Vision First? The Development of Primary Visual Cortical Networks Is More Rapid Than the Development of Primary Motor Networks in Humans

The development of cortical functions and the capacity of the mature brain to learn are largely determined by the establishment and maintenance of neocortical networks. Here we address the human development of long-range connectivity in primary visual and motor cortices, using well-established behavioral measures - a Contour Integration test and a Finger-tapping task - that have been shown to be related to these specific primary areas, and the long-range neural connectivity within those. Possible confounding factors, such as different task requirements (complexity, cognitive load) are eliminated by using these tasks in a learning paradigm. We find that there is a temporal lag between the developmental timing of primary sensory vs. motor areas with an advantage of visual development; we also confirm that human development is very slow in both cases, and that there is a retained capacity for practice induced plastic changes in adults. This pattern of results seems to point to human-specific development of the “canonical circuits” of primary sensory and motor cortices, probably reflecting the ecological requirements of human life

Perceptual learning in Williams syndrome: looking beyond averages.

Williams Syndrome is a genetically determined neurodevelopmental disorder characterized by an uneven cognitive profile and surprisingly large neurobehavioral differences among individuals. Previous studies have already shown different forms of memory deficiencies and learning difficulties in WS. Here we studied the capacity of WS subjects to improve their performance in a basic visual task. We employed a contour integration paradigm that addresses occipital visual function, and analyzed the initial (i.e. baseline) and after-learning performance of WS individuals. Instead of pooling the very inhomogeneous results of WS subjects together, we evaluated individual performance by expressing it in terms of the deviation from the average performance of the group of typically developing subjects of similar age. This approach helped us to reveal information about the possible origins of poor performance of WS subjects in contour integration. Although the majority of WS individuals showed both reduced baseline and reduced learning performance, individual analysis also revealed a dissociation between baseline and learning capacity in several WS subjects. In spite of impaired initial contour integration performance, some WS individuals presented learning capacity comparable to learning in the typically developing population, and vice versa, poor learning was also observed in subjects with high initial performance levels. These data indicate a dissociation between factors determining initial performance and perceptual learning

Age groups of participants in the CI and FT tasks.

<p>Age groups of participants in the CI and FT tasks.</p

Individual data of the WS individuals.

<p> <b>Note:</b></p><p><b>+:</b> z-scores above one standard deviation.</p><p><b>n:</b> z-scores within one standard deviation.</p><p><b>-:</b> z-scores below one standard deviation.</p><p><b>- -:</b> z-scores below two standard deviations.</p

Age-groups of TD participants.

<p>Age-groups of TD participants.</p

TD baseline and learning data.

<p>The raw baseline and learning data of the TD age-groups. The table shows mean baseline data (threshold on Day1) and the average learning data (improvement from Day1 to Day5 expressed in degree) of the TD participants.</p

Comparison of learning rates in Contour Integration and Finger-tapping.

<p>Day 1 performance is considered 100%, and performance in subsequent days is expressed relative to that. Improvements are calculated by taking the difference between thresholds in consecutive days of practice (such as, Day 1–Day 2, Day 2–Day 3, Day 3–Day 4, Day 4–Day 5). There is a larger improvement from Day 1 to Day 2 in FT than in CI across all age-groups. This difference vanishes from Day 2 to Day 3, and learning rates become nearly equivalent in the two tasks after Day 3.</p

Summary of the methods and results.

<p>(A) Sideview of the human brain with the primary visual cortex (V1, Br 17) in blue, and the primary motor cortex (M1 or Br 4) in red. The cerebral cortex is generally divided into six functionally distinct layers, and the principal source of long-range lateral intralaminar connections is layer II and III, as shown in the insets corresponding to V1 and M1. (B) Contour Integration (CI) stimuli, addressing long-range connections in the primary visual cortex. The collinear chain of oriented elements forming a horizontally placed egg-shape is hidden in the background of randomly positioned and oriented elements. The panels show three levels of difficulty in the CI task. Practice and development leads to improved performance. (C) Movement-sequence in the Finger-tapping (FT) task addressing long-range connectivity of the primary motor cortex. Accuracy and speed of carrying out this sequence improves following practice and during the course of development. (D) Developmental curves in CI (blue) and in FT (red). Day 2 performance of each age-group was normalized to that of the adult performance in each task. Small symbols: individual data; large symbols: age-group average. Curve fitting was done on the age-group average values. The horizontal lines at the bottom connect two age-groups (15 and 21 y), and significance levels of the difference in performance in the two tasks, respectively, are denoted.</p

Normalized and corrected scores in CI.

<p>(A). Normalized baseline and learning scores. The scatter-plot shows individual baseline (threshold on Day1) and learning (Day5 threshold - Day1 threshold) z-score of individual TD (dark grey cross) and WS (red cross) subjects. Average performance on both axes is at zero. The dotted grey line represents the linear regression line fitted on the TD data set. There is a significant negative correlation between baseline performance and the amount of learning in the task (r = −0,372, p<0,0001): the lower the baseline is the greater the improvement will be by the fifth day. (B). Baseline and corrected learning scores. In order to eliminate the effect of the baseline on improvement we corrected the learning data. Corrected learning z-scores were obtained by subtracting baseline values multiplied by the regression coefficient from the learning z-scores. The scatter plot represents individual baseline z-scores and corrected learning z-scores of the TD (dark grey cross) and WS (red cross) participants. Light grey and light pink zones show the range of one and two standard deviations around the mean of the baseline z-score (vertical strip) and the corrected learning z-score (horizontal strip) of the TD population, respectively.</p

Development and learning in CI.

<p>(A) Development in CI. The developmental curve of the TD group was fitted on the baseline averages of the six age-groups (goodness of fit: R = 0,9162*), the shaded area designates standard deviation. Visual CI performance increases as a function of age showing a slow developmental course of contour integration, reaching the adult level by 13–14 years of age. Colored symbols stand for individual WS subjects. Colors correspond to the appropriate age-groups shown in Fig. 2B. Performance of WS subjects has an extremely high variability and only a few subjects are within the TD range. (B) Learning in CI. Colored lines represent learning curves of each TD age-group (standard deviation is shown by light-grey shading). Younger TD subjects seem to learn at a greater speed. Colored symbols stand for individual WS subjects. Colors correspond to the appropriate age-groups. WS subjects vary a great deal in terms of learning capacity, and only a few subjects are in the TD range.</p