Can compassion be trained like a muscle? Active-controlled fMRI of compassion meditation.

Among the cognitive training literature, meditation interventions are particularly unique in that they often emphasize emotional or affective processing at least as much as classical ‘top-down’ attentional control. From a clinical and societal perspective, the idea that we might be able to “train” our “emotion muscle” is an attractive one. Recently much has been made of the “empathy deficit” in the US, ranging from empirical studies suggesting a relationship between quality-of-care and declining caregiver empathy, to a recent push by President Obama to emphasize the deficit in numerous speeches.

While much of the training literature focuses on cognitive abilities like sustained attention and working memory, many investigating meditation training have begun to study the plasticity of affective function, myself included.  A recent study by Helen Weng and colleagues in Wisconsin investigated just this question, asking if compassion (“loving-kindness”) meditation can alter altruistic behavior and associated neural processing. Her study is one of the first of its kind, in that rather than merely comparing groups of advanced practitioners and controls, she utilized a fully-randomized active-controlled design to see if compassion responds to brief training in novices while controlling for important confounds.

As many readers should be aware, a chronic problem in training studies is a lack of properly controlled longitudinal design. At best, many rely on “passive” or “no-contact” controls who merely complete both measurements without receiving any training. Even in the best of circumstances “active” controls are often poorly matched to whatever is being emphasized and tested in the intervention of interest. While having both groups do “something” is better than a passive or no-control design, problems may still arise if the measure of interest is mismatched to the demand characteristics of the study.  Stated simply, if your condition of interest receives attention training and attention tests, and your control condition receives dieting instruction or relaxation, you can expect group differences to be confounded by an explicit “expectation to improve” in the interest group.

In this regard Weng et al present an almost perfect example of everything a training study should be. Both interventions were delivered via professionally made audio CDs (you can download them yourselves here!), with participants’ daily practice experiences being recorded online. The training materials were remarkably well matched for the tests of interest and extra care was taken to ensure that the primary measures were not presented in a biased way. The only thing they could have done further would be a single blind (making sure the experimenters didn’t know the group identity of each participant), but given the high level of difficulty in blinding these kinds of studies I don’t blame them for not undertaking such a manipulation. In all the study is extremely well-controlled for research in this area and I recommend it as a guideline for best practices in training research.

Specifically, Weng et al tested the impact of loving-kindness compassion meditation or emotion reappraisal training on an emotion regulation fMRI task and behavioral economic game measuring altruistic behavior. For the fMRI task, participants viewed emotional pictures (IAPS) depicting suffering or neutral scenarios and either practiced a compassion meditation or reappraisal strategy to regulate their emotional response, before and after training. After the follow-up scan, good-old fashion experimental deception was used to administer a dictator economics-game that was ostensibly not part of the primary study and involved real live players (both deceptions).

For those not familiar with the dictator game, the concept is essentially that a participant watches a “dictator” endowed with 100$ give “unfair” offers to a “victim” without any money. Weng et al took great care in contextualizing the test purely in economic terms, limiting demand confounds:

Participants were told that they were playing the game with live players over the Internet. Effects of demand characteristics on behavior were minimized by presenting the game as a unique study, describing it in purely economic terms, never instructing participants to use the training they received, removing the physical presence of players and experimenters during game play, and enforcing real monetary consequences for participants’ behavior.

This is particularly important, as without these simple manipulations it would be easy for stodgy reviewers like myself to worry about subtle biases influencing behavior on the task. Equally important is the content of the two training programs. If for example, Weng et al used a memory training or attention task as their active-control group, it would be difficult not to worry that behavioral differences were due to one group expecting a more emotional consequence of the study, and hence acting more altruistic. In the supplementary information, Weng et al describe the two training protocols in great detail:


… Participants practiced compassion for targets by 1) contemplating and envisioning their suffering and then 2) wishing them freedom from that suffering. They first practiced compassion for a Loved One, such as a friend or family member. They imagined a time their loved one had suffered (e.g., illness, injury, relationship problem), and were instructed to pay attention to the emotions and sensations this evoked. They practiced wishing that the suffering were relieved and repeated the phrases, “May you be free from this suffering. May you have joy and happiness.” They also envisioned a golden light that extended from their heart to the loved one, which helped to ease his/her suffering. They were also instructed to pay attention to bodily sensations, particularly around the heart. They repeated this procedure for the Self, a Stranger, and a Difficult Person. The Stranger was someone encountered in daily life but not well known (e.g., a bus driver or someone on the street), and the Difficult Person was someone with whom there was conflict (e.g., coworker, significant other). Participants envisioned hypothetical situations of suffering for the stranger and difficult person (if needed) such as having an illness or experiencing a failure. At the end of the meditation, compassion was extended towards all beings. For each new meditation session, participants could choose to use either the same or different people for each target category (e.g., for the loved one category, use sister one day and use father the next day).


… Participants were asked to recall a stressful experience from the past 2 years that remained upsetting to them, such as arguing with a significant other or receiving a lower-than- expected grade. They were instructed to vividly recall details of the experience (location, images, sounds). They wrote a brief description of the event, and chose one word to best describe the feeling experienced during the event (e.g., sad, angry, anxious). They rated the intensity of the feeling during the event, and the intensity of the current feeling on a scale (0 = No feeling at all, 100 = Most intense feeling in your life). They wrote down the thoughts they had during the event in detail. Then they were asked to reappraise the event (to think about it in a different, less upsetting way) using 3 different strategies, and to write down the new thoughts. The strategies included 1) thinking about the situation from another person’s perspective (e.g., friend, parent), 2) viewing it in a way where they would respond with very little emotion, and 3) imagining how they would view the situation if a year had passed, and they were doing very well. After practicing each strategy, they rated how reasonable each interpretation was (0 = Not at all reasonable, 100 = Completely reasonable), and how badly they felt after considering this view (0 = Not bad at all, 100 = Most intense ever). Day to day, participants were allowed to practice reappraisal with the same stressful event, or choose a different event. Participants logged the amount of minutes practiced after the session.

In my view the active control is extremely well designed for the fMRI and economic tasks, with both training methods explicitly focusing on the participant altering an emotional response to other individuals.  In tests of self-rated efficacy, both groups showed significant decreases in negative emotion, further confirming the active control. Interestingly when Weng et al compared self-ratings over time, only the compassion group showed significant reduction from the first half of training sessions to the last. I’m not sure if this constitutes a limitation, as Weng et al further report that on each individual training day the reappraisal group reported significant reductions, but that the reductions themselves did not differ significantly over time. They explain this as being likely due to the fact that the reappraisal group frequently changed emotional targets, whereas the compassion group had the same 3 targets throughout the training. Either way the important point is that both groups self-reported similar overall reductions in negative emotion during the course of the study, strongly supporting the active control.

Now what about the findings? As mentioned above, Weng et al tested participants before and after training on an fMRI emotion regulation task. After the training, all participants performed the “dictator game”, shown below. After rank-ordering the data, they found that the compassion group showed significantly greater redistribution:

The dictator task (left) and increased redistribution (right).

For the fMRI analysis, they analyzed BOLD responses to negative vs neutral images at both time points, subtracted the beta coefficients, and then entered these images into a second-level design matrix testing the group difference, with the rank-ordered redistribution scores as a covariate of interest. They then tested for areas showing group differences in the correlation of redistribution scores and changes of BOLD response to negative vs neutral images (pre vs post), across the whole brain and in several ROIs, while properly correcting for multiple comparisons. Essentially this analysis asks, where in the brain do task-related changes in BOLD correlate more or less with the redistribution score in one group or another. For the group x covariate interaction they found significant differences (increased BOLD-covariate correlation) in the right inferior parietal cortex (IPC), a region of the parietal attention network, shown on the left-hand panel:

Increased correlation between negative vs neutral imagery related BOLD and redistribution scores (left), connectivity with DLPFC (right).

They further extracted signal from the IPC cluster and entered it into a conjunction analysis, testing for areas showing significant correlation  with the IPC activity, and found a strong effect in right DLPFC (right panel). Finally they performed a psychophysiological interaction (PPI) analysis with the right DLPFC activity as the seed, to determine regions showing significant task-modulated connectivity with that DLPFC activity. The found increased emotion-modulated DLPFC connectivity to nucleus accumbens, a region involved in encoding positive rewards (below, right).

Screen shot 2013-05-23 at 3.21.15 PM
PPI shows increased emotion-modulated connectivity of nucleus accumbens and DLPFC.

Together these results implicate training-related BOLD activity increases to emotional stimuli in the parietal attention network and increased parietal connectivity with regions implicated in cognitive control and reward processing, in the observed altruistic behavior differences. The authors conclude that compassion training may alter emotional processing through a novel mechanism, where top-down central-executive circuits redirect emotional information to areas associated with positive reward, reflecting the role of compassion meditation in emphasizing increased positive emotion to the aversive states of others. A fitting and interesting conclusion, I think.

Overall, the study should receive high marks for its excellent design and appropriate statistical rigor. There is quite a bit of interesting material in the supplementary info, a strategy I dislike, but that is no fault of the authors considering the publishing journal (Psych Science). The question itself is extremely novel, in terms of previous active-controlled studies. To date only one previous active-controlled study investigated the role of compassion meditation on empathy-related neuroplasticity. However that study compared compassion meditation with a memory strategy course, which (in my opinion) exposes it to serious criticism regarding demand characteristic. The authors do reference that study, but only briefly to state that both studies support a role of compassion training in altering positive emotion- personally I would have appreciated a more thorough comparison, though I suppose I can go and to that myself if I feel so inclined :).

The study does have a few limitations worth mentioning. One thing that stood out to me was that the authors never report the results of the overall group mean contrast for negative vs neutral images. I would have liked to know if the regions showing increased correlation with redistribution actually showed higher overall mean activation increases during emotion regulation. However as the authors clearly had quite specific hypotheses, leading them to  restrict their alpha to 0.01 (due to testing 1 whole-brain contrast and 4 ROIs), I can see why they left this out. Given the strong results of the study, it would in retrospect perhaps have been more prudent to skip  the ROI analysis (which didn’t seem to find much) and instead focus on testing the whole brain results.  I can’t blame them however, as it is surprising not to see anything going on in insula or amygdala for this kind of training.  It is also a bit unclear to me why the DLPFC was used as the PPI seed as opposed to the primary IPC cluster, although I am somewhat unfamiliar with the conjunction-connectivity analysis used here. Finally, as the authors themselves point out, a major limitation of the study is that the redistribution measure was collected only at time two, preventing a comparison to baseline for this measure.

Given the methodological state of the topic (quite poor, generally speaking), I am willing to grant them these mostly minor caveats. Of course, without a baseline altruism measure it is difficult to make a strong conclusion about the causal impact of the meditation training on altruism behavior, but at least their neural data are shielded from this concern. So while we can’t exhaustively conclude that compassion can be trained, the results of this study certainly suggest it is possible and perhaps even likely, providing a great starting point for future research. One interesting thing for me was the difference in DLPFC. We also found task-related increases in dorsolateral prefrontal cortex following active-controlled meditation, although in the left hemisphere and for a very different kind of training and task. One other recent study of smoking cessation also reported alteration in DLPFC following mindfulness training, leading me to wonder if we’re seeing the emergence of empirical consensus for this region’s specific involvement in meditation training. Another interesting point for me was that affective regulation here seems to involve primarily top-down or attention related neural correlates,  suggesting that bottom-up processing (insula, amygdala) may be more resilient to brief training, something we also found in our study. I wonder if the group mean-contrasts would have been revealing here (i.e. if there were differences in bottom-up processing that don’t correlate with redistribution). All together a great study that raises the bar for training research in cognitive neuroscience!

Mental Training and Neuroplasticity – PhD Complete!

I was asked to write a brief summary of my PhD research for our annual CFIN report. I haven’t blogged in a while and it turned out to be a decent little blurb, so I figured I might as well share it here. Enjoy!

In the past decade, reports concerning the natural plasticity of the human brain have taken a spotlight in the media and popular imagination. In the pursuit of neural plasticity nearly every imaginable specialization, from taxi drivers to Buddhist monks, has had their day in the scanner. These studies reveal marked functional and structural neural differences between various populations of interest, and in doing so drive a wave of interest in harnessing the brain’s plasticity for rehabilitation, education, and even increasing intelligence (Green and Bavelier, 2008). Under this new “mental training” research paradigm investigators are now examining what happens to brain and behavior when novices are randomized to a training condition, using longitudinal brain imaging.


These studies highlight a few promising domains for harnessing neural plasticity, particularly in the realm of visual attention, cognitive control, and emotional training. By randomizing novices to a brief ‘dose’ of action video game or meditation training, researchers can go beyond mere cross-section and make inferences regarding the causality of training on observed neural outcomes. Initial results are promising, suggesting that domains of great clinical relevance such as emotional and attentional processing are amenable to training (Lutz et al., 2008a; Lutz et al., 2008b; Bavelier et al., 2010). However, these findings are currently obscured by a host of methodological limitations.

These span from behavioral confounds (e.g. motivation and demand characteristic) to inadequate longitudinal processing of brain images, which present particular challenges not found in within-subject or cross-sectional design (Davidson, 2010; Jensen et al., 2011). The former can be addressed directly by careful construction of “active control” groups. Here both comparison and control groups receive putatively effective treatments, carefully designed to isolate the hypothesized “active-ingredients” involved in behavioral and neuroplasticity outcomes. In this way researchers can simultaneously make inferences in terms of mechanistic specificity while excluding non-specific confounds such as social support, demand, and participant motivation.

image2_meditationbrainWe set out to investigate one particularly popular intervention, mindfulness meditation, while controlling for these factors. Mindfulness meditation has enjoyed a great deal of research interest in recent years. This popularity is largely due to promising findings indicating good efficacy of meditation training (MT) for emotion processing and cognitive control (Sedlmeier et al., 2012). Clinical studies indicate that MT may be particularly effective for disorders that are typically non-responsive to cognitive-behavioral therapy, such as severe depression and anxiety (Grossman et al., 2004; Hofmann et al., 2010). Understanding the neural mechanism underlying such benefits remains difficult however, as most existing investigations are cross-sectional in nature or depend upon inadequate “wait-list” passive control groups.

We addressed these difficulties in an investigation of functional and structural neural plasticity before and after a 6-week active-controlled mindfulness intervention. To control demand, social support, teacher enthusiasm, and participant motivation we constructed a “shared reading and listening” active control group for comparison to MT. By eliciting daily “experience samples” regarding participants’ motivation to practice and minutes practiced, we ensured that groups did not differ on common motivational confounds.

We found that while both groups showed equivalent improvement on behavioral response-inhibition and meta-cognitive measures, only the MT group significantly reduced affective-Stroop conflict reaction times (Allen et al., 2012). Further we found that MT participants show significantly greater increases in recruitment of dorsolateral prefrontal cortex than did controls, a region implicated in cognitive control and working memory. Interestingly we did not find group differences in emotion-related reaction times or BOLD activity; instead we found that fronto-insula and medial-prefrontal BOLD responses in the MT group were significantly more correlated with practice than in controls. These results indicate that while brief MT is effective for training attention-related neural mechanisms, only participants with the greatest amount of practice showed altered neural responses to negative affective stimuli. This result is important because it underlines the differential response of various target skills to training and suggests specific applications of MT depending on time and motivation constraints.

MT related increase in DLPFC activity during affective stroop task.
MT related increase in DLPFC activity during affective stroop task.

In a second study, we utilized a longitudinally optimized pipeline to assess structural neuroplasticity in the same cohort as described above (Ashburner and Ridgway, 2012). A crucial issue in longitudinal voxel-based morphometry and similar methods is the prevalence of “asymmetrical preprocessing”, for example where normalization parameters are calculated from baseline images and applied to follow-up images, resulting in inflated risk of false-positive results. We thus applied a totally symmetrical deformation-based morphometric pipeline to assess training related expansions and contractions of gray matter volume. While we found significant increases within the MT group, these differences did not survive group-by-time comparison and thus may represent false positives; it is likely that such differences would not be ruled out by an asymmetric pipeline or non-active controlled designed. These results suggest that brief MT may act only on functional neuroplasticity and that greater training is required for more lasting anatomical alterations.

These projects are a promising advance in our understanding of neural plasticity and mental training, and highlight the need for careful methodology and control when investigating such phenomena. The investigation of neuroplasticity mechanisms may one day revolutionize our understanding of human learning and neurodevelopment, and we look forward to seeing a new wave of carefully controlled investigations in this area.

You can read more about the study in this blog post, where I explain it in detail. 

A happy day, my PhD defense!
A happy day, my PhD defense!


Allen M, Dietz M, Blair KS, van Beek M, Rees G, Vestergaard-Poulsen P, Lutz A, Roepstorff A (2012) Cognitive-Affective Neural Plasticity following Active-Controlled Mindfulness Intervention. The Journal of Neuroscience 32:15601-15610.

Ashburner J, Ridgway GR (2012) Symmetric diffeomorphic modeling of longitudinal structural MRI. Frontiers in neuroscience 6.

Bavelier D, Levi DM, Li RW, Dan Y, Hensch TK (2010) Removing brakes on adult brain plasticity: from molecular to behavioral interventions. The Journal of Neuroscience 30:14964-14971.

Davidson RJ (2010) Empirical explorations of mindfulness: conceptual and methodological conundrums. Emotion 10:8-11.

Green C, Bavelier D (2008) Exercising your brain: a review of human brain plasticity and training-induced learning. Psychology and Aging; Psychology and Aging 23:692.

Grossman P, Niemann L, Schmidt S, Walach H (2004) Mindfulness-based stress reduction and health benefits: A meta-analysis. Journal of Psychosomatic Research 57:35-43.

Hofmann SG, Sawyer AT, Witt AA, Oh D (2010) The effect of mindfulness-based therapy on anxiety and depression: A meta-analytic review. Journal of consulting and clinical psychology 78:169.

Jensen CG, Vangkilde S, Frokjaer V, Hasselbalch SG (2011) Mindfulness training affects attention—or is it attentional effort?

Lutz A, Brefczynski-Lewis J, Johnstone T, Davidson RJ (2008a) Regulation of the neural circuitry of emotion by compassion meditation: effects of meditative expertise. PLoS One 3:e1897.

Lutz A, Slagter HA, Dunne JD, Davidson RJ (2008b) Attention regulation and monitoring in meditation. Trends Cogn Sci 12:163-169.

Sedlmeier P, Eberth J, Schwarz M, Zimmermann D, Haarig F, Jaeger S, Kunze S (2012) The psychological effects of meditation: A meta-analysis.

Mindfulness and neuroplasticity – summary of my recent paper.

First, let me apologize for an overlong hiatus from blogging. I submitted my PhD thesis October 1st, and it turns out that writing two papers and a thesis in the space of about three months can seriously burn out the old muse. I’ve coaxed her back through gentle offerings of chocolate, caffeine, and a bit of videogame binging. As long as I promise not to bring her within a mile of a dissertation, I believe we’re good for at least a few posts per month.

With that taken care of, I am very happy to report the successful publication of my first fMRI paper, published last month in the Journal of Neuroscience. The paper was truly a labor of love taking nearly 3 years to complete and countless hours of head-scratching work. In the end I am quite happy with the finished product, and I do believe my colleagues and I managed to produce a useful result for the field of mindfulness training and neuroplasticity.

note: this post ended up being quite long. if you are already familiar with mindfulness research, you may want to skip ahead!

Why mindfulness?

First, depending on what brought you here, you may already be wondering why mindfulness is an interesting subject, particularly for a cognitive neuroscientist. In light of the large gaps regarding our understanding of the neurobiological foundations of neuroimaging, is it really the right time to apply these complex tools to meditation?  Can we really learn anything about something as potentially ambiguous as “mindfulness”? Although we have a long way to go, and these are certainly fair questions, I do believe that the study of meditation has a lot to contribute to our understanding of cognition and plasticity.

Generally speaking, when you want to investigate some cognitive phenomena, a firm understanding of your target is essential to successful neuroimaging. Areas with years of behavioral research and concrete theoretical models make for excellent imaging subjects, as in these cases a researcher can hope to fall back on a sort of ‘ground truth’ to guide them through the neural data, which are notoriously ambiguous and difficult to interpret. Of course well-travelled roads also have their disadvantages, sometimes providing a misleading sense of security, or at least being a bit dry. While mindfulness research still has a ways to go, our understanding of these practices is rapidly evolving.

At this point it helps to stop and ask, what is meditation (and by extension, mindfulness)? The first thing to clarify is that there is no such thing as “meditation”- rather meditation is really term describing a family resemblance of highly varied practices, covering an array of both spiritual and secular practices. Meditation or “contemplative” practices have existed for more than a thousand years and are found in nearly every spiritual tradition. More recently, here in the west our unending fascination of the esoteric has lead to a popular rise in Yoga, Tai Chi, and other physically oriented contemplative practices, all of which incorporate an element of meditation.

At the simplest level of description [mindfulness] meditation is just a process of becoming aware, whether through actual sitting meditation, exercise, or daily rituals.  Meditation (as a practice) was first popularized in the west during the rise of transcendental meditation (TM). As you can see in the figure below, interest in TM lead to an early boom in research articles. This boom was not to last, as it was gradually realized that much of this initially promising research was actually the product of zealous insiders, conducted with poor controls and in some cases outright data fabrication. As TM became known as  a cult, meditation research underwent a dark age where publishing on the topic could seriously damage a research career. We can see also that around the 1990’s, this trend started to reverse as a new generation of researchers began investigating “mindfulness” meditation.

pubmed graphy thing
Sidenote: research everywhere is expanding. Shouldn’t we start controlling these highly popular “pubs over time” figures for total publishing volume? =)

It’s easy to see from the above why when Jon Kabat-Zinn re-introduced meditation to the West, he relied heavily on the medical community to develop a totally secularized intervention-oriented version of meditation strategically called “mindfulness-based stress reduction.” The arrival of MBSR was closely related to the development of mindfulness-based cognitive therapy (MBCT), a revision of cognitive-behavioral therapy utilizing mindful practices and instruction for a variety of clinical applications. Mindfulness practice is typically described as involving at least two practices; focused attention (FA) and open monitoring (OM). FA can be described as simply noticing when attention wanders from a target (the breath, the body, or a flower for example) and gently redirecting it back to that target. OM is typically (but not always) trained at an later stage, building on the attentional skills developed in FA practice to gradually develop a sense of “non-judgmental open awareness”. While a great deal of work remains to be done, initial cognitive-behavioral and clinical research on mindfulness training (MT) has shown that these practices can improve the allocation of attentional resources, reduce physiological stress, and improve emotional well-being. In the clinic MT appears to effectively improve symptoms on a variety of pathological syndromes including anxiety and depression, at least as well as standard CBT or pharmacological treatments.

Has the quality of research on meditation improved since the dark days of TM? When answering this question it is important to note two things about the state of current mindfulness research. First, while it is true that many who research MT are also practitioners, the primary scholars are researchers who started in classical areas (emotion, clinical psychiatry, cognitive neuroscience) and gradually became involved in MT research. Further, most funding today for MT research comes not from shady religious institutions, but from well-established funding bodies such as the National Institute of Health and European Research Council. It is of course important to be aware of the impact prior beliefs can have on conducting impartial research, but with respect to today’s meditation and mindfulness researchers, I believe that most if not all of the work being done is honest, quality research.

However, it is true that much of the early MT research is flawed on several levels. Indeed several meta-analyses have concluded that generally speaking, studies of MT have often utilized poor design – in one major review only 8/22 studies met criteria for meta-analysis. The reason for this is quite simple- in the absence of pilot data, investigators had to begin somewhere. Typically it doesn’t bode well to jump into unexplored territory with an expensive, large sample, fully randomized design. There just isn’t enough to go off of- how would you know which kind of process to even measure? Accordingly, the large majority of mindfulness research to date has utilized small-scale, often sub-optimal experimental design, sacrificing experimental control in order build a basic idea of the cognitive landscape. While this exploratory research provides a needed foundation for generating likely hypotheses, it is also difficult to make any strong conclusions so long as methodological issues remain.

Indeed, most of what we know about these mindfulness and neuroplasticity comes from studies of either advanced practitioners (compared to controls) or “wait-list” control studies where controls receive no intervention. On the basis of the findings from these studies, we had some idea how to target our investigation, but there remained a nagging feeling of uncertainty. Just how much of the literature would actually replicate? Does mindfulness alter attention through mere expectation and motivation biases (i.e. placebo-like confounds), or can MT actually drive functionally relevant attentional and emotional neuroplasticity, even when controlling for these confounds?

The name of the game is active-control

Research to date links mindfulness practices to alterations in health and physiology, cognitive control, emotional regulation, responsiveness to pain, and a large array of positive clinical outcomes. However, the explicit nature of mindfulness training makes for some particularly difficult methodological issues. Group cross-sectional studies, where advanced practitioners are compared to age-matched controls, cannot provide causal evidence. Indeed, it is always possible that having a big fancy brain makes you more likely to spend many years meditating, and not that meditating gives you a big fancy brain. So training studies are essential to verifying the claim that mindfulness actually leads to interesting kinds of plasticity. However, unlike with a new drug study or computerized intervention, you cannot simply provide a sugar pill to the control group. Double-blind design is impossible; by definition subjects will know they are receiving mindfulness. To actually assess the impact of MT on neural activity and behavior, we need to compare to groups doing relatively equivalent things in similar experimental contexts. We need an active control.

There is already a well-established link between measurement outcome and experimental demands. What is perhaps less appreciated is that cognitive measures, particularly reaction time, are easily biased by phenomena like the Hawthorne effect, where the amount of attention participants receive directly contributes to experimental outcome. Wait-lists simply cannot overcome these difficulties. We know for example, that simply paying controls a moderate performance-based financial reward can erase attentional reaction-time differences. If you are repeatedly told you’re training attention, then come experiment time you are likely expect this to be true and try harder than someone who has received no such instruction. The same is true of emotional tasks; subjects told frequently they are training compassion are likely to spend more time fixating on emotional stimuli, leading to inflated self-reports and responses.

I’m sure you can quickly see how it is extremely important to control for these factors if we are to isolate and understand the mechanisms important for mindfulness training. One key solution is active-control, that is providing both groups (MT and control) with a “treatment” that is at least nominally as efficacious as the thing you are interested in. Active-control allows you exclude numerous factors from your outcome, potentially including the role of social support, expectation, and experimental demands. This is exactly what we set out to do in our study, where we recruited 60 meditation-naïve subjects, scanned them on an fMRI task, randomized them to either six weeks of MT or active-control, and then measured everything again. Further, to exclude confounds relating to social interaction, we came up with a particularly unique control activity- reading Emma together.

Jane Austen as Active Control – theory of mind vs interoception

To overcome these confounds, we constructed a specialized control intervention. As it was crucial that both groups believed in their training, we needed an instructor who could match the high level of enthusiasm and experience found in our meditation instructors. We were lucky to have the help of local scholar Mette Stineberg, who suggested a customized “shared reading” group to fit our purposes. Reading groups are a fun, attention demanding exercise, with purported benefits for stress and well-being. While these claims have not been explicitly tested, what mattered most was that Mette clearly believed in their efficacy- making for a perfect control instructor. Mette holds a PhD in literature, and we knew that her 10 years of experience participating in and leading these groups would help us to exclude instructor variables from our results.

With her help, we constructed a special condition where participants completed group readings of Jane Austin’s Emma. A sensible question to ask at this point is – “why Emma?” An essential element of active control is variable isolation, or balancing your groups in such way that, with the exception of your hypothesized “active ingredient”, the two interventions are extremely similar. As MT is thought to depend on a particular kind of non-judgmental, interoceptive kind of attention, Chris and Uta Frith suggested during an early meeting that Emma might be a perfect contrast. For those of you who haven’t read the novel, the plot is brimming over with judgment-heavy theory-of-mind-type exposition. Mette further helped to ensure a contrast with MT by emphasizing discussion sessions focused on character motives. In this way we were able to ensure that both groups met for the same amount of time each week, with equivalently talented and passionate instructors, and felt that they were working towards something worthwhile. Finally, we made sure to let every participant know at recruitment that they would receive one of two treatments intended to improve attention and well-being, and that any benefits would depend upon their commitment to the practice. To help them practice at home, we created 20-minute long CD’s for both groups, one with a guided meditation and the other with a chapter from Emma.

Unlike previous active-controlled studies that typically rely on relaxation training, reading groups depend upon a high level of social-interaction. Reading together allowed us not only to exclude treatment context and expectation from our results, but also more difficult effects of social support (the “making new friends” variable). To measure this, we built a small website for participants to make daily reports of their motivation and minutes practiced that day. As you can see in the figure below, when we averaged these reports we found that not only did the reading group practice significantly more than those in MT, but that they expressed equivalent levels of motivation to practice. Anecdotally we found that reading-group members expressed a high level of satisfaction with their class, with a sub-group of about 8 even continued their meetings after our study concluded. The meditation group by comparison, did not appear to form any lasting social relationships and did not continue meeting after the study. We were very happy with these results, which suggest that it is very unlikely our results could be explained by unbalanced motivation or expectation.

Impact of MT on attention and emotion

After we established that active control was successful, the first thing to look at was some of our outside-the-scanner behavioral results. As we were interested in the effect of meditation on both attention and meta-cognition, we used an “error-awareness task” (EAT) to examine improvement in these areas. The EAT (shown below) is a typical “go-no/go” task where subjects spend most of their time pressing a button. The difficult part comes whenever a “stop-trial” occurs and subject must quickly halt their response. In the case where the subject fails to stop, they then have the opportunity to “fix” the error by pressing a second button on the trial following the error. If you’ve ever taken this kind of task, you know that it can be frustratingly difficult to stop your finger in time – the response becomes quite habitual. Using the EAT we examined the impact of MT on both controlling responses (a variable called “stop accuracy”), as well as also on meta-cognitive self-monitoring (percent “error-awareness”).

The error-awareness task

We started by looking for significant group by time interactions on stop accuracy and error-awareness, which indicate that score fluctuation on a measure was statistically greater in the treatment (MT) group than in the control group. In repeated-measures design, this type of interaction is your first indication that the treatment may have had a greater effect than the control group. When we looked at the data, it was immediately clear that while both groups improved over time (a ‘main effect’ of time), there was no interaction to be found:

Group x time analysis of SA and EA.

While it is likely that much of the increase over time can be explained by test-retest effects (i.e. simply taking the test twice), we wanted to see if any of this variance might be explained by something specific to meditation. To do this we entered stop accuracy and error-awareness into a linear model comparing the difference of slope between each group’s practice and the EAT measures. Here we saw that practice predicted stop accuracy improvement only in the meditation group, and that the this relationship was statistically greater than in the reading group:

Practice vs Stop accuracy (MT only shown). We did of course test our interaction, see paper for GLM goodness =)

These results lead us to conclude that while we did not observe a treatment effect of MT on the error-awareness task, the presence of strong time effects and MT-only correlation with practice suggested that the improvements within each group may relate to the “active ingredients” of MT but reflect motivation-driven artifacts in the reading group. Sadly we cannot conclude this firmly- we’d have needed to include a third passive control group for comparison. Thankfully this was pointed out to us by a kind reviewer, who noted that this argument is kind of like having one’s cake and eating it, so we’ll restrict ourselves to arguing that the EAT finding serves as a nice validation of the active control- both groups improved on something, and a potential indicator of a stop-related treatment mechanism.

While the EAT served as a behavioral measure of basic cognitive processes, we also wanted to examine the neural correlates of attention and emotion, to see how they might respond to mindfulness training in our intervention. For this we partnered with Karina Blair at the National Institute of Mental Health to bring the Affective Stroop task (shown below) to Denmark .

Affective Stroop Trial Scheme

The Affective Stroop Task (AST) depends on a basic “number-counting Stroop” to investigate the neural correlates of attention, emotion, and their interaction. To complete the task, your instruction is simply “count the number of numbers in the first display (of numbers), count the number of numbers in the second display, and decide which display had more number of numbers”.  As you can see in the trial example above, conflict in the task (trial-type “C”) is driven by incongruence between the Arabic numeral (e.g. “4”) and the numeracy of the display (a display of 5 “4”’s). Meanwhile, each trial has nasty or neutral emotional stimuli selected from the international affective picture system. Using the AST, we were able to examine the neural correlates of executive attention by contrasting task (B + C > A) and emotion (negative > neutral) trials.

Since we were especially interested in changes over time, we expanded on these contrasts to examine increased or decreased neural response between the first and last scans of the study. To do this we relied on two levels of analysis (standard in imaging), where at the “first” or “subject level” we examined differences between the two time points for each condition (task and emotion), within each subject. We then compared these time-related effects (contrast images) between each group using a two-sample t-test with total minutes of practice as a co-variate. To assess the impact of meditation on performing the AST, we examined reaction times in a model with factors group, time, task, and emotion. In this way we were able to examine the impact of MT on neural activity and behavior while controlling for the kinds of artifacts discussed in the previous section.

Our analysis revealed three primary findings. First, the reaction time analysis revealed a significant effect of MT on Stroop conflict, or the difference between reaction time to incongruent versus congruent trials. Further, we did not observe any effect on emotion-related RTs- although both groups sped up significantly to negative trials vs neutral (time effect), this increase was equivalent in both groups. Below you can see the stroop-conflict related RTs:

Stroop conflict result

This became particularly interesting when we examine the neural response to these conditions, and again observed a pattern of overall [BOLD signal] increases in the dorsolateral prefrontal cortex to task performance (below):

DLPFC increase to task

Interestingly, we did not observe significant overall increases to emotional stimuli  just being in the MT group didn’t seem to be enough to change emotional processing. However, when we examined correlations with amount practice and increased BOLD to negative emotion across the whole brain, we found a striking pattern of fronto-insular BOLD increases to negative images, similar to patterns seen in previous studies of compassion and mindfulness practice:

Greater association of prefrontal-insular response to negative emotion and practice
Greater association of prefrontal-insular response to negative emotion and practice.

When we put all this together, a pattern began to emerge. Overall it seemed like MT had a relatively clear impact on attention and cognitive control. Practice-correlated increases on EAT stop accuracy, reduced Affective Stroop conflict, and increases in dorsolateral prefrontal cortex responses to task all point towards plasticity at the level of executive function. In contrast our emotion-related findings suggest that alterations in affective processing occurred only in MT participants with the most practice. Given how little we know about the training trajectories of cognitive vs affective skills, we felt that this was a very interesting result.

Conclusion: the more you do, the what you get?

For us, the first conclusion from all this was that when you control for motivation and a host of other confounds, brief MT appears to primarily train attention-related processes. Secondly, alterations in affective processing seemed to require more practice to emerge. This is interesting both for understanding the neuroscience of training and for the effective application of MT in clinical settings. While a great deal of future research is needed, it is possible that the affective system may be generally more resilient to intervention than attention. It may be the case that altering affective processes depends upon and extends increasing control over executive function. Previous research suggests that attention is largely flexible, amenable to a variety of training regimens of which MT is only one beneficial intervention. However we are also becoming increasingly aware that training attention alone does not seem to directly translate into closely related benefits.

As we begin to realize that many societal and health problems cannot be solved through medication or attention-training alone, it becomes clear that techniques to increase emotional function and well-being are crucial for future development.  I am reminded of a quote overheard at the Mind & Life Summer Research Institute and attributed to the Dalai Lama. Supposedly when asked about their goal of developing meditation programs in the west, HHDL replied that, what was truly needed in the West was not “cognitive training, as (those in the west) are already too clever. What is needed rather is emotion training, to cultivate a sense of responsibility and compassion”. When we consider falling rates of empathy in medical practitioners and the link to health outcome, I think we do need to explore the role of emotional and embodied skills in supporting a wide-array of functions in cognition and well-being. While emotional development is likely to depend upon executive function, given all the recent failures to show a transfer from training these domains to even closely related ones, I suspect we need to begin including affective processes in our understanding of optimal learning. If these differences hold, then it may be important to reassess our interventions (mindful and otherwise), developing training programs that are customized in terms of the intensity, duration, and content appropriate for any given context.

Of course, rather than end on such an inspiring note, I should point out that like any study, ours is not without flaws (you’ll have to read the paper to find out how many 😉 ) and is really just an initial step. We made significant progress in replicating common neural and behavioral effects of MT while controlling for important confounds, but in retrospect the study could have been strengthened by including measures that would better distinguish the precise mechanisms, for example a measure of body awareness or empathy. Another element that struck me was how much I wish we’d had a passive control group, which could have helped flesh out how much of our time effect was instrument reliability versus motivation. As far as I am concerned, the study was a success and I am happy to have done my part to push mindfulness research towards methodological clarity and rigor. In the future I know others will continue this trend and investigate exactly what sorts of practice are needed to alter brain and behavior, and just how these benefits are accomplished.

In the near-future, I plan to give mindfulness research a rest. Not that I don’t find it fascinating or worthwhile, but rather because during the course of my PhD I’ve become a bit obsessed with interoception and meta-cognition. At present, it looks like I’ll be spending my first post-doc applying predictive coding and dynamic causal modeling to these processes. With a little luck, I might be able to build a theoretical model that could one day provide novel targets for future intervention!

Link to paper:

Cognitive-Affective Neural Plasticity following Active-Controlled Mindfulness Intervention

Thanks to all the collaborators and colleagues who made this study possible.

Special thanks to Kate Mills (@le_feufollet) for proofing this post 🙂

Forthcoming: this is your brain on WoW

Thanks to philosopher and cognitive scientist Evan Thompson for sharing a project that was accepted today in Cerebral Cortex. I’m sure we can expect to see this one get reported all over as soon as the actual article is released (i’m looking at you Wired).

Here’s the abstract, via Evan Thompson

“How the human brain goes virtual: distinct cortical regions of the person processing-network are involved in self-identification with virtual avatars.”

Cerebral Cortex: Shanti Ganesh, Hein T. van Schie, Floris P. de Lange, Evan Thompson, and Daniel H.J. Wigboldus

“We applied functional neuroimaging to 22 long-term online gamers and 21 non-gaming controls, while they rated personality traits of self, avatar and familiar others. Strikingly, neuroimaging data revealed greater avatar-referential cortical activity in the left inferior parietal lobe, a region associated with self-identification from a third-person perspective. The magnitude of this brain activity correlated positively with the propensity to incorporate external body enhancements into one’s bodily identity. Avatar-referencing furthermore recruited greater activity in the rostral anterior cingulate gyrus, suggesting relatively greater emotional self-involvement with one’s avatar. Post-scanning behavioral data revealed superior recognition memory for avatar relative to others. Interestingly, memory for avatar positively co-varied with play duration.”

I’ll admit, I expected the usual “self x vs other x produces greater MPFC activity”. These findings are a nice extension to similiar work by Schilbach et al. I find it particularly interesting that the avatar-related activity correlated with a tendency to couple with external tools; a bit of an Andy Clark-esque vibe there. I look forward to reading the full article (and watching the media go nuts)!

Synaptic Adaptation to Environmental Alteration

From Quartz & Sejnowski: Neural Basis of Cognitive Development (1997)
Quartz and Sejnowski. The neural basis of cognitive development: a constructivist manifesto. Behav Brain Sci (1997) vol. 20 (4) pp. 537-56; discussion 556-96

Above you see an excellent summary table found in a seminal work by Quartz and Sejnowski. I’m reading this paper now, and aside from the die-hard representationalist instincts of the authors, it is an excellent overview of the development of neuroplasticity research and the relation of various forms of plasticity to learning and cognition. I find the above table fascinating simply because it demonstrates in one tidy arena the scope and temporal shape of brain development. You see for example, infamous studies in which the eyes of rats are sutured shut at birth alongside equally high-impact studies in which alterations in environmental complexity alter synaptic densities.

Overall, this is a list of studies in which the alteration of sensory motor input alters synaptic density and complexity in a dynamical fashion. I find it particularity interesting that the overall direction appears to be on in which increased complexity equals increased density. One stand out result is Valverde (1971) where an 20 day period of darkness is synaptically overcome when the mice are returned to a normal environment. Overall this table is a historically stunning account of the resilience of neural systems.

One big question though- why has it taken so long for plasticity to make its way into neurological acceptance?? Clearly the data was there… guess we needed fancy magnets to believe in it!

Zombies or Cyborgs?

On March 9th, I will be giving a talk in collaboration with my colleague Yishay Mor at the London Knowledge Lab. See below for links and the abstract of my upcoming talk

Upcoming talk @ the London Knowledge Lab

“[Social networking sites] are devoid of cohesive narrative and long-term significance. As a consequence, the mid-21st century mind might almost be infantilized, characterized by short attention spans, sensationalism, inability to empathize and a shaky sense of identity”.
-The Baroness Greenfield

“Just as I might use pen and paper to freeze my own half-baked thoughts, turning them into stable objects for further thought and reflection, so we (as a society) learned to use the written word to power a process of collective thinking and critical reason. The tools of text thus allow us at multiple scales, to create new stable objects for critical activity with speech, text, and the tradition of using them as critical tools under our belts, humankind entered the first phase of its cyborg existence”
– Andy Clark on the 1st Technocognitive Revolution, Natural Born Cyborgs

While some present the dawn of the social web as a doomsday, we believe that social media technologies represent a secondary revolution to that described above by cyborg cognition theorist Andy Clark. Trapped within this debate lies the brain; recent advances in the neurosciences have thrown open our concept of the brain, revealing a neural substrate that is highly flexible and plastic (Green and Bavelier 2008). This phenomenal level of plasticity likely underpins much of what separates us from the animal kingdom, through a profound enhancement of our ability to use new technologies and their cultural co-products (Clark and Chalmers 1998; Schoenemann, et al. 2005; Shaw, et al. 2006). Yet many fear that this plasticity represents a precise threat to our cognitive stability in light of the technological invasion of Twitter-like websites. By investigating how the brain changes as we undergo profound self alteration via digital meditation, we can begin to unravel the biological mysteries of plasticity that underpin a vast array of issues in the humanities and social sciences.

We propose to investigate functional and structural brain differences between high and low intensity users. Due to the what we view as a primarily folk psychological or narratological nature of SNS usage, we will utilize classical Theory-of-Mind tasks within the functional MRI environment, coupled with exploratory structural and functional connectivity analyses. To characterize differences in social networking behavior, we will utilize cluster-analysis and self-reported usage intensity scales. These will allow us to construct an fMRI task in which the mentalistic capacities for both real world and Facebook-specific friends are compared and contrasted, illuminating the precise impact of digitally mediated interaction on existing theory of mind capacities. We hypothesize that SNS usage intensity will positively correlate with functional brain activity increases in areas associated with theory of mind (MPFC & TPJ). We further suspect that that these measures will co-correlate with structural white matter increases within these regions, and collectively, with default mode network activity within high intensity users. Such findings would indicate that digitally mediated social networking represents a novel form of targeted social-cognitive self stimulation.

Micah Allen (neuroconscience) is a PhD student at √Örhus University, where he is working in collaboration with Interacting Minds and the Danish Center For Functionally Integrative Neuroscience (CFIN). His PhD focus is within Cognitive Neuroscience, specifically on the topic of Cognitive Neuroplasticity or the study of how biological and cognitive adaptation relate to one another. His research examines high-level brain plasticity in response to spiritual, cultural and technological practices, organized under the concept of ‘neurological self stimulation’. This research includes longitudinal investigations of meditation, structural connectivity, and default mode brain activity. Micah’s research is informed by and integrated within philosophies of embodiment, social cognition, enactivism, and cyborg phenomenology.

The Interacting Minds ( project at Aarhus University examines the links between the human capacity for minds to interact and the putative biological substrate, which enables this to happen. It is housed at the Danish National Research Foundation’s Center of Functionally Integrative Neuroscience (CFIN), a cross disciplinary brain research centre at Aarhus University and Aarhus University Hospital. CFIN does both basic research – e.g. on brain metabolism, neuroconnectivity and cognitive neuroscience and applied medical research of different neurological diseases, like Parkinson’s disease, dementia, stroke and depression.