Neuroscience and Psychology of Meditation in Everyday Life

by Dusana Dorjee

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  Chapter 2

  Long-term meditation practice

  Development of a long-term perspective in personal meditation practice as well as in research on meditation invites careful considerations about many facets of long-term vs short-term effects of meditation. For example, initial research suggests that there are marked differences in the brain changes resulting from short-term, medium-term and long-term meditation practice (e.g., Brefczynski-Lewis et al., 2007). This raises questions regarding the contribution of quantity and quality of meditation practice to such effects. Is it the hours spent in meditation or the intensity and quality of the practice or both? There are also questions about how such differences in quantity and quality translate into the well-being effects of meditation within an actual meditation practice and in everyday life outside of formal meditation sessions. And finally, there is the issue of possible adverse effects of meditation, which has been mostly neglected in meditation research over the last two decades and is increasingly more salient with meditation becoming a popular mainstream practice. Discussion about adverse effects of meditation is particularly pertinent in the context of developing a long-term meditation practice. In this chapter, we will consider each of these questions in detail, but first start with an explanation of how neural plasticity contributes to the effects of meditation.

  Neural plasticity, body physiology and meditation

  For a long time in the history of neuroscience research findings seemed to suggest that brain does not change much in the adulthood. Brain clearly develops and changes during childhood as we learn to walk, talk, read and write and acquire a range of other skills. The brain also has an amazing capacity to re-learn skills lost due to brain damage during young age provided that other parts of the brain which can overtake the skills are intact. However, much of this brain flexibility in learning is lost after the age of 12, even though development of some brain areas continues until the age of 25. Accordingly, neuroscience researchers claimed for many decades that the modulations in the brain due to learning are very limited after the developmental brain maturation is completed.

  Contrary to this long-held view, the brain research over the last two decades showed that brain can change even in adulthood when we acquire new skills. By now there are many studies in support of the overarching finding that the brain is changeable by learning new mental skills across the lifetime. For example, the research shows that the brain is modified when we learn to play a musical instrument (Herholz and Zatorre, 2012), learn a new language (Li, Legault and Litcofsky, 2014) or learn to dance (Karpati et al., 2015), etc. The training in the new skill does not need to take a long time – measurable changes have been observed after 40 hours of learning to play golf as a leisure activity (Bezzola et al., 2011) or two weeks of brief daily learning to mirror read (Ilg et al., 2008). Research into such brain changes, however, also showed that the modifications resulting from learning are reduced if the learning is not sustained. For instance, a study on learning to juggle found clear changes in brain structure after three months of practice, but these changes were largely reduced three months later after the participants in the study stopped practicing juggling (Draganski et al., 2004). Together, all these research findings highlight how our everyday choices about activities and experiences we undertake reshape our brains. The term ‘neural plasticity’ has been used to describe the ability of the brain to change as a result of our experience.

  The specific mechanisms underlying brain plasticity are complex, but it is commonly assumed that neural plasticity is linked to growth of new neural connections between brain cells (neurons). Neurons have two types of branch-like parts which enable connections to other neurons – axons, which lead information to other brain cells, and dendrites, which receive information from other brain cells. Axons are covered in fatty substance called myelin, which gives a whitish colour to brain parts mostly consisting of axons. Dendrites, in contrast, are greyish in colour and accordingly tissue containing mostly dendrites (and cell bodies plus support cells) is grey in colour. These differences in colour are used to describe the grey matter of the brain, which is located mostly on the surface of the brain, and white matter of the brain mostly forming tracts inside the brain. Research shows that both grey and white matter volume and other properties such as tissue density can be changed by neural plasticity (May, 2011).

  The relatively recent shift in our understanding of neural plasticity in the adulthood was facilitated by the development of research methods that allow investigation of changes in the brain ‘online’ – while human participants are engaging in thinking, talking, perceiving, sensing and feeling. We have also been for a long time able to learn about neural plasticity from research using reaction-time measures assessing speed of responses to carefully selected stimuli or their sequences; such responses are often recorded via button presses in experimental tasks (Ahissar and Hochstein, 2004). It might sound surprising, but reaction-time research can often provide as strong evidence on neural plasticity of learning as some imaging methods (Dorjee and Bowers, 2012). Importantly, new neuroscientific methods allowed research into subtle changes in the brain structure with learning. The new brain research methods which enabled these advances in the study of neural plasticity fall into two main categories: magnetic resonance imaging (MRI) and electroencephalography (EEG).

  The MRI measures changes in brain tissue properties using a strong magnetic field. The MRI methods are typically divided into two main categories: structural MRI and functional MRI (fMRI). While the structural methods are able to differentiate changes in, for example, volume and density of brain areas, fMRI is based on changes in magnetic properties of iron (contained in the protein haemoglobin) in the blood flow in the brain. Only fMRI is able to detect changes online, during a task performance while participants are engaging in thinking, emotional processing etc. Both structural and fMRI measures have very good spatial resolution, enabling us to understand where in the brain changes occurred. However, fMRI is not very accurate when it comes to recording timing of the changes in brain processes. This is because fMRI is based on measuring changes in blood-oxygen-level dependen
t (BOLD) signals, which are typically delayed by a couple seconds after a metabolic demand in an area of the brain arises. It is assumed that if an area requires more energy (increased metabolism and oxygen demand) it is involved in the task performed. So the inference about involvement of brain areas in learning as measured by fMRI is somewhat indirect.

  In contrast, EEG-derived measures can record brain activity from the surface of the brain with millisecond accuracy while participants are performing a task, so these measures have excellent temporal resolution. EEG methods measure the core activity of neurons – neuronal firing across large numbers of neurons – so provide more direct assessment of brain activity than fMRI signals derived from BOLD. However, EEG methods cannot assess specific localization of the changes in neuronal firing in the brain because of the natural curvature of the brain’s surface, which results in neuronal firing being projected in different directions from the source brain areas. In addition, EEG signals, for the most part, cannot capture neuronal firing from the structures inside the brain, only on its surface. Nevertheless, EEG methods have been available to researchers for much longer than MRI; hence there is a larger body of evidence in support of specific neural EEG-based markers of attention, emotion regulation, conceptual processing etc. The EEG methods are also much cheaper than MRI and the recording equipment is easily portable, allowing for recording in various environments (e.g., in meditation retreat).

  Neuroscientific research using MRI and EEG methods in investigating neural plasticity linked to meditation has clearly documented tangible changes in the brain. It supported the hypothesis that learning to meditate, just like learning other skills we have mentioned earlier, results in modifications in the brain function and brain structure. In a way, the findings regarding neural plasticity changes with meditation provide even stronger evidence of neural plasticity principles than learning to juggle, play violin, dance or play golf. This is because meditation has been stereotypically considered an elusive practice, restricted in its effects to the mind of the meditator; after all, there are no visible ‘outcomes’ of meditation practice other than perhaps subtle shifts in facial expression, unlike the number of balls juggled without falling for a certain amount of time.

  Yet, neuroscientific studies have now documented relatively consistent modifications in at least a few areas of the brain as a result of meditation practice including the anterior cingulate cortex, insula and amygdalae (Tang, Hölzel and Posner, 2015). There is also growing evidence of neural plasticity changes with meditation from event-related brain potential (ERP) research, which is an EEG-based method measuring brain responses to particular types of probes (e.g., sad faces, happy faces or rare sounds). Research using ERPs, for example, suggests that meditation practice enhances efficient use of attention resources (e.g., Slagter et al., 2007). This is an important finding because attention is a limited capacity since in every moment there is an overload of information in our environment (people’s voices and other sounds, things around us, our thoughts, emotions etc.) and we need to choose most relevant information for the task at hand. Interestingly, some of these changes in attention regulation have also been associated with health-conducive effects in body physiology such as decreases in stress hormone levels (Tang et al., 2009). The connections between bodily stress pathways and stress-related hormonal changes in the brain, discussed in the previous chapter, enable these effects. Through these connections, neural plasticity induced by meditation can enhance our ability to downregulate the stress response.

  If we examine the previous research on neural plasticity changes with meditation from the perspective of the framework of meditation research outlined in Chapter 1, most of the research focused on processes of the metacognitive self-regulatory capacity (MSRC) of the mind. The majority of the studies particularly investigated changes in attention and emotion regulation linked to meditation. Much less research has been dedicated to investigation of changes in conceptual processing with meditation, which is a relatively new topic in meditation research. Similarly, research on modes of existential awareness (MEA) is very limited. Most studies that investigate neural modulations relevant to the construal of self heavily relied on attention and emotion processes which contribute to, but are not necessarily central to, the construal of self (e.g., Lutz et al., 2016). Finally, very few neuroscientific studies considered links between changes in self-regulation or existential awareness in relation to changes in health and well-being of meditators. We will consider the research findings relevant to MSRC and MEA in detail in the following chapters.

  Despite the important contribution of neuroscientific studies to research on the neural plasticity of meditation, there is a need for caution in interpreting their findings. Neuroscientific evidence is often given stronger weight than evidence from other methods (such as reaction-time experiments) particularly by non-neuroscientists. This is often the case when sensationalist reports in the media discuss brain changes resulting from meditation. Directly speaking to this issue are findings from one research study (with other studies supporting the same conclusions) which examined trustworthiness of explanations containing neuroscience information as judged by neuroscience non-experts in comparison to neuroscience experts (Weisberg et al., 2008). The results showed that non-experts considered explanations containing neuroscience information, even when it was irrelevant (!), as more persuasive than explanations of equal quality but without neuroscientific references and language. This wasn’t the case for experts, who disregarded neuroscience information if it wasn’t relevant to the explanation.

  The state and trait effects of meditation

  Meditation practices, depending on their types, are expected to produce effects such as improved concentration or emotional balance. We might experience such effects while meditating or right after we have completed a meditation session. These changes could be considered shifts in a state – the term ‘state’ suggests that such changes are temporary, transient and closely linked to the immediate practice of meditation. Initial neuroscience findings show that these temporary changes produce measurable modulations in the brain. For example, fMRI research findings with participants without any previous meditation experience have shown clear differences in the recruitment of brain areas when the participants were engaging in a brief breath-focus meditation in comparison to unfocused attention (Dickenson et al., 2012). The particular modulation was in increased brain activity in attention control areas during the breath-focus meditation. The study thus showed that neural correlates of state difference between meditation and unfocused states are observable even in meditation novices. Another example of state shifts comes from a study, this time with experienced Vipassana meditation practitioners, which contrasted meditators’ concentration while they were engaging in focused meditation with when they were engaging in neutral unfocused thinking (Cahn and Polich, 2009). The study used ERP indexes of distractibility (P3a ERP component) and found significantly diminished brain responses to distractors during meditation than during the thinking period. This finding suggested that the meditation state was associated with increased concentration and better ability to reduce distraction, as would be expected.

  It is certainly reassuring that meditation can produce the expected effects while we are meditating or right after completion of a meditation practice, but do such effects transfer into times during the day when we are not actively engaging in a formal meditation practice? Such broader persistent impact of meditation is potentially of most relevance to our health and well-being since it would indicate that meditation can not only temporarily improve how we feel, but also produce lasting long-term changes. For example, could regular meditation practice modify the way we habitually cope with stressful events or how well we are able to concentrate on tasks during the day? We could expect that with repeated meditation practice we are changing neural plasticity in attention and emotion regulation areas of the brain and these changes gradually override usual (less healthy) ways of responding in everyday
life. This would suggest that repeated experience of meditation states could result in more lasting ‘trait’ changes in our mind and brain.

  A large body of research regarding personality traits such as neuroticisms or extraversion traditionally implicated that such traits are quite stable characteristics of personality across lifetime. However, recent research challenged this assumption and some studies showed that regular meditation practice can lead to shifts in personality traits. For example, a study on the effects of a three-month meditation retreat documented significant reductions in neuroticisms (characterized by distress and excessive worrying) after the retreat (Jacobs et al., 2011). The retreat involved training in attention regulation (Shamatha) and also cultivation of loving kindness, compassion, rejoicing and equanimity. Changes in personality-related traits were also found in a study with participants attending an eight-week mindfulness-based stress reduction (MBSR) course. This study reported changes in traits associated with type D ‘distressed’ personality – characterized by negative affectivity and inhibition of emotional expression in social interactions (not showing how we feel, which is also linked to introversion) (Nyklíček, van Beugen and Denollet, 2013). The researchers found that scores on both of the traits of distressed personality significantly reduced after the MBSR course, suggesting a trait shift. The assessments of the traits in this study also controlled for possible temporary state effects of negative affectivity.

  Neuroscience research shows that effects of meditation beyond the immediate state shifts are also associated with changes in brain structure observed outside of meditation and as early as after eight weeks of meditation training. One study assessed participants’ brain structure in areas relevant to stress processing before and after eight weeks of MBSR training (Hölzel et al., 2009). The participants were healthy but stressed adults. The results revealed significant reductions in self-reported perceived stress and also significant reductions in the density of grey matter in the right amygdala. These reductions in the density of amygdala grey matter were significantly related to the reductions in perceived stress scores of course participants, lending further support to the link between this structural change and well-being enhancing effects of the MBSR training. Amygdalae are brain regions involved in treat detection; increased activation of the amygdalae has been associated with anxiety disorders or post-traumatic stress disorder (PTSD). In another study with MBSR participants, structural modifications in the brain were found in regions involved in emotion regulation (increased grey matter density in posterior cingulate cortex and temporo-parietal junction) and memory (increased grey matter density in the hippocampus) (Hölzel et al., 2011).