Neuroconscience

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

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Twitter recommends essential reading in pupilometry

Not sure why WordPress is refusing to accept the Storify embed, but click here for some excellent suggestions on reading in pupilometry:

https://storify.com/neuroconscience/twitter-recommendations-for-essential-reading-in-p

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

Researchers begin posting article PDFs to twitter in #pdftribute to Aaron Swartz

Yesterday, as I was completing my morning coffee and internet ritual, @le_feufollet broke the sad news to me of Aaron Swartz’s death. Aaron was a leader online, a brilliant coder and developer, and sadly a casualty in the fight for freedom of information. He was essential in the development of two tools I use every day (RSS and Reddit), and though his guerilla attempt to upload all papers on JSTOR was perhaps unstrategic, it was certainly noble enough in cause. Before his death Aaron was facing nearly 35 years in prison for his role in the JSTOR debacle, an insane penalty for attempting to share information. We don’t know why Aaron chose to take his life, but when @la_feufollet and I tried to brainstorm a tribute to him, my first thought was a guerilla PDF uploading campaign in honor of his fight for open access. I’m not much of an organizer, so I posted in one of the many rising reddit threads and hoped for the best:

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My posts on reddit are usually ignored, so I went about my business and assumed it was the last i’d hear of it. It was amazing to wake up this morning and see that redditors had responded strongly to the idea and that a flood of tweets tagged #pdftribute had appeared:

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Eva Vivalt coined the #pdftribute hashtag, and helped bring anonymous onboard. Currently there are hundreds thousands of authors posting their PDFs. It’s amazing to see that the original promise of the internet – the spread of ideas- is thriving. Lately i’ve been feeling a bit pessimistic, worried that the net was becoming an overly gamed, astroturf-ridden meme-preserve for advertisers to groom to their financial needs. It’s great to see that the most exciting power of our newfound connectivity- driving ideas to spread freely and have impact without the restrictions of traditional hierarchical barriers- continues to thrive. I hope #pdftribute lives on in both force and spirit, and that we can all begin working toward a world in which all publicly funded research is available to anyone with net access.

UPDATE 13/1/13 4:00 EST:

For those of you who don’t feel comfortable violating your copyright, but want to join in #pdftribute, your best bet is to check the specifics of your publisher agreement. Most journals allow you to upload a pre-print manuscript to your personal website. Then you can go ahead and tweet the link to your website or the individual pre-print PDFs. Jonathan Eisen has a helpful list of 10 ways to post your papers on twitter here.

Otherwise, hide in the swarm today as a show of support for Aaron. By standing together we show that the future of research publishing is freedom of information. But tomorrow remember that we need to push through real copright reform. You can start by reading Aaron’s wonderful Guerilla Open Access Manifesto. If you are ready to commit to open access, you can sign the petition at http://thecostofknowledge.com/. There is also this We The People petition demanding legislation requiring journals to use an open-access publishing model Woops that petition has expired- start a new one!. As Matthew Green put it, lets push for an Aaron Swartz copyright reform act.

UPDATE 1:

Some nice folks have put together a link scraper to collect PDFs tagged #pdftribute here:

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UPDATE 2:

If I may make a humble suggestion- it may be useful to follow a specific format for sharing your papers. This will make them easier to find later, and for journalists to compile some sharing stats. Here is my suggested example.

Screen shot 2013-01-13 at 2.12.23 PM

UPDATE 3:

Eva Vivalt reports #pdftribute getting 500 tweets/hr, >2.5 million impressions!

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Thesis… almost done!

Allo- just checking in to say I’m about 8 days away from submitting my thesis. Then i’m off to Italy for some much needed R&R followed by SFN2012 in New Orleans. I’ll be blogging regularly again this fall once I’ve had a chance to recoup. Lots of fun new stuff coming including a new direction for my research, new projects, ideas, and all that comes with finally finishing a PhD. 

Video analysis software – Tracker

Neuroconscience:

This is a test post, to try out my new import services. Also, A neat post on a useful motion tracking software!

Originally posted on Computing for Psychologists:

I just came across a gosh-darn drop-dead cool (and free!) piece of software that I just had to write a quick post on. It’s called Tracker, it’s cross-platform, open-source and freely available here.

In a nutshell, it’s designed for analysis of videos, and can do various things, like track the motion of an object across frames (yielding position, velocity and acceleration data) and generate dynamic RGB colour profiles. Very cool. As an example of the kinds of things it can do, see this post on Wired.com where a physicist uses it to analyse the speed of blaster bolts in Star Wars: Episode IV. Super-geeky I know, but I love it.

An example of some motion analyses conducted using Tracker

Whenever I see a piece of software like this I immediately think about what I could use it for in psychology/neuroscience. In this case, I immediately thought about using it for…

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Top tips for new experimenters

I set out to write my top five tips for new experimenters today and found there were really only two universal suggestions I felt I could and should make:

  1. Simplify your design. There is no complex question that can’t be better asked with a simple one. Simple design means stronger statistics, a clearer interpretation, and less variables to control. If you cannot phrase your core question in a sentence, you need to drastically reduce the scope of your experiment.
  2. Know your design. Before collecting the data, you should know exactly what kind of data it will be, how many variables, and what kind of statistical test you will use to analyze it. Then you need to collect 4-5 “throw-away” participants and run them through this analysis. This ensures that the data can be readily analyzed in a rigorous way, from start to finish. You will know you are ready when your looking at the successful results of a pilot, which will discover study-killing bugs (of which there are MANY)
Those are honestly the two most important guidelines I can think of! Everything else is secondary to achieving those goals. If you pull those off, you’ll have beaten 80% of the crap that can destroy your data. In my experience the biggest mistake most people make when starting out is telling themselves that simple questions are not worth their time. You’ll build a more stable career by doing something less innovative but more solid, and knowing it more thoroughly. A lot of people don’t do this and end up with total shitpiles of worthless data- myself included. Don’t let bad data happen to you- simplify and know your design! I’d love to hear about your number 1 tips in the comments!
Edit: Tip .3 comes from a great comment by Neuroskeptic:

Good post. I would add a #3, it’s kind of an aspect of #2 although important enough to stand alone -

Make sure you are one of the pilot subjects. It’s amazing what kind of things you notice when you’re actually in the scanner that you never otherwise would – anything from the fact that the stimuli aren’t very visible, to the fact that the sequence you’re using makes the bed shake, to the fact that the task is just so long & boring that you fall asleep by the end (which is so much easier in the scanner than when you’re sitting up at a computer, which is when you probably piloted the task!)

If you’re not MRI safe, get a trusted fellow researcher to do it. But never assume that non-scientist volunteers will tell you these things because they don’t (I think because they don’t want to look stupid by questioning your authority.)

Insula and Anterior Cingulate: the ‘everything’ network or systemic neurovascular confound?

It’s no secret in cognitive neuroscience that some brain regions garner more attention than others. Particularly in fMRI research, we’re all too familiar with certain regions that seem to pop up in study after study, regardless of experimental paradigm. When it comes to areas like the anterior cingulate cortex (ACC) and insula (AIC), the trend is obvious. Generally when I see the same brain region involved in a wide a variety of tasks, I think there must be some very general level function which encompasses these paradigms. Off the top of my head, the ACC and AIC are major players in cognitive control, pain, emotion, consciousness, salience, working memory, decision making, and interoception to name a few. Maybe on a bad day I’ll look at a list like that and think, well localization is just all wrong, and really what we have is a big fat prefrontal cortex doing everything in conjunction. A paper published yesterday in Cerebral Cortex took my breath away and lead to a third, more sinister option: a serious methodological confound in a large majority of published fMRI papers.

Neurovascular coupling and the BOLD signal: a match not made in heaven

An important line of research in neuroimaging focuses on noise in fMRI signals. The essential problem of fMRI is that, while it provides decent spatial resolution, the data is acquired slowly and indirectly via the blood-oxygenation level dependent (BOLD) signal. The BOLD signal is messy, slow, and extremely complex in its origins. Although we typically assume increasing BOLD signal equals greater neural activity, the details of just what kind of activity (e.g. excitatory vs inhibitory, post-synaptic vs local field) are murky at best. Advancements in multi-modal and optogenetic imaging hold a great deal of promise regarding the signal’s true nature, but sadly we are currently at a “best guess” level of understanding. This weakness means that without careful experimental design, it can be difficult to rule out non-neural contributors to our fMRI signal. Setting aside the worry about what neural activity IS measured by BOLD signal, there is still the very real threat of non-neural sources like respiration and cardiovascular function confounding the final result. This is a whole field of research in itself, and is far too complex to summarize here in its entirety. The basic issue is quite simple though.

End-tidal C02, respiration, and the BOLD Signal

In a nutshell, the BOLD signal is thought to measure downstream changes in cerebral blood-flow (CBF) in response to neural activity. This relationship, between neural firing and blood flow, is called neurovascular coupling and is extremely complex, involving astrocytes and multiple chemical pathways. Additionally, it’s quite slow: typically one observes a 3-5 second delay between stimulation and BOLD response. This creates our first noise-related issue; the time between each ‘slice’ of the brain, or repetition time (TR), must be optimized to detect signals at this frequency. This means we sample from our participant’s brain slowly. Typically we sample every 3-5 seconds and construct our paradigms in ways that respect the natural time lag of the BOLD signal. Stimulate too fast, and the vasculature doesn’t have time to respond. Stimulation frequency also helps prevent our first simple confound: our pulse and respiration rates tend oscillate at slightly slower frequencies (approximately every 10-15 seconds). This is a good thing, and it means that so long as your design is well controlled (i.e. your events are properly staggered and your baseline is well defined) you shouldn’t have to worry too much about confounds. But that’s our first problematic assumption; consider for example when one’s paradigms use long blocks of obscure things like “decide how much you identify with these stimuli”. If cognitive load differs between conditions, or your groups (for example, a PTSD and a control group) react differently to the stimuli, respiration and pulse rates might easily begin to overlap your sampling frequency, confounding the BOLD signal.

But you say, my experiment is well controlled, and there’s no way my groups are breathing THAT differently! Fair enough, but this leads us to our next problem: end tidal CO2. Without getting into the complex physiology, end-tidal CO2 is a by-product of respiration. When you hold your breath, CO2 blood levels rise dramatically. CO2 is a potent vasodilator, meaning it opens blood vessels and increases local blood flow. You’ve probably guessed where I’m going with this: hold your breath in the fMRI and you get massive alterations in the BOLD signal. Your participants don’t even need to match the sampling frequency of the paradigm to confound the BOLD; they simply need to breath at slightly different rates in each group or condition and suddenly your results are full of CO2 driven false positives! This is a serious problem for any kind of unconstrained experimental design, especially those involving poorly conceptualized social tasks or long periods of free activity. Imagine now that certain regions of the brain might respond differently to levels of CO2.

This image is from Change & Glover’s paper, “Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI”. Here they measure both CO2 and respiration frequency during a standard resting-state scan. The image displays the results of group-level regression of these signals with BOLD. I’ve added circles in blue around the areas that respond the strongest. Without consulting an atlas, we can clearly see that bilateral anterior insula extending upwards into parietal cortex, anterior cingulate, and medial-prefrontal regions are hugely susceptible to respiration and CO2. This is pretty damning for resting-state fMRI, and makes sense given that resting state fluctuations occur at roughly the same rate as respiration. But what about well-controlled event related designs? Might variability in neurovascular coupling cause a similar pattern of response? Here is where Di et al’s paper lends a somewhat terrifying result:


Di et al recently investigated the role of vascular confounds in fMRI by administrating a common digit-symbol substitution task (DSST), a resting state, and a breath-holding paradigm. Signals related to resting-state and breath-holding were then extracted and entered into multiple regression with the DSST-related activations. This allowed Di et al to estimate what brain regions were most influenced by low-frequency fluctuation (ALFF, a common resting state measure) and purely vascular sources (breath-holding). From the figure above, you can see that regions marked with the blue arrow were the most suppressed, meaning the signal explained by the event-related model was significantly correlated with the covariates, and in red where the signal was significantly improved by removal of the covariates. The authors conclude that “(results) indicated that the adjustment tended to suppress activation in regions that were near vessels such as midline cingulate gyrus, bilateral anterior insula, and posterior cerebellum.” It seems that indeed, our old friends the anterior insula and cingulate cortex are extremely susceptible to neurovascular confound.

What does this mean for cognitive neuroscience? For one, it should be clear that even well-controlled fMRI designs can exhibit such confounds. This doesn’t mean we should throw the baby out with the bathwater though; some designs are better than others. Thankfully it’s pretty easy to measure respiration with most scanners, and so it is probably a good idea at minimum to check if one’s experimental conditions do indeed create differential respiration patterns. Further, we need to be especially cautious in cases like meditation or clinical fMRI, where special participant groups may have different baseline respiration rates or stronger parasympathetic responses to stimuli. Sadly, I’m afraid that looking back, these findings greatly limit our conclusions in any design that did not control for these issues. Remember that the insula and ACC are currently cognitive neuroscience’s hottest regions. I’m not even going to get into resting state, where these problems are all magnified 10 fold. I’ll leave you with this image from neuroskeptic, estimating the year’s most popular brain regions:

Are those spikes publication fads, every-task regions, or neurovascular artifacts? You be the judge.

 
edit:As many of you had questions or comments regarding the best way to deal with respiratory related issues, I spoke briefly with resident noise expert Torben Lund at yesterday’s lab meeting. Removal of respiratory noise is fairly simple, but the real problem is with end-tidal C02. According to Torben, most noise experts agree that regression techniques only partially remove the artifact, and that an unknown amount is left behind even following signal regression. This may be due to slow vascular saturation effects that build up and remain irrespective of shear breath frequency. A very tricky problem indeed, and certainly worth researching.
 
 
Note: credit goes to my methods teacher and fMRI noise expert Torben Lund, and CFIN neurobiologist Rasmus Aamand, for introducing and explaining the basis of the C02/respiration issue to me. Rasmus particularly, whose sharp comments lead to my including respiration and pulse measures in my last meditation project.

Daniel Wolpert on TED: Noise reduction and the Bayesian Brain

This has to be the best introduction to the Bayesian Brain hypothesis I have ever seen. I’m normally somewhat cautious about my endorsement of this view, but Daniel really draws you in. Beautiful talk, very understandable and yet not too dumbed-down.

Zombies on the Brain Redux: Return of the Baroness

Just a quick note. As most of you are probably aware, in the past week a bit of a furor has erupted on the interwebs regarding a certain UK Noblewoman’s concerns regarding our free time, and it’s impact on our brain. Susan Greenfield has written extensively, if without much depth, on the need to sound the alarm regarding how much time we spend staring at a screen. Thank goodness the kind, brillant minds have responded, and they’ve done so negatively. I’m not going to reproduce the debate here, but it suffices to say Susan’s now been shredded publicly by both world-leading Autism experts and folks who actually research our digital habits.Anyway, this is just a short post to share my view on the whole thing:

It’s not that i’m not sympathetic to the idea we spend too much time in front of a screen. It’s levying the weight of that argument around some BS neurobabble. The brain, and it’s astounding capacity for change, need not enter this debate in an alarmist fashion. If we want to discuss how our society spends its time, let’s not cling onto poorly understood scientific phenomena to do so. Let’s talk about radical capitalism and the 40 hour work week. I spend 38+ hours a week online, programming, and generally living on a computer as do many contemporary workers. Talking about the brain won’t change that- but it will sell books. And I think it puts a clear perspective on the 3-4 hours a week I might spend playing games. The problem isn’t brains, games, or anything in between. Anyone who tries to tell you otherwise wants to make a fast buck off you.

Susan is hoping to use her high-standing to kick off some kind of alarmist movement akin to the global warming debate. She’s even been so cheeky as to give her cause a similar sounding name “Mindchange”, so that her would be activists might have some nice banner around which to rally. Plus, it makes for some pretty obvious best-selling book titles. Maybe even a series of self-help audiobooks filled to the brim with half-truisms, folksy inspiration, and plenty of badly misunderstood science.

And that’s just the problem. Beneath the seemingly innocent wish to make a legacy and buck for herself, Susan has denigrated one big problem (global warming), disrespected the science, and mis-educated her audience. Not only that but I believe she’s obscuring the real problem. Call me radical, but if we don’t start to take serious the cultural-systemic problems that threaten our world, we’re fucked. By capitalizing on sensation, the Baroness has obscured a legitimate debate about the way we spend our lives.

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