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There is experimental evidence of varying correlation among the elements of the neuromuscular system over the course of the reach-and-grasp task. Several neuromuscular disorders are accompanied by anomalies in muscular coupling during the task. The aim of this study was to investigate if modifications in correlations and clustering can be detected in the Local Field Potential (LFP) recordings of the motor cortex during the task. To this end, we analyzed the LFP recordings from a previously published study on monkeys which performed a reach-and-grasp task for targets with a vertical or horizontal orientation. LFP signals were recorded from the motor and premotor cortex of macaque monkeys as they performed the task. We found very robust changes in the correlations of the multielectrode LFP recordings which corresponded to task epochs. Mean LFP correlation increased significantly during reaching and then decreased during grasp. This pattern was very robust for both left and right arm reaches irrespective of target orientation. A hierarchical cluster analysis supported the same conclusion – a decreased number of clusters during reach followed by an increase for grasp. As most previous LFP studies have focused on the question of LFP amplitude, our study has contributed to the understanding of this signal and its relationship to movement by focusing on correlations. A sliding window computation of the number of clusters was performed to probe the capacities of these LFP clusters for detecting upcoming task events. For a very high percentage of trials (97.89%), there was a downturn in cluster number following the Pellet Drop (GO signal) which reached a minimum preceding the Start of grasp, hence indicating that cluster analyses of LFP signals could add to signaling the increased probability of the Start of grasp.Significance StatementThe creation of muscular groups also called synergies for accomplishing an action is a well known feature of motor control. Since the motor cortex plays an important role in creating motor commands, it is only to be expected that such features might also be seen in this brain area. This study is among the first to show that alterations in local field potential (LFP) correlations as a function of task phase can be observed during the reach-and-grasp task by macaque monkeys. The LFPs recorded using multielectrode arrays in the motor cortex, showed increased correlations during reach, followed by decreased correlations at the start of grasp. This pattern was robust and held irrespective of which arm was employed or hand orientation.
There is experimental evidence of varying correlation among the elements of the neuromuscular system over the course of the reach-and-grasp task. Several neuromuscular disorders are accompanied by anomalies in muscular coupling during the task. The aim of this study was to investigate if modifications in correlations and clustering can be detected in the Local Field Potential (LFP) recordings of the motor cortex during the task. To this end, we analyzed the LFP recordings from a previously published study on monkeys which performed a reach-and-grasp task for targets with a vertical or horizontal orientation. LFP signals were recorded from the motor and premotor cortex of macaque monkeys as they performed the task. We found very robust changes in the correlations of the multielectrode LFP recordings which corresponded to task epochs. Mean LFP correlation increased significantly during reaching and then decreased during grasp. This pattern was very robust for both left and right arm reaches irrespective of target orientation. A hierarchical cluster analysis supported the same conclusion – a decreased number of clusters during reach followed by an increase for grasp. As most previous LFP studies have focused on the question of LFP amplitude, our study has contributed to the understanding of this signal and its relationship to movement by focusing on correlations. A sliding window computation of the number of clusters was performed to probe the capacities of these LFP clusters for detecting upcoming task events. For a very high percentage of trials (97.89%), there was a downturn in cluster number following the Pellet Drop (GO signal) which reached a minimum preceding the Start of grasp, hence indicating that cluster analyses of LFP signals could add to signaling the increased probability of the Start of grasp.Significance StatementThe creation of muscular groups also called synergies for accomplishing an action is a well known feature of motor control. Since the motor cortex plays an important role in creating motor commands, it is only to be expected that such features might also be seen in this brain area. This study is among the first to show that alterations in local field potential (LFP) correlations as a function of task phase can be observed during the reach-and-grasp task by macaque monkeys. The LFPs recorded using multielectrode arrays in the motor cortex, showed increased correlations during reach, followed by decreased correlations at the start of grasp. This pattern was robust and held irrespective of which arm was employed or hand orientation.
BackgroundThe mechanisms underlying central fatigue (CF) induced by high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) are still not fully understood.MethodsIn order to explore the effects of these exercises on the functioning of cortical and subcortical neural networks, this study investigated the effects of HIIT and MICT on local field potential (LFP) and neuronal firing in the mouse primary motor cortex (M1) and hippocampal CA1 areas. HIIT and MICT were performed on C57BL/6 mice, and simultaneous multichannel recordings were conducted in the M1 motor cortex and CA1 hippocampal region.ResultsA range of responses were elicited, including a decrease in coherence values of LFP rhythms in both areas, and an increase in slow and a decrease in fast power spectral density (PSD, n = 7–9) respectively. HIIT/MICT also decreased the gravity frequency (GF, n = 7–9) in M1 and CA1. Both exercises decreased overall firing rates, increased time lag of firing, declined burst firing rates and the number of spikes in burst, and reduced burst duration (BD) in M1 and CA1 (n = 7–9). While several neuronal firing properties showed a recovery tendency, the alterations of LFP parameters were more sustained during the 10-min post-HIIT/MICT period. MICT appeared to be more effective than HIIT in affecting LFP parameters, neuronal firing rate, and burst firing properties, particularly in CA1. Both exercises significantly affected neural network activities and local neuronal firing in M1 and CA1, with MICT associated with a more substantial and consistent suppression of functional integration between M1 and CA1.ConclusionOur study provides valuable insights into the neural mechanisms involved in exercise-induced central fatigue by examining the changes in functional connectivity and coordination between the M1 and CA1 regions. These findings may assist individuals engaged in exercise in optimizing their exercise intensity and timing to enhance performance and prevent excessive fatigue. Additionally, the findings may have clinical implications for the development of interventions aimed at managing conditions related to exercise-induced fatigue.
Time–frequency parameterization for oscillations in specific frequency bands reflects the dynamic changes in the brain. It is related to cognitive behavior and diseases and has received significant attention in neuroscience. However, many studies do not consider the impact of the aperiodic noise and neural activity, including their time-varying fluctuations. Some studies are limited by the low resolution of the time–frequency spectrum and parameter-solved operation. Therefore, this paper proposes super-resolution time–frequency periodic parameterization of (transient) oscillation (STPPTO). STPPTO obtains a super-resolution time–frequency spectrum with Superlet transform. Then, the time–frequency representation of oscillations is obtained by removing the aperiodic component fitted in a time-resolved way. Finally, the definition of transient events is used to parameterize oscillations. The performance of this method is validated on simulated data and its reliability is demonstrated on magnetoencephalography. We show how it can be used to explore and analyze oscillatory activity under rhythmic stimulation.
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