Differential recruitment of the sensorimotor putamen and frontoparietal cortex during motor chunking in humans

Beta-band amplitude oscillations in the human internal globus pallidus support the encoding of sequence boundaries during initial sensorimotor sequence learning

Sequential behavior characterizes both simple everyday tasks, such as getting dressed, and complex skills, such as music performance. The basal ganglia (BG) play an important role in the learning of motor sequences. To study the contribution of the human BG to the initial encoding of sequence boundaries, we recorded local field potentials in the sensorimotor area of the internal globus pallidus (GPi) during the early acquisition of sensorimotor sequences in patients undergoing deep brain stimulation for dystonia. We demonstrated an anticipatory modulation of pallidal beta-band neuronal oscillations that was specific to sequence boundaries, as compared to within-sequence elements, and independent of both the movement parameters and the initiation/termination of ongoing movement. The modulation at sequence boundaries emerged with training, in parallel with skill learning, and correlated with the degree of long-range temporal correlations (LRTC) in the dynamics of ongoing beta-band amplitude oscillations. The implication is that LRTC of beta-band oscillations in the sensorimotor GPi might facilitate the emergence of beta power modulations by the sequence boundaries in parallel with sequence learning. Taken together, the results reveal the oscillatory mechanisms in the human BG that contribute at an initial learning phase to the hierarchical organization of sequential behavior as reflected in the formation of boundary-delimited representations of action sequences.

Enhanced functional connectivity between putamen and supplementary motor area in Parkinson's disease patients

Parkinson's disease (PD) is a surprisingly heterogeneous disorder with symptoms including resting tremor, bradykinesia and rigidity. PD has been associated with abnormal task related brain activation in sensory and motor regions as well as reward related network. Although corticostriatal skeletomotor circuit dysfunction is implicated in the neurobiology of Parkinson's disease, the functional connectivity within this circuit at the resting state is still unclear for PD. Here we utilized resting state functional magnetic resonance imaging to measure the functional connectivity of striatum and motor cortex in 19 patients with PD and 20 healthy controls. We found that the putamen, but not the caudate, exhibited enhanced connectivity with supplementary motor area (SMA), using either the putamen or the SMA as the "seed region". Enhanced SMA-amygdala functional connectivity was also found in the PD group, compared with normal controls. Our findings highlight the key role of hyper-connected putamen-SMC circuit in the pathophysiology of PD.

Control of automated behavior: insights from the discrete sequence production task

Work with the discrete sequence production (DSP) task has provided a substantial literature on discrete sequencing skill over the last decades. The purpose of the current article is to provide a comprehensive overview of this literature and of the theoretical progress that it has prompted. We start with a description of the DSP task and the phenomena that are typically observed with it. Then we propose a cognitive model, the dual processor model (DPM), which explains performance of (skilled) discrete key-press sequences. Key features of this model are the distinction between a cognitive processor and a motor system (i.e., motor buffer and motor processor), the interplay between these two processing systems, and the possibility to execute familiar sequences in two different execution modes. We further discuss how this model relates to several related sequence skill research paradigms and models, and we outline outstanding questions for future research throughout the paper. We conclude by sketching a tentative neural implementation of the DPM.

Robust detection of dynamic community structure in networks

We describe techniques for the robust detection of community structure in some classes of time-dependent networks. Specifically, we consider the use of statistical null models for facilitating the principled identification of structural modules in semi-decomposable systems. Null models play an important role both in the optimization of quality functions such as modularity and in the subsequent assessment of the statistical validity of identified community structure. We examine the sensitivity of such methods to model parameters and show how comparisons to null models can help identify system scales. By considering a large number of optimizations, we quantify the variance of network diagnostics over optimizations ("optimization variance") and over randomizations of network structure ("randomization variance"). Because the modularity quality function typically has a large number of nearly degenerate local optima for networks constructed using real data, we develop a method to construct representative partitions that uses a null model to correct for statistical noise in sets of partitions. To illustrate our results, we employ ensembles of time-dependent networks extracted from both nonlinear oscillators and empirical neuroscience data.

The basal ganglia

Through its connections with widespread cortical areas and with dopaminergic midbrain areas, the basal ganglia are well situated to integrate patterns of cortical input with the dopaminergic reward signal originating in the midbrain. In this review, we consider the functions of the basal ganglia in relation to its gross and cellular anatomy, and discuss how these mechanisms subserve the thresholding and selection of motor and cognitive processes. We also discuss how the dopaminergic reward signal enables flexible task learning through modulation of striatal plasticity, and how reinforcement learning models have been used to account for various aspects of basal ganglia activity. Specifically, we will discuss the important role of the basal ganglia in instrumental learning, cognitive control, sequence learning, and categorization tasks. Finally, we will discuss the neurobiological and cognitive characteristics of Parkinson's disease, Huntington's disease and addiction to illustrate the relationship between the basal ganglia and cognitive function. WIREs Cogn Sci 2013, 4:135-148. doi: 10.1002/wcs.1217 For further resources related to this article, please visit the WIREs website.

Dynamic network structure of interhemispheric coordination

Fifty years ago Gazzaniga and coworkers published a seminal article that discussed the separate roles of the cerebral hemispheres in humans. Today, the study of interhemispheric communication is facilitated by a battery of novel data analysis techniques drawn from across disciplinary boundaries, including dynamic systems theory and network theory. These techniques enable the characterization of dynamic changes in the brain's functional connectivity, thereby providing an unprecedented means of decoding interhemispheric communication. Here, we illustrate the use of these techniques to examine interhemispheric coordination in healthy human participants performing a split visual field experiment in which they process lexical stimuli. We find that interhemispheric coordination is greater when lexical information is introduced to the right hemisphere and must subsequently be transferred to the left hemisphere for language processing than when it is directly introduced to the language-dominant (left) hemisphere. Further, we find that putative functional modules defined by coherent interhemispheric coordination come online in a transient manner, highlighting the underlying dynamic nature of brain communication. Our work illustrates that recently developed dynamic, network-based analysis techniques can provide novel and previously unapproachable insights into the role of interhemispheric coordination in cognition.