The Role of the Dorsal Premotor Cortex in Skilled Action Sequences

Solopchuk, Oleg; Alamia, Andrea; Zénon, Alexandre

doi:10.1523/jneurosci.1199-16.2016

Cited by 7 publications

(7 citation statements)

References 19 publications

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“…7b), and source localization (Fig. 7c), the classifier utilizes brain activity originating from motor regions, consistent with the existing literature: MRCPs are typically expressed primarily through the electrodes overlying sensorimotor gyrus (C3, C1, Cz, C2, C4) (Eimer 1998;Schultze-Kraft et al 2016;Shibasaki and Hallett 2006), but there is also evidence for a role of medial frontocentral cortex as a movement generator (Toma et al 2002) and the dorsal pre-motor cortex (Lu et al 2012;Solopchuk et al 2016) in cued movement preparation.…”

Section: Neurophysiological Plausibilitysupporting

confidence: 86%

Predicting motor behavior: an efficient EEG signal processing pipeline to detect brain states with potential therapeutic relevance for VR-based neurorehabilitation

et al. 2021

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Virtual reality (VR)-based motor therapy is an emerging approach in neurorehabilitation. The combination of VR with electroencephalography (EEG) presents further opportunities to improve therapeutic efficacy by personalizing the paradigm. Specifically, the idea is to synchronize the choice and timing of stimuli in the perceived virtual world with fluctuating brain states relevant to motor behavior. Here, we present an open source EEG single-trial based classification pipeline that is designed to identify ongoing brain states predictive of the planning and execution of movements. 9 healthy volunteers each performed 1080 trials of a repetitive reaching task with an implicit two-alternative forced choice, i.e., use of the right or left hand, in response to the appearance of a visual target. The performance of the EEG decoding pipeline was assessed with respect to classification accuracy of right vs. left arm use, based on the EEG signal at the time of the stimulus. Different features, feature extraction methods, and classifiers were compared at different time windows; the number and location of informative EEG channels and the number of calibration trials needed were also quantified, as well as any benefits from individual-level optimization of pipeline parameters. This resulted in a set of recommended parameters that achieved an average 83.3% correct prediction on never-before-seen testing data, and a state-of-the-art 77.1% in a real-time simulation. Neurophysiological plausibility of the resulting classifiers was assessed by time–frequency and event-related potential analyses, as well as by Independent Component Analysis topographies and cortical source localization. We expect that this pipeline will facilitate the identification of relevant brain states as prospective therapeutic targets in closed-loop EEG-VR motor neurorehabilitation.

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Section: Neurophysiological Plausibilitysupporting

confidence: 86%

Predicting motor behavior: an efficient EEG signal processing pipeline to detect brain states with potential therapeutic relevance for VR-based neurorehabilitation

et al. 2021

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“…In addition, the mesial frontal cortex connected with cerebellum at lobule VI, while PMd connected with dorsal surface of cerebellum (Benjamin et al 2016 ). Both PMd and mesial frontal cortex were related to action (execution and imagination) and interoception as well as memory and movement sequence processing (Pastor-Bernier et al 2012 ; Solopchuk et al 2016 ).…”

Section: Discussionmentioning

confidence: 99%

Cytoarchitecture, probability maps, and functions of the human supplementary and pre-supplementary motor areas

et al. 2018

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The dorsal mesial frontal cortex contains the supplementary motor area (SMA) and the pre-supplementary motor area (pre-SMA), which play an important role in action and cognition. Evidence from cytoarchitectonic, stimulation, and functional studies suggests structural and functional divergence between the two subregions. However, a microstructural map of these areas obtained in a representative sample of brains in a stereotaxic reference space is still lacking. In the present study we show that the dorsal mesial frontal motor cortex comprises two microstructurally different brain regions: area SMA and area pre-SMA. Area-specific cytoarchitectonic patterns were studied in serial histological sections stained for cell bodies of ten human postmortem brains. Borders of the two cortical areas were identified using image analysis and statistical features. The 3D reconstructed areas were transferred to a common reference space, and probabilistic maps were calculated by superimposing the individual maps. A coordinate-based meta-analysis of functional imaging data was subsequently performed using the two probabilistic maps as microstructurally defined seed regions. It revealed that areas SMA and pre-SMA were strongly co-activated with areas in precentral, supramarginal and superior frontal gyri, Rolandic operculum, thalamus, putamen and cerebellum. Both areas were related to motor functions, but area pre-SMA was involved in more complex processes such as learning, cognitive processes and perception. The here described subsequent analyses led to converging evidence supporting the microstructural, and functional segregation of areas SMA and pre-SMA, and maps will be made available to the scientific community to further elucidate the microstructural substrates of motor and cognitive control.Electronic supplementary materialThe online version of this article (10.1007/s00429-018-1738-6) contains supplementary material, which is available to authorized users.

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“…The M1 cortex encodes the force, direction, speed, and magnitude of voluntary movements ( Purves 2001 ). The PMC encodes motor action instructions associated with a symbolic cue, localization of external stimuli, and movement dynamics ( Solopchuk et al. 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Translational Model of Cortical Premotor-Motor Networks

et al. 2021

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Deciphering the physiological patterns of motor network connectivity is a prerequisite to elucidate aberrant oscillatory transformations and elaborate robust translational models of movement disorders. In the proposed translational approach, we studied the connectivity between premotor (PMC) and primary motor cortex (M1) by recording high-density electroencephalography in humans and between caudal (CFA) and rostral forelimb (RFA) areas by recording multi-site extracellular activity in mice to obtain spectral power, functional and effective connectivity. We identified a significantly higher spectral power in β- and γ-bands in M1compared to PMC and similarly in mice CFA layers (L) 2/3 and 5 compared to RFA. We found a strong functional β-band connectivity between PMC and M1 in humans and between CFA L6 and RFA L5 in mice. We observed that in both humans and mice the direction of information flow mediated by β- and γ-band oscillations was predominantly from PMC toward M1 and from RFA to CFA, respectively. Combining spectral power, functional and effective connectivity, we revealed clear similarities between human PMC-M1 connections and mice RFA-CFA network. We propose that reciprocal connectivity of mice RFA-CFA circuitry presents a suitable model for analysis of motor control and physiological PMC-M1 functioning or pathological transformations within this network.

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The Role of the Dorsal Premotor Cortex in Skilled Action Sequences

Cited by 7 publications

References 19 publications

Predicting motor behavior: an efficient EEG signal processing pipeline to detect brain states with potential therapeutic relevance for VR-based neurorehabilitation

Predicting motor behavior: an efficient EEG signal processing pipeline to detect brain states with potential therapeutic relevance for VR-based neurorehabilitation

Cytoarchitecture, probability maps, and functions of the human supplementary and pre-supplementary motor areas

Translational Model of Cortical Premotor-Motor Networks

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