Differential Recruitment of the Sensorimotor Putamen and Frontoparietal Cortex during Motor Chunking in Humans

Abstract

Motor chunking facilitates movement production by combining motor elements into integrated units of behavior. Previous research suggests that chunking involves two processes: concatenation, aimed at the formation of motor-motor associations between elements or sets of elements; and segmentation, aimed at the parsing of multiple contiguous elements into shorter action sets. We used fMRI to measure the trial-wise recruitment of brain regions associated with these chunking processes as healthy subjects performed a cued sequence production task. A novel dynamic network analysis identified chunking structure for a set of motor sequences acquired during fMRI and collected on three days of training. Activity in the bilateral sensorimotor putamen positively correlated with chunk concatenation, whereas a left hemisphere frontoparietal network was correlated with chunk segmentation. Across subjects, there was an aggregate increase in chunk strength (concatenation) with training, suggesting that subcortical circuits play a direct role in the creation of fluid transitions across chunks.

Robust detection of dynamic community structure in networks

“…We have previously examined both data sets in the context of neuroscientific questions. 4,13 In this paper, we use them as illustrative examples for the consideration of methodological issues in the detection of dynamic communities in temporal networks.…”

“…13, we construct an ensemble of 66 behavioral networks from 22 individuals and 3 experimental conditions. These networks represent a set of finger movements in the same simple motor learning experiment from which we constructed the brain networks in data set 1.…”

“…In Ref. 13, we examined the phenomenon of motor "chunking," which is a fascinating but poorly understood phenomenon in which groups of movements are made with similar inter-movement durations. (This is similar to remembering a phone number in groups of a few digits or grouping notes together as one masters how to play a song.)…”

“…Network layers and community assignments from two example data sets: (A) a brain network based on correlations between blood-oxygen-leveldependent (BOLD) signals 4 and (B) a behavioral network based on similarities in movement times during a simple motor learning experiment. 13 We use these data sets to illustrate situations with categorical nodes and ordered nodes, respectively. In the bottom panels, we show community assignments obtained using multilayer community detection for (C) the brain networks and (D) the behavioral networks.…”

“…2 The formalism of temporal networks is convenient for studying data drawn from areas such as person-to-person communication (e.g., via mobile phones 5,6 ), one-to-many information dissemination (such as Twitter networks 7 ), cell biology, distributed computing, infrastructure networks, neural and brain networks, and ecological networks. 2 Important phenomena that can be studied in this framework include network constraints on gang and criminal activity, 8,9 political processes, 10,11 human brain function, 4,12 human behavior, 13 and financial structures. 14,15 Time-dependent complex systems can have densely connected components in the form of cohesive groups of nodes known as "communities" (see Fig.…”