Bursts of beta oscillation differentiate postperformance activity in the striatum and motor cortex of monkeys performing movement tasks

Cortico-basal ganglia-thalamic (CBT) neural circuits are critical modulators of cognitive and motor function. When compromised, these circuits contribute to neurological and psychiatric disorders, such as Parkinson's disease (PD). In PD, motor deficits correlate with the emergence of exaggerated beta frequency (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) oscillations throughout the CBT network. However, little is known about how specific cell types within individual CBT brain regions support the generation, propagation, and interaction of oscillatory dynamics throughout the CBT circuit or how specific oscillatory dynamics are related to motor function. Here, we investigated the role of striatal cholinergic interneurons (SChIs) in generating beta and gamma oscillations in cortical-striatal circuits and in influencing movement behavior. We found that selective stimulation of SChIs via optogenetics in normal mice robustly and reversibly amplified beta and gamma oscillations that are supported by distinct mechanisms within striatal-cortical circuits. Whereas beta oscillations are supported robustly in the striatum and all layers of primary motor cortex (M1) through a muscarinic-receptor mediated mechanism, gamma oscillations are largely restricted to the striatum and the deeper layers of M1. Finally, SChI activation led to parkinsonianlike motor deficits in otherwise normal mice. These results highlight the important role of striatal cholinergic interneurons in supporting oscillations in the CBT network that are closely related to movement and parkinsonian motor symptoms.E xaggerated beta oscillations (15-30 Hz) within the cortico-basal ganglia-thalamic (CBT) neural network are putative electrophysiological correlates of bradykinesia and rigidity in Parkinson's disease (PD) (1-4). Therapies that effectively manage PD motor symptoms, such as dopamine replacement therapy and deep brain stimulation, are associated with a suppression of the exaggerated beta oscillations (4, 5). Beta oscillations are also found in the CBT circuits of patients with other movement-related disorders, such as epilepsy and dystonia (6, 7), and in normal, nonhuman primates (8, 9) and normal rodents (10, 11). Moreover, brief elevations (≤200 ms) of beta oscillations are observed in the basal ganglia of task-performing nonhuman primates and rodents during specific phases of behavioral tasks (10, 12, 13), indicating that beta oscillations may be important for motor and nonmotor functions. In contrast to the regulated temporal variability of beta oscillations in normal motor functions, temporal stability is correlated with the parkinsonian motor symptoms of bradykinesia and rigidity (2). Together, these findings suggest that brief epochs of beta oscillations are a normal aspect of basal ganglia dynamics, their temporal modulation is important for movement regulation, and loss of regulation or uncontrolled expression of beta oscillations may contribute to movement deficits, such as those observed in PD.Despite the clear link bet...

Section: Resultsmentioning

confidence: 85%

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confidence: 99%

Striatal cholinergic interneurons generate beta and gamma oscillations in the corticostriatal circuit and produce motor deficits

Kondabolu

Roberts

Bucklin

et al. 2016

“…We show that nonaveraged spontaneous beta activity in SI and frontal cortex emerges as brief events that typically last <150 ms (1,30). These beta events co-occur with varying levels of alpha activity, depending on the subject and area studied.…”

Section: Discussionmentioning

confidence: 80%

“…However, burst-like or intermittent periods of high beta power occurring stochastically within the time-averaged period could appear as continuous rhythms in averaged spectrograms, despite not ever actually being sustained. Several recent studies have shown that in nonaveraged data beta oscillations often emerge transiently, typically lasting <150 ms (2,4,(30)(31)(32).…”

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confidence: 99%

Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice

Sherman

Lee

Law

et al. 2016

415

510

Human neocortical 15-29-Hz beta oscillations are strong predictors of perceptual and motor performance. However, the mechanistic origin of beta in vivo is unknown, hindering understanding of its functional role. Combining human magnetoencephalography (MEG), computational modeling, and laminar recordings in animals, we present a new theory that accounts for the origin of spontaneous neocortical beta. In our MEG data, spontaneous beta activity from somatosensory and frontal cortex emerged as noncontinuous beta events typically lasting <150 ms with a stereotypical waveform. Computational modeling uniquely designed to infer the electrical currents underlying these signals showed that beta events could emerge from the integration of nearly synchronous bursts of excitatory synaptic drive targeting proximal and distal dendrites of pyramidal neurons, where the defining feature of a beta event was a strong distal drive that lasted one beta period (∼50 ms). This beta mechanism rigorously accounted for the beta event profiles; several other mechanisms did not. The spatial location of synaptic drive in the model to supragranular and infragranular layers was critical to the emergence of beta events and led to the prediction that beta events should be associated with a specific laminar current profile. Laminar recordings in somatosensory neocortex from anesthetized mice and awake monkeys supported these predictions, suggesting this beta mechanism is conserved across species and recording modalities. These findings make several predictions about optimal states for perceptual and motor performance and guide causal interventions to modulate beta for optimal function. beta rhythm | magnetoencephalography | computational modeling | sensorimotor processing | Parkinson's disease B eta band rhythms (15-29 Hz) are a commonly observed activity pattern in the brain. They are found with magnetoencephalography (MEG) (1-4), EEG (5, 6), and local field potential (LFP) recordings from neocortex (7-9) and are preserved across species (10). Local beta oscillations and their coordination between regions are implicated in numerous functions, including sensory perception, selective attention, and motor planning and initiation (2,3,6,7,9,(11)(12)(13)(14)(15). Neocortical beta oscillations are disrupted in various neuropathologies, most notably Parkinson's disease (PD), in which treatments that alleviate motor symptoms also reverse the neocortical beta disruption (16,17). Although associations between beta and performance suggest a crucial role in brain function, beta rhythmicity might not be important per se but instead may be an epiphenomenal consequence of other important processes. Discovering how beta emerges at the cellular and network levels is crucial to understanding why beta is such a clear predictor of performance in many domains.A major, unresolved point of debate concerns the locus of beta generation. One prominent view is that beta is generated in basal ganglia and thalamic structures and that neocortical beta is an entrained reflecti...

“…2F). We further applied NMHS decoding to local field potential (LFP) activity recorded in the putamen, caudate nucleus and motor cortex of monkeys performing reaching tasks (27). NMHS decoding demonstrated region-specific LFP synchronization between the putamen and motor cortex, which are known to be anatomically connected (Fig.…”

Section: Significancementioning

confidence: 99%

Analysis of complex neural circuits with nonlinear multidimensional hidden state models

Friedman

Slocum

Tyulmankov

et al. 2016

Self Cite

A universal need in understanding complex networks is the identification of individual information channels and their mutual interactions under different conditions. In neuroscience, our premier example, networks made up of billions of nodes dynamically interact to bring about thought and action. Granger causality is a powerful tool for identifying linear interactions, but handling nonlinear interactions remains an unmet challenge. We present a nonlinear multidimensional hidden state (NMHS) approach that achieves interaction strength analysis and decoding of networks with nonlinear interactions by including latent state variables for each node in the network. We compare NMHS to Granger causality in analyzing neural circuit recordings and simulations, improvised music, and sociodemographic data. We conclude that NMHS significantly extends the scope of analyses of multidimensional, nonlinear networks, notably in coping with the complexity of the brain.causal analysis | functional connectivity | decoding | hidden Markov models | machine learning I n analyzing complex networks, there is a crucial need for tools to analyze interaction strength among nodes of the networks, extending from genetics to economics (1), demographics (2), and ecology (3). This need is newly pressing in neuroscience (4-6) ( Fig. 1A). State-of-the-art recording techniques now allow simultaneous measurement of the activity of hundreds of neurons (7,8). Interpreting these recorded data requires the identification of groups of neurons that strongly interact with each other, known as neural microcircuits, as well as the ability to link microcircuit activity to behavior (7, 9). Currently, Granger causality (GC) (1) and cross-correlation (7) are leading methods for interaction strength analysis (5,6,10). Neither method can effectively analyze the nonlinear interactions that are common in neural circuits and systems in other fields (1,(3)(4)(5).Network decoding is an important need in many fields as well, and again, there is need for improved tools. Hidden Markov model (HMM)-based approaches are a leading method in the decoding field (11-15), but these can only decode a single element at a time. Linear dynamical systems (LDSs) are another approach based on latent variables and are an effective way to model and characterize the behavior of populations of neurons (16). However, LDS models are designed for analyzing data from large populations of similar neurons: they are less appropriate for analyzing circuits made up of heterogeneous neurons. Moreover, LDS cannot use known interactions between populations of neurons to improve decoding and prediction for both populations.A further motivating point is that most analytic methods now used are either strictly tools for interaction strength analysis or tools for decoding (5,7,17). There is a significant advantage to combining the two processes, because identifying nodes that strongly interact with each other enables robust network decoding (13). Here, we introduce the nonlinear multidimensional hidden stat...