Shane Lee

Rates and Rhythms: A Synergistic View of Frequency and Temporal Coding in Neuronal Networks

In the CNS, activity of individual neurons has a small but quantifiable relationship to sensory representations and motor outputs. Coactivation of a few 10s to 100s of neurons can code sensory inputs and behavioral task performance within psychophysical limits. However, in a sea of sensory inputs and demand for complex motor outputs how is the activity of such small subpopulations of neurons organized? Two theories dominate in this respect: increases in spike rate (rate coding) and sharpening of the coincidence of spiking in active neurons (temporal coding). Both have computational advantages and are far from mutually exclusive. Here, we review evidence for a bias in neuronal circuits toward temporal coding and the coexistence of rate and temporal coding during population rhythm generation. The coincident expression of multiple types of gamma rhythm in sensory cortex suggests a mechanistic substrate for combining rate and temporal codes on the basis of stimulus strength.

Neurosystems: brain rhythms and cognitive processing.

Neuronal rhythms are ubiquitous features of brain dynamics, and are highly correlated with cognitive processing. However, the relationship between the physiological mechanisms producing these rhythms and the functions associated with the rhythms remains mysterious. This article investigates the contributions of rhythms to basic cognitive computations (such as filtering signals by coherence and/or frequency) and to major cognitive functions (such as attention and multi‐modal coordination). We offer support to the premise that the physiology underlying brain rhythms plays an essential role in how these rhythms facilitate some cognitive operations.

Dual γ rhythm generators control interlaminar synchrony in auditory cortex.

Rhythmic activity in populations of cortical neurons accompanies, and may underlie, many aspects of primary sensory processing and short-term memory. Activity in the gamma band (30 Hz up to >100 Hz) is associated with such cognitive tasks and is thought to provide a substrate for temporal coupling of spatially separate regions of the brain. However, such coupling requires close matching of frequencies in co-active areas, and because the nominal gamma band is so spectrally broad, it may not constitute a single underlying process. Here we show that, for inhibition-based gamma rhythms in vitro in rat neocortical slices, mechanistically distinct local circuit generators exist in different laminae of rat primary auditory cortex. A persistent, 30–45 Hz, gap-junction-dependent gamma rhythm dominates rhythmic activity in supragranular layers 2/3, whereas a tonic depolarization-dependent, 50–80 Hz, pyramidal/interneuron gamma rhythm is expressed in granular layer 4 with strong glutamatergic excitation. As a consequence, altering the degree of excitation of the auditory cortex causes bifurcation in the gamma frequency spectrum and can effectively switch temporal control of layer 5 from supragranular to granular layers. Computational modeling predicts the pattern of interlaminar connections may help to stabilize this bifurcation. The data suggest that different strategies are used by primary auditory cortex to represent weak and strong inputs, with principal cell firing rate becoming increasingly important as excitation strength increases.

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

Significance Neocortical beta is one of the most prominent signatures of neural activity measured noninvasively in humans. Beta expression is a strong predictor of healthy and pathological perceptual and motor performance. However, there is considerable debate as to whether beta itself is causally important in information and disease processes. Key to resolving this debate is understanding the neural mechanisms inducing beta. Here, building on prior work, we combined human magnetoencephalography, computational modeling, and laminar recordings in mice and monkeys to establish and test a new theory explaining the emergence of spontaneous transient neocortical beta events in somatosensory and frontal cortex. Our results enable a principled understanding of neocortical beta and can help guide studies seeking to understand its relation to function. 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.

Cortical Gamma Rhythms Modulate NMDAR-Mediated Spike Timing Dependent Plasticity in a Biophysical Model

Spike timing dependent plasticity (STDP) has been observed experimentally in vitro and is a widely studied neural algorithm for synaptic modification. While the functional role of STDP has been investigated extensively, the effect of rhythms on the precise timing of STDP has not been characterized as well. We use a simplified biophysical model of a cortical network that generates pyramidal interneuronal gamma rhythms (PING). Plasticity via STDP is investigated at the excitatory pyramidal cell synapse from a gamma frequency (30–90 Hz) input independent of the network gamma rhythm. The input may represent a corticocortical or an information-specific thalamocortical connection. This synapse is mediated by N-methyl-D-aspartate receptor mediated (NMDAR) currents. For distinct network and input frequencies, the model shows robust frequency regimes of potentiation and depression, providing a mechanism by which responses to certain inputs can potentiate while responses to other inputs depress. For potentiating regimes, the model suggests an optimal amount and duration of plasticity that can occur, which depends on the time course for the decay of the postsynaptic NMDAR current. Prolonging the duration of the input beyond this optimal time results in depression. Inserting pauses in the input can increase the total potentiation. The optimal pause length corresponds to the decay time of the NMDAR current. Thus, STDP in this model provides a mechanism for potentiation and depression depending on input frequency and suggests that the slow NMDAR current decay helps to regulate the optimal amplitude and duration of the plasticity. The optimal pause length is comparable to the time scale of the negative phase of a modulatory theta rhythm, which may pause gamma rhythm spiking. Our pause results may suggest a novel role for this theta rhythm in plasticity. Finally, we discuss our results in the context of auditory thalamocortical plasticity.

Distinguishing mechanisms of gamma frequency oscillations in human current source signals using a computational model of a laminar neocortical network.

Gamma frequency rhythms have been implicated in numerous studies for their role in healthy and abnormal brain function. The frequency band has been described to encompass as broad a range as 30–150 Hz. Crucial to understanding the role of gamma in brain function is an identification of the underlying neural mechanisms, which is particularly difficult in the absence of invasive recordings in macroscopic human signals such as those from magnetoencephalography (MEG) and electroencephalography (EEG). Here, we studied features of current dipole (CD) signals from two distinct mechanisms of gamma generation, using a computational model of a laminar cortical circuit designed specifically to simulate CDs in a biophysically principled manner (Jones et al., 2007, 2009). We simulated spiking pyramidal interneuronal gamma (PING) whose period is regulated by the decay time constant of GABAA-mediated synaptic inhibition and also subthreshold gamma driven by gamma-periodic exogenous excitatory synaptic drive. Our model predicts distinguishable CD features created by spiking PING compared to subthreshold driven gamma that can help to disambiguate mechanisms of gamma oscillations in human signals. We found that gamma rhythms in neocortical layer 5 can obscure a simultaneous, independent gamma in layer 2/3. Further, we arrived at a novel interpretation of the origin of high gamma frequency rhythms (100–150 Hz), showing that they emerged from a specific temporal feature of CDs associated with single cycles of PING activity and did not reflect a separate rhythmic process. Last we show that the emergence of observable subthreshold gamma required highly coherent exogenous drive. Our results are the first to demonstrate features of gamma oscillations in human current source signals that distinguish cellular and circuit level mechanisms of these rhythms and may help to guide understanding of their functional role.

Subdural hematoma as a major determinant of short-term outcomes in traumatic brain injury

OBJECTIVE Early radiographic findings in patients with traumatic brain injury (TBI) have been studied in hopes of better predicting injury severity and outcome. However, prior attempts have generally not considered the various types of intracranial hemorrhage in isolation and have typically not excluded patients with potentially confounding extracranial injuries. Therefore, the authors examined the associations of various radiographic findings with short-term outcome to assess the potential utility of these findings in future prognostic models. METHODS The authors retrospectively identified 1716 patients who had experienced TBI without major extracranial injuries, and categorized them into the following TBI subtypes: subdural hematoma (SDH), traumatic subarachnoid hemorrhage, intraparenchymal hemorrhage (which included intraventricular hemorrhage), and epidural hematoma. They specifically considered isolated forms of hemorrhage, in which only 1 subtype was present. RESULTS In general, the presence of an isolated SDH was more likely to result in worse outcomes than the presence of other isolated forms of traumatic intracranial hemorrhage. Discharge to home was less likely and perihospital mortality rates were generally higher in patients with SDH. These findings were not simply related to age and were not fully captured by the admission Glasgow Coma Scale (GCS) score. The presence of SDH had a much higher sensitivity for poor outcome than the presence of other TBI subtypes, and was more sensitive for these poor outcomes than having a low GCS score (3-8). CONCLUSIONS In these ways, SDH was the most important finding associated with poor outcome, and the authors show that consideration of SDH, specifically, can augment age and GCS score in classification and prognostic models for TBI.

GABAB receptor-mediated, layer-specific synaptic plasticity reorganizes gamma-frequency neocortical response to stimulation.

Significance How the brain deals with the barrage of sensory information during wakefulness determines cognitive performance. Strategies include bias toward attending to novel sensory information and an ability to enhance or habituate cortical responses to repeated inputs. Here we show both enhancement and habituation occur simultaneously in different layers of cortex and that the plastic processes involved require activation of the GABAB subtype of neuronal inhibition. The work demonstrates that the brain can change the way it routes repeatedly presented sensory information in two complementary ways: It optimizes the local cortical representation, including more information as the stimulus is repeated; and it minimizes the output to other areas, preserving only outputs most closely correlated with the local cortical representation. Repeated presentations of sensory stimuli generate transient gamma-frequency (30–80 Hz) responses in neocortex that show plasticity in a task-dependent manner. Complex relationships between individual neuronal outputs and the mean, local field potential (population activity) accompany these changes, but little is known about the underlying mechanisms responsible. Here we show that transient stimulation of input layer 4 sufficient to generate gamma oscillations induced two different, lamina-specific plastic processes that correlated with lamina-specific changes in responses to further, repeated stimulation: Unit rates and recruitment showed overall enhancement in supragranular layers and suppression in infragranular layers associated with excitatory or inhibitory synaptic potentiation onto principal cells, respectively. Both synaptic processes were critically dependent on activation of GABAB receptors and, together, appeared to temporally segregate the cortical representation. These data suggest that adaptation to repetitive sensory input dramatically alters the spatiotemporal properties of the neocortical response in a manner that may both refine and minimize cortical output simultaneously.

Computational modeling to improve treatments for essential tremor

Essential tremor (ET) is a neurological disorder of unknown etiology that is typically characterized by an involuntary periodic movement of the upper limbs. No longer considered monosymptomatic, ET patients often have additional motor and even cognitive impairments. Although there are several pharmacological treatments, no drugs have been developed specifically for ET [1], and 30-70% of patients are medication-refractory [2]. A subset of medication-refractory patients may benefit from electrical deep brain stimulation (DBS) of the ventral intermediate nucleus of the thalamus (VIM), which receives cerebellar inputs. Abnormal cerebellar input to VIM is presumed to be a major contributor to tremor symptoms, which is alleviated by DBS. Computational modeling of the effects of DBS in VIM has been a powerful tool to design DBS protocols to reduce tremor activity. However, far less is known about how these therapies affect non-tremor symptoms, and more experimental and computational modeling work is required to address these growing considerations. Models capable of addressing multiple facets of ET will lead to novel, more efficient treatment.

Data-Constrained Biophysical Modeling Of Human VIM To Assess Contributions Of Intrinsic Oscillatory Activity To Essential Tremor

Essential tremor (ET) is the most common movement disorder, in which the primary symptom is a prominent, involuntary 4–10 Hz rhythmic movement. The presence of tremor frequency oscillations (TFOs) in the ventral intermediate nucleus of the thalamus (VIM) is well-established, but it is often assumed that it is driven by cerebellar tremor frequency activity, while the role of intrinsic oscillatory activity in VIM is not clear. An improved understanding of the mechanisms of tremor and non-tremor activity in VIM is critical to the development of improved pharmacological and neuromodulatory therapies. Starting from a canonical model of thalamus, we developed a biophysically-principled computational model of tremor field activity in the VIM, coupled with the thalamic reticular nucleus (TRN). We simulated TFOs in the model generated either by extrinsic tremor-periodic drive or intrinsic VIM-TRN interaction to understand whether these networks exhibited distinct biophysical properties, which may impact the efficacy of pharmacological or stimulation treatment for tremor activity. Extrinsic and intrinsic TFOs in the model depended on T-type Ca2+ channels in different ways. Each also depended on GABA modulation in a site- and type-specific manner. These results suggested that efficacy of pharmacological manipulations may depend upon the mechanisms generating tremor activity in VIM. Simulated non-tremor-related motor activity from cerebellum decreased extrinsic but increased intrinsic tremor activity. Our results suggest that both mechanisms may be important to understand the emergence and cessation of tremor activity in VIM and lead to experimentally testable predictions on how to modulate tremor frequency activity to improve treatment strategies for ET. Significance Statement Essential Tremor (ET) is a movement disorder in which the primary symptom is a prominent, involuntary, and rhythmic shaking, often of the hands. Electrical activity in many areas of the brain exhibit rhythmicity related to the patient’s tremor. One such area resides in a structure called the thalamus, but it is not fully known what gives rise to tremor-related activity. We created a computational model of this activity, which suggested how to differentiate tremor mechanisms and how these differences may contribute to other impairments in ET. Knowledge of the biophysical mechanisms contributing to tremor can ultimately lead to improvements in treatments to alleviate symptoms of ET.