scholarly journals Movement Decomposition in the Primary Motor Cortex

2018 ◽  
Vol 29 (4) ◽  
pp. 1619-1633 ◽  
Author(s):  
Naama Kadmon Harpaz ◽  
David Ungarish ◽  
Nicholas G Hatsopoulos ◽  
Tamar Flash
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Temporal Dynamics ◽  
Single Cells ◽  
Brain Regions ◽  
Specific Pattern ◽  
Neural Representation ◽  
Neural Population ◽  
Population Activity ◽  
Acceleration And Deceleration

Abstract A complex action can be described as the composition of a set of elementary movements. While both kinematic and dynamic elements have been proposed to compose complex actions, the structure of movement decomposition and its neural representation remain unknown. Here, we examined movement decomposition by modeling the temporal dynamics of neural populations in the primary motor cortex of macaque monkeys performing forelimb reaching movements. Using a hidden Markov model, we found that global transitions in the neural population activity are associated with a consistent segmentation of the behavioral output into acceleration and deceleration epochs with directional selectivity. Single cells exhibited modulation of firing rates between the kinematic epochs, with abrupt changes in spiking activity timed with the identified transitions. These results reveal distinct encoding of acceleration and deceleration phases at the level of M1, and point to a specific pattern of movement decomposition that arises from the underlying neural activity. A similar approach can be used to probe the structure of movement decomposition in different brain regions, possibly controlling different temporal scales, to reveal the hierarchical structure of movement composition.

scholarly journals Neural population dynamics in motor cortex are different for reach and grasp

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Aneesha K Suresh ◽  
James M Goodman ◽  
Elizaveta V Okorokova ◽  
Matthew Kaufman ◽  
Nicholas G Hatsopoulos ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Population Level ◽  
Basic Structure ◽  
Neuronal Population ◽  
Neural Population ◽  
Population Activity ◽  
Muscle Activations ◽  
Low Dimensional ◽  
Analytical Approaches

Low-dimensional linear dynamics are observed in neuronal population activity in primary motor cortex (M1) when monkeys make reaching movements. This population-level behavior is consistent with a role for M1 as an autonomous pattern generator that drives muscles to give rise to movement. In the present study, we examine whether similar dynamics are also observed during grasping movements, which involve fundamentally different patterns of kinematics and muscle activations. Using a variety of analytical approaches, we show that M1 does not exhibit such dynamics during grasping movements. Rather, the grasp-related neuronal dynamics in M1 are similar to their counterparts in somatosensory cortex, whose activity is driven primarily by afferent inputs rather than by intrinsic dynamics. The basic structure of the neuronal activity underlying hand control is thus fundamentally different from that underlying arm control.

scholarly journals Coherent neural representation of hand speed in humans revealed by MEG imaging

2007 ◽  
Vol 104 (18) ◽  
pp. 7676-7681 ◽  
Author(s):  
Karim Jerbi ◽  
Jean-Philippe Lachaux ◽  
Karim N′Diaye ◽  
Dimitrios Pantazis ◽  
Richard M. Leahy ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Long Range ◽  
Large Scale ◽  
Primary Motor Cortex ◽  
Phase Synchronization ◽  
Motor Behavior ◽  
Brain Regions ◽  
Neural Representation ◽  
Oscillatory Activity ◽  
Source Imaging

The spiking activity of single neurons in the primate motor cortex is correlated with various limb movement parameters, including velocity. Recent findings obtained using local field potentials suggest that hand speed may also be encoded in the summed activity of neuronal populations. At this macroscopic level, the motor cortex has also been shown to display synchronized rhythmic activity modulated by motor behavior. Yet whether and how neural oscillations might be related to limb speed control is still poorly understood. Here, we applied magnetoencephalography (MEG) source imaging to the ongoing brain activity in subjects performing a continuous visuomotor (VM) task. We used coherence and phase synchronization to investigate the coupling between the estimated activity throughout the brain and the simultaneously recorded instantaneous hand speed. We found significant phase locking between slow (2- to 5-Hz) oscillatory activity in the contralateral primary motor cortex and time-varying hand speed. In addition, we report long-range task-related coupling between primary motor cortex and multiple brain regions in the same frequency band. The detected large-scale VM network spans several cortical and subcortical areas, including structures of the frontoparietal circuit and the cerebello–thalamo–cortical pathway. These findings suggest a role for slow coherent oscillations in mediating neural representations of hand kinematics in humans and provide further support for the putative role of long-range neural synchronization in large-scale VM integration. Our findings are discussed in the context of corticomotor communication, distributed motor encoding, and possible implications for brain–machine interfaces.

scholarly journals NEURAL POPULATION DYNAMICS IN MOTOR CORTEX ARE DIFFERENT FOR REACH AND GRASP

2019 ◽  
Author(s):  
Aneesha K. Suresh ◽  
James M. Goodman ◽  
Elizaveta V. Okorokova ◽  
Matthew T. Kaufman ◽  
Nicholas G. Hatsopoulos ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Population Level ◽  
Pattern Generator ◽  
Neuronal Population ◽  
Rotational Dynamics ◽  
Neural Population ◽  
Population Activity ◽  
Reach And Grasp ◽  
Smooth Dynamics

AbstractRotational dynamics are observed in neuronal population activity in primary motor cortex (M1) when monkeys make reaching movements. This population-level behavior is consistent with a role for M1 as an autonomous pattern generator that drives muscles to produce movement. Here, we show that M1 does not exhibit smooth dynamics during grasping movements, suggesting a more input-driven circuit.

scholarly journals A novel approach for locating mice brain regions of Cryptococcus neoformans CNS invasion

2016 ◽  
Vol 11 ◽  
pp. S136-S143
Author(s):  
Chunting He ◽  
Qingfen Chen ◽  
Longkun Zhu
Keyword(s):  
Motor Cortex ◽  
Cryptococcus Neoformans ◽  
Primary Motor Cortex ◽  
Thalamic Nucleus ◽  
Molecular Layer ◽  
Brain Regions ◽  
Dorsal Lateral Geniculate Nucleus ◽  
Novel Approach ◽  
Stratum Lucidum ◽  
Lateral Posterior

Aim of this study was to locate the brain regions where Cryptococcus interact with brain cells and invade into brain. After 7 days of intratracheal inocula-tion of GFP-tagged Cryptococcus neoformans strains H99, serial cryosections (10 ?m) from 3 C57 BL/6 J mice brains were imaged with immunofluorescence microscopy. GFP-tagged H99 were found in some brain regions such as primary motor cortex-secondary motor cortex, caudate putamen, stratum lucidum of hippocampus, field CA1 of hippocampus, dorsal lateral geniculate nucleus, lateral posterior thalamic nucleus, laterorostral part, lateral posterior thalamic nucleus, mediorostral part, retrosplenial agranular cortex, lateral area of secondary visual cortex, and lacunosum molecular layer of the hippocampus. The results will be very useful for further exploring the mechanism of C. neoformans infection of brain. 

scholarly journals Latent factors and dynamics in motor cortex and their application to brain-machine interfaces

2018 ◽  
Author(s):  
Chethan Pandarinath ◽  
K. Cora Ames ◽  
Abigail A Russo ◽  
Ali Farshchian ◽  
Lee E Miller ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Movement Planning ◽  
Neural Population ◽  
Great Effort ◽  
Single Neurons ◽  
Latent Factors ◽  
Brain Machine Interfaces ◽  
Population Activity ◽  
Systems Perspective ◽  
Behaving Monkeys

In the fifty years since Evarts first recorded single neurons in motor cortex of behaving monkeys, great effort has been devoted to understanding their relation to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study network-level phenomena is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective, and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the “latent factors” underlying observed neural population activity. Finally, we discuss efforts to leverage these factors to improve the performance of brain-machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.

scholarly journals Demixed principal component analysis of neural population data

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Dmitry Kobak ◽  
Wieland Brendel ◽  
Christos Constantinidis ◽  
Claudia E Feierstein ◽  
Adam Kepecs ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Population Data ◽  
Component Analysis ◽  
Neural Representation ◽  
Neural Population ◽  
Population Activity ◽  
Cortical Areas ◽  
Dimensionality Reduction Technique ◽  
Single Figure

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.

scholarly journals Primary motor cortex neurons classified in a postural task predict muscle activation patterns in a reaching task

2016 ◽  
Vol 115 (4) ◽  
pp. 2021-2032 ◽  
Author(s):  
Ethan A. Heming ◽  
Timothy P. Lillicrap ◽  
Mohsen Omrani ◽  
Troy M. Herter ◽  
J. Andrew Pruszynski ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Muscle Activation ◽  
Primary Motor Cortex ◽  
Network Connectivity ◽  
Spatiotemporal Patterns ◽  
Neural Population ◽  
Activation Patterns ◽  
Reaching Task ◽  
Muscle Activation Patterns ◽  
Muscle Groups

Primary motor cortex (M1) activity correlates with many motor variables, making it difficult to demonstrate how it participates in motor control. We developed a two-stage process to separate the process of classifying the motor field of M1 neurons from the process of predicting the spatiotemporal patterns of its motor field during reaching. We tested our approach with a neural network model that controlled a two-joint arm to show the statistical relationship between network connectivity and neural activity across different motor tasks. In rhesus monkeys, M1 neurons classified by this method showed preferred reaching directions similar to their associated muscle groups. Importantly, the neural population signals predicted the spatiotemporal dynamics of their associated muscle groups, although a subgroup of atypical neurons reversed their directional preference, suggesting a selective role in antagonist control. These results highlight that M1 provides important details on the spatiotemporal patterns of muscle activity during motor skills such as reaching.

scholarly journals Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity

2021 ◽  
Author(s):  
Shreya Saxena ◽  
Abigail A. Russo ◽  
John P. Cunningham ◽  
Mark M. Churchland
Keyword(s):  
Motor Cortex ◽  
Muscle Activity ◽  
Single Neuron ◽  
Activity Patterns ◽  
Network Activity ◽  
Movement Speed ◽  
Neural Population ◽  
Recurrent Networks ◽  
Neural Signals ◽  
Population Activity

AbstractLearned movements can be skillfully performed at different paces. What neural strategies produce this flexibility? Can they be predicted and understood by network modeling? We trained monkeys to perform a cycling task at different speeds, and trained artificial recurrent networks to generate the empirical muscle-activity patterns. Network solutions reflected the principle that smooth well-behaved dynamics require low trajectory tangling, and yielded quantitative and qualitative predictions. To evaluate predictions, we recorded motor cortex population activity during the same task. Responses supported the hypothesis that the dominant neural signals reflect not muscle activity, but network-level strategies for generating muscle activity. Single-neuron responses were better accounted for by network activity than by muscle activity. Similarly, neural population trajectories shared their organization not with muscle trajectories, but with network solutions. Thus, cortical activity could be understood based on the need to generate muscle activity via dynamics that allow smooth, robust control over movement speed.

scholarly journals Primary motor cortex activity traces distinct trajectories of population dynamics during spontaneous facial motor sequences in mice

2021 ◽  
Author(s):  
Juan Carlos Boffi ◽  
Tristan Wiessalla ◽  
Robert Prevedel
Keyword(s):  
Population Dynamics ◽  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Neuronal Population ◽  
Future Research ◽  
Sequencing Analysis ◽  
Population Activity ◽  
Neuronal Populations ◽  
Custom Made ◽  
Facial Area

AbstractWe explore the link between on-going neuronal activity at primary motor cortex (M1) and face movement in awake mice. By combining custom-made behavioral sequencing analysis and fast volumetric Ca2+-imaging, we simultaneously tracked M1 population activity during many different facial motor sequences. We show that a facial area of M1 displays distinct trajectories of neuronal population dynamics across different spontaneous facial motor sequences, suggesting an underlying population dynamics code.Significance statementHow our brain controls a seemingly limitless diversity of body movements remains largely unknown. Recent research brings new light into this subject by showing that neuronal populations at the primary motor cortex display different dynamics during forelimb reaching movements versus grasping, which suggests that different motor sequences could be associated with distinct motor cortex population dynamics. To explore this possibility, we designed an experimental paradigm for simultaneously tracking the activity of neuronal populations in motor cortex across many different motor sequences. Our results support the concept that distinct population dynamics encode different motor sequences, bringing new insight into the role of motor cortex in sculpting behavior while opening new avenues for future research.

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