Neuroconscience

The latest thoughts, musings, and data in cognitive science and neuroscience.

Birth of a New School: PDF version and Scribus Template!

As promised, today we are releasing a copy-edited PDF of my “Birth of a New School” essay, as well as a Scribus template that anyone can use to quickly create their own professional quality PDF manuscripts. Apologies for the lengthy delay, as i’ve been in the middle of a move to the UK. We hope folks will iterate and optimize these templates for a variety of purposes, especially post-publication peer review, commentary, pre-registration, and more. Special thanks to collaborator Kate Mills, who used Scribus to create the initial layout. You might notice we deliberately styled the manuscript around the format of one of those Big Sexy Journals (see if you can guess which one). I’ve heard this elaborate process should cost somewhere in the tens of thousands of dollars per article, so I guess I owe Kate a few lunches! Seriously though, the entire copy-editing and formatting process only took about 3 or 4 hours total (most of which was just getting used to the Scribus interface), less than the time you would spend formatting and reformatting your article for a traditional publisher. With a little practice Scribus or similar tools can be used to quickly turn out a variety of high quality article types.

Here is the article on Figshare, and the direct download link:

Screen Shot 2013-12-12 at 11.50.42

The formatted manuscript. Easy!

What do you think? Personally, I’m really pleased with it! We’ve also gone ahead and uploaded the Scribus template to Figshare. You can use this to easily publish your own post-publication peer reviews, commentaries, and whatever else you like. Just copy-paste your own text into the text fields, replace the images, upload to Figshare or a similiar service, and you are good to go! In general Scribus is a really awesome open source tool for publishing, both easy to learn and cross platform. Another great alternative is Fidus. For now we’re still not exactly sure how to generate citations – in theory if you format your manuscripts according to these guidelines, Google Scholar will pick them up anywhere on the net and generate alerts. For now we are recommending everyone upload their self-publications to Figshare or a similar service, who are already working on a streamlined citation generation scheme. We hope you find these useful; now go out and publish some research!

The template:

An easy to use Scribus template for self-publishing

Our Scribus template, for quick creation of research proofs.

Storify: twitter tears apart “the neuroscientist who was a psychopath” story

Monitoring the mind: clues for a link between meta cognition and self generated thought

Neuroconscience:

Jonny Smallwood, one of my PhD mentors, just posted an interesting overview of some of his recent work on mind-wandering and metacognition (including our Frontiers paper). Check it out!

Originally posted on The Mind Wanders:

It is a relatively common experience to lose track of what one is doing: We may stop following what someone is saying during conversation, enter a room and realise we have forgotten why we came in, or lose the thread of our own thoughts leaving us with a sense that we had reached a moment of insight that is now lost forever. One important influence on making sure that we can stay on target to achieve our goals is the capacity for meta-cognition, or the ability to accurately assess our own cognitive experience. Meta cognition is important because it allows us the opportunity to correct for errors if and when they occur. I have recently become interested in this capacity for accurately assessing the contents of thought and along with two different groups of collaborators have begun to explore its neural basis.

We were interested in whether meta-cognition is a…

View original 1,192 more words

Mind-wandering and metacognition: variation between internal and external thought predicts improved error awareness

Yesterday I published my first paper on mind-wandering and metacognition, with Jonny Smallwood, Antoine Lutz, and collaborators. This was a fun project for me as I spent much of my PhD exhaustively reading the literature on mind-wandering and default mode activity, resulting in a lot of intense debate a my research center. When we had Jonny over as an opponent at my PhD defense, the chance to collaborate was simply too good to pass up. Mind-wandering is super interesting precisely because we do it so often. One of my favourite anecdotes comes from around the time I was arguing heavily for the role of the default mode in spontaneous cognition to some very skeptical colleagues.  The next day while waiting to cross the street, one such colleague rode up next to me on his bicycle and joked, “are you thinking about the default mode?” And indeed I was – meta-mind-wandering!

One thing that has really bothered me about much of the mind-wandering literature is how frequently it is presented as attention = good, mind-wandering = bad. Can you imagine how unpleasant it would be if we never mind-wandered? Just picture trying to solve a difficult task while being totally 100% focused. This kind of hyper-locking attention can easily become pathological, preventing us from altering course when our behaviour goes awry or when something internal needs to be adjusted. Mind-wandering serves many positive purposes, from stimulating our imaginations, to motivating us in boring situations with internal rewards (boring task… “ahhhh remember that nice mojito you had on the beach last year?”). Yet we largely see papers exploring the costs – mood deficits, cognitive control failure, and so on. In the meditation literature this has even been taken up to form the misguided idea that meditation should reduce or eliminate mind-wandering (even though there is almost zero evidence to this effect…)

Sometimes our theories end up reflecting our methodological apparatus, to the extent that they may not fully capture reality. I think this is part of what has happened with mind-wandering, which was originally defined in relation to difficult (and boring) attention tasks. Worse, mind-wandering is usually operationalized as a dichotomous state (“offtask” vs “ontask”) when a little introspection seems to strongly suggest it is much more of a fuzzy, dynamic transition between meta-cognitive and sensory processes. By studying mind-wandering just as the ‘amount’ (or mean) number of times you were “offtask”, we’re taking the stream of consciousness and acting as if the ‘depth’ at one point in the river is the entire story – but what about flow rate, tidal patterns, fishies, and all the dynamic variability that define the river? My idea was that one simple way get at this is by looking at the within-subject variability of mind-wandering, rather than just the overall mean “rate”.  In this way we could get some idea of the extent to which a person’s mind-wandering was fluctuating over time, rather than just categorising these events dichotomously.

The EAT task used in my study, with thought probes.

The EAT task used in my study, with thought probes.

To do this, we combined a classical meta-cognitive response inhibition paradigm, the “error awareness task” (pictured above), with standard interleaved “thought-probes” asking participants to rate on a scale of 1-7 the “subjective frequency” of task-unrelated thoughts in the task interval prior to the probe.  We then examined the relationship between the ability to perform the task or “stop accuracy” and each participant’s mean task-unrelated thought (TUT). Here we expected to replicate the well-established relationship between TUTs and attention decrements (after all, it’s difficult to inhibit your behaviour if you are thinking about the hunky babe you saw at the beach last year!). We further examined if the standard deviation of TUT (TUT variability) within each participant would predict error monitoring, reflecting a relationship between metacognition and increased fluctuation between internal and external cognition (after all, isn’t that kind of the point of metacognition?). Of course for specificity and completeness, we conducted each multiple regression analysis with the contra-variable as control predictors. Here is the key finding from the paper:

Regression analysis of TUT, TUT variability, stop accuracy, and error awareness.

Regression analysis of TUT, TUT variability, stop accuracy, and error awareness.

As you can see in the bottom right, we clearly replicated the relationship of increased overall TUT predicting poorer stop performance. Individuals who report an overall high intensity/frequency of mind-wandering unsurprisingly commit more errors. What was really interesting, however, was that the more variable a participants’ mind-wandering, the greater error-monitoring capacity (top left). This suggests that individuals who show more fluctuation between internally and externally oriented attention may be able to better enjoy the benefits of mind-wandering while simultaneously limiting its costs. Of course, these are only individual differences (i.e. correlations) and should be treated as highly preliminary. It is possible for example that participants who use more of the TUT scale have higher meta-cognitive ability in general, rather than the two variables being causally linked in the way we suggest.  We are careful to raise these and other limitations in the paper, but I do think this finding is a nice first step.

To ‘probe’ a bit further we looked at the BOLD responses to correct stops, and the parametric correlation of task-related BOLD with the TUT ratings:

Activations during correct stop trials.

Activations during correct stop trials.

Deactivations to stop trials (blue) and parametric correlation with TUT reports (red)

Deactivations to stop trials (blue) and parametric correlation with TUT reports (red)

As you can see, correct stop trials elicit a rather canonical activation pattern on the motor-inhibition and salience networks, with concurrent deactivations in visual cortex and the default mode network (second figure, blue blobs). I think of this pattern a bit like when the brain receives the ‘stop signal’ it goes, (a la Picard): “FULL STOP, MAIN VIEWER OFF, FIRE THE PHOTON TORPEDOS!”, launching into full response recovery mode. Interestingly, while we replicated the finding of medial-prefrontal co-variation with TUTS (second figure, red blob), this area was substantially more rostral than the stop-related deactivations, supporting previous findings of some degree of functional segregation between the inhibitory and mind-wandering related components of the DMN.

Finally, when examining the Aware > Unaware errors contrast, we replicated the typical salience network activations (mid-cingulate and anterior insula). Interestingly we also found strong bilateral activations in an area of the inferior parietal cortex also considered to be a part of the default mode. This finding further strengthens the link between mind-wandering and metacognition, indicating that the salience and default mode network may work in concert during conscious error awareness:

Activations to Aware > Unaware errors contrast.

Activations to Aware > Unaware errors contrast.

In all, this was a very valuable and fun study for me. As a PhD student being able to replicate the function of classic “executive, salience, and default mode” ‘resting state’ networks with a basic task was a great experience, helping me place some confidence in these labels.  I was also able to combine a classical behavioral metacognition task with some introspective thought probes, and show that they do indeed contain valuable information about task performance and related brain processes. Importantly though, we showed that the ‘content’ of the mind-wandering reports doesn’t tell the whole story of spontaneous cognition. In the future I would like to explore this idea further, perhaps by taking a time series approach to probe the dynamics of mind-wandering, using a simple continuous feedback device that participants could use throughout an experiment. In the affect literature such devices have been used to probe the dynamics of valence-arousal when participants view naturalistic movies, and I believe such an approach could reveal even greater granularity in how the experience of mind-wandering (and it’s fluctuation) interacts with cognition. Our findings suggest that the relationship between mind-wandering and task performance may be more nuanced than mere antagonism, an important finding I hope to explore in future research.

Citation: Allen M, Smallwood J, Christensen J, Gramm D, Rasmussen B, Jensen CG, Roepstorff A and Lutz A (2013) The balanced mind: the variability of task-unrelated thoughts predicts error monitoringFront. Hum. Neurosci7:743. doi: 10.3389/fnhum.2013.00743

Birth of a New School: How Self-Publication can Improve Research

Edit: click here for a PDF version and citable figshare link!

Preface: What follows is my attempt to imagine a radically different future for research publishing. Apologies for any overlooked references – the following is meant to be speculative and purposely walks the line between paper and blog post. Here is to a productive discussion regarding the future of research.

Our current systems of producing, disseminating, and evaluating research could be substantially improved. For-profit publishers enjoy extremely high taxpayer-funded profit margins. Traditional closed-door peer review is creaking under the weight of an exponentially growing knowledge base, delaying important communications and often resulting in seemingly arbitrary publication decisions1–4. Today’s young researchers are frequently dismayed to find their pain-staking work producing quality reviews overlooked or discouraged by journalistic editorial practices. In response, the research community has risen to the challenge of reform, giving birth to an ever expanding multitude of publishing tools: statistical methods to detect p-hacking5, numerous open-source publication models6–8, and innovative platforms for data and knowledge sharing9,10.

While I applaud the arrival and intent of these tools, I suspect that ultimately publication reform must begin with publication culture – with the very way we think of what a publication is and can be. After all, how can we effectively create infrastructure for practices that do not yet exist? Last summer, shortly after igniting #pdftribute, I began to think more and more about the problems confronting the publication of results. After months of conversations with colleagues I am now convinced that real reform will come not in the shape of new tools or infrastructures, but rather in the culture surrounding academic publishing itself. In many ways our current publishing infrastructure is the product of a paper-based society keen to produce lasting artifacts of scholarly research. In parallel, the exponential arrival of networked society has lead to an open-source software community in which knowledge is not a static artifact but rather an ever-expanding living document of intelligent productivity. We must move towards “research 2.0” and beyond11.

From Wikipedia to Github, open-source communities are changing the way knowledge is produced and disseminated. Already this movement has begun reach academia, with researchers across disciplines flocking to social media, blogs, and novel communication infrastructures to create a new movement of post-publication peer review4,12,13. In math and physics, researchers have already embraced self-publication, uploading preprints to the online repository arXiv, with more and more disciplines using the site to archive their research. I believe that the inevitable future of research communication is in this open-source metaphor, in the form of pervasive self-publication of scholarly knowledge. The question is thus not where are we going, but rather how do we prepare for this radical change in publication culture. In asking these questions I would like to imagine what research will look like 10, 15, or even 20 years from today. This post is intended as a first step towards bringing to light specific ideas for how this transition might be facilitated. Rather than this being a prescriptive essay, here I am merely attempting to imagine what that future may look like. I invite you to treat what follows as an ‘open beta’ for these ideas.

Part 1: Why self-publication?

I believe the essential metaphor is within the open-source software community. To this end over the past few months I have  feverishly discussed the merits and risks of self-publishing scholarly knowledge with my colleagues and peers. While at first I was worried many would find the notion of self-publication utterly absurd, I have been astonished at the responses – many have been excitedly optimistic! I was surprised to find that some of my most critical and stoic colleagues have lost so much faith in traditional publication and peer review that they are ready to consider more radical options.

The basic motivation for research self-publication is pretty simple: research papers cannot be properly evaluated without first being read. Now, by evaluation, I don’t mean for the purposes of hiring or grant giving committees. These are essentially financial decisions, e.g. “how do I effectively spend my money without reading the papers of the 200+ applicants for this position?” Such decisions will always rely on heuristics and metrics that must necessarily sacrifice accuracy for efficiency. However, I believe that self-publication culture will provide a finer grain of metrics than ever dreamed of under our current system. By documenting each step of the research process, self-publication and open science can yield rich information that can be mined for increasingly useful impact measures – but more on that later.

When it comes to evaluating research, many admit that there is no substitute for opening up an article and reading its content – regardless of journal. My prediction is, as post-publication peer review gains acceptance, some tenured researcher or brave young scholar will eventually decide to simply self-publish her research directly onto the internet, and when that research goes viral, the resulting deluge of self-publications will be overwhelming. Of course, busy lives require heuristic decisions and it’s arguable that publishers provide this editorial service. While I will address this issue specifically in Part 3, for now I want to point out that growing empirical evidence suggests that our current publisher/impact-based system provides an unreliable heuristic at best14–16. Thus, my essential reason for supporting self-publication is that in the worst-case scenario, self-publications must be accompanied by the disclaimer: “read the contents and decide for yourself.” As self-publishing practices are established, it is easy to imagine that these difficulties will be largely mitigated by self-published peer reviews and novel infrastructures supporting these interactions.

Indeed, with a little imagination we can picture plenty of potential benefits of self-publication to offset the risk that we might read poor papers. Researchers spend exorbitant amounts of their time reviewing, commenting on, and discussing articles – most of that rich content and meta-data is lost under the current system. In documenting the research practice more thoroughly, the ensuing flood of self-published data can support new quantitative metrics of reviewer trust, and be further utlized in the development of rich information about new ideas and data in near real-time. To give just one example, we might calculate how many subsequent citations or retractions a particular reviewer generates, generating a reviewer impact factor and reliability index. The more aspects of research we publish, the greater the data-mining potential. Incentivizing in-depth reviews that add clarity and conceptual content to research, rather than merely knocking down or propping up equally imperfect artifacts, will ultimately improve research quality. By self-publishing well-documented, open-sourced pilot data and accompanying digital reagents (e.g. scripts, stimulus materials, protocols, etc), researchers can get instant feedback from peers, preventing uncounted research dollars from being wasted. Previously closed-door conferences can become live records of new ideas and conceptual developments as they unfold. The metaphor here is research as open-source – an ever evolving, living record of knowledge as it is created.

Now, let’s contrast this model to the current publishing system. Every publisher (including open-access) obliges researchers to adhere to randomly varied formatting constraints, presentation rules, submission and acceptance fees, and review cultures. Researchers perform reviews for free for often publically subsidized work, so that publishers can then turn around and sell the finished product back to those same researchers (and the public) at an exorbitant mark-up. These constraints introduce lengthy delays – ranging from 6+ months in the sciences all the way up to two years in some humanities disciplines. By contrast, how you self-publish your research is entirely up to you – where, when, how, the formatting, and the openness. Put simply, if you could publish your research how and when you wanted, and have it generate the same “impact” as traditional venues, why would you use a publisher at all?

One obvious reason to use publishers is copy-editing, i.e. the creation of pretty manuscripts. Another is the guarantee of high-profile distribution. Indeed, under the current system these are legitimate worries. While it is possible to produce reasonably formatted papers, ideally the creation of an open-source, easy to use copy-editing software is needed to facilitate mainstream self-publication. Innovators like figshare are already leading the way in this area. In the next section, I will try to theorize some different ways in which self-publication can overcome these and other potential limitations, in terms of specific applications and guidelines for maximizing the utility of self-published research. To do so, I will outline a few specific cases with the most potential for self-publication to make a positive impact on research right away, and hopefully illuminate the ‘why’ question a bit further with some concrete examples.

 Part 2: Where to begin self-publishing

What follows is the “how-to” part of this document. I must preface by saying that although I have written so far with researchers across the sciences and humanities in mind, I will now focus primarily on the scientific examples with which I am more experienced.  The transition to self-publication is already happening in the forms of academic tweets, self-archives, and blogs, at a seemingly exponential growth rate. To be clear, I do not believe that the new publication culture will be utopian. As in many human endeavors the usual brandism3, politics, and corruption can be expected to appear in this new culture. Accordingly, the transition is likely to be a bit wild and woolly around the edges. Like any generational culture shift, new practices must first emerge before infrastructures can be put in place to support them. My hope is to contribute to that cultural shift from artifact to process-based research, outlining particularly promising early venues for self-publication. Once these practices become more common, there will be huge opportunities for those ready and willing to step in and provide rich informational architectures to support and enhance self-publication – but for now we can only step into that wild frontier.

In my discussions with others I have identified three particularly promising areas where self-publication is either already contributing or can begin contributing to research. These are: the publication of exploratory pilot-data, post-publication peer reviews, and trial pre-registration. I will cover each in turn, attempting to provide examples and templates where possible. Finally, Part 3 will examine some common concerns with self-publication. In general, I think that successful reforms should resemble existing research practices as much as possible: publication solutions are most effective when they resemble daily practices that are already in place, rather than forcing individuals into novel practices or infrastructures with an unclear time-commitment. A frequent criticism of current solutions such as the comments section on Frontiers, PLOS One, or the newly developed PubPeer, is that they are rarely used by the general academic population. It is reasonable to conclude that this is because already over-worked academics currently see little plausible benefit from contributing to these discussions given the current publishing culture (worse still, they may fear other negative repercussions, discussed in Part 3). Thus a central theme of the following examples is that they attempt to mirror practices in which many academics are already engaged, with complementary incentive structures (e.g. citations).

Example 1: Exploratory Pilot Data 

This previous summer witnessed a fascinating clash of research cultures, with the eruption of intense debate between pre-registration advocates and pre-registration skeptics. I derived some useful insights from both sides of that discussion. Many were concerned about what would happen to exploratory data under these new publication regimes. Indeed, a general worry with existing reform movements is that they appear to emphasize a highly conservative and somewhat cynical “perfect papers” culture. I do not believe in perfect papers – the scientific model is driven by replication and discovery. No paper can ever be 100% flawless – otherwise there would be no reason for further research! Inevitably, some will find ways to cheat the system. Accordingly, reform must incentivize better reporting practices over stricter control, or at least balance between the two extremes.

Exploratory pilot data is an excellent avenue for this. By their very nature such data are not confirmatory – they are exciting in that they do not conform well to prior predictions. Such data benefit from rapid communication and feedback. Imagine an intuition-based project – a side or pet project conducted on the fly for example. The researcher might feel that the project has potential, but also knows that there could be serious flaws. Most journals won’t publish these kinds of data. Under the current system these data are lost, hidden, obscured, or otherwise forgotten.

Compare to a self-publication world: the researcher can upload the data, document all the protocols, make the presentation and analysis scripts open-source, and provide some well-written documentation explaining why she thinks the data are of interest. Some intrepid graduate student might find it, and follow up with a valuable control analysis, pointing out an excellent feature or fatal flaw, which he can then upload as a direct citation to the original data. Both publications are citable, giving credit to originator and reviewer alike. Armed with this new knowledge, the original researcher could now pre-register an altered protocol and conduct a full study on the subject (or alternatively, abandon the project entirely). In this exchange, it is likely that hundreds of hours and research dollars will have been saved. Additionally, the entire process will have been documented, making it both citable and minable for impact metrics. Tools already exist for each of these steps – but largely cultural fears prevent it from happening. How would it be perceived? Would anyone read it? Will someone steal my idea? To better frame these issues, I will now examine a self-publication practice that has already emerged in force.

 Example 2: Post-publication peer review

This is a particularly easy case, precisely because high-profile scholars are already regularly engaged in the practice. As I’ve frequently joked on twitter, we’re rapidly entering an era where publishing in a glam-mag has no impact guarantee if the paper itself isn’t worthwhile – you may as well hang a target on your head for post-publication peer reviewers. However, I want to emphasize the positive benefits and not just the conservative controls. Post-publication peer review (PPPR) has already begun to change the way we view research, with reviewers adding lasting content to papers, enriching the conclusions one can draw, and pointing out novel connections that were not extrapolated upon by the authors themselves. Here I like to draw an analogy to the open source movement, where code (and its documentation) is forkable, versioned, and open to constant revision – never static but always evolving.

Indeed, just last week PubMed launched their new “PubMed Commons” system, an innovative PPPR comment system, whereby any registered person (with at least one paper on PubMed) can leave scientific comments on articles.  Inevitably, the reception on twitter and Facebook mirrored previous attempts to introduce infrastructure-based solutions – mixed excitement followed by a lot of bemused cynicism – bring out the trolls many joked. To wit, a brief scan of the average comment on another platform, PubPeer, revealed a generally (but not entirely) poor level of comment quality. While many comments seem to be on topic, most had little to no formatting and were given with little context. At times comments can seem trollish, pointing out minor flaws as if they render the paper worthless. In many disciplines like my own, few comments could be found at all. This compounds the central problem with PPPR; why would anyone acknowledge such a system if the primary result is poorly formed nitpicking of your research? The essential problem here is again incentive – for reviews to be quality there needs to be incentive. We need a culture of PPPR that values positive and negative comments equally. This is common to both traditional and self-publication practices.

To facilitate easy, incentivized self-publication of comments and PPPRs, my colleague Hauke Hillebrandt and I have attempted to create a simple template that researchers can use to quickly and easily publish these materials. The idea is that by using these templates and uploading them to figshare or similar services, Google Scholar will automatically index them as citations, provide citation alerts to the original authors, and even include the comments in its h-index calculation. This way researchers can begin to get credit for what they are already doing, in an easy to use and familiar format. While the template isn’t quite working yet (oddly enough, Scholar is counting citations from my blog, but not the template), you can take a look at it here and maybe help us figure out why it isn’t working! In the near future we plan to get this working, and will follow-up this post with the full template, ready for you to use.

Example 3: Pre-registration of experimental trials

As my final example, I suggest that for many researchers, self-publication of trial pre-registrations (PR) may be an excellent way to test the waters of PR in a format with a low barrier to entry. Replication attempts are a particularly promising venue for PR, and self-publication of such registrations is a way to quickly move from idea to registration to collection (as in the above pilot data example), while ensuring that credit for the original idea is embedded in the infamously hard to erase memory of the internet.

A few benefits of PR self-publication, rather than relying on for-profit publishers, is that PR templates can be easily open-sourced themselves, allowing various research fields to generate community-based specialized templates adhering to the needs of that field. Self-published PRs, as well as high quality templates, can be cited – incentivizing the creation and dissemination of both. I imagine the rapid emergence of specialized templates within each community, tailored to the needs of that research discipline.

Part 3: Criticism and limitations

Here I will close by considering some common concerns with self-publication:

Quality of data

A natural worry at this point is quality control. How can we be sure that what is published without the seal of peer review isn’t complete hooey? The primary response is that we cannot, just like we cannot be sure that peer reviewed materials are quality without first reading them ourselves. Still, it is for this reason that I tried to suggest a few particularly ripe venues for self-publication of research. The cultural zeitgeist supporting full-blown scholarly self-publication has not yet arrived, but we can already begin to prepare for it. With regards to filtering noise, I argue that by coupling post-publication peer review and social media, quality self-publications will rise to the top. Importantly, this issue points towards flaws in our current publication culture. In many research areas there are effects that are repeatedly published but that few believe, largely due to the presence of biases against null-findings. Self-publication aims to make as much of the research process publicly available as possible, preventing this kind of knowledge from slipping through the editorial cracks and improving our ability to evaluate the veracity of published effects. If such data are reported cleanly and completely, existing quantitative tools can further incorporate them to better estimate the likelihood of p-hacking within a literature. That leads to the next concern – quality of presentation.

Hemingway's thoughts on data.

Quality of presentation

Many ask: how in this brave new world will we separate signal from noise? I am sure that every published researcher already receives at least a few garbage citations a year from obscure places in obscure journals with little relevance to actual article contents. But, so the worry goes, what if we are deluged with a vast array of poorly written, poorly documented, self-published crud. How would we separate the signal from the noise?

 The answer is Content, Presentation, and Clarity. These must be treated as central guidelines for self-publication to be worth anyone’s time. The Internet memesphere has already generated one rule for ranking interest: content rules. Content floats and is upvoted, blogspam sinks and is downvoted. This is already true for published articles – twitter, reddit, facebook, and email circles help us separate the wheat from the chaff at least as much as impact factor if not more. But presentation and clarity are equally important. Poorly conducted research is not shared, or at least is shared with vehemence. Similarly, poorly written self-publications, or poorly documented data/reagents are unlikely to generate positive feedback, much less impact-generating eyeballs. I like to imagine a distant future in which self-publication has given rise to a new generation of well-regarded specialists: reviewers who are prized for their content, presentation, and clarity; coders who produce cleanly documented pipelines; behaviorists producing powerful and easily customized paradigm scripts; and data collection experts who produce the smoothest, cleanest data around. All of these future specialists will be able to garner impact for the things they already do, incentivizing each step of the research processes rather than only the end product.

Being scooped, intellectual credit

Another common concern is “what if my idea/data/pilot is scooped?” I acknowledge that particularly in these early days, the decision to self-publish must be weighted against this possibility. However, I must also point out that in the current system authors must also weight the decision to develop an idea in isolation against the benefits of communicating with peers and colleagues. Both have risks and benefits – an idea or project in isolation can easily over-estimate its own quality or impact. The decision to self-publish must similarly be weighted against the need for feedback. Furthermore, a self-publication culture would allow researchers to move more quickly from project to publication, ensuring that they are readily credited for their work. And again, as research culture continues to evolve, I believe this concern will increasingly fade. It is notoriously difficult to erase information from The Internet (see the “Streisand effect”) – there is no reason why self-published ideas and data cannot generate direct credit for the authors. Indeed, I envision a world in which these contributions can themselves be independently weighted and credited.

 Prevention of cheating, corruption, self-citations

To some, this will be an inevitable point of departure. Without our time-tested guardian of peer review, what is to prevent a flood of outright fabricated data? My response is: what prevents outright fabrication under the current system? To misquote Jeff Goldblum in Jurassic Park, cheaters will always find a way. No matter how much we tighten our grip, there will be those who respond to the pressures of publication by deliberate misconduct. I believe that the current publication system directly incentivizes such behavior by valuing end product over process. By creating incentives for low-barrier post-publication peer review, pre-registration, and rich pilot data publication, researchers are given the opportunity to generate impact for each step of the research process. When faced with the vast penalties of cheating due to a null finding, versus doing one’s best to turn those data into something useful for someone, I suspect most people will choose the honest and less risky option.

 Corruption and self-citations are perhaps a subtler, more sinister factor. In my discussions with colleagues, a frequent concern is that there is nothing to prevent high-impact “rich club” institutions from banding together to provide glossy post-publication reviews, citation farming, or promoting one another’s research to the top of the pile regardless of content. I again answer: how is this any different from our current system? Papers are submitted to an editor who makes a subjective evaluation of the paper’s quality and impact, before sending it to four out of a thousand possible reviewers who will make an obscure  decision about the content of the paper. Sometimes this system works well, but increasingly it does not2. Many have witnessed great papers rejected for political reasons, or poor ones accepted for the same. Lowering the barrier to post-publication peer review means that even when these factors drive a paper to the top, it will be far easier to contextualize that research with a heavy dose of reality. Over time, I believe self-publication will incentivize good research. Cheating will always be a factor – and this new frontier is unlikely to be a utopia. Rather, I hope to contribute to the development of a bridge between our traditional publishing models and a radically advanced not-too-distant future.

Conclusion

Our current systems of producing, disseminating, and evaluating research increasingly seem to be out of step with cultural and technological realities. To take back the research process and bolster the ailing standard of peer-review I believe research will ultimately adopt an open and largely publisher-free model. In my view, these new practices will be entirely complementary to existing solutions including such as the p-curve5, open-source publication models6–8, and innovative platforms for data and knowledge sharing such as PubPeer, PubMed Commons, and figshare9,10. The next step from here will be to produce useable templates for self-publication. You can expect to see a PDF version of this post in the coming weeks as a further example of self-publishing practices. In attempting to build a bridge to the coming technological and social revolution, I hope to inspire others to join in the conversation so that we can improve all aspects of research.

 Acknowledgments

Thanks to Hauke Hillebrandt, Kate Mills, and Francesca Fardo for invaluable discussion, comments, and edits of this work. Many of the ideas developed here were originally inspired by this post envisioning a self-publication future. Thanks also to PubPeer, PeerJ,  figshare, and others in this area for their pioneering work in providing some valuable tools and spaces to begin engaging with self-publication practices.

Addendum

Excellent resources already exist for the many of the ideas presented here. I want to give special notice to researchers who have already begun self-publishing their work either as preprints, archives, or as direct blog posts. Parallel publishing is an attractive transitional option where researchers can prepublish their work for immediate feedback before submitting it to a traditional publisher. Special notice should be given to Zen Faulkes whose excellent pioneering blog posts demonstrated that it is reasonably easy to self-produce well formatted publications. Here are a few pioneering self-published papers you can use as examples – feel free to add your own in the comments:

The distal leg motor neurons of slipper lobsters, Ibacus spp. (Decapoda, Scyllaridae), Zen Faulkes

http://neurodojo.blogspot.dk/2012/09/Ibacus.html

Eklund, Anders (2013): Multivariate fMRI Analysis using Canonical Correlation Analysis instead of Classifiers, Comment on Todd et al. figshare.

http://dx.doi.org/10.6084/m9.figshare.787696

Automated removal of independent components to reduce trial-by-trial variation in event-related potentials, Dorothy Bishop

http://bishoptechbits.blogspot.dk/2011_05_01_archive.html

Deep Impact: Unintended consequences of journal rank

Björn Brembs, Marcus Munafò

http://arxiv.org/abs/1301.3748

A novel platform for open peer to peer review and publication:

http://thewinnower.com/

A platform for open PPPRs:

https://pubpeer.com/

Another PPPR platform:

http://f1000.com/

References

1. Henderson, M. Problems with peer review. BMJ 340, c1409 (2010).

2. Ioannidis, J. P. A. Why Most Published Research Findings Are False. PLoS Med 2, e124 (2005).

3. Peters, D. P. & Ceci, S. J. Peer-review practices of psychological journals: The fate of published articles, submitted again. Behav. Brain Sci. 5, 187 (2010).

4. Hunter, J. Post-publication peer review: opening up scientific conversation. Front. Comput. Neurosci. 6, 63 (2012).

5. Simonsohn, U., Nelson, L. D. & Simmons, J. P. P-Curve: A Key to the File Drawer. (2013). at <http://papers.ssrn.com/abstract=2256237>

6.  MacCallum, C. J. ONE for All: The Next Step for PLoS. PLoS Biol. 4, e401 (2006).

7. Smith, K. A. The frontiers publishing paradigm. Front. Immunol. 3, 1 (2012).

8. Wets, K., Weedon, D. & Velterop, J. Post-publication filtering and evaluation: Faculty of 1000. Learn. Publ. 16, 249–258 (2003).

9. Allen, M. PubPeer – A universal comment and review layer for scholarly papers? | Neuroconscience on WordPress.com. Website/Blog (2013). at <http://neuroconscience.com/2013/01/25/pubpeer-a-universal-comment-and-review-layer-for-scholarly-papers/>

10. Hahnel, M. Exclusive: figshare a new open data project that wants to change the future of scholarly publishing. Impact Soc. Sci. blog (2012). at <http://eprints.lse.ac.uk/51893/1/blogs.lse.ac.uk-Exclusive_figshare_a_new_open_data_project_that_wants_to_change_the_future_of_scholarly_publishing.pdf>

11. Yarkoni, T., Poldrack, R. A., Van Essen, D. C. & Wager, T. D. Cognitive neuroscience 2.0: building a cumulative science of human brain function. Trends Cogn. Sci. 14, 489–496 (2010).

12. Bishop, D. BishopBlog: A gentle introduction to Twitter for the apprehensive academic. Blog/website (2013). at <http://deevybee.blogspot.dk/2011/06/gentle-introduction-to-twitter-for.html>

13. Hadibeenareviewer. Had I Been A Reviewer on WordPress.com. Blog/website (2013). at <http://hadibeenareviewer.wordpress.com/>

14. Tressoldi, P. E., Giofré, D., Sella, F. & Cumming, G. High Impact = High Statistical Standards? Not Necessarily So. PLoS One 8, e56180 (2013).

15.  Brembs, B. & Munafò, M. Deep Impact: Unintended consequences of journal rank. (2013). at <http://arxiv.org/abs/1301.3748>

16.  Eisen, J. A., Maccallum, C. J. & Neylon, C. Expert Failure: Re-evaluating Research Assessment. PLoS Biol. 11, e1001677 (2013).

Short post: why I share (and share often)

If you follow my social media activities I am sure by now that you know me as a compulsive share-addict. Over the past four years I have gradually increased both the amount of incoming and outgoing information I attempt to integrate on a daily basis. I start every day with a now routine ritual of scanning new publications from 60+ journals and blogs using my firehose RSS feed, as well as integrating new links from various Science sub-reddits, my curated twitter cogneuro list, my friends and colleagues on Facebook, and email lists. I then in turn curate the best, most relevant to my interests, or in some cases the most outrageous of these links and share them back to twitter, facebook, reddit, and colleagues.

Of course in doing so, a frequent response from (particularly more senior) colleagues is: why?! Why do I choose to spend the time to both take in all that information and to share it back to the world? The answer is quite simple- in sharing this stuff I get critical feedback from an ever-growing network of peers and collaborators. I can’t even count the number of times someone has pointed out something (for better or worse) that I would have otherwise missed in an article or idea. That’s right, I share it so I can see what you think of it!  In this way I have been able to not only stay up to date with the latest research and concepts, but to receive constant invaluable feedback from all of you lovely brains :). In some sense I literally distribute my cognition throughout my network – thanks for the extra neurons!

From the beginning, I have been able not only to assess the impact of this stuff, but also gain deeper and more varied insights into its meaning. When I began my PhD I had the moderate statistical training of a BSc in psychology with little direct knowledge of neuroimaging methods or theory. Frankly it was bewildering. Just figuring out which methods to pay attention to, or what problems to look out for, was a headache-inducing nightmare. But I had to start somewhere and so I started by sharing, and sharing often. As a result almost every day I get amazing feedback pointing out critical insights or flaws in the things I share that I would have otherwise missed. In this way the entire world has become my interactive classroom! It is difficult to overstate the degree to which this interaction has enriched my abilities as a scientists and thinker.

It is only natural however for more senior investigators to worry about how much time one might spend on all this. I admit in the early days of my PhD I may have spent a bit too long lingering amongst the RSS trees and twitter swarms. But then again, it is difficult to place a price on the knowledge and know-how I garnered in this process (not to mention the invaluable social capital generated in building such a network!). I am a firm believer in “power procrastination”, which is just the process of regularly switching from more difficult but higher priority to more interesting but lower priority tasks. I believe that by spending my downtime taking in and sharing information, I’m letting my ‘default mode’ take a much needed rest, while still feeding it with inputs that will actually make the hard tasks easier.

In all, on a good day I’d say I spend about 20 minutes each morning taking in inputs and another 20 minutes throughout the day sharing them. Of course some days (looking at you Fridays) I don’t always adhere to that and there are those times when I have to ‘just say no’ and wait until the evening to get into that workflow. Productivity apps like Pomodoro have helped make sure I respect the balance when particularly difficult tasks arise. All in all however, the time I spend sharing is paid back tenfold in new knowledge and deeper understanding.

Really I should be thanking all of you, the invaluable peers, friends, colleagues, followers, and readers who give me the feedback that is so totally essential to my cognitive evolution. So long as you keep reading- I’ll keep sharing! Thanks!!

Notes: I haven’t even touched on the value of blogging and post-publication peer review, which of course sums with the benefits mentioned here, but also has vastly improved my writing and comprehension skills! But that’s a topic for another post!

( don’t worry, the skim-share cycle is no replacement for deep individual learning, which I also spend plenty of time doing!)

“you are a von economo neuron!” – Francesca :)

Fun fact – I read the excellent scifi novel Accelerando just prior to beginning my PhD. In the novel the main character is an info-addict who integrates so much information he gains a “5 second” prescience on events as they unfold. He then shares these insights for free with anyone who wants them, generating billion dollar companies (of which he owns no part in) and gradually manipulating global events to bring about a technological singularity. I guess you could say I found this to be a pretty neat character :) In a serious vein though, I am a firm believer in free and open science, self-publication, and sharing-based economies. Information deserves to be free!

When is expectation not a confound? On the necessity of active controls.

Learning and plasticity are hot topics in neuroscience. Whether exploring old world wisdom or new age science fiction, the possibility that playing videogames might turn us into attention superheroes or that practicing esoteric meditation techniques might heal troubled minds is an exciting avenue for research. Indeed findings suggesting that exotic behaviors or novel therapeutic treatments might radically alter our brain (and behavior) are ripe for sensational science-fiction headlines purporting vast brain benefits.  For those of you not totally bored of methodological crisis, here we have one brewing anew. You see the standard recommendation for those interested in intervention research is the active-controlled experimental design. Unfortunately in both clinical research on psychotherapy (including meditation) and more Sci-Fi areas of brain training and gaming, use of active controls is rare at best when compared to the more convenient (but causally ineffective) passive control group. Now a new article in Perspectives in Psychological Science suggests that even standard active controls may not be sufficient to rule out confounds in the treatment effect of interest.

Why is that? And why exactly do we need  active controls in the first place? As the authors clearly point out, what you want to show with such a study is the causal efficacy of the treatment of interest. Quite simply what that means is that the thing you think should have some interesting effect should actually be causally responsible for creating that effect. If you want to argue that standing upside down for twenty minutes a day will make me better at playing videogames in Australia, it must be shown that it is actually standing upside down that causes my increased performance down under. If my improved performance on Minecraft Australian Edition is simply a product of my belief in the power of standing upside down, or my expectation that standing upside down is a great way to best kangaroo-creepers, then we have no way of determining what actually produced that performance benefit. Research on placebos and the power of expectations shows that these kinds of subjective beliefs can have a big impact on everything from attentional performance to mortality rates.

Useful flowchart from Boot et al on whether or not a study can make causal claims for treatment.

Useful flowchart from Boot et al on whether or not a study can make causal claims for treatment.

Typically researchers attempt to control for such confounds through the use of a control group performing a task as similar as possible to the intervention of interest. But how do we know participants in the two groups don’t end up with different expectations about how they should improve as a result of the training? Boot et al point out that without actually measuring these variables, we have no idea and no way of knowing for sure that expectation biases don’t produce our observed improvements. They then provide a rather clever demonstration of their concern, in an experiment where participants view videos of various cognition tests as well as videos of a training task they might later receive, in this case either the first-person shooter Unreal Tournament or the spatial puzzle game Tetris. Finally they asked the participants in each group which tests they thought they’d do better on as a result of the training video. Importantly the authors show that not only did UT and Tetris lead to significantly different expectations, but also that those expectation benefits were specific to the modality of trained and tested tasks. Thus participant who watched the action-intensive Unreal Tournament videos expected greater improvements on tests of reaction time and visual performance, whereas participants viewing Tetris rated themselves as likely to do better on tests of spatial memory.

This is a critically important finding for intervention research. Many researchers, myself included, have often thought of the expectation and demand characteristic confounds in a rather general way. Generally speaking until recently I wouldn’t have expected the expectation bias to go much beyond a general “I’m doing something effective” belief. Boot et al show that our participants are a good deal cleverer than that, forming expectations-for-improvement that map onto specific dimensions of training. This means that to the degree that an experimenter’s hypothesis can be discerned from either the training or the test, participants are likely to form unbalanced expectations.

The good news is that the authors provide several reasonable fixes for this dilemma. The first is just to actually measure participant’s expectations, specifically in relation to the measures of interest. Another useful suggestion is to run pilot studies ensuring that the two treatments do not evoke differential expectations, or similarly to check that your outcome measures are not subject to these biases. Boot and colleagues throw the proverbial glove down, daring readers to attempt experiments where the “control condition” actually elicits greater expectations yet the treatment effect is preserved. Further common concerns, such as worries about balancing false positives against false negatives, are address at length.

The entire article is a great read, timely and full of excellent suggestions for caution in future research. It also brought something I’ve been chewing on for some time quite clearly into focus. From the general perspective of learning and plasticity, I have to ask at what point is an expectation no longer a confound. Boot et al give an interesting discussion on this point, in which they suggest that even in the case of balanced expectations and positive treatment effects, an expectation dependent response (in which outcome correlates with expectation) may still give cause for concern as to the causal efficacy of the trained task. This is a difficult question that I believe ventures far into the territory of what exactly constitutes the minimal necessary features for learning. As the authors point out, placebo and expectations effects are “real” products of the brain, with serious consequences for behavior and treatment outcome. Yet even in the medical community there is a growing understanding that such effects may be essential parts of the causal machinery of healing.

Possible outcome of a training experiment, in which the control shows no dependence between expectation and outcome (top panel) and the treatment of interest shows dependence (bottom panel). Boot et al suggest that such a case may invalidate causal claims for treatment efficacy.

Possible outcome of a training experiment, in which the control shows no dependence between expectation and outcome (top panel) and the treatment of interest shows dependence (bottom panel). Boot et al suggest that such a case may invalidate causal claims for treatment efficacy.

To what extent might this also be true of learning or cognitive training? For sure we can assume that expectations shape training outcomes, otherwise the whole point about active controls would be moot. But can one really have meaningful learning if there is no expectation to improve? I realize that from an experimental/clinical perspective, the question is not “is expectation important for this outcome” but “can we observe a treatment outcome when expectations are balanced”. Still when we begin to argue that the observation of expectation-dependent responses in a balanced design might invalidate our outcome findings, I have to wonder if we are at risk of valuing methodology over phenomena. If expectation is a powerful, potentially central mechanism in the causal apparatus of learning and plasticity, we shouldn’t be surprised when even efficacious treatments are modulated by such beliefs. In the end I am left wondering if this is simply an inherent limitation in our attempt to apply the reductive apparatus of science to increasingly holistic domains.

Please do read the paper, as it is an excellent treatment of a critically ignored issue in the cognitive and clinical sciences. Anyone undertaking related work should expect this reference to appear in reviewer’s replies in the near future.

EDIT:
Professor Simons, a co-author of the paper, was nice enough to answer my question on twitter. Simons pointed out that a study that balanced expectation, found group outcome differences, and further found correlations of those differences with expectation could conclude that the treatment was causally efficacious, but that it also depends on expectations (effect + expectation). This would obviously be superior to an unbalanced designed or one without measurement of expectation, as it would actually tell us something about the importance of expectation in producing the causal outcome. Be sure to read through the very helpful FAQ they’ve posted as an addendum to the paper, which covers these questions and more in greater detail. Here is the answer to my specific question:

What if expectations are necessary for a treatment to work? Wouldn’t controlling for them eliminate the treatment effect?

No. We are not suggesting that expectations for improvement must be eliminated entirely. Rather, we are arguing for the need to equate such expectations across conditions. Expectations can still affect the treatment condition in a double-blind, placebo-controlled design. And, it is possible that some treatments will only have an effect when they interact with expectations. But, the key to that design is that the expectations are equated across the treatment and control conditions. If the treatment group outperforms the control group, and expectations are equated, then something about the treatment must have contributed to the improvement. The improvement could have resulted from the critical ingredients of the treatment alone or from some interaction between the treatment and expectations. It would be possible to isolate the treatment effect by eliminating expectations, but that is not essential in order to claim that the treatment had an effect.

In a typical psychology intervention, expectations are not equated between the treatment and control condition. If the treatment group improves more than the control group, we have no conclusive evidence that the ingredients of the treatment mattered. The improvement could have resulted from the treatment ingredients alone, from expectations alone, or from an interaction between the two. The results of any intervention that does not equate expectations across the treatment and control condition cannot provide conclusive evidence that the treatment was necessary for the improvement. It could be due to the difference in expectations alone. That is why double blind designs are ideal, and it is why psychology interventions must take steps to address the shortcomings that result from the impossibility of using a double blind design. It is possible to control for expectation differences without eliminating expectations altogether.

Can the reward prediction error hypothesis explain addiction and reward? Great video on incentive-salience theory.

If you are interested in predictive coding, learning, motivation, addiction, or reward, don’t miss this excellent video by Kent Berridge. The incentive salience theory has long fascinated me as it may potentially explain data not accounted for by the hedonic-aversive accounts of addiction and reward. Essentially Incentive Salience argues that rather than reward or addiction being purely the function of seeking hedonic rewards or avoiding aversive punishments (e.g. withdrawal), salient cues trigger direct “wanting” responses irrespective of the reward itself. This may explain why for example, mice often continue to seek rewards even when dopamine is blocked and no pleasurable outcome can be obtained, or conversely why even in the absence of withdrawal addicts will seek drugs in response to salient cues.  Anyway, Kent explains it much better than I can- so watch it!

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:

Compassion

… 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).

Reappraisal

… 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!

Is the resting BOLD signal physiological noise? What about resting EEG?

Over the past 5 years, resting-state fMRI (rsfMRI) has exploded in popularity. Literally dozens of papers are published each day examining slow (< .1 hz) or “low frequency” fluctuations in the BOLD signal. When I first moved to Europe I was caught up in the somewhat North American frenzy of resting state networks. I couldn’t understand why my Danish colleagues, who specialize in modelling physiological noise in fMRI, simply did not take the literature seriously. The problem is essentially that the low frequencies examined in these studies are the same as those that dominate physiological rhythms. Respiration and cardiac pulsation can make up a massive amount of variability in the BOLD signal. Before resting state fMRI came along, nearly every fMRI study discarded any data frequencies lower than one oscillation every 120 seconds (e.g. 1/120 Hz high pass filtering). Simple things like breath holding and pulsatile motion in vasculature can cause huge effects in BOLD data, and it just so happens that these artifacts (which are non-neural in origin) tend to pool around some of our favorite “default” areas: medial prefrontal cortex, insula, and other large gyri near draining veins.

Naturally this leads us to ask if the “resting state networks” (RSNs) observed in such studies are actually neural in origin, or if they are simply the result of variations in breath pattern or the like. Obviously we can’t answer this question with fMRI alone. We can apply something like independent component analysis (ICA) and hope that it removes most of the noise- but we’ll never really be 100% sure we’ve gotten it all that way. We can measure the noise directly (e.g. “nuisance covariance regression”) and include it in our GLM- but much of the noise is likely to be highly correlated with the signal we want to observe. What we need are cross-modality validations that low-frequency oscillations do exist, that they drive observed BOLD fluctuations, and that these relationships hold even when controlling for non-neural signals. Some of this is already established- for example direct intracranial recordings do find slow oscillations in animal models. In MEG and EEG, it is well established that slow fluctuations exist and have a functional role.

So far so good. But what about in fMRI? Can we measure meaningful signal while controlling for these factors? This is currently a topic of intense research interest. Marcus Raichle, the ‘father’ of the default mode network, highlights fascinating multi-modal work from a Finnish group showing that slow fluctuations in behavior and EEG signal coincide (Raichle and Snyder 2007; Monto, Palva et al. 2008). However, we should still be cautious- I recently spoke to a post-doc from the Helsinki group about the original paper, and he stressed that slow EEG is just as contaminated by physiological artifacts as fMRI. Except that the problem is even worse, because in EEG the artifacts may be several orders of magnitude larger than the signal of interest[i].

Understandably I was interested to see a paper entitled “Correlated slow fluctuations in respiration, EEG, and BOLD fMRI” appear in Neuroimage today (Yuan, Zotev et al. 2013). The authors simultaneously collected EEG, respiration, pulse, and resting fMRI data in 9 subjects, and then perform cross-correlation and GLM analyses on the relationship of these variables, during both eyes closed and eyes open rest. They calculate Respiratory Volume per Time (RVT), a measure developed by Rasmus Birn, to assign a respiratory phase to each TR (Birn, Diamond et al. 2006). One key finding is that the global variations in EEG power are strongly predicted by RVT during eyes closed rest, with a maximum peak correlation coefficient of .40. Here are the two time series:

RVTalpha 

You can clearly see that there is a strong relationship between global alpha (GFP) and respiration (RVT). The authors state that “GFP appears to lead RVT” though I am not so sure. Regardless, there is a clear relationship between eyes closed ‘alpha’ and respiration. Interestingly they find that correlations between RVT and GFP with eyes open were not significantly different from chance, and that pulse did not correlate with GFP. They then conduct GLM analyses with RVT and GFP as BOLD regressors. Here is what their example subject looked like during eyes-closed rest:

RVT_GFP_BOLD

Notice any familiar “RSNs” in the RVT map? I see anti-correlated executive deactivation and default mode activation! Very canonical.  Too bad they are breath related. This is why noise regression experts tend to dislike rsfMRI, particularly when you don’t measure the noise. We also shouldn’t be too surprised that the GFP-BOLD and RVT-BOLD maps look similar, considering that GFP and RVT are highly correlated. After looking at these correlations separately, Yuan et al perform RETROICOR physiological noise correction and then reexamine the contrasts. Here are the group maps:

group_map

Things look a bit less default-mode-like in the group RVT map, but the RVT and GFP maps are still clearly quite similar. In panel D you can see that physiological noise correction has a large global impact on GFP-BOLD correlations, suggesting that quite a bit of this co-variance is driven by physiological noise. Put simply, respiration is explaining a large degree of alpha-BOLD correlation; any experiment not modelling this covariance is likely to produce strongly contaminated results. Yuan et al go on to examine eyes-open rest and show that, similar to their RVT-GFP cross-correlation analysis, not nearly as much seems to be happening in eyes open compared to closed:

eyesopen

The authors conclude that “In particular, this correlation between alpha EEG and respiration is much stronger in eyes-closed resting than in eyes-open resting” and that “[the] results also suggest that eyes-open resting may be a more favorable condition to conduct brain resting state fMRI and for functional connectivity analysis because of the suppressed correlation between low-frequency respiratory fluctuation and global alpha EEG power, therefore the low-frequency physiological noise predominantly of non-neuronal origin can be more safely removed.” Fair enough- one conclusion is certainly that eyes closed rest seems much more correlated with respiration than eyes open. This is a decent and useful result of the study. But then they go on to make this really strange statement, which appears in the abstract, introduction, and discussion:

“In addition, similar spatial patterns were observed between the correlation maps of BOLD with global alpha EEG power and respiration. Removal of respiration related physiological noise in the BOLD signal reduces the correlation between alpha EEG power and spontaneous BOLD signals measured at eyes-closed resting. These results suggest a mutual link of neuronal origin between the alpha EEG power, respiration, and BOLD signals”’ (emphasis added)

That’s one way to put it! The logic here is that since alpha = neural activity, and respiration correlates with alpha, then alpha must be the neural correlate of respiration. I’m sorry guys, you did a decent experiment, but I’m afraid you’ve gotten this one wrong. There is absolutely nothing that implies alpha power cannot also be contaminated by respiration-related physiological noise. In fact it is exactly the opposite- in the low frequencies observed by Yuan et al the EEG data is particularly likely to be contaminated by physiological artifacts! And that is precisely what the paper shows – in the author’s own words: “impressively strong correlations between global alpha and respiration”. This is further corroborated by the strong similarity between the RVT-BOLD and alpha-BOLD maps, and the fact that removing respiratory and pulse variance drastically alters the alpha-BOLD correlations!

So what should we take away from this study? It is of course inconclusive- there are several aspects of the methodology that are puzzling to me, and sadly the study is rather under-powered at n = 9. I found it quite curious that in each of the BOLD-alpha maps there seemed to be a significant artifact in the lateral and posterior ventricles, even after physiological noise correction (check out figure 2b, an almost perfect ventricle map). If their global alpha signal is specific to a neural origin, why does this artifact remain even after physiological noise correction? I can’t quite put my finger on it, but it seems likely to me that some source of noise remained even after correction- perhaps a reader with more experience in EEG-fMRI methods can comment. For one thing their EEG motion correction seems a bit suspect, as they simply drop outlier timepoints. One way or another, I believe we should take one clear message away from this study – low frequency signals are not easily untangled from physiological noise, even in electrophysiology. This isn’t a damnation of all resting state research- rather it is a clear sign that we need be to measuring these signals to retain a degree of control over our data, particularly when we have the least control at all.

References:

Birn, R. M., J. B. Diamond, et al. (2006). “Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI.” Neuroimage 31(4): 1536-1548.

Monto, S., S. Palva, et al. (2008). “Very slow EEG fluctuations predict the dynamics of stimulus detection and oscillation amplitudes in humans.” The Journal of Neuroscience 28(33): 8268-8272.

Raichle, M. E. and A. Z. Snyder (2007). “A default mode of brain function: a brief history of an evolving idea.” Neuroimage 37(4): 1083-1090.

Yuan, H., V. Zotev, et al. (2013). “Correlated Slow Fluctuations in Respiration, EEG, and BOLD fMRI.” NeuroImage pp. 1053-8119.

 


[i] Note that this is not meant to be in anyway a comprehensive review. A quick literature search suggests that there are quite a few recent papers on resting BOLD EEG. I recall a well done paper by a group at the Max Planck Institute that did include noise regressors, and found unique slow BOLD-EEG relations. I cannot seem to find it at the moment however!

 

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