Even the adult primary visual cortex is sufficiently plastic to allow performance enhancement.

We studied the neural correlates of visuomotor sequence learning using functional magnetic resonance imaging (fMRI). In the test condition, subjects learned, by trial and error, the correct order of pressing two buttons consecutively for 10 pairs of buttons (2 × 10 task); in the control condition, they pressed buttons in any order. Comparison between the test condition and the control condition revealed four brain areas specifically related to learning: the dorsolateral prefrontal cortex (DLPFC), the presupplementary motor area (pre-SMA), the precuneus, and the intraparietal sulcus (IPS). We found that the time course of activation during learning was different between these areas. To normalize the individual differences in the speed of learning, we classified the performance of each subject into three learning stages: early, intermediate, and advanced stages. Both the relative increase of signal intensity and the number of activated pixels within the four areas showed significant changes across the learning stages, with different time courses. The two frontal areas, DLPFC and pre-SMA, were activated in the earlier stages of learning, whereas the two parietal areas, precuneus and IPS, were activated in the later stages. Specifically, DLPFC, pre-SMA, precuneus, and IPS were most highly activated in the early stage, in both the early and intermediate stages, in the intermediate stage, and in both the intermediate and advanced stages, respectively. The results suggest that the acquisition of visuomotor sequences requires frontal activation, whereas the retrieval of visuomotor sequences requires parietal activation, which might reflect the transition from the declarative stage to the procedural stage.

Although autism spectrum disorder (ASD) is a serious lifelong condition, its underlying neural mechanism remains unclear. Recently, neuroimaging-based classifiers for ASD and typically developed (TD) individuals were developed to identify the abnormality of functional connections (FCs). Due to over-fitting and interferential effects of varying measurement conditions and demographic distributions, no classifiers have been strictly validated for independent cohorts. Here we overcome these difficulties by developing a novel machine-learning algorithm that identifies a small number of FCs that separates ASD versus TD. The classifier achieves high accuracy for a Japanese discovery cohort and demonstrates a remarkable degree of generalization for two independent validation cohorts in the USA and Japan. The developed ASD classifier does not distinguish individuals with major depressive disorder and attention-deficit hyperactivity disorder from their controls but moderately distinguishes patients with schizophrenia from their controls. The results leave open the viable possibility of exploring neuroimaging-based dimensions quantifying the multiple-disorder spectrum.