Performance in most complex cognitive and psychomotor tasks improves with training, yet the extent of improvement varies among individuals. Is it possible to forecast the benefit that a person might reap from training? Several behavioral measures have been used to predict individual differences in task improvement, but their predictive power is limited. Here we show that individual differences in patterns of time-averaged T2*-weighted MRI images in the dorsal striatum recorded at the initial stage of training predict subsequent learning success in a complex video game with high accuracy. These predictions explained more than half of the variance in learning success among individuals, suggesting that individual differences in neuroanatomy or persistent physiology predict whether and to what extent people will benefit from training in a complex task. Surprisingly, predictions from white matter were highly accurate, while voxels in the gray matter of the dorsal striatum did not contain any information about future training success. Prediction accuracy was higher in the anterior than the posterior half of the dorsal striatum. The link between trainability and the time-averaged T2*-weighted signal in the dorsal striatum reaffirms the role of this part of the basal ganglia in learning and executive functions, such as task-switching and task coordination processes. The ability to predict who will benefit from training by using neuroimaging data collected in the early training phase may have far-reaching implications for the assessment of candidates for specific training programs as well as the study of populations that show deficiencies in learning new skills.

Funding: This work was funded by the Office of Naval Research (grant award number N00014-07-1-0903). The Office of Naval Research had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2011 Vo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

With a few exceptions (e.g., the volumetric study by Erickson et al. [6] ), learning has so far mostly been investigated with functional MRI (fMRI), making use of contrasts in the blood-oxygen-level dependent (BOLD) effect [18] . Measured with gradient-echo echo planar imaging (EPI), functional BOLD activity is obtained by contrasting the EPI images of an experimental condition of interest with those of a baseline condition. This emphasizes the differences between the two conditions and eliminates the part of the BOLD signal that they have in common. Here we focus on the common part, which we obtain by averaging the EPI volumes over time. The result is a time-averaged T2*-weighted image. Unlike the T1-weighted magnetization prepared rapid acquisition gradient echo (MPRAGE) image, which reflects the tissue's proton density, the T2*-weighted image depends mostly on the magnetic susceptibility of the tissue. Using multi-voxel pattern analysis (MVPA) we identified patterns of time-averaged T2*-weighted activity that predict subjects' future improvements in playing a complex video game with high accuracy.

In our analysis we focused on the dorsal striatum, consisting of the caudate nucleus and the putamen, and on the nucleus accumbens in the ventral striatum because of these structures' involvement in learning and execution of complex responses. The dorsal striatum plays a role in procedural and habit learning and in carrying out or initiating complex goal-directed tasks such as task-switching or reaction-time tasks [4] , [5] , [6] , [7] , [8] , [9] , [10] . The ventral striatum, typically related to reinforcement and motivation [8] , [11] , [12] , is also recruited during early stages of learning [13] , [14] , [15] . Both the dorsal and ventral striatum show increased release and binding of dopamine, which has been associated with better performance in a video game [16] . Furthermore, an increase in the functional activity in the striatum has been associated with the transfer of updating skills in working memory tasks, possibly regulated by dopaminergic modulation [17] .

People vary in their ability to improve cognitive and psychomotor performance with practice and training. Cognitive tests predict who will benefit from training [1] , [2] , but they usually account for only a small proportion of the variance among individuals [3] . Here we use brain magnetic resonance imaging (MRI) data to predict individual learning success with unprecedented accuracy. Specifically, we show that patterns of time-averaged T2*-weighted images in the dorsal striatum at the start of training in a complex video-game account for more than half of the variance in the amount of subsequent learning among individuals.

Results

Thirty-four young adults with little experience in playing video games were trained to play Space Fortress (Figure 1A), a complex video game developed as a test bed to study skill acquisition and learning [19], [20] (see details in the Materials and Methods section). After an initial instruction session to familiarize participants with the game controls and objectives, they played Space Fortress inside an MRI scanner with an MR-compatible joystick. We recorded high-resolution anatomical T1-weighted MRI scans with an MPRAGE sequence as well as T2*-weighted images with a gradient-echo EPI sequence. The total game score during this first session inside the scanner was used as a measure of participants' abilities prior to extensive training. Over the course of the next three to eight weeks (38 days on average) participants completed ten two-hour training sessions playing Space Fortress outside the scanner (Figure 1B). Following these 20 hours of training, participants underwent a second MRI session identical to the first. The score improvement from the first to the second MRI session, i.e., the difference between the game scores in MRI sessions 2 and 1, served as a measure of individual learning success. Note that we only consider game performance during the two MRI scans in this paper, since the main focus of the paper is on predicting learning success from imaging data. For details of the progression of training outside the scanner see reference [21].

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larger image TIFF original image Download: Figure 1. Space Fortress game, experimental time line and pre-processing flow. (A) Schematic interface of the Space Fortress video game. The objective of the game is to destroy the space fortress (yellow, center of the display) by shooting missiles at it from a space ship (yellow, upper-left corner), while moving the space ship inside the hexagon with thruster commands to evade mines (red diamond) and to collect resources (‘$’ sign). (B) Timeline of the experiment for a typical participant. After initial instructions, participants played Space Fortress in the MRI scanner while their brain activity was recorded. Next, participants underwent a total of 20 hours of training, followed by a second MRI session. We used the difference in total game score between the two MRI sessions (i.e. the score improvement) as a measure of learning success. (C) MRI preprocessing workflow: EPI volume series (1st MR session) of different subjects are registered to the common space (MNI space) by linear and non-linear registration. After normalization, temporal averages of the EPI volumes are used for the subsequent analysis. https://doi.org/10.1371/journal.pone.0016093.g001

The T2*-weighted images acquired for each participant during MRI session 1 were registered linearly (7 degrees of freedom) to the T1 volume recorded in the same session. Next, a non-linear transformation was computed from the high-resolution T1 volumes to the standard Montreal Neurological Institute (MNI) space. The concatenation of these two transformations was then applied to register each subject's T2*-weighted images into MNI space. This registration was followed by a normalization step to account for variations of scanner settings between runs. The resulting T2* volumes were averaged over 16 minutes of active game play in order to suppress signal variations due to functional activity and other sources of noise. We then performed two different types of region-of-interest (ROI) based analysis with this average T2* signal to predict subjects' score improvement: spatial mean activity analysis and multi-voxel pattern analysis (MVPA). Unlike the spatial mean analysis, MVPA utilizes the distributed pattern of voxel activity within an ROI.

For the spatial mean activity analysis, we averaged the intensity of all voxels inside an anatomically defined region. As a first test, we divided subjects into groups of good and poor learners based on a median split of their score improvements. We found significantly higher mean activity for good than poor learners in the dorsal striatum (p = 0.011), but not in the ventral striatum (p = 0.75, two-sample t tests with n1 = n2 = 17). To determine the relationship between subjects' numerical score improvements and mean activity within an ROI we computed their Pearson correlation. In the dorsal striatum, the correlation was significant (r = 0.47, p = 0.0053; see Figure 2A), but again not in the ventral striatum (r = −0.09).

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larger image TIFF original image Download: Figure 2. Predicting score improvement from MRI activity in the dorsal striatum. (A) Correlation of measured score improvement with the spatial mean of the time-averaged T2*-weighted signal in the dorsal striatum. Mean activity of 34 subjects is significantly correlated with score improvement. (B) Correlation of measured score improvements with score improvement predicted from multi-voxel patterns of the T2*-weighted signal in the dorsal striatum. It shows an even higher correlation than in A). The dashed lines show the least-squares best linear fits in figures A and B. **p<0.01, ***p<0.001. https://doi.org/10.1371/journal.pone.0016093.g002

Although analysis of spatial mean activity can predict score improvements to some extent, it provides merely summary statistics of the activity in an ROI, ignoring subtle differences in activity patterns. Indeed, after subtracting out each individual's average activity, good and poor learners differed in the multi-voxel patterns of time-averaged T2* activity in the dorsal striatum (Figure 3). The color patches in Figure 3 suggest a subdivision of the dorsal striatum roughly along the anterior-posterior line. In other words, good and poor learners not only differ in their level of mean activity in the dorsal striatum, but also in the local activity patterns within the dorsal striatum. These differences allow us to predict learning success for individual participants from the patterns of the temporally compounded EPI images recorded at the beginning of training with much higher accuracy than from the spatial mean of activity alone.

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larger image TIFF original image Download: Figure 3. Pattern of differences between good and poor learners. Differences in activation patterns in the dorsal striatum between good and poor learners overlaid on top of six anatomical slices with z-coordinates respectively, −14, −6, 2, 10, 18, and 26. For this visualization the group of 34 subjects was split into 17 good and 17 poor learners based on the median of score improvements in Space Fortress over the course of 20 hours of training. Each subject's mean activity was subtracted from her or his activity in the dorsal striatum. The activity patterns were then averaged separately for good and poor learners. The figure shows the difference between the average patterns of good and poor learners. https://doi.org/10.1371/journal.pone.0016093.g003

To exploit these differences in a multivariate analysis, we first excluded data from one subject and used activity patterns of the voxels from the remaining subjects, together with their score improvements, to train a support vector regression (SVR) algorithm [22], [23]. The algorithm then generated a prediction for the performance improvement of the excluded subject from her or his pattern of time-averaged T2*-weighted activity. The procedure was repeated so that each subject was excluded once in a leave-one-subject-out (LOSO) cross validation procedure, thereby generating predictions for each subject based on the performance and activity patterns of the other subjects. Details about the SVR algorithm and the LOSO procedure can be found in the Materials and Methods section.

The algorithmically predicted score improvements were then correlated with the actual performance improvements in Space Fortress to determine the prediction accuracy. Figure 2B shows that the predictions based on pre-training activity patterns in the dorsal striatum were highly correlated with the actual improvements that resulted from 20 hours of training (Pearson correlation coefficient r = 0.74, p = 6.1·10−7). Activity patterns before training accounted for more than half of the variance (R2 = 0.55) among individuals in how much they benefited from training. This represents a substantial improvement in prediction accuracy compared with the spatial mean analysis over the same regions of interest, which explained less than a quarter of the variance (22%; r = 0.47; Figure 2A).

Within the dorsal striatum, predictions based on the pattern of activity in the caudate nucleus (r = 0.77, p = 1.3·10−7) were more accurate than those based on activity in the putamen (r = 0.47, p = 0.0046; Figure 4), with a marginally significant difference (p = 0.051). Furthermore, the left dorsal striatum (r = 0.80, p = 1.0·10−8) showed significantly higher (p = 0.0037) predictive power than the right dorsal striatum (r = 0.36, p = 0.039). Since all subjects were right-handed and controlled the movements of the space ship with their right hand, this may be related to motor learning in the contralateral (left) hemisphere. In contrast to good predictions from the dorsal striatum, predictions based on activity patterns in the ventral striatum (nucleus accumbens) were not correlated with measured score improvements (r = 0.08).

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larger image TIFF original image Download: Figure 4. Accuracy of predicting individual score improvement from MVPA of the time-averaged T2*-weighted signal. In the dorsal striatum, correlation of predicted and measured score improvement for 34 subjects was highly significant. Within the dorsal striatum, correlation for pattern analysis was just as high in the caudate nucleus, but lower in the putamen. Predictions were even less accurate in the ventral striatum (nucleus accumbens). In the dorsal striatum, predictions were significantly more accurate based on activity patterns in the left than in the right hemisphere. The caudate nucleus showed similar lateralization, whereas the putamen did not show strong lateralization. †p = 0.051, *p<0.05, **p<0.01, ***p<0.001. https://doi.org/10.1371/journal.pone.0016093.g004

The score of the Space Fortress game was composed of four sub-scores: Control of the space ship's position; maintaining ship Velocity within a predefined range; Speed with which subjects discriminated between and responded to different types of mines; and Points for successfully destroying the fortress. We repeated the SVR analysis separately for each of the sub-scores. As shown in Figure 5, the speed sub-score shows the same pattern of results as the total score, including the high correlation of predicted and measured score improvement in the left but not the right dorsal striatum, the higher correlation in the caudate nucleus than the putamen, and the low correlation in the ventral striatum (nucleus accumbens). This suggests that learning success with respect to discrimination and working memory (needed to identify a mine as friendly or hostile and to react to it quickly) is best predicted by time-averaged T2* activity in the dorsal striatum. Improvement in motor control, which is reflected in the control and velocity sub-scores, is not predicted to the same extent by the dorsal striatum, although both of these sub-scores are predicted at some level by T2* activity in the left nucleus accumbens. Improvements in the points sub-score are not predicted by activity in the striatum, except for a small but significant correlation of predicted and measured score improvement in the left caudate nucleus.

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larger image TIFF original image Download: Figure 5. Accuracy of predicting improvements in sub-scores from the time-averaged T2*-weighted signal. (A) Improvement in the control sub-score is predicted to a limited extent by the time-averaged T2* activity in the left ventral striatum (nucleus accumbens). (B) The velocity sub-score shows small but significant correlations in the left caudate nucleus and the left nucleus accumbens. (C) Improvement in the speed sub-score is predicted highly significantly by time-averaged T2*-weighted activity in the dorsal striatum, in particular the caudate nucleus, but not by the ventral striatum. Correlation of predicted and measured score improvements is higher in the left than the right hemisphere. This pattern of results matches that of the total score shown in figure 4. (D) The points sub-score shows no significant prediction except for a small but significant correlation of predicted and measures score improvement in the left caudate nucleus. *p<0.05, **p<0.01, ***p<0.001. https://doi.org/10.1371/journal.pone.0016093.g005

Previously, striatal brain volume was reported to predict score improvement to some extent [6], and volume of an area and its time-averaged T2* signal may be related. Another potential predictor for score improvement could be the initial score from the games played during the first MRI session. On the one hand, participants with high initial scores may already have reached ceiling performance, showing little further improvement. On the other hand, higher initial score could indicate higher cognitive abilities, enabling participants to benefit more from extensive training. To account for these factors, we used the volume of regions as reported in [6] and the initial score as two additional explanatory variables (covariates) of measured score improvements, in addition to the score improvements predicted by the SVR analysis. We used a second-order partial correlation analysis for each of the three explanatory variables to assess the unique predictive power of each of them irrespective of the other two. Table 1 shows the correlation of the SVR prediction with measured score improvement to be highly significant, even after removing the effects of striatal volume and initial score. Note that for this analysis, only those 32 of our 34 subjects were used for whom the volumetric data were available from [6]. Also, one might wonder about the use of improvement in game score during the first MRI session (e.g., from game 1 to game 4) as another predictor. However, we found no significant correlation between improvement within the first MRI session and the improvement from the first to the second MRI session (r = −0.17).

It is important to emphasize that although we recorded the same kind of T2*-weighted EPI images that are used for functional MRI, the time-averaged EPI volumes that we used for our MVPA analysis are unlikely to be functional, because here we consider the part of the EPI images that is common across the time course rather than modeling the differences of BOLD activity over different stimulus conditions. Therefore, our signal is more likely to capture individual differences in some aspect of neuroanatomy or persistent physiology, such as differences in blood supply to the dorsal striatum or the iron concentration in this region. This view is further supported by the observation that it is not necessary to use the EPI images recorded during active game play. We obtained almost identical accuracies of predicting score improvement in Space Fortress when we use EPI images from blocks with an acoustic oddball task (r = 0.75, p = 2.9·10−7) or from blocks of passively watching Space Fortress games (r = 0.74, p = 5.6·10−7).

Contrast in MR images can be obtained based on the transverse relaxation time T2 (or T2* in the case of field inhomogeneity) or the longitudinal relaxation time, T1. These two contrasts are determined by intrinsic properties of the imaged tissues. In fact, different T1 and T2 (or T2*) values help to differentiate white and gray matter in anatomical images. To test if we can predict score improvement just as well based on T1-weighted as T2*-weighted images, we subsampled the MPRAGE images that were acquired during the first scanning sessions to the same resolution as the EPI images (3.4375 mm×3.4375 mm×4 mm) and performed the MVPA analysis as described above. Correlation of predicted score improvements with measured score improvement was significantly lower for T1-weighted than T2*-weighted images (p = 0.031), although at 0.38 it was still significantly above zero (p = 0.027; Figure 6). The higher prediction accuracy in T2* compared to T1 images might hint at the importance of magnetic susceptibility of the tissue, which affects T2* but not T1. One possible source of susceptibility variations could be iron in the tissues, for instance in supplied blood [24].

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larger image TIFF original image Download: Figure 6. Comparison of prediction accuracy for various signal sources. Predictions based on patterns of T1-weighted images (MPRAGE) in the dorsal striatum were significantly less accurate than those based on time-averaged T2*-weighted images (EPI). Voxels located in white matter allowed for much better predictions than those in gray matter within the dorsal striatum. Finally, decoding was significantly better from the anterior than the posterior half of the left dorsal striatum. Error bars indicate the 95% confidence interval for the Pearson correlation coefficients. *p<0.05, **p<0.01, ***p<0.001. https://doi.org/10.1371/journal.pone.0016093.g006

Both white and gray matter contain blood vessels. In the white matter, capillaries are embedded in the myelin sheaths of axons that project over relatively long distances. In the gray matter, vessels supply mostly the somas and dendrites of neurons. Determining which tissue contributes more to the patterns that let us predict individual learning success could elucidate the anatomical and/or physiological phenomena underlying our effects. We used FSL's FAST automatic segmentation tool to separate white from gray matter in the T1 image of each individual. We then performed the LOSO cross validation analysis separately on the white matter and on the gray matter voxels (Figure 6). Correlation of predicted with observed score improvement was significantly higher (p = 0.0026) in the white matter (r = 0.65, p = 2.8·10−5) than in the gray matter (r = 0.02). This suggests that the long-range, myelinated connections in the white matter are critical for our ability to predict score improvement in Space Fortress.

In Figure 3 we had noted an apparent anterior/posterior organization of the dorsal striatum based on the difference in activity patterns between good and poor learners. To investigate this organization further, we split the left dorsal striatum in each participant with a coronal plane such that approximately equal numbers of voxels were anterior as posterior of the division. We then repeated the LOSO cross validation analysis separately for the anterior and the posterior half. Prediction accuracy was significantly higher (p = 0.0024) from the anterior (r = 0.82, p = 2.4·10−9) than the posterior (r = 0.38, p = 0.028) half of the left dorsal striatum (Figure 6), accounting for 68% of the variance among individuals. This result substantiates the qualitative observation in Figure 3 with a quantitative difference between anterior and posterior parts of the dorsal striatum.