Learning sculpts the spontaneous activity of the resting human brain

Abstract

The brain is not a passive sensory-motor analyzer driven by environmental stimuli, but actively maintains ongoing representations that may be involved in the coding of expected sensory stimuli, prospective motor responses, and prior experience. Spontaneous cortical activity has been proposed to play an important part in maintaining these ongoing, internal representations, although its functional role is not well understood. One spontaneous signal being intensely investigated in the human brain is the interregional temporal correlation of the blood-oxygen level-dependent (BOLD) signal recorded at rest by functional MRI (functional connectivity-by-MRI, fcMRI, or BOLD connectivity). This signal is intrinsic and coherent within a number of distributed networks whose topography closely resembles that of functional networks recruited during tasks. While it is apparent that fcMRI networks reflect anatomical connectivity, it is less clear whether they have any dynamic functional importance. Here, we demonstrate that visual perceptual learning, an example of adult neural plasticity, modifies the resting covariance structure of spontaneous activity between networks engaged by the task. Specifically, after intense training on a shape-identification task constrained to one visual quadrant, resting BOLD functional connectivity and directed mutual interaction between trained visual cortex and frontal-parietal areas involved in the control of spatial attention were significantly modified. Critically, these changes correlated with the degree of perceptual learning. We conclude that functional connectivity serves a dynamic role in brain function, supporting the consolidation of previous experience.

Fig. 1.

Behavioral training and psychophysics results. (A) Illustration of timeline for 2 trials. On each trial subjects fixated a central spot for 200 ms (fixation), after which the target shape (an inverted T) was presented at the center of the screen for 2,000 ms (target presentation); finally, an array of 12 stimuli, differently oriented Ts (distracters) with or without an inverted T (target), was briefly flashed for 150 ms (array presentation). Subjects attended to the lower left visual quadrant and indicated the presence or absence of the target shape (red ○) in that visual quadrant (○ was not present in the display; see SI Methods for more details). (B) Example of a single subject's learning curve. Each block contains 45 trials. The red line indicates a learning threshold of 80% accuracy in 10 consecutive trial blocks. (C) Psychophysical comparison of accuracy in all quadrants. In a control session at the end of training, subjects were asked to discriminate between trained and uniquely shaped orientations in all visual quadrants. A repeated-measure ANOVA, with shape (trained, untrained) and quadrant (trained Lower Left, Lower Right, Upper Left, Upper Right) as factors, showed a significant main effect of quadrant [F (3, 21) = 3.52, P < 0.05], and a significant interaction of shape by quadrant [F (3, 21) = 8.49, P < 0.001 ]. Posthoc contrasts (Newman-Keuls test) showed that performance in the trained condition (trained shape in the trained visual quadrant) was better with respect to any other condition. (n = 6; Error bars, ± SEM; *, P < 0.05).

Fig. 2.

Task-evoked modulation of the visual cortex after perceptual learning. (Center) Stimulus array with colored squares (not present in real display) indicating 4 visual quadrants. (Flat maps) visual cortex ROIs obtained from passive localizer scans by stimulating one quadrant at a time (Fig. S2). ROIs are projected onto a flattened representation of the posterior occipital cortex using the PALS (population-average, landmark, and surface-based) atlas (25). Bar plots: % signal change of BOLD in each quadrant when attending to the lower left quadrant and discriminating trained or untrained targets. Note that all 4 quadrants of the visual cortex were stimulated by the stimulus array, but only the trained visual quadrant in the right dorsal and the homologous area in left dorsal visual cortex show a shape-specific modulation. (Posthoc Newman-Keuls test, n = 12; Error bars, ± SEM; *, P < 0.05).

Fig. 3.

Whole-brain task-evoked modulation and spontaneous functional connectivity after perceptual learning. (A) Whole-brain voxel-wise z-map of trained minus untrained shape conditions, corrected for multiple comparisons (Monte-Carlo, P < 0.05) and projected onto an inflated representation of the PALS atlas. Central inset shows activation in the right dorsal visual cortex projected onto a flattened representation of the occipital lobe. Blue regions represent untrained > trained; orange regions represent response for trained > untrained. n = 12. (B) Pre- and postlearning spontaneous fcMRI. Color bar indicates z-transformed correlation values for each region pair, positive for red cells and negative for blue cells. Note stability of the correlation matrix across sessions (separated by >1 week), indicating that within-network functional connectivity is very stable over time (n = 14). (C) Post- minus prelearning changes in spontaneous fcMRI. Correlation matrix (Fisher z-transformed Pearson coefficient) of all possible ROI pairs in the visual cortex, dorsal attention network (DAN), and default mode network (DMN). Color bar indicates post- minus prelearning z-transformed correlation values (rest 2 – rest 1 scans) for each region pair. Blue cells represent significant correlation difference between dorsal attention and trained visual cortex ROIs (t test, P < 0.03, corrected for multiple comparisons): prelearning > postlearning. Red cells represent significant correlation difference between default network and untrained visual cortex ROIs, postlearning > prelearning; n = 14.

Fig. 4.

Modulation of spontaneous functional connectivity after perceptual learning. Flattened brain representation with ROIs in trained visual cortex and dorsal attention network (A) and in untrained visual cortex and default network (B). Bar graphs report correlation values (r) between trained visual cortex and dorsal attention ROIs and untrained visual cortices and default network ROIs before (black) and after (gray) perceptual learning. n = 14. r, Pearson correlation coefficient; Student's t test, 2 tails, P < 0.05; error bars ± SEM.

Fig. 5.

Modulation of GC after perceptual learning. Changes in GC directional modulation after learning that correlate with behavioral performance. (A) Bottom-up GC change. Scatter plots show the positive correlation between bottom-up GC modulation measured as F-statistic score (y-axis) and behavioral improvement measured as trained minus untrained shape accuracy score (x-axis). (r = 0.68, P = 0.0074 for right V1/V2d/V3 → right FEF; r = 0.557, P = 0.037 for V3A/LO → right FEF). (B) Top-down GC change. Scatter plots show the positive correlation between top-down GC modulation measured as F-statistic score (y-axis) and behavioral improvement measured as trained minus untrained shape accuracy score (green) or untrained minus trained shape reaction time score (x-axis). (r = 0.687, P = 0.0065 for right FEF → right V1/V2d/V3; r = 0.55, P = 0.0409 for right FEF → right LO). Green arrows and outlines indicate increased GC (postlearning > prelearning); red dashed arrows and outlines indicate decreased GC (prelearning > postlearning). n = 14.