Stephanie R. Jones

A critical role for NMDA receptors in parvalbumin interneurons for gamma rhythm induction and behavior

Synchronous recruitment of fast-spiking (FS) parvalbumin (PV) interneurons generates gamma oscillations, rhythms that emerge during performance of cognitive tasks. Administration of N-methyl-D-aspartate (NMDA) receptor antagonists alters gamma rhythms, and can induce cognitive as well as psychosis-like symptoms in humans. The disruption of NMDA receptor (NMDAR) signaling specifically in FS PV interneurons is therefore hypothesized to give rise to neural network dysfunction that could underlie these symptoms. To address the connection between NMDAR activity, FS PV interneurons, gamma oscillations and behavior, we generated mice lacking NMDAR neurotransmission only in PV cells (PV-Cre/NR1f/f mice). Here, we show that mutant mice exhibit enhanced baseline cortical gamma rhythms, impaired gamma rhythm induction after optogenetic drive of PV interneurons and reduced sensitivity to the effects of NMDAR antagonists on gamma oscillations and stereotypies. Mutant mice show largely normal behaviors except for selective cognitive impairments, including deficits in habituation, working memory and associative learning. Our results provide evidence for the critical role of NMDAR in PV interneurons for expression of normal gamma rhythms and specific cognitive behaviors.

Cued Spatial Attention Drives Functionally Relevant Modulation of the Mu Rhythm in Primary Somatosensory Cortex

Cued spatial attention modulates functionally relevant alpha rhythms in visual cortices in humans. Here, we present evidence for analogous phenomena in primary somatosensory neocortex (SI). Using magnetoencephalography, we measured changes in the SI mu rhythm containing mu-alpha (7–14 Hz) and mu-beta (15–29 Hz) components. We found that cued attention impacted mu-alpha in the somatopically localized hand representation in SI, showing decreased power after attention was cued to the hand and increased power after attention was cued to the foot, with significant differences observed 500–1100 ms after cue. Mu-beta showed differences in a time window 800–850 ms after cue. The visual cue also drove an early evoked response beginning ∼70 ms after cue with distinct peaks modulated with cued attention. Distinct components of the tactile stimulus-evoked response were also modulated with cued attention. Analysis of a second dataset showed that, on a trial-by-trial basis, tactile detection probabilities decreased linearly with prestimulus mu-alpha and mu-beta power. These results support the growing consensus that cue-induced alpha modulation is a functionally relevant sensory gating mechanism deployed by attention. Further, while cued attention had a weaker effect on the allocation of mu-beta, oscillations in this band also predicted tactile detection.

Quantitative Analysis and Biophysically Realistic Neural Modeling of the MEG Mu Rhythm: Rhythmogenesis and Modulation of Sensory-Evoked Responses

Variations in cortical oscillations in the alpha (7-14 Hz) and beta (15-29 Hz) range have been correlated with attention, working memory, and stimulus detection. The mu rhythm recorded with magnetoencephalography (MEG) is a prominent oscillation generated by Rolandic cortex containing alpha and beta bands. Despite its prominence, the neural mechanisms regulating mu are unknown. We characterized the ongoing MEG mu rhythm from a localized source in the finger representation of primary somatosensory (SI) cortex. Subjects showed variation in the relative expression of mu-alpha or mu-beta, which were nonoverlapping for roughly 50% of their respective durations on single trials. To delineate the origins of this rhythm, a biophysically principled computational neural model of SI was developed, with distinct laminae, inhibitory and excitatory neurons, and feedforward (FF, representative of lemniscal thalamic drive) and feedback (FB, representative of higher-order cortical drive or input from nonlemniscal thalamic nuclei) inputs defined by the laminar location of their postsynaptic effects. The mu-alpha component was accurately modeled by rhythmic FF input at approximately 10-Hz. The mu-beta component was accurately modeled by the addition of approximately 10-Hz FB input that was nearly synchronous with the FF input. The relative dominance of these two frequencies depended on the delay between FF and FB drives, their relative input strengths, and stochastic changes in these variables. The model also reproduced key features of the impact of high prestimulus mu power on peaks in SI-evoked activity. For stimuli presented during high mu power, the model predicted enhancement in an initial evoked peak and decreased subsequent deflections. In agreement, the MEG-evoked responses showed an enhanced initial peak and a trend to smaller subsequent peaks. These data provide new information on the dynamics of the mu rhythm in humans and the model provides a novel mechanistic interpretation of this rhythm and its functional significance.

Neural Correlates of Tactile Detection: A Combined Magnetoencephalography and Biophysically Based Computational Modeling Study

Previous reports conflict as to the role of primary somatosensory neocortex (SI) in tactile detection. We addressed this question in normal human subjects using whole-head magnetoencephalography (MEG) recording. We found that the evoked signal (0–175 ms) showed a prominent equivalent current dipole that localized to the anterior bank of the postcentral gyrus, area 3b of SI. The magnitude and timing of peaks in the SI waveform were stimulus amplitude dependent and predicted perception beginning at ∼70 ms after stimulus. To make a direct and principled connection between the SI waveform and underlying neural dynamics, we developed a biophysically realistic computational SI model that contained excitatory and inhibitory neurons in supragranular and infragranular layers. The SI evoked response was successfully reproduced from the intracellular currents in pyramidal neurons driven by a sequence of lamina-specific excitatory input, consisting of output from the granular layer (∼25 ms), exogenous input to the supragranular layers (∼70 ms), and a second wave of granular output (∼135 ms). The model also predicted that SI correlates of perception reflect stronger and shorter-latency supragranular and late granular drive during perceived trials. These findings strongly support the view that signatures of tactile detection are present in human SI and are mediated by local neural dynamics induced by lamina-specific synaptic drive. Furthermore, our model provides a biophysically realistic solution to the MEG signal and can predict the electrophysiological correlates of human perception.

Alpha-Frequency Rhythms Desynchronize over Long Cortical Distances: A Modeling Study

Neocortical networks of excitatory and inhibitory neurons can display alpha(α)-frequency rhythms when an animal is in a resting or unfocused state. Unlike some γ- and β-frequency rhythms, experimental observations in cats have shown that these α-frequency rhythms need not synchronize over long cortical distances. Here, we develop a network model of synaptically coupled excitatory and inhibitory cells to study this asynchrony. The cells of the local circuit are modeled on the neurons found in layer V of the neocortex where α-frequency rhythms are thought to originate. Cortical distance is represented by a pair of local circuits coupled with a delay in synaptic propagation. Mathematical analysis of this model reveals that the h and T currents present in layer V pyramidal (excitatory) cells not only produce and regulate the α-frequency rhythm but also lead to the occurrence of spatial asynchrony. In particular, these inward currents cause excitation and inhibition to have nonintuitive effects in the network, with excitation delaying and inhibition advancing the firing time of cells; these reversed effects create the asynchrony. Moreover, increased excitatory to excitatory connections can lead to further desynchronization. However, the local rhythms have the property that, in the absence of excitatory to excitatory connections, if the participating cells are brought close to synchrony (for example, by common input), they will remain close to synchrony for a substantial time.

Computational Modeling of Distinct Neocortical Oscillations Driven by Cell-Type Selective Optogenetic Drive: Separable Resonant Circuits Controlled by Low-Threshold Spiking and Fast-Spiking Interneurons

Selective optogenetic drive of fast-spiking (FS) interneurons (INs) leads to enhanced local field potential (LFP) power across the traditional “gamma” frequency band (20–80 Hz; Cardin et al., 2009). In contrast, drive to regular-spiking (RS) pyramidal cells enhances power at lower frequencies, with a peak at 8 Hz. The first result is consistent with previous computational studies emphasizing the role of FS and the time constant of GABAA synaptic inhibition in gamma rhythmicity. However, the same theoretical models do not typically predict low-frequency LFP enhancement with RS drive. To develop hypotheses as to how the same network can support these contrasting behaviors, we constructed a biophysically principled network model of primary somatosensory neocortex containing FS, RS, and low-threshold spiking (LTS) INs. Cells were modeled with detailed cell anatomy and physiology, multiple dendritic compartments, and included active somatic and dendritic ionic currents. Consistent with prior studies, the model demonstrated gamma resonance during FS drive, dependent on the time constant of GABAA inhibition induced by synchronous FS activity. Lower-frequency enhancement during RS drive was replicated only on inclusion of an inhibitory LTS population, whose activation was critically dependent on RS synchrony and evoked longer-lasting inhibition. Our results predict that differential recruitment of FS and LTS inhibitory populations is essential to the observed cortical dynamics and may provide a means for amplifying the natural expression of distinct oscillations in normal cortical processing.

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.

Attention Drives Synchronization of Alpha and Beta Rhythms between Right Inferior Frontal and Primary Sensory Neocortex

The right inferior frontal cortex (rIFC) is specifically associated with attentional control via the inhibition of behaviorally irrelevant stimuli and motor responses. Similarly, recent evidence has shown that alpha (7–14 Hz) and beta (15–29 Hz) oscillations in primary sensory neocortical areas are enhanced in the representation of non-attended stimuli, leading to the hypothesis that allocation of these rhythms plays an active role in optimal inattention. Here, we tested the hypothesis that selective synchronization between rIFC and primary sensory neocortex occurs in these frequency bands during inattention. We used magnetoencephalography to investigate phase synchrony between primary somatosensory (SI) and rIFC regions during a cued-attention tactile detection task that required suppression of response to uncertain distractor stimuli. Attentional modulation of synchrony between SI and rIFC was found in both the alpha and beta frequency bands. This synchrony manifested as an increase in the alpha-band early after cue between non-attended SI representations and rIFC, and as a subsequent increase in beta-band synchrony closer to stimulus processing. Differences in phase synchrony were not found in several proximal control regions. These results are the first to reveal distinct interactions between primary sensory cortex and rIFC in humans and suggest that synchrony between rIFC and primary sensory representations plays a role in the inhibition of irrelevant sensory stimuli and motor responses.

Analysis of State-Dependent Transitions in Frequency and Long-Distance Coordination in a Model Oscillatory Cortical Circuit

Changes in behavioral state are typically accompanied by changes in the frequency and spatial coordination of rhythmic activity in the neocortex. In this article, we analyze the effects of neuromodulation on ionic conductances in an oscillating cortical circuit model. The model consists of synaptically-coupled excitatory and inhibitory neurons and supports rhythmic activity in the alpha, beta, and gamma ranges. We find that the effects of neuromodulation on ionic conductances are, by themselves, sufficient to induce transitions between synchronous gamma and beta rhythms and asynchronous alpha rhythms. Moreover, these changes are consistent with changes in behavioral state, with the rhythm transitioning from the slower alpha to the faster gamma and beta as arousal increases. We also observe that it is the same set of underlying intrinsic and network mechanisms that appear to be simultaneously responsible for both the observed transitions between the rhythm types and between their synchronization properties. Spike time response curves (STRCs) are used to study the relationship between the transitions in rhythm and the underlying biophysics.

Transformations in oscillatory activity and evoked responses in primary somatosensory cortex in middle age: A combined computational neural modeling and MEG study

Oscillatory brain rhythms and evoked responses are widely believed to impact cognition, but relatively little is known about how these measures are affected by healthy aging. The present study used MEG to examine age-related changes in spontaneous oscillations and tactile evoked responses in primary somatosensory cortex (SI) in healthy young (YA) and middle-aged (MA) adults. To make specific predictions about neurophysiological changes that mediate age-related MEG changes, we applied a biophysically realistic model of SI that accurately reproduces SI MEG mu rhythms, containing alpha (7-14 Hz) and beta (15-30 Hz) components, and evoked responses. Analyses of MEG data revealed a significant increase in prestimulus mu power in SI, driven predominately by greater mu-beta dominance, and a larger and delayed M70 peak in the SI evoked response in MA. Previous analysis with our computational model showed that the SI mu rhythm could be reproduced with a stochastic sequence of rhythmic approximately 10 Hz feedforward (FF) input to the granular layers of SI (representative of lemniscal thalamic input) followed nearly simultaneously by approximately 10 Hz feedback (FB) input to the supragranular layers (representative of input from high order cortical or non-specific thalamic sources) (Jones et al., 2009). In the present study, the model further predicted that the rhythmic FF and FB inputs become stronger with age. Further, the FB input is predicted to arrive more synchronously to SI on each cycle of the 10 Hz input in MA. The simulated neurophysiological changes are sufficient to account for the age-related differences in both prestimulus mu rhythms and evoked responses. Thus, the model predicts that a single set of neurophysiological changes intimately links these age-related changes in neural dynamics.