Accelerated Learning fMRI Study

Functional magnetic resonance imaging (fMRI) measures the activity level in regions of the brain while a subject is in the scanner. The network of partial correlations among regions of interest (ROI) in the brain, is called a functional brain network because it indicates which regions of the brain have activity patterns that appear to be exchanging information with each other. A common question is whether these dependencies are di↵erent in subjects under di↵erent conditions.

Using data from the Accelerated Learning fMRI Study, we want to see how brain regions interact before and after a person learns a new skill (Clark et al., 2012). In this study, subjects are asked to identify concealed objects in still images taken from a virtual reality environment. Initially, all subjects are considered Novice, that is they are not significantly better than random at identifying images with concealed objects.

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(a) False discovery rate versus transfer

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Number of Learned Differences (logscale)

1 − FDR

(b) False discovery rate versus number of di↵er-ences

Figure 5.5: Accelerated learning fMRI study false discovery rate.

fMRI data are collected from these subjects while performing this identification task.

Then, subjects are trained until they reach a level of Intermediate (midway between chance and perfect) competency. At this point, fMRI data are again collected while performing the identification task. In total, we have data from 12 subjects at the Novice stage and 4 at the Intermediate stage. For each subject, there are 1056 samples of brain activity from 116 regions of interest (ROIs) in the brain. The ROIs are defined by the AAL atlas (Tzourio-Mazoyer et al., 2002). Our goal is to identify dependencies among the ROIs that are di↵erent in the Intermediate stage from the Novice stage.

Looking at the networks (rather than the activity levels of individual ROIs) shows us which ROIs are most critical for performing a cognitive task (Clark et al., 2012).

Our false discovery experiments with fMRI data show that without transfer, there are always di↵erences learned between the stages. As the transfer strength is increased, these known false di↵erences disappear quickly. We compare this behavior against what happens when we perform similar bootstrap sampling from the two classes of data that we would like to compare. We see that many more di↵erences are learned and these di↵erences do not disappear so quickly as the transfer strength is increased.

Figure 5.5a illustrates this phenomenon by showing the ratio of fake di↵erences (those

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learned between sets of data from the same population) to the di↵erences learned between sets of data from the two populations of interest. At the left end of the plot, when there is no transfer, the number of fake di↵erences learned is about 80% the number of potentially-real di↵erences between the two classes. As the transfer strength parameter increases, this percentage drops, indicating that the rate of decrease of the fake di↵erences is faster than that of the potentially-real di↵erences.

Figure 5.5b shows the tradeo↵ between estimated precision (1-FDR) and the num-ber of di↵erences found (analogous to the precision recall curves in Figure 5.3). There are edge di↵erences between Novice and Intermediate that are more resistant to trans-fer bias than the di↵erences between two sets of samples from the same class. We are therefore more confident that these Novice vs Intermediate edge di↵erences represent true di↵erences than those found without transfer. However, we are not sure what percentage of these edges are expected to be real because our estimate of the false discovery rate may be low.

Learned Brain Networks

Figure 5.6 shows the networks learned for the two classes of data (Novice and Interme-diate). Figure 5.6a shows the edges that are learned in Novice but not Intermediate.

Figure 5.6b shows the edges that are learned in Intermediate but not Novice.

In order to gain evidence that we are learning good networks, we look at the pathways activated by the visual exercise in both stages of learning (Novice and Intermediate). These results show that for both stages, groups of brain regions are found that share information, which correlate well with sensory-motor pathways found in humans (Figure 5.7). This includes the occipito-parietal dorsal visual pathway that computes the location of objects, the occipito-temporal ventral pathway that determines the identity of objects, collections of frontal and cingulate regions that help to make decisions about responses, as well as separate cerebellar and middle temporal networks, along with other smaller networks of brain regions (Mishkin, Ungerleider, and Macko, 1983). With learning to identify hidden objects in this task,

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(a) Novice but not Intermediate (b) Intermediate but not Novice

Figure 5.6: Di↵erential dependency networks learned from Accelerated Learning fMRI Study with 1 = 0.5 and 2 = 0.2.

it was found that portions of the ventral pathway increased in strength, suggesting that learning resulted in greater information flow among regions that specialize in visual object identification.

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Figure 5.7: Network of dependencies shared among Novice and Intermediate stages of the Accelerated Learning study.

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(a) False discovery rate versus transfer

1 5 10 50 100 500

0.20.40.60.81.0

Number of Learned Differences (logscale)

1−FDR

(b) False discovery rate versus number of di↵er-ences

Figure 5.8: Ovarian cancer study false discovery rate.