Burst firing synchronizes prefrontal and anterior cingulate cortex during attentional control

L-Type Calcium Channel Modulates Low-Intensity Pulsed Ultrasound-Induced Excitation in Cultured Hippocampal Neurons

As a noninvasive technique, ultrasound stimulation is known to modulate neuronal activity both in vitro and in vivo. The latest explanation of this phenomenon is that the acoustic wave can activate the ion channels and further impact the electrophysiological properties of targeted neurons. However, the underlying mechanism of low-intensity pulsed ultrasound (LIPUS)-induced neuro-modulation effects is still unclear. Here, we characterize the excitatory effects of LIPUS on spontaneous activity and the intracellular Ca2+ homeostasis in cultured hippocampal neurons. By whole-cell patch clamp recording, we found that 15 min of 1-MHz LIPUS boosts the frequency of both spontaneous action potentials and spontaneous excitatory synaptic currents (sEPSCs) and also increases the amplitude of sEPSCs in hippocampal neurons. This phenomenon lasts for > 10 min after LIPUS exposure. Together with Ca2+ imaging, we clarified that LIPUS increases the [Ca2+]cyto level by facilitating L-type Ca2+ channels (LTCCs). In addition, due to the [Ca2+]cyto elevation by LIPUS exposure, the Ca2+-dependent CaMKII-CREB pathway can be activated within 30 min to further regulate the gene transcription and protein expression. Our work suggests that LIPUS regulates neuronal activity in a Ca2+-dependent manner via LTCCs. This may also explain the multi-activation effects of LIPUS beyond neurons. LIPUS stimulation potentiates spontaneous neuronal activity by increasing Ca2+ influx.

Cingulate microstimulation induces negative decision-making via reduced top-down influence on primate fronto-cingulo-striatal network

The dorsolateral prefrontal cortex (dlPFC) is crucial for regulation of emotion that is known to aid prevention of depression. The broader fronto-cingulo-striatal (FCS) network, including cognitive dlPFC and limbic cingulo-striatal regions, has been associated with a negative evaluation bias often seen in depression. The mechanism by which dlPFC regulates the limbic system remains largely unclear. Here we have successfully induced a negative bias in decision-making in female primates performing a conflict decision-making task, by directly microstimulating the subgenual cingulate cortex while simultaneously recording FCS local field potentials (LFPs). The artificially induced negative bias in decision-making was associated with a significant decrease in functional connectivity from cognitive to limbic FCS regions, represented by a reduction in Granger causality in beta-range LFPs from the dlPFC to the other regions. The loss of top-down directional influence from cognitive to limbic regions, we suggest, could underlie negative biases in decision-making as observed in depressive states.

Contributions of narrow- and broad-spiking prefrontal and parietal neurons on working memory tasks

Neurons that generate persistent activity in the primate dorsolateral prefrontal and posterior parietal cortex have been shown to be predictive of behavior in working memory tasks, though subtle differences between them have been observed in how information is represented. The role of different neuron types in each of these areas has not been investigated at depth. We thus compared the activity of neurons classified as narrow-spiking, putative interneurons, and broad-spiking, putative pyramidal neurons, recorded from the dorsolateral prefrontal and posterior parietal cortex of male monkeys, to analyze their role in the maintenance of working memory. Our results demonstrate that narrow-spiking neurons are active during a range of tasks and generate persistent activity during the delay period over which stimuli need to be maintained in memory. Furthermore, the activity of narrow-spiking neurons was predictive of the subject's recall no less than that of broad-spiking neurons, which are exclusively projection neurons in the cortex. Our results show that putative interneurons play an active role during the maintenance of working memory and shed light onto the fundamental neural circuits that determine subjects' memories and judgments.

Phase of neural oscillations as a reference frame for attention-based routing in visual cortex

Selective attention allows the brain to efficiently process the image projected onto the retina, selectively focusing neural processing resources on behaviorally relevant visual information. While previous studies have documented the crucial role of the action potential rate of single neurons in relaying such information, little is known about how the activity of single neurons relative to their neighboring network contributes to the efficient representation of attended stimuli and transmission of this information to downstream areas. Here, we show in the dorsal visual pathway of monkeys (medial superior temporal area) that neurons fire spikes preferentially at a specific phase of the ongoing population beta (∼20 Hz) oscillations of the surrounding local network. This preferred spiking phase shifts towards a later phase when monkeys selectively attend towards (rather than away from) the receptive field of the neuron. This shift of the locking phase is positively correlated with the speed at which animals report a visual change. Furthermore, our computational modeling suggests that neural networks can manipulate the preferred phase of coupling by imposing differential synaptic delays on postsynaptic potentials. This distinction between the locking phase of neurons activated by the spatially attended stimulus vs. that of neurons activated by the unattended stimulus, may enable the neural system to discriminate relevant from irrelevant sensory inputs and consequently filter out distracting stimuli information by aligning the spikes which convey relevant/irrelevant information to distinct phases linked to periods of better/worse perceptual sensitivity for higher cortices. This strategy may be used to reserve the narrow windows of highest perceptual efficacy to the processing of the most behaviorally relevant information, ensuring highly efficient responses to attended sensory events.

Silences, spikes and bursts: Three-part knot of the neural code

When a neuron breaks silence, it can emit action potentials in a number of patterns. Some responses are so sudden and intense that electrophysiologists felt the need to single them out, labelling action potentials emitted at a particularly high frequency with a metonym - bursts. Is there more to bursts than a figure of speech? After all, sudden bouts of high-frequency firing are expected to occur whenever inputs surge. The burst coding hypothesis advances that the neural code has three syllables: silences, spikes and bursts. We review evidence supporting this ternary code in terms of devoted mechanisms for burst generation, synaptic transmission and synaptic plasticity. We also review the learning and attention theories for which such a triad is beneficial.

Top-down control of exogenous attentional selection is mediated by beta coherence in prefrontal cortex

Salience-driven exogenous and goal-driven endogenous attentional selection are two distinct forms of attention that guide selection of task-irrelevant and task-relevant targets in primates. Top-down attentional control mechanisms enable selection of the task-relevant target by limiting the influence of sensory information. Although the lateral prefrontal cortex (LPFC) is known to mediate top-down control, the neuronal mechanisms of top-down control of attentional selection are poorly understood. Here, we trained two rhesus monkeys on a two-target, free-choice luminance-reward selection task. We demonstrate that visual-movement (VM) neurons and nonvisual neurons or movement neurons encode exogenous and endogenous selection. We then show that coherent beta activity selectively modulates mechanisms of exogenous selection specifically during conflict and consequently may support top-down control. These results reveal the VM-neuron-specific network mechanisms of attentional selection and suggest a functional role for beta-frequency coherent neural dynamics in the modulation of sensory communication channels for the top-down control of attentional selection.

Routing states transition during oscillatory bursts and attentional selection

Brain-wide information routing relies on the spatio-temporal dynamics of neural activity, but it remains unclear how routing states emerge at fast spiking timescales and relate to slower activity dynamics during cognitive processes. Here, we show that localized spiking events participate in directional routing states with spiking activity in distant brain areas that dynamically switch or amplify states during oscillatory bursts, attentional selection, and decision-making. Modeling and neural recordings from lateral prefrontal cortex (LPFC), anterior cingulate cortex (ACC), and striatum of nonhuman primates revealed that cross-regional routing states arise within 20 ms following individual neuron spikes, with LPFC spikes leading the activity in ACC and striatum. The baseline routing state amplified during LPFC beta bursts between LPFC and striatum and switched direction during ACC theta/alpha bursts between ACC and LPFC. Selective attention amplified theta-/alpha-band-specific lead ensembles in ACC, while decision-making increased the lead of ACC and LPFC spikes to the striatum.

Putative cell-type-specific multiregional mode in posterior parietal cortex during coordinated visual behavior

In the reach and saccade regions of the posterior parietal cortex (PPC), multiregional communication depends on the timing of neuronal activity with respect to beta-frequency (10-30 Hz) local field potential (LFP) activity, termed dual coherence. Neural coherence is believed to reflect neural excitability, whereby spiking tends to occur at a particular phase of LFP activity, but the mechanisms of multiregional dual coherence remain unknown. Here, we investigate dual coherence in the PPC of non-human primates performing eye-hand movements. We computationally model dual coherence in terms of multiregional neural excitability and show that one latent component, a multiregional mode, reflects shared excitability across distributed PPC populations. Analyzing the power in the multiregional mode with respect to different putative cell types reveals significant modulations with the spiking of putative pyramidal neurons and not inhibitory interneurons. These results suggest a specific role for pyramidal neurons in dual coherence supporting multiregional communication in PPC.

Spatial Attention Modulates Neuronal Interactions between Simple and Complex Cells in V1

Visual perception is profoundly modulated by spatial attention, which can selectively prioritize goal-related information. Previous studies found spatial attention facilitated the efficacy of neuronal communication between visual cortices with hierarchical organizations. In the primary visual cortex (V1), there is also a hierarchical connection between simple (S) and complex (C) cells. We wonder whether and how spatial attention modulates neuronal communication within V1, especially for neuronal pairs with heterogeneous visual input. We simultaneously recorded the pairs' activity from macaque monkeys when they performed a spatial-attention-involved task, then applied likelihood-based Granger causality analysis to explore attentional modulation of neuronal interactions. First, a significant attention-related decrease in Granger causality was found in S-C pairs, which primarily displayed in the S-to-C feedforward connection. Second, the interaction strength of the feedforward connection was significantly higher than that of the feedback under attend toward (AT) conditions. Although information flow did not alter as the attentional focus shifted, the strength of communications between target- and distractor-stimuli-covered neurons differed only when attending to complex cells' receptive fields (RFs). Furthermore, pairs' communications depended on the attentional modulation of neurons' firing rates. Our findings demonstrate spatial attention does not induce specific information flow but rather amplifies directed communication within V1.

Top-down control of exogenous attentional selection is mediated by beta coherence in prefrontal cortex

Salience-driven exogenous and goal-driven endogenous attentional selection are two distinct forms of attention that guide selection of task-irrelevant and task-relevant targets in primates. During conflict i.e, when salience and goal each favor the selection of different targets, endogenous selection of the task-relevant target relies on top-down control. Top-down attentional control mechanisms enable selection of the task-relevant target by limiting the influence of sensory information. Although the lateral prefrontal cortex (LPFC) is known to mediate top-down control, the neuronal mechanisms of top-down control of attentional selection are poorly understood. Here, using a two-target free-choice luminance-reward selection task, we demonstrate that visual-movement neurons and not visual neurons or movement neurons encode exogenous and endogenous selection. We then show that coherent-beta activity selectively modulates mechanisms of exogenous selection specifically during conflict and consequently may support top-down control. These results reveal the VM-neuron-specific network mechanisms of attentional selection and suggest a functional role for beta-frequency coherent neural dynamics in the modulation of sensory communication channels for the top-down control of attentional selection.

Spatial Attention Modulates Spike Count Correlations and Granger Causality in the Primary Visual Cortex

The influence of spatial attention on neural interactions has been revealed even in early visual information processing stages. It resolves the process of competing for sensory information about objects perceived as targets and distractors. However, the attentional modulation of the interaction between pairs of neurons with non-overlapping receptive fields (RFs) is not well known. Here, we investigated the activity of anatomically distant neurons in two behaving monkeys’ primary visual cortex (V1), when they performed a spatial attention task detecting color change. We compared attentional modulation from the perspective of spike count correlations and Granger causality among simple and complex cells. An attention-related increase in spike count correlations and a decrease in Granger causality were found. The results showed that spatial attention significantly influenced only the interactions between rather than within simple and complex cells. Furthermore, we found that the attentional modulation of neuronal interactions changed with neuronal pairs’ preferred directions differences. Thus, we found that spatial attention increased the functional communications and competing connectivities when attending to the neurons’ RFs, which impacts the interactions only between simple and complex cells. Our findings enrich the model of simple and complex cells and further understand the way that attention influences the neurons’ activities.

Inhibitory control of frontal metastability sets the temporal signature of cognition

Cortical dynamics are organized over multiple anatomical and temporal scales. The mechanistic origin of the temporal organization and its contribution to cognition remain unknown. Here we demonstrate the cause of this organization by studying a specific temporal signature (time constant and latency) of neural activity. In monkey frontal areas, recorded during flexible decisions, temporal signatures display specific area-dependent ranges, as well as anatomical and cell-type distributions. Moreover, temporal signatures are functionally adapted to behaviorally relevant timescales. Fine-grained biophysical network models, constrained to account for experimentally observed temporal signatures, reveal that after-hyperpolarization potassium and inhibitory GABA-B conductances critically determine areas' specificity. They mechanistically account for temporal signatures by organizing activity into metastable states, with inhibition controlling state stability and transitions. As predicted by models, state durations non-linearly scale with temporal signatures in monkey, matching behavioral timescales. Thus, local inhibitory-controlled metastability constitutes the dynamical core specifying the temporal organization of cognitive functions in frontal areas.

Distinct neural codes in primate hippocampus and lateral prefrontal cortex during associative learning in virtual environments

The hippocampus (HPC) and the lateral prefrontal cortex (LPFC) are two cortical areas of the primate brain deemed essential to cognition. Here, we hypothesized that the codes mediating neuronal communication in the HPC and LPFC microcircuits have distinctively evolved to serve plasticity and memory function at different spatiotemporal scales. We used a virtual reality task in which animals selected one of the two targets in the arms of the maze, according to a learned context-color rule. Our results show that during associative learning, HPC principal cells concentrate spikes in bursts, enabling temporal summation and fast synaptic plasticity in small populations of neurons and ultimately facilitating rapid encoding of associative memories. On the other hand, layer II/III LPFC pyramidal cells fire spikes more sparsely distributed over time. The latter would facilitate broadcasting of signals loaded in short-term memory across neuronal populations without necessarily triggering fast synaptic plasticity.

The neural hierarchy of consciousness

The chief undertaking in the studies of consciousness is that of unravelling "the minimal set of neural processes that are together sufficient for the conscious experience of a particular content - the neural correlates of consciousness". To this day, this crusade remains at an impasse, with a clash of two main theories: consciousness may arise either in a graded and cortically-localised fashion, or in an all-or-none and widespread one. In spite of the long-lasting theoretical debates, neurophysiological theories of consciousness have been mostly dissociated from them. Herein, a theoretical review will be put forth with the aim to change that. In its first half, we will cover the hard available evidence on the neurophysiology of consciousness, whereas in its second half we will weave a series of considerations on both theories and substantiate a novel take on conscious awareness: the levels of processing approach, partitioning the conscious architecture into lower- and higher-order, graded and nonlinear.

Neural Burst Firing and Its Roles in Mental and Neurological Disorders

Neural firing patterns are critical for specific information coding and transmission, and abnormal firing is implicated in a series of neural pathologies. Recent studies have indicated that enhanced burst firing mediated by T-type voltage-gated calcium channels (T-VGCCs) in specific neuronal subtypes is involved in several mental or neurological disorders such as depression and epilepsy, while suppression of T-VGCCs relieve related symptoms. Burst firing consists of groups of relatively high-frequency spikes separated by quiescence. Neurons in a variety of brain areas, including the thalamus, hypothalamus, cortex, and hippocampus, display burst firing, but the ionic mechanisms that generating burst firing and the related physiological functions vary among regions. In this review, we summarize recent findings on the mechanisms underlying burst firing in various brain areas, as well as the roles of burst firing in several mental and neurological disorders. We also discuss the ion channels and receptors that may regulate burst firing directly or indirectly, with these molecules highlighted as potential intervention targets for the treatment of mental and neurological disorders.

Comprehensive characterization of oscillatory signatures in a model circuit with PV- and SOM-expressing interneurons

Neural circuits contain a wide variety of interneuron types, which differ in their biophysical properties and connectivity patterns. The two most common interneuron types, parvalbumin-expressing and somatostatin-expressing cells, have been shown to be differentially involved in many cognitive functions. These cell types also show different relationships with the power and phase of oscillations in local field potentials. The mechanisms that underlie the emergence of different oscillatory rhythms in neural circuits with more than one interneuron subtype, and the roles specific interneurons play in those mechanisms, are not fully understood. Here, we present a comprehensive analysis of all possible circuit motifs and input regimes that can be achieved in circuits comprised of excitatory cells, PV-like fast-spiking interneurons and SOM-like low-threshold spiking interneurons. We identify 18 unique motifs and simulate their dynamics over a range of input strengths. Using several characteristics, such as oscillation frequency, firing rates, phase of firing and burst fraction, we cluster the resulting circuit dynamics across motifs in order to identify patterns of activity and compare these patterns to behaviors that were generated in circuits with one interneuron type. In addition to the well-known PING and ING gamma oscillations and an asynchronous state, our analysis identified three oscillatory behaviors that were generated by the three-cell-type motifs only: theta-nested gamma oscillations, stable beta oscillations and theta-locked bursting behavior, which have also been observed in experiments. Our characterization provides a map to interpret experimental activity patterns and suggests pharmacological manipulations or optogenetics approaches to validate these conclusions.

Kv2.1 Potassium Channels Regulate Repetitive Burst Firing in Extratelencephalic Neocortical Pyramidal Neurons

Coincidence detection and cortical rhythmicity are both greatly influenced by neurons' propensity to fire bursts of action potentials. In the neocortex, repetitive burst firing can also initiate abnormal neocortical rhythmicity (including epilepsy). Bursts are generated by inward currents that underlie a fast afterdepolarization (fADP) but less is known about outward currents that regulate bursting. We tested whether Kv2 channels regulate the fADP and burst firing in labeled layer 5 PNs from motor cortex of the Thy1-h mouse. Kv2 block with guangxitoxin-1E (GTx) converted single spike responses evoked by dendritic stimulation into multispike bursts riding on an enhanced fADP. Immunohistochemistry revealed that Thy1-h PNs expressed Kv2.1 (not Kv2.2) channels perisomatically (not in the dendrites). In somatic macropatches, GTx-sensitive current was the largest component of outward current with biophysical properties well-suited for regulating bursting. GTx drove ~40% of Thy1 PNs stimulated with noisy somatic current steps to repetitive burst firing and shifted the maximal frequency-dependent gain. A network model showed that reduction of Kv2-like conductance in a small subset of neurons resulted in repetitive bursting and entrainment of the circuit to seizure-like rhythmic activity. Kv2 channels play a dominant role in regulating onset bursts and preventing repetitive bursting in Thy1 PNs.

Neural burst codes disguised as rate codes

The burst coding hypothesis posits that the occurrence of sudden high-frequency patterns of action potentials constitutes a salient syllable of the neural code. Many neurons, however, do not produce clearly demarcated bursts, an observation invoked to rule out the pervasiveness of this coding scheme across brain areas and cell types. Here we ask how detrimental ambiguous spike patterns, those that are neither clearly bursts nor isolated spikes, are for neuronal information transfer. We addressed this question using information theory and computational simulations. By quantifying how information transmission depends on firing statistics, we found that the information transmitted is not strongly influenced by the presence of clearly demarcated modes in the interspike interval distribution, a feature often used to identify the presence of burst coding. Instead, we found that neurons having unimodal interval distributions were still able to ascribe different meanings to bursts and isolated spikes. In this regime, information transmission depends on dynamical properties of the synapses as well as the length and relative frequency of bursts. Furthermore, we found that common metrics used to quantify burstiness were unable to predict the degree with which bursts could be used to carry information. Our results provide guiding principles for the implementation of coding strategies based on spike-timing patterns, and show that even unimodal firing statistics can be consistent with a bivariate neural code.

Interneuron-specific gamma synchronization indexes cue uncertainty and prediction errors in lateral prefrontal and anterior cingulate cortex

Inhibitory interneurons are believed to realize critical gating functions in cortical circuits, but it has been difficult to ascertain the content of gated information for well-characterized interneurons in primate cortex. Here, we address this question by characterizing putative interneurons in primate prefrontal and anterior cingulate cortex while monkeys engaged in attention demanding reversal learning. We find that subclasses of narrow spiking neurons have a relative suppressive effect on the local circuit indicating they are inhibitory interneurons. One of these interneuron subclasses showed prominent firing rate modulations and (35–45 Hz) gamma synchronous spiking during periods of uncertainty in both, lateral prefrontal cortex (LPFC) and anterior cingulate cortex (ACC). In LPFC, this interneuron subclass activated when the uncertainty of attention cues was resolved during flexible learning, whereas in ACC it fired and gamma-synchronized when outcomes were uncertain and prediction errors were high during learning. Computational modeling of this interneuron-specific gamma band activity in simple circuit motifs suggests it could reflect a soft winner-take-all gating of information having high degree of uncertainty. Together, these findings elucidate an electrophysiologically characterized interneuron subclass in the primate, that forms gamma synchronous networks in two different areas when resolving uncertainty during adaptive goal-directed behavior.

Neurophysiological basis of the N400 deflection, from Mismatch Negativity to Semantic Prediction Potentials and late positive components

The first theoretical model on the neurophysiological basis of the N400: the deflection reflects layer I dendritic plateaus on a preparatory state of synaptic integration that precedes layer V somatic burst firing for conscious identification of the higher-order features of the stimulus (a late positive shift). Plateaus ensue from apical disinhibition by vasoactive intestinal polypeptide-positive interneurons (VIPs) through suppression of Martinotti cells, opening the gates for glutamatergic feedback to trigger dendritic regenerative potentials. Cholinergic transients contribute to these dynamics directly, holding a central role in the N400 deflection. The stereotypical timing of the (frontal) glutamatergic feedback and the accompanying cholinergic transients account for the enigmatic "invariability" of the peak latency in the face of a gamut of different stimuli and paradigms. The theoretical postulations presented here may bring about unprecedented level of detail for the N400 deflection to be used in the study of schizophrenia, Alzheimer's disease and other higher-order pathologies. The substrates of a late positive component, the Mismatch Negativity and the Semantic Prediction Potentials are also surveyed.

Attention reduces the burstiness of V1 neurons involved in attended target enhancement

Attention-dependent reduction in the tendency for neurons to fire bursts (burstiness) is widely observed in the visual cortex. However, the underlying mechanism and the functional role of this phenomenon remain unclear. We recorded well-isolated single-unit activities in primary visual cortex (V1) from two primates (Macaca mulatta) while they performed a detection task engaging spatial attention with two levels of difficulty (hard/easy). We found that attention modulated burstiness of V1 neurons in a cell-type specific manner. For neurons whose net response enhanced with the increase of task difficulty (difficulty-enhanced neuron), representing their involvement in boosting the signal of the attended stimulus, attention led to a reduction in burstiness during hard task but not during easy task. In contrast, regardless of the level of task difficulty, attention-dependent reduction in burstiness was not observed in neurons that showed a net suppression in firing rate with the increase of task difficulty (difficulty-suppressed neuron), indicating their commitment in filtering out the interference of distractor. This differentiation in the effects of attentional modulation on burstiness among the cells with distinct functional roles in attention suggests that the reduction in burstiness by attention is linked to target enhancement and is not associated with distractor suppression.

Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well established that it depends on pre- and postsynaptic activity. However, models that rely solely on pre- and postsynaptic activity for synaptic changes have, so far, not been able to account for learning complex tasks that demand credit assignment in hierarchical networks. Here we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then pyramidal neurons higher in a hierarchical circuit can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when paired with short-term synaptic dynamics, regenerative activity in the apical dendrites and synaptic plasticity in feedback pathways, a burst-dependent learning rule can solve challenging tasks that require deep network architectures. Our results demonstrate that well-known properties of dendrites, synapses and synaptic plasticity are sufficient to enable sophisticated learning in hierarchical circuits.

The chapeau de gendarme sign can be evoked by lateral prefrontal epileptic seizures

PURPOSE

The 'chapeau de gendarme' (CDG) sign, characterised by a symmetric down-turned mouth, has been considered as a semiological hallmark of focal epileptic seizures with cingulate or insular involvement. We report three cases in which the CDG sign featured in habitual seizures.

METHODS

The epileptogenic zones of these three patients were localized in the lateral prefrontal cortex by video-stereoelectroencephalography monitoring. A sulcal resection has led to a seizure-free outcome in each patient.

RESULTS

Ictal stereoelectroencephalography demonstrated that ictal discharges arising from the lateral prefrontal cortices were immediately followed by low-voltage fast activity, concomitantly involving the anterior insulo-cingulate cortices, and preceding the onset of the CDG signs. Limited sulcal resection and seizure freedom after surgery confirmed the restricted distributions of the epileptogenic zones in the lateral prefrontal cortex.

CONCLUSION

Our observations led us to speculate that the epileptic discharges could enter the emotional insulo-cingulate cortical system through the prefrontal-cingulate inhibitory projections (feedforward pattern) -arising from the supragranular layers of the lateral prefrontal cortex-and proceed to the deeper layers of the anterior-middle cingulate cortex, trigger the co-occurrence of gamma bands, and evoke a set of exaggerated behaviours, which is often accompanied by the unique facial sign.

Oscillations as a window into neuronal mechanisms underlying dorsal anterior cingulate cortex function

The function of dorsal Anterior Cingulate Cortex (dACC) remains poorly understood. While many methods, spanning bottom-up and top-down approaches, have been deployed, the view they offer is often conflicting. Integrating bottom-up and top-down approaches requires an intermediary with sufficient explanatory power, theoretical development, and empirical support. Oscillations in the local field potential (LFP) provide such a link. LFP oscillations arise from empirically well-characterized neuronal circuit motifs. Synchronizing the firing of individual units has appealing properties to bind disparate brain regions and propagate information, including gating, routing, and coding. Moreover, the LFP, rather than single unit activity, more closely relates to macro-scale recordings, such as the electroencephalogram and functional magnetic resonance imaging. Thus, LFP oscillations are a critical link that allow for the inference of neuronal micro-circuitry underlying macroscopic brain recordings.

Layer and rhythm specificity for predictive routing

An established theoretical model, predictive coding, states that the brain is constantly building models (signifying changing predictions) of the environment. The brain does this by forming predictions and signaling sensory inputs which deviate from predictions (“prediction errors”). Various hypotheses exist about how predictive coding could be implemented in the brain. We recorded neural spiking and oscillations with laminar resolution in a network of cortical areas as monkeys performed a working memory task with changing stimulus predictability. Predictability modulated the patterns of feedforward/feedback flow, cortical layers, and oscillations used to process a visual stimulus. These data support the theory of predictive coding but suggest an alternate model for its neural implementation: predictive routing.

In predictive coding, experience generates predictions that attenuate the feeding forward of predicted stimuli while passing forward unpredicted “errors.” Different models have suggested distinct cortical layers, and rhythms implement predictive coding. We recorded spikes and local field potentials from laminar electrodes in five cortical areas (visual area 4 [V4], lateral intraparietal [LIP], posterior parietal area 7A, frontal eye field [FEF], and prefrontal cortex [PFC]) while monkeys performed a task that modulated visual stimulus predictability. During predictable blocks, there was enhanced alpha (8 to 14 Hz) or beta (15 to 30 Hz) power in all areas during stimulus processing and prestimulus beta (15 to 30 Hz) functional connectivity in deep layers of PFC to the other areas. Unpredictable stimuli were associated with increases in spiking and in gamma-band (40 to 90 Hz) power/connectivity that fed forward up the cortical hierarchy via superficial-layer cortex. Power and spiking modulation by predictability was stimulus specific. Alpha/beta power in LIP, FEF, and PFC inhibited spiking in deep layers of V4. Area 7A uniquely showed increases in high-beta (∼22 to 28 Hz) power/connectivity to unpredictable stimuli. These results motivate a conceptual model, predictive routing. It suggests that predictive coding may be implemented via lower-frequency alpha/beta rhythms that “prepare” pathways processing-predicted inputs by inhibiting feedforward gamma rhythms and associated spiking.

Contribution of AMPA and NMDA receptors in the spontaneous firing patterns of single neurons in autaptic culture

Single neurons in an autaptic culture exhibit various types of firing pattern with different firing durations and rhythms. However, a neuron with autapses has often been modeled as an oscillator providing a monotonic firing pattern with a constant periodicity because of the lack of a mathematical model. In the work described in this study, we use computational simulation and whole-cell patch-clamp recording to elucidate and model the mechanism by which such neurons generate various firing pattens. In the computational simulation, three types of spontaneous firing pattern, i.e., short, long-lasting, and periodic burst firing patterns are realized by changing the combination ratio of N-methyl-d-aspartate (NMDA) to α-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) conductance. These three types of firing patterns are also observed in the experiments where neurons are cultured in isolation on micropatterned substrates. Using the AMPA and NMDA current models, we discuss that, in principle, autapses can regulate rhythmicity and information selection in neuronal networks.

Beta-band modulation in the human hippocampus during a conflict response task

Objective Identify the role of beta-band (13-30 Hz) power modulation in the human hippocampus during conflict processing. Approach We investigated changes in the spectral power of the beta band (13-30 Hz) as measured by depth electrode leads in the hippocampus during a modified Stroop task in six patients with medically-refractory epilepsy. Previous work done with direct electrophysiological recordings in humans has shown hippocampal theta-band (3-8 Hz) modulation during conflict processing. Local field potentials (LFP) sampled at 2k Hz were used for analysis and a non-parametric cluster-permutation t-test was used to identify the time period and frequency ranges of significant power change during cue processing (i.e. post-stimulus, pre-response). Main Results In five of the six patients, we observe a statistically significant increase in hippocampal beta-band power during successful conflict processing in the incongruent trial condition (cluster-based correction for multiple comparisons, p < 0.05). There was no significant beta-band power change observed during the cue processing period of the congruent condition in the hippocampus of these patients. Significance The beta-power changes during conflict processing represented here are consistent with previous studies suggesting that the hippocampus plays a role in conflict processing, but it is the first time that the beta band has been shown to be involved in humans with direct electrophysiological evidence. We propose that beta-band modulation plays a role in successful conflict detection and automatic response inhibition in the human hippocampus as studied during a conflict response task.

Phase of firing coding of learning variables across the fronto-striatal network during feature-based learning

The prefrontal cortex and striatum form a recurrent network whose spiking activity encodes multiple types of learning-relevant information. This spike-encoded information is evident in average firing rates, but finer temporal coding might allow multiplexing and enhanced readout across the connected network. We tested this hypothesis in the fronto-striatal network of nonhuman primates during reversal learning of feature values. We found that populations of neurons encoding choice outcomes, outcome prediction errors, and outcome history in their firing rates also carry significant information in their phase-of-firing at a 10–25 Hz band-limited beta frequency at which they synchronize across lateral prefrontal cortex, anterior cingulate cortex and anterior striatum when outcomes were processed. The phase-of-firing code exceeds information that can be obtained from firing rates alone and is evident for inter-areal connections between anterior cingulate cortex, lateral prefrontal cortex and anterior striatum. For the majority of connections, the phase-of-firing information gain is maximal at phases of the beta cycle that were offset from the preferred spiking phase of neurons. Taken together, these findings document enhanced information of three important learning variables at specific phases of firing in the beta cycle at an inter-areally shared beta oscillation frequency during goal-directed behavior.

The average spiking frequency in the fronto-striatal network encodes multiple types of learning-relevant information. Here, the authors show that populations of neurons in non-human primates also carry significant information in their phase-of-firing when learning-relevant outcomes are processed.

Thalamus Modulates Consciousness via Layer-Specific Control of Cortex

Functional MRI and electrophysiology studies suggest that consciousness depends on large-scale thalamocortical and corticocortical interactions. However, it is unclear how neurons in different cortical layers and circuits contribute. We simultaneously recorded from central lateral thalamus (CL) and across layers of the frontoparietal cortex in awake, sleeping, and anesthetized macaques. We found that neurons in thalamus and deep cortical layers are most sensitive to changes in consciousness level, consistent across different anesthetic agents and sleep. Deep-layer activity is sustained by interactions with CL. Consciousness also depends on deep-layer neurons providing feedback to superficial layers (not to deep layers), suggesting that long-range feedback and intracolumnar signaling are important. To show causality, we stimulated CL in anesthetized macaques and effectively restored arousal and wake-like neural processing. This effect was location and frequency specific. Our findings suggest layer-specific thalamocortical correlates of consciousness and inform how targeted deep brain stimulation can alleviate disorders of consciousness.

Cell-Type Specific Burst Firing Interacts with Theta and Beta Activity in Prefrontal Cortex During Attention States

Population-level theta and beta band activity in anterior cingulate and prefrontal cortices (ACC/PFC) are prominent signatures of self-controlled, adaptive behaviors. But how these rhythmic activities are linked to cell-type specific activity has remained unclear. Here, we suggest such a cell-to-systems level linkage. We found that the rate of burst spiking events is enhanced particularly during attention states and that attention-specific burst spikes have a unique temporal relationship to local theta and beta band population-level activities. For the 5-10 Hz theta frequency range, bursts coincided with transient increases of local theta power relative to nonbursts, particularly for bursts of putative interneurons. For the 16-30 Hz beta frequency, bursts of putative interneurons phase synchronized stronger than nonbursts, and were associated with larger beta power modulation. In contrast, burst of putative pyramidal cells showed similar beta power modulation as nonbursts, but were accompanied by stronger beta power only when they occurred early in the beta cycle. These findings suggest that in the ACC/PFC during attention states, mechanisms underlying burst firing are intimately linked to narrow band population-level activities, providing a cell-type specific window into rhythmic inhibitory gating and the emergence of rhythmically coherent network states during goal directed behavior.

Adaptive spike-artifact removal from local field potentials uncovers prominent beta and gamma band neuronal synchronization

BACKGROUND

Many neurons synchronize their action potentials to the phase of local field potential (LFP) fluctuations in one or more frequency bands. Analyzing this spike-to-LFP synchronization is challenging, however, when neural spikes and LFP are generated in the same local circuit, because the spike's action potential waveform leak into the LFP and distort phase synchrony estimates. Existing approaches to address this spike bleed-through artifact relied on removing the average action potential waveforms of neurons, but this leaves artifacts in the LFP and distorts synchrony estimates.

NEW METHOD

We describe a spike-removal method that surpasses these limitations by decomposing individual action potentials into their frequency components before their removal from the LFP. The adaptively estimated frequency components allow for variable spread, strength and temporal variation of the spike artifact.

RESULTS

This adaptive approach effectively removes spike bleed-through artifacts in synthetic data with known ground truth, and in single neuron and LFP recordings in nonhuman primate striatum. For a large population of neurons with both narrow and broad action potential waveforms, the use of adaptive artifact removal uncovered 20-35 Hz beta and 35-45 Hz gamma band spike-LFP synchronization that would have remained contaminated otherwise.

COMPARISON WITH EXISTING METHODS

We demonstrate that adaptive spike-artifact removal cleans LFP data that remained contaminated when applying existing Bayesian and non-Bayesian methods of average spike-artifact removal.

CONCLUSIONS

Applying adaptive spike-removal from field potentials allows to estimate the phase at which neurons synchronize and the consistency of their phase-locked firing for both beta and low gamma frequencies. These metrics may prove essential to understand cell-to-circuit neuronal interactions in multiple brain systems.

Incisional Nociceptive Input Impairs Attention-related Behavior and Is Associated with Reduced Neuronal Activity in the Prefrontal Cortex in Rats

WHAT WE ALREADY KNOW ABOUT THIS TOPIC

WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: Cognitive capacity may be reduced from inflammation, surgery, anesthesia, and pain. In this study, we hypothesized that incision-induced nociceptive input impairs attentional performance and alters neuronal activity in the prefrontal cortex.

METHODS

Attentional performance was measured in rats by using the titration variant of the 5-choice serial reaction time to determine the effect of surgical incision and anesthesia in a visual attention task. Neuronal activity (single spike and local field potentials) was measured in the medial prefrontal cortex in animals during the task.

RESULTS

Incision significantly impaired attention postoperatively (area under curve of median cue duration-time 97.2 ± 56.8 [n = 9] vs. anesthesia control 25.5 ± 14.5 s-days [n = 9], P = 0.002; effect size, η = 0.456). Morphine (1 mg/kg) reduced impairment after incision (area under curve of median cue duration-time 31.6 ± 36.7 [n = 11] vs. saline 110 ± 64.7 s-days [n = 10], P < 0.001; η = 0.378). Incision also decreased cell activity (n = 24; 1.48 ± 0.58 vs. control, 2.93 ± 2.02 bursts/min; P = 0.002; η = 0.098) and local field potentials (n = 28; η = 0.111) in the medial prefrontal cortex.

CONCLUSIONS

These results show that acute postoperative nociceptive input from incision reduces attention-related task performance and decreases neuronal activity in the medial prefrontal cortex. Decreased neuronal activity suggests nociceptive input is more than just a distraction because neuronal activity increases during audiovisual distraction with similar behavioral impairment. This suggests that nociceptive input and the medial prefrontal cortex may contribute to attentional impairment and mild cognitive dysfunction postoperatively. In this regard, pain may affect postoperative recovery and return to normal activities through attentional impairment by contributing to lapses in concentration for routine and complex tasks.

Classes of dendritic information processing

Dendrites are much more than passive neuronal components. Mounting experimental evidence and decades of computational work have decisively shown that dendrites leverage a host of nonlinear biophysical phenomena and actively participate in sophisticated computations, at the level of the single neuron and at the level of the network. However, a coherent view of their processing power is still lacking and dendrites are largely neglected in neural network models. Here, we describe four classes of dendritic information processing and delineate their implications at the algorithmic level. We propose that beyond the well-known spatiotemporal filtering of their inputs, dendrites are capable of selecting, routing and multiplexing information. By separating dendritic processing from axonal outputs, neuron networks gain a degree of freedom with implications for perception and learning.

Electrophysiological Brain Connectivity: Theory and Implementation

We review the theory and algorithms of electrophysiological brain connectivity analysis. This tutorial is aimed at providing an introduction to brain functional connectivity from electrophysiological signals, including electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), stereoelectroencephalography (SEEG). Various connectivity estimators are discussed, and algorithms introduced. Important issues for estimating and mapping brain functional connectivity with electrophysiology are discussed.

Feature-specific prediction errors and surprise across macaque fronto-striatal circuits

To adjust expectations efficiently, prediction errors need to be associated with the precise features that gave rise to the unexpected outcome, but this credit assignment may be problematic if stimuli differ on multiple dimensions and it is ambiguous which feature dimension caused the outcome. Here, we report a potential solution: neurons in four recorded areas of the anterior fronto-striatal networks encode prediction errors that are specific to feature values of different dimensions of attended multidimensional stimuli. The most ubiquitous prediction error occurred for the reward-relevant dimension. Feature-specific prediction error signals a) emerge on average shortly after non-specific prediction error signals, b) arise earliest in the anterior cingulate cortex and later in dorsolateral prefrontal cortex, caudate and ventral striatum, and c) contribute to feature-based stimulus selection after learning. Thus, a widely-distributed feature-specific eligibility trace may be used to update synaptic weights for improved feature-based attention.

In order to adjust expectations efficiently, prediction errors need to be associated with the features that gave rise to the unexpected outcome. Here, the authors show that neurons in anterior fronto-striatal networks encode prediction errors that are specific to feature values of different stimulus dimensions.

Autapses enhance bursting and coincidence detection in neocortical pyramidal cells

Autapses are synaptic contacts of a neuron’s axon onto its own dendrite and soma. In the neocortex, self-inhibiting autapses in GABAergic interneurons are abundant in number and play critical roles in regulating spike precision and network activity. Here we examine whether the principal glutamatergic pyramidal cells (PCs) also form functional autapses. In patch-clamp recording from both rodent and human PCs, we isolated autaptic responses and found that these occur predominantly in layer-5 PCs projecting to subcortical regions, with very few in those projecting to contralateral prefrontal cortex and layer 2/3 PCs. Moreover, PC autapses persist during development into adulthood. Surprisingly, they produce giant postsynaptic responses (∼5 fold greater than recurrent PC-PC synapses) that are exclusively mediated by AMPA receptors. Upon activation, autapses enhance burst firing, neuronal responsiveness and coincidence detection of synaptic inputs. These findings indicate that PC autapses are functional and represent an important circuit element in the neocortex.

While autapses are synapses made by a neuron onto itself, its functional significance in pyramidal cells are not clear. Here, the authors show that in the mammalian neocortex, autapses of pyramidal cells can enhance burst firing and coincidence detection from other inputs.

Plasticity in the Sensitivity to Light in Aging: Decreased Non-visual Impact of Light on Cognitive Brain Activity in Older Individuals but No Impact of Lens Replacement

Beyond its essential visual role, light, and particularly blue light, has numerous non-visual effects, including stimulating cognitive functions and alertness. Non-visual effects of light may decrease with aging and contribute to cognitive and sleepiness complaints in aging. However, both the brain and the eye profoundly change in aging. Whether the stimulating effects light on cognitive brain functions varies in aging and how ocular changes may be involved is not established. We compared the impact of blue and orange lights on non-visual cognitive brain activity in younger (23.6 ± 2.5 years), and older individuals with their natural lenses (NL; 66.7 ± 5.1 years) or with intraocular lens (IOL) replacement following cataract surgery (69.6 ± 4.9 years). Analyses reveal that blue light modulates executive brain responses in both young and older individuals. Light effects were, however, stronger in young individuals including in the hippocampus and frontal and cingular cortices. Light effects did not significantly differ between older-IOL and older-NL while regression analyses indicated that differential brain engagement was not underlying age-related differences in light effects. These findings show that, although its impact decreases, light can stimulate cognitive brain activity in aging. Since lens replacement did not affect light impact, the brain seems to adapt to the progressive decrease in retinal light exposure in aging.

Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner

Optimal decision-making is based on integrating information from several dimensions of decisional space (e.g., reward expectation, cost estimation, effort exertion). Despite considerable empirical and theoretical efforts, the computational and neural bases of such multidimensional integration have remained largely elusive. Here we propose that the current theoretical stalemate may be broken by considering the computational properties of a cortical-subcortical circuit involving the dorsal anterior cingulate cortex (dACC) and the brainstem neuromodulatory nuclei: ventral tegmental area (VTA) and locus coeruleus (LC). From this perspective, the dACC optimizes decisions about stimuli and actions, and using the same computational machinery, it also modulates cortical functions (meta-learning), via neuromodulatory control (VTA and LC). We implemented this theory in a novel neuro-computational model–the Reinforcement Meta Learner (RML). We outline how the RML captures critical empirical findings from an unprecedented range of theoretical domains, and parsimoniously integrates various previous proposals on dACC functioning.

A major challenge for all organisms is selecting optimal behaviour to obtain resources while minimizing energetic and other expenses. Evolution provided mammals with exceptional decision-making capabilities to face this challenge. Even though neuroscientists have identified a heterogeneous and distributed set of brain structures to be involved, a comprehensive theory about the biological and computational basis of such decision-making is yet to be formulated. We propose that the interaction between the medial prefrontal cortex (a part of the frontal lobes) and the subcortical nuclei releasing catecholaminergic neuromodulators will be key to such a theory. We argue that this interaction allows both the selection of optimal behaviour and, more importantly, the optimal modulation of the very brain circuits that drive such behavioral selection (i.e., meta-learning). We implemented this theory in a novel neuro-computational model, the Reinforcement Meta-Learner (RML). By means of computer simulations we showed that the RML provides a biological and computational account for a set of neuroscientific data with unprecedented scope, thereby suggesting a critical mechanism of decision-making in the mammalian brain.

Flexible resonance in prefrontal networks with strong feedback inhibition

Oscillations are ubiquitous features of brain dynamics that undergo task-related changes in synchrony, power, and frequency. The impact of those changes on target networks is poorly understood. In this work, we used a biophysically detailed model of prefrontal cortex (PFC) to explore the effects of varying the spike rate, synchrony, and waveform of strong oscillatory inputs on the behavior of cortical networks driven by them. Interacting populations of excitatory and inhibitory neurons with strong feedback inhibition are inhibition-based network oscillators that exhibit resonance (i.e., larger responses to preferred input frequencies). We quantified network responses in terms of mean firing rates and the population frequency of network oscillation; and characterized their behavior in terms of the natural response to asynchronous input and the resonant response to oscillatory inputs. We show that strong feedback inhibition causes the PFC to generate internal (natural) oscillations in the beta/gamma frequency range (>15 Hz) and to maximize principal cell spiking in response to external oscillations at slightly higher frequencies. Importantly, we found that the fastest oscillation frequency that can be relayed by the network maximizes local inhibition and is equal to a frequency even higher than that which maximizes the firing rate of excitatory cells; we call this phenomenon population frequency resonance. This form of resonance is shown to determine the optimal driving frequency for suppressing responses to asynchronous activity. Lastly, we demonstrate that the natural and resonant frequencies can be tuned by changes in neuronal excitability, the duration of feedback inhibition, and dynamic properties of the input. Our results predict that PFC networks are tuned for generating and selectively responding to beta- and gamma-rhythmic signals due to the natural and resonant properties of inhibition-based oscillators. They also suggest strategies for optimizing transcranial stimulation and using oscillatory networks in neuromorphic engineering.

Sparse bursts optimize information transmission in a multiplexed neural code

Many cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing-rate output, which collapses all input streams into one. We analyze the extent to which neurons can simultaneously represent multiple input streams by using a code that distinguishes spike timing patterns at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. Neurons can also demultiplex this information, using specific connectivity patterns. The anatomy of the adult mammalian cortex suggests that these connectivity patterns are used by the nervous system to maintain sparse bursting and optimal multiplexing. Contrary to firing-rate coding, our findings indicate that the physiology and anatomy of the cortex may be interpreted as optimizing the transmission of multiple independent signals to different targets.

A novel approach to probabilistic characterisation of neural firing patterns

BACKGROUND

The advances in extracellular neural recording techniques result in big data volumes that necessitate fast, reliable, and automatic identification of statistically similar units. This study proposes a single framework yielding a compact set of probabilistic descriptors that characterise the firing patterns of a single unit.

NEW METHOD

Probabilistic features are estimated from an inter-spike-interval time series, without assumptions about the firing distribution or the stationarity. The first level of proposed firing patterns decomposition divides the inter-spike intervals into bursting, moderate and idle firing modes, yielding a coarse feature set. The second level identifies the successive bursting spikes, or the spiking acceleration/deceleration in the moderate firing mode, yielding a refined feature set. The features are estimated from simulated data and from experimental recordings from the lateral prefrontal cortex in awake, behaving rhesus monkeys.

RESULTS

An efficient and stable partitioning of neural units is provided by the ensemble evidence accumulation clustering. The possibility of selecting the number of clusters and choosing among coarse and refined feature sets provides an opportunity to explore and compare different data partitions.

CONCLUSIONS

The estimation of features, if applied to a single unit, can serve as a tool for the firing analysis, observing either overall spiking activity or the periods of interest in trial-to-trial recordings. If applied to massively parallel recordings, it additionally serves as an input to the clustering procedure, with the potential to compare the functional properties of various brain structures and to link the types of neural cells to the particular behavioural states.

Solving the Credit Assignment Problem With the Prefrontal Cortex

In naturalistic multi-cue and multi-step learning tasks, where outcomes of behavior are delayed in time, discovering which choices are responsible for rewards can present a challenge, known as the credit assignment problem. In this review, I summarize recent work that highlighted a critical role for the prefrontal cortex (PFC) in assigning credit where it is due in tasks where only a few of the multitude of cues or choices are relevant to the final outcome of behavior. Collectively, these investigations have provided compelling support for specialized roles of the orbitofrontal (OFC), anterior cingulate (ACC), and dorsolateral prefrontal (dlPFC) cortices in contingent learning. However, recent work has similarly revealed shared contributions and emphasized rich and heterogeneous response properties of neurons in these brain regions. Such functional overlap is not surprising given the complexity of reciprocal projections spanning the PFC. In the concluding section, I overview the evidence suggesting that the OFC, ACC and dlPFC communicate extensively, sharing the information about presented options, executed decisions and received rewards, which enables them to assign credit for outcomes to choices on which they are contingent. This account suggests that lesion or inactivation/inhibition experiments targeting a localized PFC subregion will be insufficient to gain a fine-grained understanding of credit assignment during learning and instead poses refined questions for future research, shifting the focus from focal manipulations to experimental techniques targeting cortico-cortical projections.

Spatial Attention Reduces Burstiness in Macaque Visual Cortical Area MST

Visual attention modulates the firing rate of neurons in many primate cortical areas. In V4, a cortical area in the ventral visual pathway, spatial attention has also been shown to reduce the tendency of neurons to fire closely separated spikes (burstiness). A recent model proposes that a single mechanism accounts for both the firing rate enhancement and the burstiness reduction in V4, but this has not been empirically tested. It is also unclear if the burstiness reduction by spatial attention is found in other visual areas and for other attentional types. We therefore recorded from single neurons in the medial superior temporal area (MST), a key motion-processing area along the dorsal visual pathway, of two rhesus monkeys while they performed a task engaging both spatial and feature-based attention. We show that in MST, spatial attention is associated with a clear reduction in burstiness that is independent of the concurrent enhancement of firing rate. In contrast, feature-based attention enhances firing rate but is not associated with a significant reduction in burstiness. These results establish burstiness reduction as a widespread effect of spatial attention. They also suggest that in contrast to the recently proposed model, the effects of spatial attention on burstiness and firing rate emerge from different mechanisms.

Functional integration across oscillation frequencies by cross-frequency phase synchronization

Neuronal oscillations and their inter-areal synchronization may be instrumental in regulating neuronal communication in distributed networks. Several lines of research have, however, shown that cognitive tasks engage neuronal oscillations simultaneously in multiple frequency bands that have distinct functional roles in cognitive processing. Gamma oscillations (30-120 Hz) are associated with bottom-up processing, while slower oscillations in delta (1-4 Hz), theta (4-7 Hz), alpha (8-14 Hz) and beta (14-30 Hz) frequency bands may have roles in executive or top-down controlling functions, although also other distinctions have been made. Identification of the mechanisms that integrate such spectrally distributed processing and govern neuronal communication among these networks is crucial for understanding how cognitive functions are achieved in neuronal circuits. Cross-frequency interactions among oscillations have been recognized as a likely candidate mechanism for such integration. We advance here the hypothesis that phase-phase synchronization of neuronal oscillations in two different frequency bands, cross-frequency phase synchrony (CFS), could serve to integrate, coordinate and regulate neuronal processing distributed into neuronal assemblies concurrently in multiple frequency bands. A trail of studies over the past decade has revealed the presence of CFS among cortical oscillations and linked CFS with roles in cognitive integration. We propose that CFS could connect fast and slow oscillatory networks and thereby integrate distributed cognitive functions such as representation of sensory information with attentional and executive functions.

Neuroelectric Tuning of Cortical Oscillations by Apical Dendrites in Loop Circuits

Bundles of relatively long apical dendrites dominate the neurons that make up the thickness of the cerebral cortex. It is proposed that a major function of the apical dendrite is to produce sustained oscillations at a specific frequency that can serve as a common timing unit for the processing of information in circuits connected to that apical dendrite. Many layer 5 and 6 pyramidal neurons are connected to thalamic neurons in loop circuits. A model of the apical dendrites of these pyramidal neurons has been used to simulate the electric activity of the apical dendrite. The results of that simulation demonstrated that subthreshold electric pulses in these apical dendrites can be tuned to specific frequencies and also can be fine-tuned to narrow bandwidths of less than one Hertz (1 Hz). Synchronous pulse outputs from the circuit loops containing apical dendrites can tune subthreshold membrane oscillations of neurons they contact. When the pulse outputs are finely tuned, they function as a local “clock,” which enables the contacted neurons to synchronously communicate with each other. Thus, a shared tuning frequency can select neurons for membership in a circuit. Unlike layer 6 apical dendrites, layer 5 apical dendrites can produce burst firing in many of their neurons, which increases the amplitude of signals in the neurons they contact. This difference in amplitude of signals serves as basis of selecting a sub-circuit for specialized processing (e.g., sustained attention) within the typically larger layer 6-based circuit. After examining the sustaining of oscillations in loop circuits and the processing of spikes in network circuits, we propose that cortical functioning can be globally viewed as two systems: a loop system and a network system. The loop system oscillations influence the network system’s timing and amplitude of pulse signals, both of which can select circuits that are momentarily dominant in cortical activity.

Theta and beta synchrony coordinate frontal eye fields and anterior cingulate cortex during sensorimotor mapping

The frontal eye fields (FEFs) and the anterior cingulate cortex (ACC) are commonly coactivated for cognitive saccade tasks, but whether this joined activation indexes coordinated activity underlying successful guidance of sensorimotor mapping is unknown. Here we test whether ACC and FEF circuits coordinate through phase synchronization of local field potential and neural spiking activity in macaque monkeys performing memory-guided and pro- and anti-saccades. We find that FEF and ACC showed prominent synchronization at a 3–9 Hz theta and a 12–30 Hz beta frequency band during the delay and preparation periods with a strong Granger-causal influence from ACC to FEF. The strength of theta- and beta-band coherence between ACC and FEF but not variations in power predict correct task performance. Taken together, the results support a role of ACC in cognitive control of frontoparietal networks and suggest that narrow-band theta and to some extent beta rhythmic activity indexes the coordination of relevant information during periods of enhanced control demands.

Frontal eye fields (FEF) and anterior cingulate cortex (ACC) are coactivated during cognitive tasks, but the precise format of their interaction is not known. Here the authors show that phase coupling between ACC -FEF in theta and beta frequency bands better predicts behavioural performance.

Attenuated contact heat-evoked potentials associated with sensory and social-emotional symptoms in individuals with autism spectrum disorder

Sensory disturbance is associated with socio-emotional problems in individuals with autism spectrum disorder (ASD). Most studies assess sensory symptoms by self-reports, which are largely limited by the language ability and self-awareness of the individuals. This study aims to investigate sensory disturbance by contact heat-evoked potentials (CHEP) in ASD individuals, and to examine the clinical correlates of CHEP parameters. We compared the CHEP parameters and reported pain between 31 ASD individuals (aged 20.5 ± 5.2 years) and and 22 typically-developing controls (TD, aged 21.4 ± 2.6), and correlated the CHEP parameters with self-reported sensory symptoms and attention/socio-emotional symptoms. We found that ASD individuals showed smaller P2-wave amplitudes than TD, even though they reported a similar level of pain. In TD individuals, a smaller P2-wave amplitude was related to higher scores on ‘low registration,’ ‘attention to detail,’ and ‘attention switching difficulties.’ In ASD individuals, longer N2-wave latency was related to higher scores on ‘sensory sensitivity’ and socio-emotional problems; while higher reported pain was associated with higher scores on ‘low registration,’ overall autistic severity, and longer N2-wave latency. Our findings of attenuated CHEP response in ASD, which was associated with sensory symptoms and socio-emotional problems, suggest a potential role for CHEP in studying sensory disturbances in ASD.

Cell-type-specific modulation of targets and distractors by dopamine D1 receptors in primate prefrontal cortex

The prefrontal cortex (PFC) is crucial for maintaining relevant information in working memory and resisting interference. PFC neurons are strongly regulated by dopamine, but it is unknown whether dopamine receptors are involved in protecting target memories from distracting stimuli. We investigated the prefrontal circuit dynamics and dopaminergic modulation of targets and distractors in monkeys trained to ignore interfering stimuli in a delayed-match-to-numerosity task. We found that dopamine D1 receptors (D1Rs) modulate the recovery of task-relevant information following a distracting stimulus. The direction of modulation is cell-type-specific: in putative pyramidal neurons, D1R inhibition enhances and D1R stimulation attenuates coding of the target stimulus after the interference, while the opposite pattern is observed in putative interneurons. Our results suggest that dopaminergic neuromodulation of PFC circuits regulates mental representations of behaviourally relevant stimuli that compete with task-irrelevant input and could play a central role for cognitive functioning in health and disease.