Single-unit Recordings Research Articles

A high-density 1,024-channel probe for brain-wide recordings in non-human primates.

Large-scale neural population recordings with single-cell resolution across the primate brain remain challenging. Here we introduce the Neuroscroll probe that isolates single neuronal activities simultaneously from 1,024 densely spaced channels that are flexibly distributed across the shank of the probe. The Neuroscroll probe length is easily tunable for individual probes from 10 mm to 90 mm, covering the brain size of non-human primates and humans, and the probes remain intact and functional after repeated bending deformations. The Neuroscroll probes provided reliable recordings from large neural populations with high chronic stability up to 105 weeks in rats. Recording with each Neuroscroll probe yielded hundreds of well-isolated single units simultaneously from multiple brain regions distributed across the entire depth of the rhesus macaque brain. With the thousand simultaneously recorded channels, unprecedented probe length, excellent mechanical stability and flexible recording site distribution, the Neuroscroll probes enable a wide range of new experimental paradigms in system neuroscience studies with great versatility.

Theta-phase locking of single neurons during human spatial memory.

The precise timing of single-neuron activity in relation to local field potentials may support various cognitive functions. Extensive research in rodents, along with some evidence in humans, suggests that single-neuron activity at specific phases of theta oscillations plays a crucial role in memory processes. Our fundamental understanding of such theta-phase locking in humans and its dependency on basic electrophysiological properties of the local field potential is still limited, however. Here, using single-neuron recordings in epilepsy patients performing a spatial memory task, we thus aimed at improving our understanding of factors modulating theta-phase locking in the human brain. Combining a generalized-phase approach for frequency-adaptive theta-phase estimation with time-resolved spectral parameterization, our results show that theta-phase locking is a strong and prevalent phenomenon across human medial temporal lobe regions, both during spatial memory encoding and retrieval. Neuronal theta-phase locking increased during periods of elevated theta power, when clear theta oscillations were present, and when aperiodic activity exhibited steeper slopes. Theta-phase locking was similarly strong during successful and unsuccessful memory, and most neurons activated at similar theta phases between encoding and retrieval. Some neurons changed their preferred theta phases between encoding and retrieval, in line with the idea that different memory processes are separated within the theta cycle. Together, these results help disentangle how different properties of local field potentials and memory states influence theta-phase locking of human single neurons. This contributes to a better understanding of how interactions between single neurons and local field potentials may support human spatial memory.

A prefrontal motor circuit initiates persistent movement

Persistence reinforces continuous action, which benefits animals in many aspects. Diverse external or internal signals may trigger animals to start a persistent movement. However, it is unclear how the brain decides to persist with current actions by selecting specific information. Using single-unit extracellular recordings and opto-tagging in awake mice, we demonstrated that a group of dorsal mPFC (dmPFC) motor cortex projecting (MP) neurons initiate a persistent movement by selectively encoding contextual information rather than natural valence. Inactivation of dmPFC MP neurons impairs the initiation and reduces neuronal activity in the insular and motor cortex. After the persistent movement is initiated, the dmPFC MP neurons are not required to maintain it. Finally, a computational model suggests that a successive sensory stimulus acts as an input signal for the dmPFC MP neurons to initiate a persistent movement. These results reveal a neural initiation mechanism on the persistent movement.

Modulation of neuronal activity in human centromedian nucleus during an auditory attention and working memory task

Centromedian nucleus (CM) is one of several intralaminar nuclei of the thalamus and is thought to be involved in consciousness, arousal, and attention. CM has been suggested to play a key role in the control of attention, by regulating the flow of information to different brain regions such as the ascending reticular system, basal ganglia, and cortex. While the neurophysiology of attention in visual and auditory systems has been studied in animal models, combined single unit and LFP recordings in human have not, to our knowledge, been reported. Here, we recorded neuronal activity in the CM nucleus in 11 patients prior to insertion of deep brain stimulation electrodes for the treatment of epilepsy while subjects performed an auditory attention task. Patients were requested to attend and count the infrequent (p = 0.2) odd or "deviant" tones, ignore the frequent standard tones and report the total number of deviant tones at trial completion. Spikes were discriminated, and LFPs were band pass filtered (5–45 Hz). Average peri‑stimulus time histograms and spectra were constructed by aligning on tone onsets and statistically compared. The firing rate of CM neurons showed selective, multi-phasic responses to deviant tones in 81% of the tested neurons. Local field potential analysis showed selective beta and low gamma (13–45 Hz) modulations in response to deviant tones, also in a multi-phasic pattern. The current study demonstrates that CM neurons are under top-down control and participate in the selective processing during auditory attention and working memory. These results, taken together, implicate the CM in selective auditory attention and working memory and support a role of beta and low gamma oscillatory activity in cognitive processes. It also has potential implications for DBS therapy for epilepsy and non-motor symptoms of PD, such as apathy and other disorders of attention.

Temporal Coherence Shapes Cortical Responses to Speech Mixtures in a Ferret Cocktail Party.

Segregation of complex sounds such as speech, music and animal vocalizations as they simultaneously emanate from multiple sources (referred to as the "cocktail party problem") is a remarkable ability that is common in humans and animals alike. The neural underpinnings of this process have been extensively studied behaviorally and physiologically in non-human animals primarily with simplified sounds (tones and noise sequences). In humans, segregation experiments utilizing more complex speech mixtures are common; but physiological experiments have relied on EEG/MEG/ECoG recordings that sample activity from thousands of neurons, often obscuring the detailed processes that give rise to the observed segregation. The present study combines the insights from animal single-unit physiology with segregation of speech-like mixtures. Ferrets were trained to attend to a female voice and detect a target word, both in presence or absence of a concurrent, equally salient male voice. Single neuron recordings were obtained from primary and secondary ferret auditory cortical fields, as well as frontal cortex. During task performance, representation of the female words became more enhanced relative to those of the (distractor) male in all cortical regions, especially in the higher auditory cortical field. Analysis of the temporal and spectral response characteristics during task performance reveals how speech segregation gradually emerges in the auditory cortex. A computational model evaluated on the same voice mixtures replicates and extends these results to different attentional targets (attention to female or male voices). These findings are consistent with the temporal coherence theory whereby attention to a target voice anchors neural activity in cortical networks hence binding together channels that are coherently temporally-modulated with the target, and ultimately forming a common auditory stream.

Spiking networks that efficiently process dynamic sensory features explain receptor information mixing in somatosensory cortex.

How do biological neural systems efficiently encode, transform and propagate information between the sensory periphery and the sensory cortex about sensory features evolving at different time scales? Are these computations efficient in normative information processing terms? While previous work has suggested that biologically plausible models of of such neural information processing may be implemented efficiently within a single processing layer, how such computations extend across several processing layers is less clear. Here, we model propagation of multiple time-varying sensory features across a sensory pathway, by extending the theory of efficient coding with spikes to efficient encoding, transformation and transmission of sensory signals. These computations are optimally realized by a multilayer spiking network with feedforward networks of spiking neurons (receptor layer) and recurrent excitatory-inhibitory networks of generalized leaky integrate-and-fire neurons (recurrent layers). Our model efficiently realizes a broad class of feature transformations, including positive and negative interaction across features, through specific and biologically plausible structures of feedforward connectivity. We find that mixing of sensory features in the activity of single neurons is beneficial because it lowers the metabolic cost at the network level. We apply the model to the somatosensory pathway by constraining it with parameters measured empirically and include in its last node, analogous to the primary somatosensory cortex (S1), two types of inhibitory neurons: parvalbumin-positive neurons realizing lateral inhibition, and somatostatin-positive neurons realizing winner-take-all inhibition. By implementing a negative interaction across stimulus features, this model captures several intriguing empirical observations from the somatosensory system of the mouse, including a decrease of sustained responses from subcortical networks to S1, a non-linear effect of the knock-out of receptor neuron types on the activity in S1, and amplification of weak signals from sensory neurons across the pathway.

Neural Activity in Quarks Language: Lattice Field Theory for a Network of Real Neurons.

Brain-computer interfaces have seen extraordinary surges in developments in recent years, and a significant discrepancy now exists between the abundance of available data and the limited headway made in achieving a unified theoretical framework. This discrepancy becomes particularly pronounced when examining the collective neural activity at the micro and meso scale, where a coherent formalization that adequately describes neural interactions is still lacking. Here, we introduce a mathematical framework to analyze systems of natural neurons and interpret the related empirical observations in terms of lattice field theory, an established paradigm from theoretical particle physics and statistical mechanics. Our methods are tailored to interpret data from chronic neural interfaces, especially spike rasters from measurements of single neuron activity, and generalize the maximum entropy model for neural networks so that the time evolution of the system is also taken into account. This is obtained by bridging particle physics and neuroscience, paving the way for particle physics-inspired models of the neocortex.

Rule-based modulation of a sensorimotor transformation across cortical areas.

Flexible responses to sensory stimuli based on changing rules are critical for adapting to a dynamic environment. However, it remains unclear how the brain encodes and uses rule information to guide behavior. Here, we made single-unit recordings while head-fixed mice performed a cross-modal sensory selection task where they switched between two rules: licking in response to tactile stimuli while rejecting visual stimuli, or vice versa. Along a cortical sensorimotor processing stream including the primary (S1) and secondary (S2) somatosensory areas, and the medial (MM) and anterolateral (ALM) motor areas, single-neuron activity distinguished between the two rules both prior to and in response to the tactile stimulus. We hypothesized that neural populations in these areas would show rule-dependent preparatory states, which would shape the subsequent sensory processing and behavior. This hypothesis was supported for the motor cortical areas (MM and ALM) by findings that (1) the current task rule could be decoded from pre-stimulus population activity; (2) neural subspaces containing the population activity differed between the two rules; and (3) optogenetic disruption of pre-stimulus states impaired task performance. Our findings indicate that flexible action selection in response to sensory input can occur via configuration of preparatory states in the motor cortex.

Contributions of repeated learning to memory in humans: insights from single-neuron recordings in the hippocampus and amygdala

Abstract Despite the well-established phenomenon of improved memory performance through repeated learning, studies investigating the associated neural mechanisms have yielded complex and sometimes contradictory findings, and direct evidence from human neuronal recordings has been lacking. This study employs single-neuron recordings with exceptional spatial–temporal resolution, combined with representational similarity analysis, to explore the neural dynamics within the hippocampus and amygdala during repeated learning. Our results demonstrate that in the hippocampus, repetition enhances both representational specificity and fidelity, with these features predicting learning times. Conversely, the amygdala exhibits heightened representational specificity and fidelity during initial learning but does not show improvement with repetition, suggesting functional specialization of the hippocampus and amygdala during different stages of the learning repetition. Specifically, the hippocampus appears to contribute to sustained engagement necessary for benefiting from repeated learning, while the amygdala may play a role in the representation of novel items. These findings contribute to a comprehensive understanding of the intricate interplay between these brain regions in memory processes. Significance statement For over a century, understanding how repetition contributes to memory enhancement has captivated researchers, yet direct neuronal evidence has been lacking, with a primary focus on the hippocampus and a neglect of the neighboring amygdala. Employing advanced single-neuron recordings and analytical techniques, this study unveils a nuanced functional specialization within the amygdala-hippocampal circuit during various learning repetition. The results highlight the hippocampus’s role in sustaining engagement for improved memory with repetition, contrasting with the amygdala’s superior ability in representing novel items. This exploration not only deepens our comprehension of memory enhancement intricacies but also sheds light on potential interventions to optimize learning and memory processes.

ElecFeX is a user-friendly toolbox for efficient feature extraction from single-cell electrophysiological recordings

Characterizing neurons by their electrophysiological phenotypes is essential for understanding the neural basis of behavioral and cognitive functions. Technological developments have enabled the collection of hundreds of neural recordings; this calls for new tools capable of performing feature extraction efficiently. To address the urgent need for a powerful and accessible tool, we developed ElecFeX, an open-source MATLAB-based toolbox that (1) has an intuitive graphical user interface, (2) provides customizable measurements for a wide range of electrophysiological features, (3) processes large-size datasets effortlessly via batch analysis, and (4) yields formatted output for further analysis. We implemented ElecFeX on a diverse set of neural recordings; demonstrated its functionality, versatility, and efficiency in capturing electrical features; and established its significance in distinguishing neuronal subgroups across brain regions and species. ElecFeX is thus presented as a user-friendly toolbox to benefit the neuroscience community by minimizing the time required for extracting features from their electrophysiological datasets.

Mapping mouse axial progenitor dynamics in vitro

In this issue of Developmental Cell, Bolondi etal. systematically assesses neuro-mesodermal progenitor (NMP) dynamics by combining a mouse stem-cell-based embryo model with molecular recording of single cells, shedding light on the dynamics of neural tube and paraxial mesoderm formation during mammalian development.

Temporal interference stimulation disrupts spike timing in the primate brain

Electrical stimulation can regulate brain activity, producing clear clinical benefits, but focal and effective neuromodulation often requires surgically implanted electrodes. Recent studies argue that temporal interference (TI) stimulation may provide similar outcomes non-invasively. During TI, scalp electrodes generate multiple electrical fields in the brain, modulating neural activity only at their intersection. Despite considerable enthusiasm for this approach, little empirical evidence demonstrates its effectiveness, especially under conditions suitable for human use. Here, using single-neuron recordings in non-human primates, we establish that TI reliably alters the timing, but not the rate, of spiking activity. However, we show that TI requires strategies—high carrier frequencies, multiple electrodes, and amplitude-modulated waveforms—that also limit its effectiveness. Combined, these factors make TI 80 % weaker than other forms of non-invasive brain stimulation. Although unlikely to cause widespread neuronal entrainment, TI may be ideal for disrupting pathological oscillatory activity, a hallmark of many neurological disorders.

Preictal dysfunctions of inhibitory interneurons paradoxically lead to their rebound hyperactivity and to low-voltage-fast onset seizures in Dravet syndrome

Epilepsies have numerous specific mechanisms. The understanding of neural dynamics leading to seizures is important for disclosing pathological mechanisms and developing therapeutic approaches. We investigated electrographic activities and neural dynamics leading to convulsive seizures in patients and mouse models of Dravet syndrome (DS), a developmental and epileptic encephalopathy in which hypoexcitability of GABAergic neurons is considered to be the main dysfunction. We analyzed EEGs from DS patients carrying a SCN1A pathogenic variant, as well as epidural electrocorticograms, hippocampal local field potentials, and hippocampal single-unit neuronal activities in Scn1a+/- and Scn1aRH/+ DS mice. Strikingly, most seizures had low-voltage-fast onset in both patients and mice, which is thought to be generated by hyperactivity of GABAergic interneurons, the opposite of the main pathological mechanism of DS. Analyzing single-unit recordings, we observed that temporal disorganization of the firing of putative interneurons in the period immediately before the seizure (preictal) precedes the increase of their activity at seizure onset, together with the entire neuronal network. Moreover, we found early signatures of the preictal period in the spectral features of hippocampal and cortical field potential of Scn1a mice and of patients' EEG, which are consistent with the dysfunctions that we observed in single neurons and that allowed seizure prediction. Therefore, the perturbed preictal activity of interneurons leads to their hyperactivity at the onset of generalized seizures, which have low-voltage-fast features that are similar to those observed in other epilepsies and are triggered by hyperactivity of GABAergic neurons. Preictal spectral features may be used as predictive seizure biomarkers.

Effect of Noisy Galvanic Vestibular Stimulation on Spatial Learning and Memory of Rats

Background and Aim: Previous studies have shown promising findings on effectiveness of noisy Galvanic Vestibular Stimulation (nGVS) in various cognitive disorders. The connections of the vestibular system with the hippocampus has been proven. Here we investigated the effect of vestibular galvanic stimulation on the improvement of spatial learning and memory of rats. Methods: Twelve Wistar rats were randomly divided into control and nGVS groups. The nGVS group underwent 30-minute sessions of stimulation at sub-threshold levels for a duration of fourteen days. Following the intervention, both groups underwent assessments of cognitive indices through the Morris water maze task, hippocampal neuronal spike rate by Single-Unit Recording (SUR) and the concentrations of c-fos protein in the hippocampus were measured using ELISA device. Results: The nGVS group exhibited a significant difference compared to the control group in both the time taken to reach the target platform and the percentage of time spent in the goal quarter during the Morris water maze test. The nGVS treatment significantly enhanced spike rate of hippocampal dentate gyrus (p<0.01) compared to the control group. Additionally, c-fos protein concentrations were increased in the nGVS (5.833) than the control group (4.126), (p<0.001). Conclusion: According to the obtained results, nGVS plays a role in improving spatial memory, and a longer duration of intervention is suggested to achieve more obvious improvement results. Keywords: Galvanic vestibular stimulation; spatial cognition; single-unit recording; hippocampus; rat

Cellular resolution contributions to ictal population signals.

The increased amplitude of ictal activity is a common feature of epileptic seizures, but the determinants of this amplitude have not been identified. Clinically, ictal amplitudes are measured electrographically (using, e.g., electroencephalography, electrocorticography, and depth electrodes), but these methods do not enable the assessment of the activity of individual neurons. Population signal may increase from three potential sources: (1) increased synchrony (i.e., more coactive neurons); (2) altered active state, from bursts of action potentials and/or paroxysmal depolarizing shifts in membrane potential; and (3) altered subthreshold state, which includes all lower levels of activity. Here, we quantify the fraction of ictal signal from each source. To identify the cellular determinants of the ictal signal, we measured single cell and population electrical activity and neuronal calcium levels via optical imaging of the genetically encoded calcium indicator (GECI) GCaMP. Spontaneous seizure activity was assessed with microendoscopy in an APP/PS1 mouse with focal cortical injury and via widefield imaging in the organotypic hippocampal slice cultures (OHSCs) model of posttraumatic epilepsy. Single cell calcium signals were linked to a range of electrical activities by performing simultaneous GECI-based calcium imaging and whole-cell patch-clamp recordings in spontaneously seizing OHSCs. Neuronal resolution calcium imaging of spontaneous seizures was then used to quantify the cellular contributions to population-level ictal signal. The seizure onset signal was primarily driven by increased subthreshold activity, consistent with either barrages of excitatory postsynaptic potentials or sustained membrane depolarization. Unsurprisingly, more neurons entered the active state as seizure activity progressed. However, the increasing fraction of active cells was primarily driven by synchronous reactivation and not from continued recruitment of new populations of neurons into the seizure. This work provides a critical link between single neuron activity and population measures of seizure activity.

Advanced age is not a barrier to chronic intracortical single-unit recording in rat cortex.

Available evidence suggests that as we age, our brain and immune system undergo changes that increase our susceptibility to injury, inflammation, and neurodegeneration. Since a significant portion of the potential patients treated with a microelectrode-based implant may be older, it is important to understand the recording performance of such devices in an aged population. We studied the chronic recording performance and the foreign body response (FBR) to a clinically used microelectrode array implanted in the cortex of 18-month-old Sprague Dawley rats. To the best of our knowledge, this is the first preclinical study of its type in the older mammalian brain. Here, we show that single-unit recording performance was initially robust then gradually declined over a 12-week period, similar to what has been previously reported using younger adult rats and in clinical trials. In addition, we show that FBR biomarker distribution was similar to what has been previously described for younger adult rats implanted with multi-shank recording arrays in the motor cortex. Using a quantitative immunohistochemcal approach, we observed that the extent of astrogliosis and tissue loss near the recording zone was inversely related to recording performance. A comparison of recording performance with a younger cohort supports the notion that aging, in and of itself, is not a limiting factor for the clinical use of penetrating microelectrode recording arrays for the treatment of certain CNS disorders.

Machine learning decoding of single neurons in the thalamus for speech brain-machine interfaces

Objective. Our goal is to decode firing patterns of single neurons in the left ventralis intermediate nucleus (Vim) of the thalamus, related to speech production, perception, and imagery. For realistic speech brain-machine interfaces (BMIs), we aim to characterize the amount of thalamic neurons necessary for high accuracy decoding. Approach. We intraoperatively recorded single neuron activity in the left Vim of eight neurosurgical patients undergoing implantation of deep brain stimulator or RF lesioning during production, perception and imagery of the five monophthongal vowel sounds. We utilized the Spade decoder, a machine learning algorithm that dynamically learns specific features of firing patterns and is based on sparse decomposition of the high dimensional feature space. Main results. Spade outperformed all algorithms compared with, for all three aspects of speech: production, perception and imagery, and obtained accuracies of 100%, 96%, and 92%, respectively (chance level: 20%) based on pooling together neurons across all patients. The accuracy was logarithmic in the amount of neurons for all three aspects of speech. Regardless of the amount of units employed, production gained highest accuracies, whereas perception and imagery equated with each other. Significance. Our research renders single neuron activity in the left Vim a promising source of inputs to BMIs for restoration of speech faculties for locked-in patients or patients with anarthria or dysarthria to allow them to communicate again. Our characterization of how many neurons are necessary to achieve a certain decoding accuracy is of utmost importance for planning BMI implantation.

Multimodal convergence in the pedunculopontine tegmental nucleus: Motor, sensory and theta-frequency inputs influence activity of single neurons.

The pedunculopontine tegmental nucleus of the brainstem (PPTg) has extensive interconnections and neuronal-behavioural correlates. It is implicated in movement control and sensorimotor integration. We investigated whether single neuron activity in freely moving rats is correlated with components of skilled forelimb movement, and whether individual neurons respond to both motor and sensory events. We found that individual PPTg neurons showed changes in firing rate at different times during the reach. This type of temporally specific modulation is like activity seen elsewhere in voluntary movement control circuits, such as the motor cortex, and suggests that PPTg neural activity is related to different specific events occurring during the reach. In particular, many neuronal modulations were time-locked to the end of the extension phase of the reach, when fine distal movements related to food grasping occur, indicating strong engagement of PPTg in this phase of skilled individual forelimb movements. In addition, some neurons showed brief periods of apparent oscillatory firing in the theta range at specific phases of the reach-to-grasp movement. When movement-related neurons were tested with tone stimuli, many also responded to this auditory input, allowing for sensorimotor integration at the cellular level. Together, these data extend the concept of the PPTg as an integrative structure in generation of complex movements, by showing that this function extends to the highly coordinated control of the forelimb during skilled reach to grasp movement, and that sensory and motor-related information converges on single neurons, allowing for direct integration at the cellular level.

Optogenetic inhibition of the orbitofrontal cortex disrupts inhibitory control during stop-change performance in male rats.

Historically, the orbitofrontal cortex (OFC) has been implicated in a variety of behaviors ranging from reversal learning and inhibitory control to more complex representations of reward value and task space. While modern interpretations of the OFC's function have focused on a role in outcome evaluation, these cognitive processes often require an organism to inhibit a maladaptive response or strategy. Single unit recordings from the OFC in rats performing a stop-change task show that the OFC responds strongly to STOP trials. To investigate the role that the OFC plays in stop-change performance we expressed halorhodopsin (eNpHR3.0) in excitatory neurons in the OFC and tested rats on the stop-change task. Previous work suggests that the OFC differentiates between STOP trials based on trial sequence (i.e., gS trials: STOP trials preceded by a GO versus sS trials: STOP trials preceded by a STOP). We found that yellow light activation of the eNpHR3.0 expressing neurons significantly decreased accuracy only on STOP trials that followed GO trials (gS trials). Further, optogenetic inhibition of OFC speeded reaction times on error trials. This suggests that the OFC plays a role in inhibitory control processes, and that this role needs to be accounted for in modern interpretations of OFC function.Significance Statement The orbitofrontal cortex (OFC) is a highly interconnected brain region thought to be essential for healthy cognitive function. Historically, research has implicated the OFC in the inhibition of inappropriate actions, however, more recent work has focused on its role in guiding value-based decision making. Using a modified version of a canonical inhibitory control task in combination with optogenetics, we show that while important for value-based decision making, disruption of excitatory neurons in the OFC impairs inhibitory control processes as well. This highlights the need for theoretical accounts of OFC function to accommodate its role in both types of cognitive processes.

Subthalamic nucleus synchronization between beta band local field potential and single-unit activity in Parkinson's disease.

Local field potential (LFP) oscillations in the beta band (13-30 Hz) in the subthalamic nucleus (STN) of Parkinson's disease patients have been implicated in disease severity and treatment response. The relationship between single-neuron activity in the STN and regional beta power changes remains unclear. We used spike-triggered average (STA) to assess beta synchronization in STN. Beta power and STA magnitude at the beta frequency range were compared in three conditions: STN versus other subcortical structures, dorsal versus ventral STN, and high versus low beta power STN recordings. Magnitude of STA-LFP was greater within the STN compared to extra-STN structures along the trajectory path, despite no difference in percentage of the total power. Within the STN, there was a higher percent beta power in dorsal compared to ventral STN but no difference in STA-LFP magnitude. Further refining the comparison to high versus low beta peak power recordings inside the STN to evaluate if single-unit activity synchronized more strongly with beta band activity in areas of high beta power resulted in a significantly higher STA magnitude for areas of high beta power. Overall, these results suggest that STN single units strongly synchronize to beta activity, particularly units in areas ofhigh beta power.