Self-reflection is positively associated with cognition late in life as well as glucose metabolism, a marker of brain health, finds a new study led by UCL researchers.
The authors of the new study, published in Neurology, say that older adults who engage in self-reflection may have a reduced risk of dementia.
Lead author, Ph.D. student Harriet Demnitz-King (UCL Psychiatry), says that “there is a growing body of evidence finding that positive psychological factors, such as purpose in life and conscientiousness, may reduce the risk of dementia.
Finding further ways to reduce the risk of dementia is an urgent priority, so we hope that as self-reflection capabilities can be improved upon, it could be a useful tool in helping people to stay cognitively healthy as they age.”
The study used cross-sectional data (rather than reporting results of the trial interventions) from two clinical trials, Age-Well and SCD-Well, that included a total of 259 participants with mean ages of 69 and 73. They answered questions about reflective pondering, measuring how often they think about and try to understand their thoughts and feelings.
Previous research has shown that self-reflection capabilities can be improved with a recently tested psychological intervention, and the researchers say that such a program might be useful for people at risk of dementia.
Harriet Demnitz-King explained that “other studies have found that a self-reflective thinking style leads to a more adaptive stress response, with evidence even showing improvements in inflammatory responses to stress and better cardiovascular health, so this may be how self-reflection could improve our resilience against cognitive decline.”
They caution that while their findings suggest that engagement in self-reflection helps to preserve cognition, they cannot rule out that it might instead be that people with better cognition are also better able to self-reflect, and suggest that more, longitudinal research is needed to determine the direction of causation.
Senior author Dr. Natalie Marchant (UCL Psychiatry) says that “with no disease-modifying treatments yet available, it is important that we find ways to prevent dementia; by finding out which factors make dementia or cognitive decline more or less likely, we may be able to develop ways to target these factors and reduce dementia risk.”
Previous studies by Dr. Marchant have found that repetitive negative thinking may increase the risk of Alzheimer’s disease, while mindfulness may help to improve cognition in older adults.
Dr. Richard Oakley, Associate Director of Research at Alzheimer’s Society, commented that “in this study researchers showed for the first time that self-reflection—reflecting on your own thoughts, feelings and behaviors—was linked to better brain function in areas of the brain known to be affected by dementia.”
“While more research is needed to fully understand the implications of this finding, if self-reflection does seem to have a positive effect on brain function, there’s a chance one day we could reduce the risk of dementia with psychological treatments that help people build healthy thought patterns.”
There is evidence that meditation influences affective regulation and can reduce negative affect (Travis et al., 2018). It has also been shown to influence cognition and perception as well as physiological processes. Several studies have addressed physiological (see Ooi et al. (Ooi et al., 2017) for a review) and cerebral processing in experienced and/or non-experienced meditation practitioners.
The results show overall reduced alertness as well as a shift to vagal dominance. However, results have depended not only on meditation experience but also on the technique used. Focused attention meditation (FAM) has been associated with increased gamma electroencephalography (EEG) power and coherence (Braboszcz et al., 2017), and open monitoring meditation (OMM) with increases in theta, alpha, and beta bands (Ahani et al., 2013). Self-transcending meditation has been described as related to alpha EEG bands (Travis et al., 2010).
The rapidly progressing science of meditation has led to insights into the neural correlates of different forms of meditation (FAM, OMM, compassion meditation [CM] and loving kindness meditation [LKM]) in regard to states and traits. In most techniques, attention is an important part of the process of meditation. Traditional yoga teaching describes two stages of meditation that occur in sequence: meditative focusing (dharana) and effortless meditation (dhyana) (Boccia et al., 2015).
Especially the first step, meditative focusing, is a well-described form of FAM that leads to effortless meditation. This mediation method can thus be used as a straightforward method to teach FAM in an experimental setting. Therefore, the intention of our study was to evaluate FAM. As an introduction to possible influences of FAM on cerebral processes, we provide an overview of published results dealing with this type of meditative practice to identify potential influences on cerebral processes.
With Resting state fMRI (rsfMRI), an influence of meditation on functional connectivity between affective networks and the posterior cingulate (PCC) and precuneus has been described—specifically, in regions involved in self-referential processing was analyzed. The extent of this influence has depended on the different meditative stages (Baerentsen et al., 2010).
Default mode network (DMN) functional connectivity and visual network analyses are presented by Berkovich-Ohana et al. (2016), showing that connectivity within both networks was lower in meditators and correlated with meditative experience. Especially in mindfulness meditation and FAM, deactivation of DMN has been described, which underscores the theory that these techniques are goal-oriented and result in the directing of attention (Brewer et al., 2011; Simon & Engstrom, 2015).
An extensive review of fMRI and PET studies with meditators provides an overview of various meditative techniques and their influence on brain activation patterns (Fox et al., 2016). Even though there are some brain areas that are recruited consistently across multiple techniques, including insula, pre/SMA, dorsal ACC, and the frontopolar cortex, a convergence of these areas, that is, recruitment of the same areas for all of the meditation techniques investigated, is described by the authors as the exception.
This suggests that studies should choose meditation techniques carefully and ensure that they are performed in a standardized manner for all participants. Longitudinal studies should thus be helpful in overcoming such confounding variables. This underscores the choice of FAM in our longitudinal study as a structured, focused method that leads to comparable results within a group of participants.
To date, however, longitudinal studies addressing the effects of meditation on cerebral processing are rare. One recent study reported effects of a 4-week Sahaja Yoga meditation training program on GM density and spontaneous resting-state brain activity in a group of 12 meditation-naïve healthy adults (Dodich et al., 2019). The authors showed that meditators had an increase in GM and changes in brain activation coherence in the right inferior frontal gyrus.
A second study, with a longitudinal design and graph-based analysis of rsfMRI, included elderly participants taking part in meditation training versus relaxation training for 8 weeks. The results showed that meditation training led to decreased intra-connectivity in the DMN, salience network and SMN modules post-training and decreased connectivity strength between the DMN and other modules. At a local level, meditation training lowered nodal strength in the left PCC, bilateral paracentral lobule and middle cingulate gyrus. The authors conclude that these changes represent a movement toward a more self-detached perspective as well as more efficient cerebral processing. The results also support the hypothesis that short-term meditation may be a beneficial method of mental training for the elderly (Cotier et al., 2017).
So far, capturing the BOLD effect (fMRI) has played a central role in measuring brain function, which is an indirect way to measure energy expenditure of the human brain. A more direct approach is the dynamic measurement of tissue concentrations of high-energy phosphates as done in the current study. Resting state fMRI (rsfMRI) may be the method closest to the 31P-MRS approach. MRS studies of meditative state are rare and have thus far been conducted only with 1H-MRS which does not measure the energetic state of the brain.
31P-MRS is a particularly promising MRS method in this context as it allows the measurement of cerebral energy metabolism in vivo. Some authors have even used this method for functional MRI of the visual cortex to indicate the connection between cerebral function and 31P MRS (Chen et al., 1997). To date, there have been no studies using phosphorous MR spectroscopy (31P-MRS) under meditation.
The unique advantage of 31P-MRS is that it allows the measurement of various metabolites of energy metabolism and membrane turnover: adenosine triphosphate (ATP), phosphocreatine (PCr), inorganic phosphate (Pi) as well as various phosphomonoesters (PME) and phosphodiesters (PDE) (Chaumeil et al., 2009; Hugg et al., 1992; Wijnen et al., 2010). Direct quantification of metabolite concentrations from 31P spectra is complicated due to factors such as coil sensitivity, field inhomogeneity, and relaxation time.
However, some metabolite ratios have been shown to offer relevant and stable results and may therefore be seen as established ratios for the interpretation of energetic changes in cerebral areas. Changes in phosphorylation—indicated by Pi/ATP ratio—have been described as a marker of brain bioenergetics (D’Rozario et al., 2018). The PCr/ATP ratio has been interpreted as a marker of energetic state and phosphorylation potential. In ATP deficiency, creatine kinase equilibrium buffers ATP, resulting in a decrease of PCr and an increase of Pi and free creatine. Therefore, the Pi/ATP ratio may be interpreted as a marker of the amount of ATP turnover.
Furthermore, brain pH can be measured using 31P-MRS by calculating the chemical shift difference between PCr and Pi (Cichocka et al., 2015; Petroff & Prichard, 1983). Normal brain activity can be associated with task-related pH changes, as can environmental factors such as altitude (Shi et al., 2014). Therefore, 31P-MRS seems to be an interesting additional method (to fMRI or volume-based analysis) to identify changes in cerebral processes under meditative training and to provide additional insights into results from fMRI while direct measurement of energy metabolism.
Even short-term meditative practice seems to affect brain activation and connectivity in rsfMRI studies, which led to our hypothesis that meditation should also affect brain energy metabolism in a longitudinal study. As changes in energy metabolites represent a form of functional information of the brain without direct dependency on blood oxygenation as in fMRI, MRS can be an addition to the already reported functional changes due to meditation. However, it is essential to bear in mind that individual mental state varies over time and throughout the day. Therefore, paradigms and settings used to study meditative effects on the brain should be designed carefully in order to exclude confounding factors as much as possible.
reference link : https://onlinelibrary.wiley.com/doi/full/10.1002/brb3.1914
Original Research: Closed access.
“Association Between Self-Reflection, Cognition, and Brain Health in Cognitively Unimpaired Older Adults” by Harriet Demnitz-King et al. Neurology
