Quote of the Week - Fisher
“Modern statisticians are familiar with the notion that any finite body of data contains only a limited amount of information on any point under examination; that this limit is set by the nature of the data themselves, and cannot be increased by any amount of ingenuity expended in their statistical examination: that the statistician’s task, in fact, is limited to the extraction of the whole of the available information on any particular issue.” - R. A. Fisher
Summer Heatwave
Santa Barbara is an absolutely horrible place to be in the summertime. I mean, come on, 77 degrees? Who can stand that?
Principal Components of Individual Differences
I have been spending the last few weeks exploring principal components analysis (PCA) of functional imaging data. PCA has been around for over a century, having first been invented by Karl Pearson in 1901. I have always been taught that PCA was a powerful data reduction technique, allowing a handful of components to represent the variability of a far greater number of variables. However, my recent interest in PCA is from the perspective of exploratory data analysis, where PCA can be used to reveal the underlying structure of a dataset.
PCA is based on the idea that any group of variables will vary together to some degree. This covariance will be greater in variables that measure similar quantities. PCA capitalizes on the covariance of variables by using eigenvalue decomposition to extract components that can explain the greatest amount of variability in the data. A simple, two-dimensional example of this process is below.
The left figure above depicts the plot of two variables with high covariance. You can easily see that there is structure in the data, with levels of one variable highly related to levels of the other. PCA examines the cloud of data and asks, “along what dimension is the greatest amount of variability found?”. In the case of our plot, the greatest variability is found along the diagonal axis, which becomes the first component. After the variability of the first component is accounted for, a second orthogonal component will then be found. In this case the second component explains the spread of the data around the first component.
Combined the two components in the example explain 100% of the variability in the data. Still, they are not equal in their contribution. In this case the first component explains 97% of the variance, meaning that you could reduce your dataset by half using just the first component and you would lost only 3% of the variability.
We are trying to use PCA to examine individual differences between people in our fMRI study. By taking each person’s analysis results and running them through the PCA algorithm we are hoping to identify the underlying structure of variability between people. In conjunction with clustering algorithms, we can observe not only how people vary, but where in the brain the variability is strongest, and how people group together. Time will tell if this approach bears fruit, but it is a lot of fun to explore.
Cedrus Lumina Serial Emulator
The scanner at UCSB is always busy, leaving precious little time to get in and test your new experiment. You could stay late or come in during the weekend to get a turn on the magnet, or you can debug your experiment at your desk with a response emulator. This page on the prefrontal.org wiki has a set of instructions for creating your own emulator to provide a facsimile of the output provided by the Cedrus Lumina LP-400 box. It provides scanner trigger events every 2000ms with artificial subject responses following 250ms after each trigger.
I used this device extensively when I was programming some Psychtoolbox scripts. By getting all the big bugs out of your experiment before ‘going live’ and testing on a MR phantom you can save a lot of time, energy, and frustration.
Summer Teaching: Discover Technology
I always hope that a pause in the stream of weblog posts will be justified. The last several weeks have been pretty quiet around prefrontal.org, but I do like to think that the time went to a good cause.
For most of July I have been in Lawrence, Kansas as an instructor for the KU Educational Talent Search summer camp ‘Discover Technology’. Talent Search is an educational opportunity program whose main goal is to shepherd students from underrepresented backgrounds though high school and encourage them to engage in post-secondary education. The Discover Technology summer camp is just one part of this year-round program.
I have been an instructor for Discover Technology for almost 10 years now. What brings me back year after year? Well, they pay me for my time, which is a plus. The biggest motivator though is that the course genuinely impacts the lives of the students. The graduation rate of most Kansas City, Kansas high schools is around 50% - Talent Search gets over 95% of their students through to their graduation. That is huge, and definitely worth a little time and effort on my part.
The Emergence of Collaborative Brain Function
It doesn’t take a neuroimaging study to see that adolescents are in a state of flux. It is the time in our lives that takes us from having the mind and body of a child to possessing the full mental and physical faculties of a young adult. In terms of cognitive ability it is during this time that a number of very advanced cognitive abilities are slowly coming online. These abilities include risk perception [consequence representation], metacognition [thinking about thinking], counterfactual thinking [alternate possibilities], and abstract reasoning [higher-order relationships]. In short, we are able to represent a much larger amount of information in new and complex ways, relating this information together in ways that are impossible for a 10-year-old brain.
Now, here’s the $10,000 question: what is going on in the brain to accomplish this?
One piece of that puzzle has been investigated by Beatriz Luna and John Sweeney. They conducted a series of experiments investigating the development of response inhibition by using an antisaccade task with adolescents and adults. The antisaccade task is founded on our natural tendency to move our eyes to the appearance of a new object in our visual field. When we are instructed to move our eyes to these new objects it is called a prosaccade - you are reinforcing an already strong natural behavior. When we are instructed to move our eyes away from the new objects it is called an antisaccade - you are having to inhibit the natural response and engage in the opposite behavior. Naturally, this is much more difficult than the prosaccade condition and is considered by many to be a metric for cognitive flexibility (Hutton and Ettinger, 2006).
There are two main conclusions that I have taken away from the Luna and Sweeney papers. These are conclusions so important that they end up getting cited in just about every paper that I write.
1. Just because an adolescent has shown an adult level of performance does not mean that they are arriving there in an adult-like way. As adolescents get older they tend to make fewer and fewer errors during the antisaccade task. This trend continues until roughly the age of 15, when they have nearly reached an adult level of performance (Luna, Garver, Urban, Lazar, and Sweeney, 2004). However, looking at the PET or fMRI data for a 15-year-old during an antisaccade task tells a different story. The adolescents have different levels of activity in the frontal eye fields, intraparietal sulcus, thalamus, cerebellum, and superior colliculus (Luna et al., 2001). So, while the adolescents could complete the antisaccade task looking like an adult, their brain was accomplishing this feat using an immature pattern of regional activity.
2. A large part of the cognitive maturation observed in adolescence comes from the ability for brain regions to increasingly communicate and collaborate. This runs contrary to frontal theories, which theorize that developmental increases in cognitive ability are largely due to the late maturation of prefrontal executive function Instead, Luna and Sweeney argue that we cannot ignore the functional integration of brain regions in our examination of developmental change (Luna and Sweeney, 2004). This idea is supported by recent DTI studies of the brain, which show that behavioral performance is related to inter-region white matter connectivity (Tuch et al., 2005). As an isolated computational unit a single cortical region is rather useless. It is only through the dynamic exchange of information between regions that advanced cognitive abilities can flourish.
The Luna and Sweeney papers are a staple of my research library. While their evidence is very domain-specific (response inhibition), their conclusions speak volumes about how the brain matures during adolescence. Just because an individual might look and sound like a young adult doesn’t mean that they are one, and hen they finally do make it to adulthood it will have required the combined resources of the whole brain working together.
Refs
* Hutton SB, Ettinger U. (2006). The antisaccade task as a research tool in psychopathology: a critical review. Psychophysiology, May;43(3):302-13. Pubmed: 16805870
* Luna B, Thulborn KR, Munoz DP, Merriam EP, Garver KE, Minshew NJ, Keshavan MS, Genovese CR, Eddy WF, Sweeney JA. (2001). Maturation of widely distributed brain function subserves cognitive development. Neuroimage, 13(5):786-793. Pubmed: 11304075
* Luna B, Garver KE, Urban TA, Lazar NA, Sweeney JA. (2004). Maturation of cognitive processes from late childhood to adulthood. Child Development, Sep-Oct;75(5):1357-72. Pubmed: 15369519
* Luna B, Sweeney JA. (2004). The emergence of collaborative brain function: FMRI studies of the development of response inhibition. Annals of the New York Academy of Sciences, Jun;1021:296-309. Pubmed: 15251900
* Tuch DS, Salat DH, Wisco JJ, Zaleta AK, Hevelone ND, Rosas HD. (2005). Choice reaction time performance correlates with diffusion anisotropy in white matter pathways supporting visuospatial attention. Proceedings of the National Academy of Sciences, Aug 23;102(34):12212-7. Pubmed: 16103359
Quote of the Week - Rousseau
“We are born, so to speak, twice over; born into existence, and born into life; born a human being and born a man.” Jean-Jacques Rousseau, Emile, 1762
Good Science vs Public Awareness (Iacoboni)
In cognitive neuroscience debates are usually quite subdued and people rarely, if ever, point fingers negatively. However, in the last 24 hours there has been a rather dramatic reversal of this norm in a debate that has carried on for several years now: whether Marco Iacoboni and his collaborators overstep the bounds of good science when they bypass peer review and take their findings on Superbowl ads and political candidates directly to the public. Prefrontal.org touched briefly on the topic several months ago in a post entitled “Political Pseudoscience“. Since that time Adam Kolber of the Neuroethics and Law Blog invited Iacoboni to repond to his critics in a post on his weblog. The response was posted this morning, an excerpt of which is printed below: Read the rest of this post »
Writing Tools: Scrivener
Writing is one of the most difficult things that I have to do as a scientist. The problem is that writing is a necessary part of both grant applications and article publication, meaning that we really do live and die by the impact our words have on people. That fact is rarely lost on me as I stare at the laptop screen silently begging my mind to, please, let me have one more great mediocre sentence.
Fortunately I have been able to find several tools that help the writing process along. A good outline of the project before I begin writing is probably the biggest step I have taken in making the final product more coherent. I will write about OmniOutliner sometime in the near future. Also helpful has been the habit of keeping all of my experiment notes in a single notebook, putting relevant information at my fingertips when the act of writing begins. Probably the single most beneficial tool I have found is the writing project management software Scrivener. Having this one program that I can live in while I write has made a gigantic impact on my productivity. Read the rest of this post »
Phoenix Lander Descent Photo
It might not seem immediately obvious, but the above picture is off-the-scale awesome.
The two white blobs in the photo represent the Phoenix Mars lander and its parachute as it was descending into the martian atmosphere. But, wait, where did the photo come from? Well, the Mars Reconnaissance Orbiter just happened to be passing by at 30,000 miles per hour and angled its HiRISE camera over to sneak a few pics. Now that is engineering. It is amazing in its own right that we had a successful landing on Mars after traversing 423 million miles. It is icing on the cake though to have calculations accurate enough to enable this ‘action shot’ of the landing.