Predicting learning plateau of working memory from whole-brain intrinsic network connectivity patterns

Abstract

Individual learning performance of cognitive function is related to functional connections within 'task-activated' regions where activities increase during the corresponding cognitive tasks. On the other hand, since any brain region is connected with other regions and brain-wide networks, learning is characterized by modulations in connectivity between networks with different functions. Therefore, we hypothesized that learning performance is determined by functional connections among intrinsic networks that include both task-activated and less-activated networks. Subjects underwent resting-state functional MRI and a short period of training (80-90 min) in a working memory task on separate days. We calculated functional connectivity patterns of whole-brain intrinsic networks and examined whether a sparse linear regression model predicts a performance plateau from the individual patterns. The model resulted in highly accurate predictions (R(2) = 0.73, p = 0.003). Positive connections within task-activated networks, including the left fronto-parietal network, accounted for nearly half (48%) of the contribution ratio to the prediction. Moreover, consistent with our hypothesis, connections of the task-activated networks with less-activated networks showed a comparable contribution (44%). Our findings suggest that learning performance is potentially constrained by system-level interactions within task-activated networks as well as those between task-activated and less-activated networks.

Figure 1. Estimation of individual performance plateau.

(a) Example of a session in the 3-back task. Subjects respond to the target stimulus, which was the letter identical to the one presented three trials back. Red and blue arrows indicate trials in which responses were detected. d-prime was calculated from the hit and false-alarm rates for each session (see Methods). (b) Learning curves (thin lines) of individual subjects (n = 20; coded by color) after smoothing with five-session moving average. Learning curves significantly fitted with an inverse curve (bold line; y = a − b/x) are presented (F-test, p < 0.05; see Supplementary Fig. S1 for remaining subjects). Gray curves indicate three subjects who were excluded from further analysis due to excessive head motions during the resting-state fMRI scan (n = 3, see Methods). Note that the number of sessions was reduced from 25 to 21 due to the moving average.

Figure 2. Matrices for functional connectivity and selection count of 18 intrinsic connectivity networks.

Diagonal and non-diagonal elements show within- and between-network connectivity, respectively. (a) Functional connectivity matrix averaged across subjects (n = 17). Color bar indicates Z-transformed correlation coefficient. See Figure 4b for regions included in each network. (b) Selection count matrix. Red circles indicate connections having a significantly greater selection count than the chance level according to the binomial distribution (p < 0.05 after Bonferroni correction for number of connections, see Methods).

Figure 3. Scatter plot of predicted versus observed individual performance plateaus (n = 17).

Solid line is the regression line with a 95% confidence interval (gray area). Filled and open circles indicate subjects who first underwent resting-state fMRI (i.e., Rest First; n = 10) and those who first received training of the 3-back task (i.e., Task First; n = 7), respectively.

Figure 4. Contribution of connections to prediction of performance plateau.

(a) Circle plot of the 18 intrinsic connectivity networks in order of relevance to working memory according to the metadata of BrainMap ICA. Contribution ratios (see text) of the nine connections that were consistently selected by a sparse linear regression model (red circles in Figure 2b) are presented as the thickness of connection lines (edges). Red and blue edges indicate positive and negative functional connectivity, respectively. (b) Network labels (left column) and their detailed definitions in BrainMap ICA (right column). Color bar indicates weight values that quantify how strongly each network is related to working memory function.