Learning-induced autonomy of sensorimotor systems

Abstract

Distributed networks of brain areas interact with one another in a time-varying fashion to enable complex cognitive and sensorimotor functions. Here we used new network-analysis algorithms to test the recruitment and integration of large-scale functional neural circuitry during learning. Using functional magnetic resonance imaging data acquired from healthy human participants, we investigated changes in the architecture of functional connectivity patterns that promote learning from initial training through mastery of a simple motor skill. Our results show that learning induces an autonomy of sensorimotor systems and that the release of cognitive control hubs in frontal and cingulate cortices predicts individual differences in the rate of learning on other days of practice. Our general statistical approach is applicable across other cognitive domains and provides a key to understanding time-resolved interactions between distributed neural circuits that enable task performance.

Figure 1

Schematic of methods. (a) We parcellated the brain into 112 cortical and subcortical regions based on the Harvard-Oxford atlas. (b) We calculated the functional connectivity between these regions to create a functional network and clustered regions within the functional network using community-detection techniques. (c) We collated the community assignments (‘partitions’) across different time scales of learning (naive, early, middle and late), different depths of training (extensive (EXT), moderate (MOD) and minimal (MIN)) and different subjects. (d) We created a module-allegiance matrix indicating the probability that any two regions will be classified into the same network community.

Figure 2

Summary architecture of learning. (a) The module-allegiance matrix indicating the probability that two nodes will be located in the same functional community across subjects, scanning sessions, sequence types and trial blocks. (b,c) The module-allegiance matrix displays two putative functional modules composed of brain regions that are consistently grouped into the same network community: one composed of primary and secondary sensorimotor areas (b), and one composed of primary visual cortex (c). Brain regions in a were ordered to maximize strong associations along the diagonal. For brain-surface visualizations we used Caret software (http://brainvis.wustl.edu/wiki/index.php/Caret:About).

Figure 3

Dynamic brain architecture associated with task practice. (a) In naive, early, middle and late learning, the motor and visual modules evident in the stable architecture (Fig. 2a) were also present. We observed a decrease in the strength of allegiance between regions in the non-motor, non-visual set. (b) Magnified views of the motor and visual modules from a demonstrating the decrease in the strength of allegiance between these modules as learning progressed.

Figure 4

Recruitment and integration were modulated by training. (a,b) Motor-visual integration (a) and non-motor, non-visual recruitment (b) as a function of the number of trials practiced. Solid diagonal lines indicate the best linear fit, and r values indicate Pearson correlation coefficients. Error bars indicate s.d. of the mean across participants.

Figure 5

Individual differences in brain network architecture map to task learning. Scatter plot of learning rate and training-dependent modulation of non-motor, non-visual recruitment (r = 0.59, P = 0.0062).

Figure 6

The release of a fronto-cingulate control network predicts individual differences in learning. (a) Elements in the driver network are given by the statistically significant (P < 0.05, uncorrected) Pearson correlation between individual differences in training-induced modulation and individual differences in learning. Colors indicate the magnitude of the Pearson correlation coefficient. (b) The strength of brain areas mapped onto the cortical surface using Caret. The strength of area i is given by the sum of column i in the driver network. Warm colors indicate high strength in the driver network, and cool colors indicate low strength in the driver network. For brain-surface visualizations we used Caret software (http://brainvis.wustl.edu/wiki/index.php/Caret:About).