Andy's Brain Blog

Recently the journal Cerebral Cortex accepted one of our lab's papers for publication, which has made me ecstatically, deliriously happy. This represents one of the highwater marks of my publishing career, even greater than the erotically-tinged agitprop novel I published serially in my college's Pravda-inspired newspaper. Entitled A Long Caress: Twilight of the Capitalist Idols, the book followed the lives of two dreamy radicals pursued by the CIA, of which I provide an excerpt:

Boris laid aside his Chernyshevsky pamphlet and looked at Olga. Hearing his declaiming against the capitalist dogs had brought her to a fever heat; and now, as he watched her, he noticed the graceful curves of her neck, brought into relief by the delicate strands of jet-black hair gently brushing against her collarbone and the edges of her heaving bosom. She gazed at him adoringly, sloe-eyed, her cheeks flushed, the glistening sweat making her Party-issued uniform cling to her skin like fuzz on a peach. Around her waist he wrapped his strong, powerful arms, and she offered herself up like a prize.

A few miles away in an underground bunker, all of this was transmitted through a hidden wire to CIA agent John Davies. Pressing his headphones closer to his ears, Davies frowned. "Blimey," he said.

While our new paper does not come close to the rhapsodic heights of A Long Caress, it still goes a long way to resolving fundamental issues with studying different forms of abstraction. One form of abstraction, temporal abstraction, refers to maintaining information over time, with more remote events requiring correspondingly greater levels of temporal abstraction; while a related form of abstraction, relational abstraction, refers to processing higher-level information, such as complex features of stimuli. Unhappily, they are often confounded in the same experiment. This study attempted to tease apart both of these forms of abstraction, as well as independently assess the effects of integration, wherein several different pieces of information need to be collectively processed in order to make a correct response. Temporal abstraction was nonexistent, while relational abstraction effects were found in lateral premotor cortex and rostrolateral prefrontal cortex. Integration was associated with increased activation in superior frontal sulcus and frontopolar cortex, consistent with this region's handling more abstract representations between items.

A link to the paper can be found here. Documentary footage of Stalin ordering the deaths of his generals and field marshals can be found in the following video.

Pictured: FSL User

[Before we begin: According to my traffic sources, the majority of my viewers, outside of the United States, are from Russia. If the history books I have read and the video games I have played are any guide, they are probably visiting this site in order to learn enough about cognitive neuroscience to produce some kind of supersoldier in order to restore communist hardliners to power and launch an assault on America. So, to all of my Russian readers: Hola!]

Finally, we have arrived at the end of the FEAT interface. The last two tabs, post-stats and registration, allow the user to specify how the results will be visualized, what kinds of multiple comparison corrections to carry out, and how to register and normalize the data.

One might wonder why FSL chooses to perform coregistration and normalization as the last step, instead of at a previous step in the preprocessing pipeline as do other software analysis packages. The reasoning is that because these steps introduce spatial correlations, it is better to introduce them after having run the statistical analysis, in order to prevent any sort of biases that may be introduced into the data as a result of applying these steps. Personally, I don't think it matters that much either way, since you have to do it at some point; however, that is the way it is built into the FSL stream, and if you don't like it, tough bananas.

Most of the defaults are fine; the only tab that requires any input before you can move forward is the Registration tab, which requires a skullstripped brain to normalize to a standardized space. This includes atlases such as Talairach or Montreal Neurological Institute (MNI), although I believe FSL only uses MNI. The point of normalization is that every subject's brain will be twisted, rotated, warped, and undergo various other uncomfortable transformations until it is located within a box that has equal dimensions to the standard space. Furthermore, certain anatomical landmarks will be at a specific coordinate position relative to every other part of the brain; for example, in Talairach space, the anterior commissure - a bundle of nerve fibers connecting the hemispheres, located at the base of the anterior columns of the fornix - will be positioned at coordinates 0, 0, 0. Thus, according to the Talairach atlas in this example, any other brain regions can be defined based on their distance from this origin (although the researcher should always check to make sure that what the atlas says matches up with what is directly in front of him).

A couple of other useful options are in the post-stats tab. For example, Pre-threshold masking allows the user to perform region of interest (ROI) analyses which define an a priori region either based on anatomical regions defined by an atlas or a binary mask generated by a program like Marsbar. Contrast masking has a similar role, masking out certain regions of the brain based on whether they are covered by another contrast in the analysis; although caution should be exercised here as well, in order to make sure that the masking contrast is orthogonal to the one being investigated. For more information about ROI analyses, as well as potential pitfalls, see an earlier post about the topic.

More tutorials will be up soon to guide the user through what all those HTML output files mean, as well as looking at and interpreting results.