To investigate the representation of motor sequence, we tested transfer effects in a motor sequence learning paradigm. We hypothesize that there are two sequence representations, effector independent and dependent. Further, we postulate that the effector independent representation is in visual/spatial coordinates, that the effector dependent representation is in motor coordinates, and that their time courses of acquisition during learning are different. Twelve subjects were tested in a modified 2x10 task. Subjects learned to press two keys (called a set) successively on a keypad in response to two lighted squares on a 3x3 display. The complete sequence to be learned was composed of ten such sets, called a hyperset. Training was given in the normal condition and sequence recall was assessed in the early, intermediate, and late stages in three conditions, normal, visual, and motor. In the visual condition, finger-keypad mapping was rotated 90 degrees while the keypad-display mapping was kept identical to normal. In the motor condition, the keypad-display mapping was also rotated 90 degrees, resulting in an identical finger-display mapping as in normal. Subjects formed two groups with each group using a different normal condition. One group learned the sequence in a standard keypad-hand setting and subsequently recalled the sequence using a rotated keypad-hand setting in the test conditions. The second group learned the sequence with a rotated keypad-hand setting and subsequently recalled the sequence with a standard keypad-hand setting in the test conditions. Response time (RT) and sequencing errors during recall were recorded. Although subjects committed more sequencing errors in both testing conditions, visual and motor, as compared to the normal condition, the errors were below chance level. Sequencing errors did not differ significantly between visual and motor conditions. Further, the sequence recall accuracy was over 70% even by the early stage when the subjects performed the sequence for the first time with the altered conditions, visual and motor. There were parallel improvements thereafter in all the conditions. These results of positive transfer of sequence knowledge across conditions that use dissimilar finger movements point to an effector independent sequence representation, possibly in visual/spatial coordinates. Initially the RTs were similar in the visual and the motor conditions, but with training RTs in the motor condition became significantly shorter than in the visual condition, as revealed by significant interaction for the testing stage and condition term in the repeated measures ANOVA. Moreover, using RTs for single key pressing in the three conditions as baseline indices, it was again observed that RTs in the visual and motor conditions were not significantly different in the early stage, but motor RTs became significantly shorter by the late testing stage. These results support the hypothesis that the motor condition benefits more than the visual because it uses identical effector movements...

Basal ganglia (BG) constitute a network of seven deep brain nuclei involved in a variety of crucial brain functions including: action selection, action gating, reward based learning, motor preparation, timing, etc. In spite of the immense amount of data available today, researchers continue to wonder how a single deep brain circuit performs such a bewildering range of functions. Computational models of BG have focused on individual functions and fail to give an integrative picture of BG function. A major breakthrough in our understanding of BG function is perhaps the insight that activities of mesencephalic dopaminergic cells represent some form of 'reward' to the organism. This insight enabled application of tools from 'reinforcement learning,' a branch of machine learning, in the study of BG function. Nevertheless, in spite of these bright spots, we are far from the goal of arriving at a comprehensive understanding of these 'mysterious nuclei.' A comprehensive knowledge of BG function has the potential to radically alter treatment and management of a variety of BG-related neurological disorders (Parkinson's disease, Huntington's chorea, etc.) and neuropsychiatric disorders (schizophrenia, obsessive compulsive disorder, etc.) also. In this article, we review the existing modeling literature on BG and hypothesize an integrative picture of the function of these nuclei.

Resting-state functional connectivity (FC) analyses have shown atypical connectivity in autism spectrum disorder (ASD) as compared to typically developing (TD). However, this view emerges from investigating static FC overlooking the whole brain transient connectivity patterns. In our study, we investigated how age and disease influence the dynamic changes in functional connectivity of TD and ASD. We used resting-state functional magnetic resonance imaging (rs-fMRI) data stratified into three cohorts: children (7–11 years), adolescents (12–17 years), and adults (18+ years) for the analysis. The dynamic variability in the connection strength and the modular organization in terms of measures such as flexiblity, cohesion strength, and disjointness were explored for each subject to characterize the differences between ASD and TD. In ASD, we observed significantly higher inter-subject dynamic variability in connection strength as compared to TD. This hyper-variability relates to the symptom severity in ASD. We also found that whole-brain flexibility correlates with static modularity only in TD. Further, we observed a core-periphery organization in the resting-state, with Sensorimotor and Visual regions in the rigid core; and DMN and attention areas in the flexible periphery. TD also develops a more cohesive organization of sensorimotor areas. However, in ASD we found a strong positive correlation of symptom severity with flexibility of rigid areas and with disjointness of sensorimotor areas. The regions of the brain showing high predictive power of symptom severity were distributed across the cortex, with stronger bearings in the frontal, motor, and occipital cortices. Our study demonstrates that the dynamic framework best characterizes the variability in ASD.

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