-3022. doi: 10.1073/pnas.1015950108
Etkin, A., & Wager, T. D. (2007). Functional neuroimaging of anxiety: A meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia. The American Journal of Psychiatry, 164(10), 1476-1488. doi: 10.1176/appi.ajp.2007.07030504
Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: Attentional control theory. Emotion, 7(2), 336-353. doi: 10.1037/1528-3518.104.22.1686
Fabiani, M. (2012). It was the best of times, it was the worst of times: A psychophysiologist's view of cognitive aging. Psychophysiology, 49(3), 283-304. doi: 10.1111/j.1469-8986.2011.01331.x
Fales, C. L., Becerril, K. E., Luking, K. R., & Barch, D. M. (2010). Emotional-stimulus processing in trait anxiety is modulated by stimulus valence during neuroimaging of a working-memory task. Cognition & Emotion, 24(2), 200-222. doi: 10.1080/02699930903384691
Fisher, H., Aron, A., & Brown, L. L. (2005). Romantic love: An fMRI study of a neural mechanism for mate choice. The Journal of Comparative Neurology, 493(1), 58-62. doi: 10.1002/cne.20772
Fisler, M. S., Federspiel, A., Horn, H., Dierks, T., Schmitt, W., Wiest, R., de Quervain, D. J. F., & Soravia, L. M. (2013). Spider phobia is associated with decreased left amygdala volume: A cross-sectional study. BMC Psychiatry, 13. doi: 10.1186/1471-244x-13-70
Fjell, A. M., Westlye, L. T., Amlien, I., Espeseth, T., Reinvang, I., Raz, N., Agartz, I., Salat, D. H., Greve, D. N., Fischl, B., Dale, A. M., & Walhovd, K. B. (2009). High consistency of regional cortical thinning in aging across multiple samples. Cerebral Cortex, 19(9), 2001-2012. doi: 10.1093/cercor/bhn232
Foster, P. P. (2015). Role of physical and mental training in brain network configuration. Frontiers in Aging Neuroscience, 7, 117. doi: 10.3389/fnagi.2015.00117
Fox, E. (2013). Rainy brain, sunny brain: How to retrain your brain to overcome pessimism and achieve a more positive outlook. New York, NY: Basic Books.
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 and Biobehavioral Reviews, 43, 48-73. doi: 10.1016/j.neubiorev.2014.03.016
Gajewski, P. D., & Falkenstein, M. (2016). Physical activity and neurocognitive functioning in aging - a condensed updated review. European Review of Aging and Physical Activity, 13, 1. doi: 10.1186/s11556-016-0161-3
Garcia-Segura, L. M. (2009). Hormones and brain plasticity. New York: Oxford University Press.
Gotink, R. A., Meijboom, R., Vernooij, M. W., Smits, M., & Hunink, M. G. M. (2016). 8-week mindfulness based stress reduction induces brain changes similar to traditional long-term meditation practice—a systematic review. Brain and Cognition, 108, 32-41. doi: 10.1016/j.bandc.2016.07.001
Gotlib, I. H., Krasnoperova, E., Yue, D. N., & Joormann, J. (2004). Attentional biases for negative interpersonal stimuli in clinical depression. Journal of Abnormal Psychology, 113(1), 121-135. doi: 10.1037/0021-843X.113.1.121
Grabenhorst, F., & Rolls, E. T. (2011). Value, pleasure and choice in the ventral prefrontal cortex. Trends in Cognitive Sciences, 15(2), 56-67. doi: 10.1016/J.Tics.2010.12.004
Groenewold, N. A., Opmeer, E. M., de Jonge, P., Aleman, A., & Costafreda, S. G. (2013). Emotional valence modulates brain functional abnormalities in depression: Evidence from a meta-analysis of fMRI studies. Neuroscience and Biobehavioral Reviews, 37(2), 152-163. doi: 10.1016/j.neubiorev.2012.11.015
Grühn, D., Scheibe, S., & Baltes, P. B. (2007). Reduced negativity effect in older adults’ memory for emotional pictures: The heterogeneity–homogeneity list paradigm. Psychology and Aging, 22(3), 644-649. doi: 10.1037/0882-7922.214.171.1244
Gutierrez, J. L. G., Moreno-Jimenez, B., Garrosa, E., & Puente, C. P. (2005). Personality and subjective well-being: Big five correlates and demographic variables. Personality and Individual Differences, 38(7), 1561-1569. doi: 10.1016/j.paid.2004.09.015
Haas, B. W., & Canli, T. (2008). Emotional memory function, personality structure and psychopathology: A neural system approach to the identification of vulnerability markers. Brain Research Review, 58(1), 71-84. doi: 10.1016/j.brainresrev.2007.10.014
Hahn, A., Stein, P., Windischberger, C., Weissenbacher, A., Spindelegger, C., Moser, E., Kasper, S., & Lanzenberger, R. (2011). Reduced resting-state functional connectivity between amygdala and orbitofrontal cortex in social anxiety disorder. NeuroImage, 56(3), 881-889. doi: 10.1016/j.neuroimage.2011.02.064
Hamilton, J. P., & Gotlib, I. H. (2008). Neural substrates of increased memory sensitivity for negative stimuli in major depression. Biological Psychiatry, 63(12), 1155-1162. doi: 10.1016/j.biopsych.2007.12.015
Hamilton, J. P., Siemer, M., & Gotlib, I. H. (2008). Amygdala volume in major depressive disorder: A meta-analysis of magnetic resonance imaging studies. Molecular Psychiatry, 13(11), 993-1000. doi: 10.1038/mp.2008.57
Hassmén, P., Koivula, N., & Uutela, A. (2000). Physical exercise and psychological well-being: A population study in Finland. Preventive Medicine, 30(1), 17-25. doi: 10.1006/pmed.1999.0597
Hayano, F., Nakamura, M., Asami, T., Uehara, K., Yoshida, T., Roppongi, T., Otsuka, T., Inoue, T., & Hirayasu, Y. (2009). Smaller amygdala is associated with anxiety in patients with panic disorder. Psychiatry and Clinical Neurosciences, 63(3), 266-276. doi: 10.1111/j.1440-1819.2009.01960.x
Hayes, N., & Joseph, S. (2003). Big 5 correlates of three measures of subjective well-being. Personality and Individual Differences, 34(4), 723-727. doi: 10.1016/S0191-8869(02)00057-0
Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. New York: Wiley.
Hecht, D. (2013). The neural basis of optimism and pessimism. Experimental Neurobiology, 22(3), 173-199.
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). doi: 10.1177/0956797613490744
Herrington, J. D., Heller, W., Mohanty, A., Engels, A. S., Banich, M. T., Webb, A. G., & Miller, G. A. (2010). Localization of asymmetric brain function in emotion and depression. Psychophysiology, 47(3), 442-454. doi: 10.1111/j.1469-8986.2009.00958.x
Hillman, C. H., Erickson, K. I., & Kramer, A. F. (2008). Be smart, exercise your heart: Exercise effects on brain and cognition. Nature Reviews Neuroscience, 9(1), 58-65. doi: 10.1038/nrn2298
Hillman, C. H., Pontifex, M. B., Castelli, D. M., Khan, N. A., Raine, L. B., Scudder, M. J., Drollette, E. S., Moore, R. D., Wu, C. T., & K., K. (2014). Effects of the FITKids randomized controlled trial on executive control and brain function. Pediatrics, 134(4), e1063-e1071. doi: 10.1542/peds.2013-3219
Holt-Lunstad, J., Smith, T. B., & Layton, J. B. (2010). Social relationships and mortality risk: A meta-analytic review. PLoS Medicine, 7(7), e1000316. doi: 10.1371/journal.pmed.1000316
Hölzel, B. K., Carmody, J., Evans, K. C., Hoge, E. A., Dusek, J. A., Morgan, L., Pitman, R. K., & Lazar, S. W. (2010). Stress reduction correlates with structural changes in the amygdala. Social Cognitive and Affective Neuroscience, 5(1), 11-17. doi: 10.1093/scan/nsp034
Hölzel, B. K., Carmody, J., Vangel, M., Congleton, C., Yerramsetti, S. M., Gard, T., & Lazar, S. W. (2011). Mindfulness practice leads to increases in regional brain gray matter density. Psychiatry Research: Neuroimaging, 191(1), 36-43. doi: 10.1016/j.pscychresns.2010.08.006
Hooker, C. I., Verosky, S. C., Miyakawa, A., Knight, R. T., & D'Esposito, M. (2008). The influence of personality on neural mechanisms of observational fear and reward learning. Neuropsychologia, 46(11), 2709-2724. doi: 10.1016/j.neuropsychologia.2008.05.005
House, J. S., Landis, K. R., & Umberson, D. (1988). Social relationships and health. Science, 241(4865), 540-545. doi: 10.1126/science.3399889
Hu, Y., & Dolcos, S. (2017). Trait anxiety mediates the link between inferior frontal cortex volume and negative affective bias in healthy adults. Social Cognitive and Affective Neuroscience, 12(5), 775-782. doi: 10.1093/scan/nsx008
Hu, Y., Moore, M., Bertels, Z., Phan, K. L., Dolcos, F., & Dolcos, S. (in press). Smaller amygdala volume and increased neuroticism predict anxiety symptoms in healthy subjects: A volumetric approach using manual tracing. Neuropsychologia. doi: 10.1016/j.neuropsychologia.2017.11.008
Irle, E., Ruhleder, M., Lange, C., Seidler-Brandler, U., Salzer, S., Dechent, P., Weniger, G., Leibing, E., & Leichsenring, F. (2010). Reduced amygdalar and hippocampal size in adults with generalized social phobia. Journal of Psychiatry & Neuroscience, 35(2), 126-131. doi: 10.1503/jpn.090041
Isaacowitz, D. M., Wadlinger, H. A., Goren, D., & Wilson, H. R. (2006). Is there an age-related positivity effect in visual attention? A comparison of two methodologies. Emotion, 6(3), 511-516. doi: 10.1037/1528-35126.96.36.1991
Jain, F. A., Walsh, R. N., Eisendrath, S. J., Christensen, S., & Cahn, B. R. (2015). Critical analysis of the efficacy of meditation therapies for acute and subacute phase treatment of depressive disorders: A systematic review. Psychosomatics, 56(2), 140-152. doi: 10.1016/j.psym.2014.10.007
Jones, W. H., Freemon, J. E., & Goswick, R. A. (1981). The persistence of loneliness: Self and other determinants. Journal of Personality, 49(1), 27-48. doi: 10.1111/j.1467-6494.1981.tb00844.x
Kabat-Zinn, J. (2003). Mindfulness-based interventions in context: Past, present, and future. Clinical Psychology: Science and Practice, 10(2), 144-156. doi: 10.1093/clipsy/bpg016
Kahneman, D., Krueger, A. B., Schkade, D., Schwarz, N., & Stone, A. (2004). Toward national well-being accounts. American Economic Review, 94(2), 429-434. doi: 10.1257/0002828041301713
Kallapiran, K., Koo, S., Kirubakaran, R., & Hancock, K. (2015). Review: Effectiveness of mindfulness in improving mental health symptoms of children and adolescents: A meta-analysis. Child and Adolescent Mental Health, 20(4), 182-194. doi: 10.1111/camh.12113
Kanai, R., Bahrami, B., Duchaine, B., Janik, A., Banissy, M. J., & Rees, G. (2012). Brain structure links loneliness to social perception. Current Biology, 22(20), 1975-1979. doi: 10.1016/j.cub.2012.08.045
Kattenstroth, J., Kalisch, T., Holt, S., Tegenthoff, M., & Dinse, H. (2013). Six months of dance intervention enhances postural, sensorimotor, and cognitive performance in elderly without affecting cardio-respiratory functions. Frontiers in Aging Neuroscience, 5(5). doi: 10.3389/fnagi.2013.00005
Kendler, K. S., Gardner, C. O., & Prescott, C. A. (1999). Clinical characteristics of major depression that predict risk of depression in relatives. Archives of General Psychiatry, 56(4), 322-327. doi: 10.1001/archpsyc.56.4.322
Kim, M. J., Loucks, R. A., Palmer, A. L., Brown, A. C., Solomon, K. M., Marchante, A. N., & Whalen, P. J. (2011). The structural and functional connectivity of the amygdala: From normal emotion to pathological anxiety. Behavioural Brain Research, 223(2), 403-410. doi: 10.1016/j.bbr.2011.04.025
Kim, M. J., & Whalen, P. J. (2009). The structural integrity of an amygdala-prefrontal pathway predicts trait anxiety. The Journal of Neuroscience, 29(37), 11614-11618. doi: 10.1523/JNEUROSCI.2335-09.2009
Kleim, J. A., Barbay, S., Cooper, N. R., Hogg, T. M., Reidel, C. N., Remple, M. S., & Nudo, R. J. (2002). Motor learning-dependent synaptogenesis is localized to functionally reorganized motor cortex. Neurobiology of Learning and Memory, 77(1), 63-77. doi: 10.1006/nlme.2001.4004
Kleim, J. A., Barbay, S., & Nudo, R. J. (1998). Functional reorganization of the rat motor cortex following motor skill learning. Journal of Neurophysiology, 80(6), 3321-3325. doi: 10.1152/jn.19188.8.131.5221
Klumpp, H., Angstadt, M., & Phan, K. L. (2012). Insula reactivity and connectivity to anterior cingulate cortex when processing threat in generalized social anxiety disorder. Biological Psychology, 89(1), 273-276. doi: 10.1016/j.biopsycho.2011.10.010
Knight, M., Seymour, T. L., Gaunt, J. T., Baker, C., Nesmith, K., & Mather, M. (2007). Aging and goal-directed emotional attention: Distraction reverses emotional biases. Emotion, 7(4), 705-714. doi: 10.1037/1528-35184.108.40.2065
Kong, F., Zhao, J., & You, X. (2013). Self-esteem as mediator and moderator of the relationship between social support and subjective well-being among Chinese university students. Social Indicators Research, 112(1), 151-161. doi: 10.1007/s11205-012-0044-6
Koolschijn, P. C., van Haren, N. E., Lensvelt-Mulders, G. J., Hulshoff Pol, H. E., & Kahn, R. S. (2009). Brain volume abnormalities in major depressive disorder: A meta-analysis of magnetic resonance imaging studies. Human Brain Mapping, 30(11), 3719-3735. doi: 10.1002/hbm.20801
Kramer, A. F., & Erickson, K. I. (2007). Effects of physical activity on cognition, well-being, and brain: Human interventions. Alzheimer's & Dementia, 3(2), S45-S51. doi: 10.1016/j.jalz.2007.01.008
Kringelbach, M. L., & Berridge, K. C. (2009). Towards a functional neuroanatomy of pleasure and happiness. Trends in Cognitive Sciences, 13(11), 479-487. doi: 10.1016/J.Tics.2009.08.006
Kringelbach, M. L., & Berridge, K. C. (2010). The functional neuroanatomy of pleasure and happiness. Discovery Medicine, 9(49), 579-587.
Kuyken, W., Warren, F. C., Taylor, R. S., Whalley, B., Crane, C., Bondolfi, G., Hayes, R., Huijbers, M., Ma, H. L., Schweizer, S., Segal, Z., Speckens, A., Teasdale, J. D., Van Heeringen, K., Williams, M., Byford, S., Byng, R., & Dalgleish, T. (2016). Efficacy of mindfulness-based cognitive therapy in prevention of depressive relapse: An individual patient data meta-analysis from randomized trials. JAMA Psychiatry, 73(6), 565-574. doi: 10.1001/jamapsychiatry.2016.0076
Laxer, R. M. (1964). Relation of real self-rating to mood and blame and their interaction in depression. Journal of Consulting Psychology, 28(6), 538-546. doi: 10.1037/H0041700
Liddle, P. F., Kiehl, K. A., & Smith, A. M. (2001). Event-related fMRI study of response inhibition. Human Brain Mapping, 12(2), 100-109. doi: 10.1002/1097-0193(200102)12:2<100::AID-HBM1007>3.0.CO;2-6
Lieberman, M. (2013). Social: Why our brains are wired to connect. New York: Crown Publishers.
Lindquist, K. A., Wager, T. D., Kober, H., Bliss-Moreau, E., & Barrett, L. F. (2012). The brain basis of emotion: A meta-analytic review. The Behavioral and Brain Sciences, 35(3), 121-143.
Lomas, T., Ivtzan, I., & Fu, C. H. Y. (2015). A systematic review of the neurophysiology of mindfulness on EEG oscillations. Neuroscience and Biobehavioral Reviews, 57, 401-410. doi: 10.1016/j.neubiorev.2015.09.018
López-Muñoz, F., & Alamo, C. (2009). Monoaminergic neurotransmission: The history of the discovery of antidepressants from 1950s until today. Current Pharmaceutical Design, 15(14), 1563-1586. doi: 10.2174/138161209788168001
Lucas, R. E., & Diener, E. (2008). Subjective well-being. In M. Lewis, J. M. Haviland & L. F. Barrett (Eds.), Handbook of emotions (3rd ed., pp. 471-484). New York: Guilford Press.
Luders, E., Phillips, O. R., Clark, K., Kurth, F., Toga, A. W., & Narr, K. L. (2012). Bridging the hemispheres in meditation: Thicker callosal regions and enhanced fractional anisotropy (FA) in long-term practitioners. NeuroImage, 61(1), 181-187. doi: 10.1016/j.neuroimage.2012.02.026
Luhmann, M., Schimmack, U., & Eid, M. (2011). Stability and variability in the relationship between subjective well-being and income. Journal of Research in Personality, 45(2), 186-197. doi: 10.1016/j.jrp.2011.01.004
Mars, R. B., Neubert, F. X., Noonan, M. P., Sallet, J., Toni, I., & Rushworth, M. F. (2012). On the relationship between the "default mode network" and the "social brain". Frontiers in Human Neuroscience, 6, 189. doi: 10.3389/fnhum.2012.00189
Master, S. L., Eisenberger, N. I., Taylor, S. E., Nalibboff, B. D., Shirinyan, D., & Lieberman, M. D. (2009). A picture's worth: Partner photographs reduce experimentally induced pain. Psychological Science, 20(11), 1316-1318. doi: 10.1111/j.1467-9280.2009.02444.x
Mather, M. (2012). The emotion paradox in the aging brain. Annals of the New York Academy of Sciences, 1251, 33-49. doi: 10.1111/j.1749-6632.2012.06471.x
Mather, M. (2016). The affective neuroscience of aging. Annual Review of Psychology, 67, 213-238. doi: 10.1146/annurev-psych-122414-033540
Mather, M., & Carstensen, L. L. (2003). Aging and attentional biases for emotional faces. Psychological Science, 14(5), 409-415. doi: 10.1111/1467-9280.01455
May, A. (2011). Experience-dependent structural plasticity in the adult human brain. Trends in Cognitive Sciences, 15(10), 475-482. doi: 10.1016/j.tics.2011.08.002
Mayo, P. R. (1983). Personality traits and the retrieval of positive and negative memories. Personality and Individual Differences, 4, 465-471. doi: 10.1016/0191-8869(83)90076-4
McEwen, B. S., Nasca, C., & Gray, J. D. (2016). Stress effects on neuronal structure: Hippocampus, amygdala, and prefrontal cortex. Neuropsychopharmacology, 41(1), 3-23. doi: 10.1038/npp.2015.171
McIntyre, C. W., Watson, D., & Cunningham, A. C. (1990). The effects of social interaction, exercise, and test stress on positive and negative affect. Bulletin of the Psychonomic Society, 28(2), 141-143. doi: 10.3758/bf03333988
Meeusen, R., & De Meirleir, K. (1995). Exercise and brain neurotransmission. Sports Medicine, 20(3), 160-188.
Meng, Y. J., Lui, S., Qiu, C. J., Qiu, L. H., Lama, S., Huang, X. Q., Feng, Y., Zhu, C. Y., Gong, Q. Y., & Zhang, W. (2013). Neuroanatomical deficits in drug-naive adult patients with generalized social anxiety disorder: A voxel-based morphometry study. Psychiatry Research: Neuroimaging, 214(1), 9-15. doi: 10.1016/j.pscychresns.2013.06.002
Monat, A., Lazarus, R. S., & Reevy, G. (2007). The Praeger handbook on stress and coping (Vol. 1). Westport, CT: Praeger.
Moran, J. M., Macrae, C. N., Heatherton, T. F., Wyland, C. L., & Kelley, W. M. (2006). Neuroanatomical evidence for distinct cognitive and affective components of self. Journal of Cognitive Neuroscience, 18(9), 1586-1594. doi: 10.1162/jocn.2006.18.9.1586
Morey, R. A., Gold, A. L., LaBar, K. S., Beall, S. K., Brown, V. M., Haswell, C. C., Nasser, J. D., Workgroup, M.-A. M., Wagner, H. R., & McCarthy, G. (2012). Amygdala volume changes with posttraumatic stress disorder in a large case-controlled veteran group. Archives of General Psychiatry, 69(11), 1169-1178. doi: 10.1001/archgenpsychiatry.2012.50
Murakami, H., Nakao, T., Matsunaga, M., Kasuya, Y., Shinoda, J., Yamada, J., & Ohira, H. (2012). The structure of mindful brain. PLoS ONE, 7(9). doi: 10.1371/journal.pone.0046377
Myers, D. G. (1992). The pursuit of happiness. New York: Harper Collins Publishing Inc.
Nakagawa, S., Takeuchi, H., Taki, Y., Nouchi, R., Sekiguchi, A., Kotozaki, Y., Miyauchi, C. M., Iizuka, K., Yokoyama, R., Shinada, T., Yamamoto, Y., Hanawa, S., Araki, T., Hashizume, H., Kunitoki, K., Sassa, Y., & Kawashima, R. (2015). White matter structures associated with loneliness in young adults. Scientific Reports, 5, 17001. doi: 10.1038/srep17001
Nes, L. S., & Segerstrom, S. C. (2006). Dispositional optimism and coping: A meta-analytic review. Personality and Social Psychology Review, 10(3), 235-251. doi: 10.1207/S15327957pspr1003_3
Netz, Y., Wu, M. J., Becker, B. J., & Tenenbaum, G. (2005). Physical activity and psychological well-being in advanced age: A meta-analysis of intervention studies. Psychology and Aging, 20(2), 272-284. doi: 10.1037/0882-79220.127.116.112
Nudo, R. J., Milliken, G. W., Jenkins, W. M., & Merzenich, M. M. (1996). Use-dependent alterations of movement representations in primary motor cortex of adult squirrel monkeys. The Journal of Neuroscience, 16(2), 785-807.
Nyklicek, I., & Kuijpers, K. F. (2008). Effects of mindfulness-based stress reduction intervention on psychological well-being and quality of life: Is increased mindfulness indeed the mechanism? Annals of Behavioral Medicine, 35(3), 331-340. doi: 10.1007/s12160-008-9030-2
Onoda, K., Okamoto, Y., Nakashima, K., Nittono, H., Yoshimura, S., Yamawaki, S., Yamaguchi, S., & Ura, M. (2010). Does low self-esteem enhance social pain? The relationship between trait self-esteem and anterior cingulate cortex activation induced by ostracism. Social Cognitive and Affective Neuroscience, 5(4), 385-391. doi: 10.1093/scan/nsq002
Ortigue, S., Bianchi-Demicheli, F., Patel, N., Frum, C., & Lewis, J. W. (2010). Neuroimaging of love: fMRI meta-analysis evidence toward new perspectives in sexual medicine. The Journal of Sexual Medicine, 7(11), 3541-3552. doi: 10.1111/j.1743-6109.2010.01999.x
Pan, W. G., Liu, C. C., Yang, Q., Gu, Y., Yin, S. H., & Chen, A. T. (2016). The neural basis of trait self-esteem revealed by the amplitude of low-frequency fluctuations and resting state functional connectivity. Social Cognitive and Affective Neuroscience, 11(3), 367-376. doi: 10.1093/scan/nsv119
Paulus, M. P., & Frank, L. R. (2003). Ventromedial prefrontal cortex activation is critical for preference judgments. NeuroReport, 14(10), 1311-1315. doi: 10.1097/01.wnr.0000078543.07662.02
Penedo, F. J., & Dahn, J. R. (2005). Exercise and well-being: A review of mental and physical health benefits associated with physical activity. Current Opinion in Psychiatry, 18(2), 189-193. doi: 10.1097/00001504-200503000-00013
Pereira, A. C., Huddleston, D. E., Brickman, A. M., Sosunov, A. A., Hen, R., McKhann, G. M., Sloan, R., Gage, F. H., Brown, T. R., & Small, S. A. (2007). An in vivo correlate of exercise-induced neurogenesis in the adult dentate gyrus. Proceedings of the National Academy of Sciences of the United States of America, 104(13), 5638-5643. doi: 10.1073/pnas.0611721104
Phelps, E. A., Lempert, K. M., & Sokol-Hessner, P. (2014). Emotion and decision making: Multiple modulatory neural circuits. Annual Review of Neuroscience, 37, 263-287. doi: 10.1146/annurev-neuro-071013-014119
Pickut, B. A., Van Hecke, W., Kerckhofs, E., Marien, P., Vanneste, S., Cras, P., & Parizel, P. M. (2013). Mindfulness based intervention in Parkinson's disease leads to structural brain changes on MRI: A randomized controlled longitudinal trial. Clinical Neurology and Neurosurgery, 115(12), 2419-2425. doi: 10.1016/j.clineuro.2013.10.002
Pruessner, J. C., Baldwin, M. W., Dedovic, K., Renwick, R., Mahani, N. K., Lord, C., Meaney, M., & Lupien, S. (2005). Self-esteem, locus of control, hippocampal volume, and cortisol regulation in young and old adulthood. NeuroImage, 28(4), 815-826. doi: 10.1016/j.neuroimage.2005.06.014
Quaglia, J. T., Braun, S. E., Freeman, S. P., McDaniel, M. A., & Brown, K. W. (2016). Meta-analytic evidence for effects of mindfulness training on dimensions of self-reported dispositional mindfulness. Psychological Assessment, 28(7), 803-818. doi: 10.1037/pas0000268
Raglin, J. S., & Wilson, M. (1996). State anxiety following 20 minutes of bicycle ergometer exercise at selected intensities. International Journal of Sports Medicine, 17(06), 467-471. doi: 10.1055/s-2007-972880
Rayner, G., Jackson, G., & Wilson, S. (2016). Cognition-related brain networks underpin the symptoms of unipolar depression: Evidence from a systematic review. Neuroscience and Biobehavioral Reviews, 61, 53-65. doi: 10.1016/j.neubiorev.2015.09.022
Raz, N., Ghisletta, P., Rodrigue, K. M., Kennedy, K. M., & Lindenberger, U. (2010). Trajectories of brain aging in middle-aged and older adults: Regional and individual differences. NeuroImage, 51(2), 501-511. doi: 10.1016/j.neuroimage.2010.03.020
Raz, N., Lindenberger, U., Rodrigue, K. M., Kennedy, K. M., Head, D., Williamson, A., Dahle, C., Gerstorf, D., & Acker, J. D. (2005). Regional brain changes in aging healthy adults: General trends, individual differences and modifiers. Cerebral Cortex, 15(11), 1676-1689. doi: 10.1093/cercor/bhi044
Reed, A. E., & Carstensen, L. L. (2012). The theory behind the age-related positivity effect. Frontiers in Psychology, 3. doi: 10.3389/fpsyg.2012.00339
Reed, A. E., Chan, L., & Mikels, J. A. (2014). Meta-analysis of the age-related positivity effect: Age differences in preferences for positive over negative information. Psychology and Aging, 29(1), 1-15. doi: 10.1037/a0035194
Reuter-Lorenz, P. A., & Park, D. C. (2014). How does it STAC up? Revisiting the scaffolding theory of aging and cognition. Neuropsychology Review, 24(3), 355-370. doi: 10.1007/s11065-014-9270-9
Riccelli, R., Toschi, N., Nigro, S., Terracciano, A., & Passamonti, L. (2017). Surface-based morphometry reveals the neuroanatomical basis of the five-factor model of personality. Social Cognitive and Affective Neuroscience, 12(4), 671-684. doi: 10.1093/scan/nsw175
Rosenberg, M. (1965). Rosenberg self-esteem scale (RSE). Acceptance and commitment therapy: Measures package, 61, 52.
Roy, M., Shohamy, D., & Wager, T. D. (2012). Ventromedial prefrontal-subcortical systems and the generation of affective meaning. Trends in Cognitive Sciences, 16(3), 147-156. doi: 10.1016/j.tics.2012.01.005
Russo-Neustadt, A., Beard, R. C., & Cotman, C. W. (1999). Exercise, antidepressant medications, and enhanced brain derived neurotrophic factor expression. Neuropsychopharmacology, 21(5), 679-682. doi: 10.1016/S0893-133X(99)00059-7
Russo, R., Whittuck, D., Roberson, D., Dutton, K., Georgiou, G., & Fox, E. (2006). Mood-congruent free recall bias in anxious individuals is not a consequence of response bias. Memory, 14(4), 393-399. doi: 10.1080/09658210500343166
Rusting, C. L. (1999). Interactive effects of personality and mood on emotion-congruent memory and judgment. Journal of Personality and Social Psychology, 77(5), 1073-1086. doi: 10.1037/0022-3518.104.22.1683
Ryff, C. D., Heller, A. S., Schaefer, S. M., Van Reekum, C., & Davidson, R. J. (2016). Purposeful engagement, healthy aging, and the brain. Current Behavioral Neuroscience Reports, 3(4), 318-327. doi: 10.1007/s40473-016-0096-z
Sampaio, A., Soares, J. M., Coutinho, J., Sousa, N., & Goncalves, O. F. (2014). The big five default brain: Functional evidence. Brain Structure & Function, 219(6), 1913-1922. doi: 10.1007/s00429-013-0610-y
Schaeffer, D. J., Krafft, C. E., Schwarz, N. F., Chi, L., Rodrigue, A. L., Pierce, J. E., Allison, J. D., Yanasak, N. E., Liu, T., Davis, C. L., & McDowell, J. E. (2014). An 8-month exercise intervention alters fronto-temporal white matter integrity in overweight children. Psychophysiology, 51(8), 728-733. doi: 10.1111/psyp.12227
Scheibe, S., & Carstensen, L. L. (2010). Emotional aging: Recent findings and future trends. The Journals of Gerontology: Series B, 65B(2), 135-144. doi: 10.1093/geronb/gbp132
Scheier, M. F., & Carver, C. S. (1985). Optimism, coping, and health: Assessment and implications of generalized outcome expectancies. Health Psychology 4(3), 219-247. doi: 10.1037/0278-622.214.171.124
Schuch, F. B., Vancampfort, D., Sui, X. M., Rosenbaum, S., Firth, J., Richards, J., Ward, P. B., & Stubbs, B. (2016). Are lower levels of cardiorespiratory fitness associated with incident depression? A systematic review of prospective cohort studies. Preventive Medicine, 93, 159-165. doi: 10.1016/j.ypmed.2016.10.011
Schulz, K. P., Fan, J., Magidina, O., Marks, D. J., Hahn, B., & Halperin, J. M. (2007). Does the emotional go/no-go task really measure behavioral inhibition? Convergence with measures on a non-emotional analog. Archives of Clinical Neuropsychology, 22(2), 151-160. doi: 10.1016/j.acn.2006.12.001
Seeman, T. E. (1996). Social ties and health: The benefits of social integration. Annals of Epidemiology, 6(5), 442-451. doi: 10.1016/S1047-2797(96)00095-6
Segerstrom, S. C. (2001). Optimism and attentional bias for negative and positive stimuli. Personality and Social Psychology Bulletin, 27(10), 1334-1343. doi: 10.1177/01461672012710009
Seligman, M. E. P., Steen, T. A., Park, N., & Peterson, C. (2005). Positive psychology progress: Empirical validation of interventions. American Psychologist, 60(5), 410-421. doi: 10.1037/0003-066X.60.5.410
Shang, J., Fu, Y., Ren, Z., Zhang, T., Du, M., Gong, Q., Lui, S., & Zhang, W. (2014). The common traits of the ACC and PFC in anxiety disorders in the DSM-5: Meta-analysis of voxel-based morphometry studies. PLoS ONE, 9(3), e93432. doi: 10.1371/journal.pone.0093432
Shapiro, S. L., Brown, K. W., & Biegel, G. M. (2007). Teaching self-care to caregivers: Effects of mindfulness-based stress reduction on the mental health of therapists in training. Training and Education in Professional Psychology, 1(2), 105-115. doi: 10.1037/1931-39126.96.36.199
Sharot, T., Riccardi, A. M., Raio, C. M., & Phelps, E. A. (2007). Neural mechanisms mediating optimism bias. Nature, 450(7166), 102-105. doi: 10.1038/nature06280
Sidaros, A., Skimminge, A., Liptrot, M. G., Sidaros, K., Engberg, A. W., Herning, M., Paulson, O. B., Jernigan, T. L., & Rostrup, E. (2009). Long-term global and regional brain volume changes following severe traumatic brain injury: A longitudinal study with clinical correlates. NeuroImage, 44(1), 1-8. doi: 10.1016/j.neuroimage.2008.08.030
Silveira, H., Moraes, H., Oliveira, N., Coutinho, E. S. F., Laks, J., & Deslandes, A. (2013). Physical exercise and clinically depressed patients: A systematic review and meta-analysis. Neuropsychobiology, 67(2), 61-68. doi: 10.1159/000345160
Sofi, F., Valecchi, D., Bacci, D., Abbate, R., Gensini, G. F., Casini, A., & Macchi, C. (2011). Physical activity and risk of cognitive decline: A meta-analysis of prospective studies. Journal of Internal Medicine, 269(1), 107-117. doi: 10.1111/j.1365-2796.2010.02281.x
Somerville, L. H., Kelley, W. M., & Heatherton, T. F. (2010). Self-esteem modulates medial prefrontal cortical responses to evaluative social feedback. Cerebral Cortex, 20(12), 3005-3013. doi: 10.1093/cercor/bhq049
St. Jacques, P. L., Dolcos, F., & Cabeza, R. (2010). Effects of aging on functional connectivity of the amygdala during negative evaluation: A network analysis of fMRI data. Neurobiology of Aging, 31(315-327). doi: 10.1016/j.neurobiolaging.2008.03.012
Steptoe, A., Kearsley, N., & Walters, N. (1993). Cardiovascular activity during mental stress following vigorous exercise in sportsmen and inactive men. Psychophysiology, 30(3), 245-252. doi: 10.1111/j.1469-8986.1993.tb03350.x
Steptoe, A., Kimbell, J., & Basford, P. (1998). Exercise and the experience and appraisal of daily stressors: A naturalistic study. Journal of Behavioral Medicine, 214, 363-374.
Swick, D., Ashley, V., & Turken, U. (2008). Left inferior frontal gyrus is critical for response inhibition. BMC Neuroscience, 9. doi: 10.1186/1471-2202-9-102
Swick, D., Ashley, V., & Turken, U. (2011). Are the neural correlates of stopping and not going identical? Quantitative meta-analysis of two response inhibition tasks. NeuroImage, 56(3), 1655-1665. doi: 10.1016/j.neuroimage.2011.02.070
Talati, A., Pantazatos, S. P., Schneier, F. R., Weissman, M. M., & Hirsch, J. (2013). Gray matter abnormalities in social anxiety disorder: Primary, replication, and specificity studies. Biological Psychiatry, 73(1), 75-84. doi: 10.1016/J.Biopsych.2012.05.022
Tang, Y. Y., Hölzel, B. K., & Posner, M. I. (2015). The neuroscience of mindfulness meditation. Nature Reviews Neuroscience, 16(4), 213-225. doi: 10.1038/nrn3916
Tang, Y. Y., Lu, Q. L., Fan, M., Yang, Y. H., & Posner, M. I. (2012). Mechanisms of white matter changes induced by meditation. Proceedings of the National Academy of Sciences of the United States of America, 109(26), 10570-10574. doi: 10.1073/pnas.1207817109
Tang, Y. Y., Lu, Q. L., Geng, X. J., Stein, E. A., Yang, Y. H., & Posner, M. I. (2010). Short-term meditation induces white matter changes in the anterior cingulate. Proceedings of the National Academy of Sciences of the United States of America, 107(35), 15649-15652. doi: 10.1073/pnas.1011043107
van der Velden, A. M., Kuyken, W., Wattar, U., Crane, C., Pallesen, K. J., Dahlgaard, J., Fjorback, L. O., & Piet, J. (2015). A systematic review of mechanisms of change in mindfulness-based cognitive therapy in the treatment of recurrent major depressive disorder. Clinical Psychology Review, 37, 26-39. doi: 10.1016/j.cpr.2015.02.001
VanderWeele, T. J., Hawkley, L. C., & Cacioppo, J. T. (2012). On the reciprocal association between loneliness and subjective well-being. American Journal of Epidemiology, 176(9), 777-784. doi: 10.1093/aje/kws173
VanderWeele, T. J., Hawkley, L. C., Thisted, R. A., & Cacioppo, J. T. (2011). A marginal structural model analysis for loneliness: Implications for intervention trials and clinical practice. Journal of Consulting and Clinical Psychology, 79(2), 225-235. doi: 10.1037/a0022610
Van Praag, H. M. (1982). Depression. The Lancet, 320(8310), 1259-1264. doi: 10.1016/S0140-6736(82)90115-5
Visted, E., Vollestad, J., Nielsen, M. B., & Nielsen, G. H. (2015). The impact of group-based mindfulness training on self-reported mindfulness: A systematic review and meta-analysis. Mindfulness, 6(3), 501-522. doi: 10.1007/s12671-014-0283-5
Vollestad, J., Nielsen, M. B., & Nielsen, G. H. (2012). Mindfulness- and acceptance-based interventions for anxiety disorders: A systematic review and meta-analysis. The British Journal of Clinical Psychology, 51(3), 239-260. doi: 10.1111/j.2044-8260.2011.02024.x
Vollestad, J., Sivertsen, B., & Nielsen, G. F. (2011). Mindfulness-based stress reduction for patients with anxiety disorders: Evaluation in a randomized controlled trial. Behaviour Research and Therapy, 49(4), 281-288. doi: 10.1016/j.brat.2011.01.007
Voss, M. W., Erickson, K. I., Prakash, R. S., Chaddock, L., Kim, J. S., Alves, H., Szabo, A., White, S. M., Wójcicki, T. R., Mailey, E. L., Olson, E. A., Gothe, N., Potter, V. V., Martin, S. A., Pence, B. D., Cook, M. D., Woods, J. A., McAuley, E., & Kramer, A. F. (2013). Neurobiological markers of exercise-related brain plasticity in older adults. Brain, Behavior, and Immunity, 28, 90-99. doi: 10.1016/j.bbi.2012.10.021
Voss, M. W., Nagamatsu, L. S., Liu-Ambrose, T., & Kramer, A. F. (2011). Exercise, brain, and cognition across the life span. Journal of Applied Physiology, 111(5), 1505-1513. doi: 10.1152/japplphysiol.00210.2011
Wessman, A. E., & Ricks, D. F. (1966). Mood and personality. New York: Holt.
Whalen, P. J. (2007). The uncertainty of it all. Trends in Cognitive Sciences, 11(12), 499-500. doi: 10.1016/j.tics.2007.08.016
Wilson, W., & Marsden, C. (1996). In vivo measurement of extracellular serotonin in the ventral hippocampus during treadmill running. Behavioural Pharmacology, 7(1), 101-104.
Wood, S., & Kisley, M. A. (2006). The negativity bias is eliminated in older adults: Age-related reduction in event-related brain potentials associated with evaluative categorization. Psychology and Aging, 21, 815–820. doi: 10.1037/0882-79188.8.131.525
Xu, J. S., & Potenza, M. N. (2012). White matter integrity and five-factor personality measures in healthy adults. NeuroImage, 59(1), 800-807. doi: 10.1016/j.neuroimage.2011.07.040
Yeung, R. R. (1996). The acute effects of exercise on mood state. Journal of Psychosomatic Research, 40(2), 123-141. doi: 10.1016/0022-3999(95)00554-4
Younger, J., Aron, A., Parke, S., Chatterjee, N., & Mackey, S. (2010). Viewing pictures of a romantic partner reduces experimental pain: Involvement of neural reward systems. PLoS ONE, 5(10), e13309. doi: 10.1371/journal.pone.0013309
Zhang, A. F., Leow, A., Ajilore, O., Lamar, M., Yang, S. L., Joseph, J., Medina, J., Zhan, L., & Kumar, A. (2012). Quantitative tract-specific measures of uncinate and cingulum in major depression using diffusion tensor imaging. Neuropsychopharmacology, 37(4), 959-967. doi: 10.1038/npp.2011.279
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