Rapid neocortical dynamics: cellular and network mechanisms

Clinicopathologic Dissociation: Robust Lafora Body Accumulation in Malin KO Mice Without Observable Changes in Home-Cage Behavior

Lafora disease (LD) is a syndrome of progressive myoclonic epilepsy and cumulative neurocognitive deterioration caused by recessively inherited genetic lesions of EPM2A (laforin) or NHLRC1 (malin). Neuropsychiatric symptomatology in LD is thought to be directly downstream of neuronal and astrocytic polyglucosan aggregates, termed Lafora bodies (LBs), which faithfully accumulate in an age-dependent manner in all mouse models of LD. In this study, we applied home-cage monitoring to examine the extent of neurobehavioral deterioration in a model of malin-deficient LD as a means to identify robust preclinical endpoints that may guide the selection of novel genetic treatments. At 6 weeks, ∼6-7 months, and ∼12 months of age, malin-deficient mice ("KO") and wild-type (WT) littermates underwent a standardized home-cage behavioral assessment designed to non-obtrusively appraise features of rest/arousal, consumptive behaviors, risk aversion, and voluntary wheel-running. At all timepoints, and over a range of metrics that we report transparently, WT and KO mice were essentially indistinguishable. In contrast, within WT mice compared across the same timepoints, we identified age-related nocturnal hypoactivity, diminished sucrose preference, and reduced wheel-running. Neuropathological examinations in subsets of the same mice revealed expected age-dependent LB accumulation, gliosis, and microglial activation in cortical and subcortical brain regions. At 12 months of age, despite the burden of neocortical LBs, we did not identify spontaneous seizures during an electroencephalographic (EEG) survey, and KO and WT mice exhibited similar spectral EEG features. However, in an in vitro assay of neocortical function, paroxysmal bursts of network activity (UP states) in KO slices were more prolonged at 3 and 6 months of age, but similar to WT at 12 months. KO mice displayed a distinct response to pentylenetetrazole, with a greater incidence of clonic seizures and a more pronounced postictal suppression of movement, feeding, and drinking behavior. Together, these results highlight the clinicopathologic dissociation in a mouse model of LD, where the accrual of LBs may latently modify cortical circuit function and seizure threshold without clinically meaningful changes in home-cage behavior. Our findings allude to a delay between LB accumulation and neurobehavioral decline in LD: one that may provide a window for treatment, and whose precise duration may be difficult to ascertain within the typical lifespan of a laboratory mouse.

Female-specific dysfunction of sensory neocortical circuits in a mouse model of autism mediated by mGluR5 and estrogen receptor α

Little is known of the brain mechanisms that mediate sex-specific autism symptoms. Here, we demonstrate that deletion of the autism spectrum disorder (ASD)-risk gene, Pten, in neocortical pyramidal neurons (NSEPten knockout [KO]) results in robust cortical circuit hyperexcitability selectively in female mice observed as prolonged spontaneous persistent activity states. Circuit hyperexcitability in females is mediated by metabotropic glutamate receptor 5 (mGluR5) and estrogen receptor α (ERα) signaling to mitogen-activated protein kinases (Erk1/2) and de novo protein synthesis. Pten KO layer 5 neurons have a female-specific increase in mGluR5 and mGluR5-dependent protein synthesis. Furthermore, mGluR5-ERα complexes are generally elevated in female cortices, and genetic reduction of ERα rescues enhanced circuit excitability, protein synthesis, and neuron size selectively in NSEPten KO females. Female NSEPten KO mice display deficits in sensory processing and social behaviors as well as mGluR5-dependent seizures. These results reveal mechanisms by which sex and a high-confidence ASD-risk gene interact to affect brain function and behavior.

GABAergic/Glycinergic and Glutamatergic Neurons Mediate Distinct Neurodevelopmental Phenotypes of STXBP1 Encephalopathy

An increasing number of pathogenic variants in presynaptic proteins involved in the synaptic vesicle cycle are being discovered in neurodevelopmental disorders. The clinical features of these synaptic vesicle cycle disorders are diverse, but the most prevalent phenotypes include intellectual disability, epilepsy, movement disorders, cerebral visual impairment, and psychiatric symptoms ( Verhage and Sørensen, 2020; Bonnycastle et al., 2021; John et al., 2021; Melland et al., 2021). Among this growing list of synaptic vesicle cycle disorders, the most frequent is STXBP1 encephalopathy caused by de novo heterozygous pathogenic variants in syntaxin-binding protein 1 (STXBP1, also known as MUNC18-1; Verhage and Sørensen, 2020; John et al., 2021). STXBP1 is an essential protein for presynaptic neurotransmitter release. Its haploinsufficiency is the main disease mechanism and impairs both excitatory and inhibitory neurotransmitter release. However, the disease pathogenesis and cellular origins of the broad spectrum of neurological phenotypes are poorly understood. Here we generate cell type-specific Stxbp1 haploinsufficient male and female mice and show that Stxbp1 haploinsufficiency in GABAergic/glycinergic neurons causes developmental delay, epilepsy, and motor, cognitive, and psychiatric deficits, recapitulating majority of the phenotypes observed in the constitutive Stxbp1 haploinsufficient mice and STXBP1 encephalopathy. In contrast, Stxbp1 haploinsufficiency in glutamatergic neurons results in a small subset of cognitive and seizure phenotypes distinct from those caused by Stxbp1 haploinsufficiency in GABAergic/glycinergic neurons. Thus, the contrasting roles of excitatory and inhibitory signaling reveal GABAergic/glycinergic dysfunction as a key disease mechanism of STXBP1 encephalopathy and suggest the possibility to selectively modulate disease phenotypes by targeting specific neurotransmitter systems.

Visual surround suppression at the neural and perceptual levels

Surround suppression was initially identified as a phenomenon at the neural level in which stimuli outside the neuron's receptive field alone cannot activate responses but can modulate neural responses to stimuli covered inside the receptive field. Subsequent studies showed that surround suppression is not only a critical property of neurons across species and brain areas but also has been found in visual perceptions. More importantly, surround suppression varies across individuals and shows significant differences between normal controls and patients with certain mental disorders. Here, we combined results from related literature and summarized the findings derived from physiological and psychophysical evidence. We first outline the basic properties of surround suppression in the visual system and perceptions. Then, we mainly summarize the differences in perceptual surround suppression among different human subjects. Our review suggests that there is no consensus regarding whether the strength of perceptual surround suppression could be used as an effective index to distinguish particular populations. Then, we summarized the similar mechanisms for surround suppression and cognitive impairments to further explore the potential clinical applications of surround suppression. A clearer understanding of the mechanisms of surround suppression in neural responses and perceptions is necessary for facilitating its clinical applications.

Adaptation-induced sharpening of orientation tuning curves in the mouse visual cortex

OBJECTIVE

Orientation selectivity is an emergent property of visual neurons across species with columnar and noncolumnar organization of the visual cortex. The emergence of orientation selectivity is more established in columnar cortical areas than in noncolumnar ones. Thus, how does orientation selectivity emerge in noncolumnar cortical areas after an adaptation protocol? Adaptation refers to the constant presentation of a nonoptimal stimulus (adapter) to a neuron under observation for a specific time. Previously, it had been shown that adaptation has varying effects on the tuning properties of neurons, such as orientation, spatial frequency, motion and so on.

BASIC METHODS

We recorded the mouse primary visual neurons (V1) at different orientations in the control (preadaptation) condition. This was followed by adapting neurons uninterruptedly for 12 min and then recording the same neurons postadaptation. An orientation selectivity index (OSI) for neurons was computed to compare them pre- and post-adaptation.

MAIN RESULTS

We show that 12-min adaptation increases the OSI of visual neurons ( n  = 113), that is, sharpens their tuning. Moreover, the OSI postadaptation increases linearly as a function of the OSI preadaptation.

CONCLUSION

The increased OSI postadaptation may result from a specific dendritic neural mechanism, potentially facilitating the rapid learning of novel features.

Female specific dysfunction of sensory neocortical circuits in a mouse model of autism mediated by mGluR5 and Estrogen Receptor α

Autism manifests differently in males and females and the brain mechanisms that mediate these sex-dependent differences are unknown. Here, we demonstrate that deletion of the ASD-risk gene, Pten, in neocortical pyramidal neurons (NSE Pten KO) results in robust hyperexcitability of local neocortical circuits in female, but not male, mice, observed as prolonged, spontaneous persistent activity states (UP states). Circuit hyperexcitability in NSE Pten KO mice is mediated by enhanced and/or altered signaling of metabotropic glutamate receptor 5 (mGluR5) and estrogen receptor α (ERα) to ERK and protein synthesis selectively in Pten deleted female neurons. In support of this idea, Pten deleted Layer 5 cortical neurons have female-specific increases in mGluR5 and mGluR5-driven protein synthesis. In addition, mGluR5-ERα complexes are elevated in female cortex and genetic reduction of ERα in Pten KO cortical neurons rescues circuit excitability, protein synthesis and enlarged neurons selectively in females. Abnormal timing and hyperexcitability of neocortical circuits in female NSE Pten KO mice are associated with deficits in temporal processing of sensory stimuli and social behaviors as well as mGluR5-dependent seizures. Female-specific cortical hyperexcitability and mGluR5-dependent seizures are also observed in a human disease relevant mouse model, germline Pten +/- mice. Our results reveal molecular mechanisms by which sex and a high impact ASD-risk gene interact to affect brain function and behavior.

On the physiological and structural contributors to the overall balance of excitation and inhibition in local cortical networks

Overall balance of excitation and inhibition in cortical networks is central to their functionality and normal operation. Such orchestrated co-evolution of excitation and inhibition is established through convoluted local interactions between neurons, which are organized by specific network connectivity structures and are dynamically controlled by modulating synaptic activities. Therefore, identifying how such structural and physiological factors contribute to establishment of overall balance of excitation and inhibition is crucial in understanding the homeostatic plasticity mechanisms that regulate the balance. We use biologically plausible mathematical models to extensively study the effects of multiple key factors on overall balance of a network. We characterize a network's baseline balanced state by certain functional properties, and demonstrate how variations in physiological and structural parameters of the network deviate this balance and, in particular, result in transitions in spontaneous activity of the network to high-amplitude slow oscillatory regimes. We show that deviations from the reference balanced state can be continuously quantified by measuring the ratio of mean excitatory to mean inhibitory synaptic conductances in the network. Our results suggest that the commonly observed ratio of the number of inhibitory to the number of excitatory neurons in local cortical networks is almost optimal for their stability and excitability. Moreover, the values of inhibitory synaptic decay time constants and density of inhibitory-to-inhibitory network connectivity are critical to overall balance and stability of cortical networks. However, network stability in our results is sufficiently robust against modulations of synaptic quantal conductances, as required by their role in learning and memory. Our study based on extensive bifurcation analyses thus reveal the functional optimality and criticality of structural and physiological parameters in establishing the baseline operating state of local cortical networks.

Spontaneous slow wave oscillations in extracellular field potential recordings reflect the alternating dominance of excitation and inhibition

In the resting state, cortical neurons can fire action potentials spontaneously but synchronously (Up state), followed by a quiescent period (Down state) before the cycle repeats. Extracellular recordings in the infragranular layer of cortex with a micro-electrode display a negative deflection (depth-negative) during Up states and a positive deflection (depth-positive) during Down states. The resulting slow wave oscillation (SWO) has been studied extensively during sleep and under anaesthesia. However, recent research on the balanced nature of synaptic excitation and inhibition has highlighted our limited understanding of its genesis. Specifically, are excitation and inhibition balanced during SWOs? We analyse spontaneous local field potentials (LFPs) during SWOs recorded from anaesthetised rats via a multi-channel laminar micro-electrode and show that the Down state consists of two distinct synaptic states: a Dynamic Down state associated with depth-positive LFPs and a prominent dipole in the extracellular field, and a Static Down state with negligible (≈ 0 mV $ \approx 0{\mathrm{\;mV}}$ ) LFPs and a lack of dipoles extracellularly. We demonstrate that depth-negative and -positive LFPs are generated by a shift in the balance of synaptic excitation and inhibition from excitation dominance (depth-negative) to inhibition dominance (depth-positive) in the infragranular layer neurons. Thus, although excitation and inhibition co-tune overall, differences in their timing lead to an alternation of dominance, manifesting as SWOs. We further show that Up state initiation is significantly faster if the preceding Down state is dynamic rather than static. Our findings provide a coherent picture of the dependence of SWOs on synaptic activity. KEY POINTS: Cortical neurons can exhibit repeated cycles of spontaneous activity interleaved with periods of relative silence, a phenomenon known as 'slow wave oscillation' (SWO). During SWOs, recordings of local field potentials (LFPs) in the neocortex show depth-negative deflection during the active period (Up state) and depth-positive deflection during the silent period (Down state). Here we further classified the Down state into a dynamic phase and a static phase based on a novel method of classification and revealed non-random, stereotypical sequences of the three states occurring with significantly different transitional kinetics. Our results suggest that the positive and negative deflections in the LFP reflect the shift of the instantaneous balance between excitatory and inhibitory synaptic activity of the local cortical neurons. The differences in transitional kinetics may imply distinct synaptic mechanisms for Up state initiation. The study may provide a new approach for investigating spontaneous brain rhythms.

Increased presynaptic excitability in a migraine with aura mutation

Migraine is a common and disabling neurological disorder. The headache and sensory amplifications of migraine are attributed to hyperexcitable sensory circuits, but a detailed understanding remains elusive. A mutation in casein kinase 1 delta (CK1δ) was identified in non-hemiplegic familial migraine with aura and advanced sleep phase syndrome. Mice carrying the CK1δT44A mutation were more susceptible to spreading depolarization (the phenomenon that underlies migraine aura), but mechanisms underlying this migraine-relevant phenotype were not known. We used a combination of whole-cell electrophysiology and multiphoton imaging, in vivo and in brain slices, to compare CK1δT44A mice (adult males) to their wild-type littermates. We found that despite comparable synaptic activity at rest, CK1δT44A neurons were more excitable upon repetitive stimulation than wild-type, with a reduction in presynaptic adaptation at excitatory but not inhibitory synapses. The mechanism of this adaptation deficit was a calcium-dependent enhancement of the size of the readily releasable pool of synaptic vesicles, and a resultant increase in glutamate release, in CK1δT44A compared to wild-type synapses. Consistent with this mechanism, CK1δT44A neurons showed an increase in the cumulative amplitude of excitatory post-synaptic currents, and a higher excitation-to-inhibition ratio during sustained activity compared to wild-type. At a local circuit level, action potential bursts elicited in CK1δT44A neurons triggered an increase in recurrent excitation compared to wild-type, and at a network level, CK1δT44A mice showed a longer duration of 'up state' activity, which is dependent on recurrent excitation. Finally, we demonstrated that the spreading depolarization susceptibility of CK1δT44A mice could be returned to wild-type levels with the same intervention (reduced extracellular calcium) that normalized presynaptic adaptation. Taken together, these findings show a stimulus-dependent presynaptic gain of function at glutamatergic synapses in a genetic model of migraine, that accounts for the increased spreading depolarization susceptibility and may also explain the sensory amplifications that are associated with the disease.

Lateral inhibition in V1 controls neural & perceptual contrast sensitivity

Lateral inhibition is a central principle for sensory system function. It is thought to operate by the activation of inhibitory neurons that restrict the spatial spread of sensory excitation. Much work on the role of inhibition in sensory systems has focused on visual cortex; however, the neurons, computations, and mechanisms underlying cortical lateral inhibition remain debated, and its importance for visual perception remains unknown. Here, we tested how lateral inhibition from PV or SST neurons in mouse primary visual cortex (V1) modulates neural and perceptual sensitivity to stimulus contrast. Lateral inhibition from PV neurons reduced neural and perceptual sensitivity to visual contrast in a uniform subtractive manner, whereas lateral inhibition from SST neurons more effectively changed the slope (or gain) of neural and perceptual contrast sensitivity. A neural circuit model identified spatially extensive lateral projections from SST neurons as the key factor, and we confirmed this with direct subthreshold measurements of a larger spatial footprint for SST versus PV lateral inhibition. Together, these results define cell-type specific computational roles for lateral inhibition in V1, and establish their unique consequences on sensitivity to contrast, a fundamental aspect of the visual world.

Prelimbic area to lateral hypothalamus circuit drives social aggression

Controlling aggression is a vital skill in social species such as rodents and humans and has been associated with the medial prefrontal cortex (mPFC). In this study, we showed that during aggressive behavior, the activity of GABAergic neurons in the prelimbic area (PL) of the mPFC was significantly suppressed. Specific activation of GABAergic PL neurons significantly curbed male-to-male aggression and inhibited conditioned place preference (CPP) for aggression-paired contexts, whereas specific inhibition of GABAergic PL neurons brought about the opposite effect. Moreover, GABAergic projections from PL neurons to the lateral hypothalamus (LH) orexinergic neurons mediated aggressive behavior. Finally, directly modulated LH-orexinergic neurons influence aggressive behavior. These results suggest that GABAergic PL-orexinergic LH projection is an important control circuit for intermale aggressive behavior, both of which could be targets for curbing aggression.

Transthalamic input to higher-order cortex selectively conveys state information

Communication among different neocortical areas is largely thought to be mediated by long-range synaptic interactions between cortical neurons, with the thalamus providing only an initial relay of information from the sensory periphery. Higher-order thalamic nuclei receive strong synaptic inputs from the cortex and send robust projections back to other cortical areas, providing a distinct and potentially critical route for cortico-cortical communication. However, the relative contributions of corticocortical and thalamocortical inputs to higher-order cortical function remain unclear. Using imaging of cortical neurons and projection axon terminals in combination with optogenetic manipulations, we find that the higher-order visual thalamus of mice conveys a specialized stream of information to higher-order visual cortex. Whereas corticocortical projections from lower cortical areas convey robust visual information, higher-order thalamocortical projections convey strong behavioral state information. Together, these findings suggest a key role for higher-order thalamus in providing contextual signals that flexibly modulate sensory processing in higher-order cortex.

Clinicopathologic Dissociation: Robust Lafora Body Accumulation in Malin KO Mice Without Observable Changes in Home-cage Behavior

Lafora Disease (LD) is a syndrome of progressive myoclonic epilepsy and cumulative neurocognitive deterioration caused by recessively inherited genetic lesions of EPM2A (laforin) or NHLRC1 (malin). Neuropsychiatric symptomatology in LD is thought to be directly downstream of neuronal and astrocytic polyglucosan aggregates, termed Lafora bodies (LBs), which faithfully accumulate in an age-dependent manner in all mouse models of LD. In this study, we applied home-cage monitoring to examine the extent of neurobehavioral deterioration in a model of malin-deficient LD, as a means to identify robust preclinical endpoints that may guide the selection of novel genetic treatments. At 6 weeks, ~6-7 months and ~12 months of age, malin deficient mice ("KO") and wild type (WT) littermates underwent a standardized home-cage behavioral assessment designed to non-obtrusively appraise features of rest/arousal, consumptive behaviors, risk aversion and voluntary wheel-running. At all timepoints, and over a range of metrics that we report transparently, WT and KO mice were essentially indistinguishable. In contrast, within WT mice compared across timepoints, we identified age-related nocturnal hypoactivity, diminished sucrose preference and reduced wheel-running. Neuropathological examinations in subsets of the same mice revealed expected age dependent LB accumulation, gliosis and microglial activation in cortical and subcortical brain regions. At 12 months of age, despite the burden of neocortical LBs, we did not identify spontaneous seizures during an electroencephalographic (EEG) survey, and KO and WT mice exhibited similar spectral EEG features. Using an in vitro assay of neocortical function, paroxysmal increases in network activity (UP states) in KO slices were more prolonged at 3 and 6 months of age, but were similar to WT at 12 months. KO mice displayed a distinct response to pentylenetetrazole, with a greater incidence of clonic seizures and a more pronounced post-ictal suppression of movement, feeding and drinking behavior. Together, these results highlight a stark clinicopathologic dissociation in a mouse model of LD, where LBs accrue substantially without clinically meaningful changes in overall wellbeing. Our findings allude to a delay between LB accumulation and neurobehavioral decline: one that may provide a window for treatment, and whose precise duration may be difficult to ascertain within the typical lifespan of a laboratory mouse.

Beyond rhythm - a framework for understanding the frequency spectrum of neural activity

Cognitive and behavioral processes are often accompanied by changes within well-defined frequency bands of the local field potential (LFP i.e., the voltage induced by neuronal activity). These changes are detectable in the frequency domain using the Fourier transform and are often interpreted as neuronal oscillations. However, aside some well-known exceptions, the processes underlying such changes are difficult to track in time, making their oscillatory nature hard to verify. In addition, many non-periodic neural processes can also have spectra that emphasize specific frequencies. Thus, the notion that spectral changes reflect oscillations is likely too restrictive. In this study, we use a simple yet versatile framework to understand the frequency spectra of neural recordings. Using simulations, we derive the Fourier spectra of periodic, quasi-periodic and non-periodic neural processes having diverse waveforms, illustrating how these attributes shape their spectral signatures. We then show how neural processes sum their energy in the local field potential in simulated and real-world recording scenarios. We find that the spectral power of neural processes is essentially determined by two aspects: (1) the distribution of neural events in time and (2) the waveform of the voltage induced by single neural events. Taken together, this work guides the interpretation of the Fourier spectrum of neural recordings and indicates that power increases in specific frequency bands do not necessarily reflect periodic neural activity.

Simulation of oscillatory dynamics induced by an approximation of grid cell output

Grid cells, in entorhinal cortex (EC) and related structures, signal animal location relative to hexagonal tilings of 2D space. A number of modeling papers have addressed the question of how grid firing behaviors emerge using (for example) ideas borrowed from dynamical systems (attractors) or from coupled oscillator theory. Here we use a different approach: instead of asking how grid behavior emerges, we take as a given the experimentally observed intracellular potentials of superficial medial EC neurons during grid firing. Employing a detailed neural circuit model modified from a lateral EC model, we then ask how the circuit responds when group of medial EC principal neurons exhibit such potentials, simultaneously with a simulated theta frequency input from the septal nuclei. The model predicts the emergence of robust theta-modulated gamma/beta oscillations, suggestive of oscillations observed in an in vitro medial EC experimental model (Cunningham, M.O., Pervouchine, D.D., Racca, C., Kopell, N.J., Davies, C.H., Jones, R.S.G., Traub, R.D., and Whittington, M.A. (2006). Neuronal metabolism governs cortical network response state. Proc. Natl. Acad. Sci. U S A 103: 5597-5601). Such oscillations result because feedback interneurons tightly synchronize with each other - despite the varying phases of the grid cells - and generate a robust inhibition-based rhythm. The lack of spatial specificity of the model interneurons is consistent with the lack of spatial periodicity in parvalbumin interneurons observed by Buetfering, C., Allen, K., and Monyer, H. (2014). Parvalbumin interneurons provide grid cell-driven recurrent inhibition in the medial entorhinal cortex. Nat. Neurosci. 17: 710-718. If in vivo EC gamma rhythms arise during exploration as our model predicts, there could be implications for interpreting disrupted spatial behavior and gamma oscillations in animal models of Alzheimer's disease and schizophrenia. Noting that experimental intracellular grid cell potentials closely resemble cortical Up states and Down states, during which fast oscillations also occur during Up states, we propose that the co-occurrence of slow principal cell depolarizations and fast network oscillations is a general property of the telencephalon, in both waking and sleep states.

The h-current controls cortical recurrent network activity through modulation of dendrosomatic communication

A fundamental feature of the cerebral cortex is the ability to rapidly turn on and off maintained activity within ensembles of neurons through recurrent excitation balanced by inhibition. Here we demonstrate that reduction of the h-current, which is especially prominent in pyramidal cell dendrites, strongly increases the ability of local cortical networks to generate maintained recurrent activity. Reduction of the h-current resulted in hyperpolarization and increase in input resistance of both the somata and apical dendrites of layer 5 pyramidal cells, while strongly increasing the dendrosomatic transfer of low (<20 Hz) frequencies, causing an increased responsiveness to dynamic clamp-induced recurrent network-like activity injected into the dendrites and substantially increasing the duration of spontaneous Up states. We propose that modulation of the h-current may strongly control the ability of cortical networks to generate recurrent persistent activity and the formation and dissolution of neuronal ensembles.

Beyond rhythm - A framework for understanding the frequency spectrum of neural activity

Cognitive and behavioral processes are often accompanied by changes within well-defined frequency bands of the local field potential (LFP i.e., the voltage induced by neuronal activity). These changes are detectable in the frequency domain using the Fourier transform and are often interpreted as neuronal oscillations. However, aside some well-known exceptions, the processes underlying such changes are difficult to track in time, making their oscillatory nature hard to verify. In addition, many non-periodic neural processes can also have spectra that emphasize specific frequencies. Thus, the notion that spectral changes reflect oscillations is likely too restrictive. In this study, we propose a simple yet versatile framework to understand the frequency spectra of neural recordings. Using simulations, we derive the Fourier spectra of periodic, quasi-periodic and non-periodic neural processes having diverse waveforms, illustrating how these attributes shape their spectral signatures. We then show how neural processes sum their energy in the local field potential in simulated and real-world recording scenarios. We find that the spectral power of neural processes is essentially determined by two aspects: 1) the distribution of neural events in time and 2) the waveform of the voltage induced by single neural events. Taken together, this work guides the interpretation of the Fourier spectrum of neural recordings and indicates that power increases in specific frequency bands do not necessarily reflect periodic neural activity.

Biophysical characterization and modelling of SCN1A gain-of-function predicts interneuron hyperexcitability and a predisposition to network instability through homeostatic plasticity

SCN1A gain-of-function variants are associated with early onset developmental and epileptic encephalopathies (DEEs) that possess distinct clinical features compared to Dravet syndrome caused by SCN1A loss-of-function. However, it is unclear how SCN1A gain-of-function may predispose to cortical hyper-excitability and seizures. Here, we first report the clinical features of a patient carrying a de novo SCN1A variant (T162I) associated with neonatal-onset DEE, and then characterize the biophysical properties of T162I and three other SCN1A variants associated with neonatal-onset DEE (I236V) and early infantile DEE (P1345S, R1636Q). In voltage clamp experiments, three variants (T162I, P1345S and R1636Q) exhibited changes in activation and inactivation properties that enhanced window current, consistent with gain-of-function. Dynamic action potential clamp experiments utilising model neurons incorporating Na_v1.1. channels supported a gain-of-function mechanism for all four variants. Here, the T162I, I236V, P1345S, and R1636Q variants exhibited higher peak firing rates relative to wild type and the T162I and R1636Q variants produced a hyperpolarized threshold and reduced neuronal rheobase. To explore the impact of these variants upon cortical excitability, we used a spiking network model containing an excitatory pyramidal cell (PC) and parvalbumin positive (PV) interneuron population. SCN1A gain-of-function was modelled by enhancing the excitability of PV interneurons and then incorporating three simple forms of homeostatic plasticity that restored pyramidal cell firing rates. We found that homeostatic plasticity mechanisms exerted differential impact upon network function, with changes to PV-to-PC and PC-to-PC synaptic strength predisposing to network instability. Overall, our findings support a role for SCN1A gain-of-function and inhibitory interneuron hyperexcitability in early onset DEE. We propose a mechanism through which homeostatic plasticity pathways can predispose to pathological excitatory activity and contribute to phenotypic variability in SCN1A disorders.

On the physiological and structural contributors to the overall balance of excitation and inhibition in local cortical networks

Overall balance of excitation and inhibition in cortical networks is central to their functionality and normal operation. Such orchestrated co-evolution of excitation and inhibition is established through convoluted local interactions between neurons, which are organized by specific network connectivity structures and are dynamically controlled by modulating synaptic activities. Therefore, identifying how such structural and physiological factors contribute to establishment of overall balance of excitation and inhibition is crucial in understanding the homeostatic plasticity mechanisms that regulate the balance. We use biologically plausible mathematical models to extensively study the effects of multiple key factors on overall balance of a network. We characterize a network's baseline balanced state by certain functional properties, and demonstrate how variations in physiological and structural parameters of the network deviate this balance and, in particular, result in transitions in spontaneous activity of the network to high-amplitude slow oscillatory regimes. We show that deviations from the reference balanced state can be continuously quantified by measuring the ratio of mean excitatory to mean inhibitory synaptic conductances in the network. Our results suggest that the commonly observed ratio of the number of inhibitory to the number of excitatory neurons in local cortical networks is almost optimal for their stability and excitability. Moreover, the values of inhibitory synaptic decay time constants and density of inhibitory-to-inhibitory network connectivity are critical to overall balance and stability of cortical networks. However, network stability in our results is sufficiently robust against modulations of synaptic quantal conductances, as required by their role in learning and memory.

Assessing the mechanisms of brain plasticity by transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is a non-invasive technique for focal brain stimulation based on electromagnetic induction where a fluctuating magnetic field induces a small intracranial electric current in the brain. For more than 35 years, TMS has shown promise in the diagnosis and treatment of neurological and psychiatric disorders in adults. In this review, we provide a brief introduction to the TMS technique with a focus on repetitive TMS (rTMS) protocols, particularly theta-burst stimulation (TBS), and relevant rTMS-derived metrics of brain plasticity. We then discuss the TMS-EEG technique, the use of neuronavigation in TMS, the neural substrate of TBS measures of plasticity, the inter- and intraindividual variability of those measures, effects of age and genetic factors on TBS aftereffects, and then summarize alterations of TMS-TBS measures of plasticity in major neurological and psychiatric disorders including autism spectrum disorder, schizophrenia, depression, traumatic brain injury, Alzheimer's disease, and diabetes. Finally, we discuss the translational studies of TMS-TBS measures of plasticity and their therapeutic implications.

Early adolescent psychological distress and cognition, correlates of resting-state EEG, interregional phase-amplitude coupling

Delineating neurobiological markers of youth mental health is crucial for early identification and treatment. One promising marker is phase-amplitude coupling (PAC), cross-frequency coupling between the phase of slower oscillatory activity and the amplitude of faster oscillatory activity in the brain. Prior research has demonstrated that PAC is associated with both cognition and mental health and can be modulated using neurostimulation. However, to date research investigating PAC has focused primarily on adults, and only within-region theta-gamma coupling in the context of mental health. We investigated associations between interregional resting-state PAC (posterior-anterior cortex), and cognition and psychological distress in N = 77 (M_age = 12.58 years, SD = 0.31; 51 % female) 12-year-olds. Firstly, while left theta-beta PAC showed a moderate positive correlation (r = 0.529, p < .01), right theta-gamma PAC showed a weak positive correlation, with psychological distress (r = 0.283, p < .05). In terms of cognition, moderate correlations were observed between: (i) increased left theta-beta PAC and increased psychomotor speed (r = -0.367, p < .05); (ii) increased left alpha-beta PAC and decreased attention (r = 0.355, p ≤0.01); and (iii) increased left alpha-beta PAC and decreased verbal learning and memory (r = -0.352, p < .01). Whereas weak associations were observed for: (i) increased left alpha-beta PAC and decreased executive functioning scores (r = 0.284, p < .05); and (ii) increased left alpha-gamma PAC and increased attention (r = -0.272, p < .05). The overall findings of this exploratory study are encouraging, although all the correlations were in the weak-to-moderate range and require replication. Further research may confirm interregional resting-state PAC as a biomarker that can help us better understand the link between mental health and cognition in adolescents and improve treatment of cognitive related deficits in mental illness.

Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding

Synchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed discharges in cortical networks and relate those mechanisms to their possible roles in coding and cognitive functions. To accommodate the need for a selective, directed synchronization of cells, I propose that synchronous firing of distributed neurons is a natural consequence of spike-timing-dependent plasticity (STDP) that associates cells repetitively receiving temporally coherent input: the “synchrony through synaptic plasticity” hypothesis. Neurons that are excited by a repeated sequence of synaptic inputs may learn to selectively respond to the onset of this sequence through synaptic plasticity. Multiple neurons receiving coherent input could thus actively synchronize their firing by learning to selectively respond at corresponding temporal positions. The hypothesis makes several predictions: first, the position of the cells in the network, as well as the source of their input signals, would be irrelevant as long as their input signals arrive simultaneously; second, repeating discharge patterns should get compressed until all or some part of the signals are synchronized; and third, this compression should be accompanied by a sparsening of signals. In this way, selective groups of cells could emerge that would respond to some recurring event with synchronous firing. Such a learned response pattern could further be modulated by synchronous network oscillations that provide a dynamic, flexible context for the synaptic integration of distributed signals. I conclude by suggesting experimental approaches to further test this new hypothesis.

Decoding of attentional state using local field potentials

We review recent efforts to decode visual spatial attention from different types of brain signals, such as spikes and local field potentials (LFPs). Combining signals from more electrodes improves decoding, but the pattern of improvement varies considerably depending on the signal as well as the task (for example, decoding of sensory stimulus/motor intention versus location of attention). We argue that this pattern of results conveys important information not only about the usefulness of a particular brain signal for decoding attention, but also about the spatial scale over which attention operates in the brain. The spatial scale, in turn, likely depends on the extent of underlying mechanisms such as normalization, gain control via excitation-inhibition interactions, and neuromodulatory regulation of attention.

Audiovisual task switching rapidly modulates sound encoding in mouse auditory cortex

In everyday behavior, sensory systems are in constant competition for attentional resources, but the cellular and circuit-level mechanisms of modality-selective attention remain largely uninvestigated. We conducted translaminar recordings in mouse auditory cortex (AC) during an audiovisual (AV) attention shifting task. Attending to sound elements in an AV stream reduced both pre-stimulus and stimulus-evoked spiking activity, primarily in deep-layer neurons and neurons without spectrotemporal tuning. Despite reduced spiking, stimulus decoder accuracy was preserved, suggesting improved sound encoding efficiency. Similarly, task-irrelevant mapping stimuli during inter-trial intervals evoked fewer spikes without impairing stimulus encoding, indicating that attentional modulation generalized beyond training stimuli. Importantly, spiking reductions predicted trial-to-trial behavioral accuracy during auditory attention, but not visual attention. Together, these findings suggest auditory attention facilitates sound discrimination by filtering sound-irrelevant background activity in AC, and that the deepest cortical layers serve as a hub for integrating extramodal contextual information.

Cascaded normalizations for spatial integration in the primary visual cortex of primates

Spatial integration of visual information is an important function in the brain. However, neural computation for spatial integration in the visual cortex remains unclear. In this study, we recorded laminar responses in V1 of awake monkeys driven by visual stimuli with grating patches and annuli of different sizes. We find three important response properties related to spatial integration that are significantly different between input and output layers: neurons in output layers have stronger surround suppression, smaller receptive field (RF), and higher sensitivity to grating annuli partially covering their RFs. These interlaminar differences can be explained by a descriptive model composed of two global divisions (normalization) and a local subtraction. Our results suggest suppressions with cascaded normalizations (CNs) are essential for spatial integration and laminar processing in the visual cortex. Interestingly, the features of spatial integration in convolutional neural networks, especially in lower layers, are different from our findings in V1.

Not so spontaneous: Multi-dimensional representations of behaviors and context in sensory areas

Sensory areas are spontaneously active in the absence of sensory stimuli. This spontaneous activity has long been studied; however, its functional role remains largely unknown. Recent advances in technology, allowing large-scale neural recordings in the awake and behaving animal, have transformed our understanding of spontaneous activity. Studies using these recordings have discovered high-dimensional spontaneous activity patterns, correlation between spontaneous activity and behavior, and dissimilarity between spontaneous and sensory-driven activity patterns. These findings are supported by evidence from developing animals, where a transition toward these characteristics is observed as the circuit matures, as well as by evidence from mature animals across species. These newly revealed characteristics call for the formulation of a new role for spontaneous activity in neural sensory computation.

Spike-Gamma Phase Relationship in the Visual Cortex

Gamma oscillations (30-70 Hz) have been hypothesized to play a role in cortical function. Most of the proposed mechanisms involve rhythmic modulation of neuronal excitability at gamma frequencies, leading to modulation of spike timing relative to the rhythm. I first show that the gamma band could be more privileged than other frequencies in observing spike-field interactions even in the absence of genuine gamma rhythmicity and discuss several biases in spike-gamma phase estimation. I then discuss the expected spike-gamma phase according to several hypotheses. Inconsistent with the phase-coding hypothesis (but not with others), the spike-gamma phase does not change with changes in stimulus intensity or attentional state, with spikes preferentially occurring 2-4 ms before the trough, but with substantial variability. However, this phase relationship is expected even when gamma is a byproduct of excitatory-inhibitory interactions. Given that gamma occurs in short bursts, I argue that the debate over the role of gamma is a matter of semantics. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Distinct ensembles in the noradrenergic locus coeruleus are associated with diverse cortical states

Brainstem locus coeruleus (LC) noradrenergic neurons produce an arousal-related state characterized by a broadband increase in high-frequency oscillations. This perspective was built upon electrical or optogenetic stimulation that artificially activates LC neurons synchronously. This has led to the conceptual model that LC activation is associated with a single cortical state. Here, we show that natural, spontaneously occurring LC single-unit activity consists of ensembles with largely nonoverlapping activation dynamics. Spontaneous activations of different LC ensembles are associated with different cortical states. Our results suggest that the role of the LC in controlling a single type of cortical state associated with arousal is an oversimplification. Instead, ensembles of LC neurons may control a diverse multitude of cortical states.

The noradrenergic locus coeruleus (LC) is a controller of brain and behavioral states. Activating LC neurons en masse by electrical or optogenetic stimulation promotes a stereotypical “activated” cortical state of high-frequency oscillations. However, it has been recently reported that spontaneous activity of LC cell pairs has sparse yet structured time-averaged cross-correlations, which is unlike the highly synchronous neuronal activity evoked by stimulation. Therefore, LC population activity could consist of distinct multicell ensembles each with unique temporal evolution of activity. We used nonnegative matrix factorization (NMF) to analyze large populations of simultaneously recorded LC single units in the rat LC. NMF identified ensembles of spontaneously coactive LC neurons and their activation time courses. Since LC neurons selectively project to specific forebrain regions, we hypothesized that distinct ensembles activate during different cortical states. To test this hypothesis, we calculated band-limited power and spectrograms of local field potentials in cortical area 24a aligned to spontaneous activations of distinct LC ensembles. A diversity of state modulations occurred around activation of different LC ensembles, including a typical activated state with increased high-frequency power as well as other states including decreased high-frequency power. Thus—in contrast to the stereotypical activated brain state evoked by en masse LC stimulation—spontaneous activation of distinct LC ensembles is associated with a multitude of cortical states.

A Circuit Model for Working Memory Based on Hybrid Positive and Negative-Derivative Feedback Mechanism

Working memory (WM) plays an important role in cognitive activity. The WM system is used to temporarily store information in learning and decision-making. WM always functions in many aspects of daily life, such as the short-term memory of words, cell phone verification codes, and cell phone numbers. In young adults, studies have shown that a central memory store is limited to three to five meaningful items. Little is known about how WM functions at the microscopic neural level, but appropriate neural network computational models can help us gain a better understanding of it. In this study, we attempt to design a microscopic neural network model to explain the internal mechanism of WM. The performance of existing positive feedback models depends on the parameters of a synapse. We use a negative-derivative feedback mechanism to counteract the drift in persistent activity, making the hybrid positive and negative-derivative feedback (HPNF) model more robust to common disturbances. To fulfill the mechanism of WM at the neural circuit level, we construct two main neural networks based on the HPNF model: a memory-storage sub-network (the memory-storage sub-network is composed of several sets of neurons, so we call it "SET network", or "SET" for short) with positive feedback and negative-derivative feedback and a storage distribution network (SDN) designed by combining SET for memory item storage and memory updating. The SET network is a neural information self-sustaining mechanism, which is robust to common disturbances; the SDN constructs a storage distribution network at the neural circuit level; the experimental results show that our network can fulfill the storage, association, updating, and forgetting of information at the level of neural circuits, and it can work in different individuals with little change in parameters.

Inference of Multiplicative Factors Underlying Neural Variability in Calcium Imaging Data

Understanding brain function requires disentangling the high-dimensional activity of populations of neurons. Calcium imaging is an increasingly popular technique for monitoring such neural activity, but computational tools for interpreting extracted calcium signals are lacking. While there has been a substantial development of factor-analysis-type methods for neural spike train analysis, similar methods targeted at calcium imaging data are only beginning to emerge. Here we develop a flexible modeling framework that identifies low-dimensional latent factors in calcium imaging data with distinct additive and multiplicative modulatory effects. Our model includes spike-and-slab sparse priors that regularize additive factor activity and gaussian process priors that constrain multiplicative effects to vary only gradually, allowing for the identification of smooth and interpretable changes in multiplicative gain. These factors are estimated from the data using a variational expectation-maximization algorithm that requires a differentiable reparameterization of both continuous and discrete latent variables. After demonstrating our method on simulated data, we apply it to experimental data from the zebrafish optic tectum, uncovering low-dimensional fluctuations in multiplicative excitability that govern trial-to-trial variation in evoked responses.

Biomarkers Obtained by Transcranial Magnetic Stimulation in Neurodevelopmental Disorders

SUMMARY

Transcranial magnetic stimulation (TMS) is a method for focal brain stimulation that is based on the principle of electromagnetic induction where small intracranial electric currents are generated by a powerful fluctuating magnetic field. Over the past three decades, TMS has shown promise in the diagnosis, monitoring, and treatment of neurological and psychiatric disorders in adults. However, the use of TMS in children has been more limited. We provide a brief introduction to the TMS technique; common TMS protocols including single-pulse TMS, paired-pulse TMS, paired associative stimulation, and repetitive TMS; and relevant TMS-derived neurophysiological measurements including resting and active motor threshold, cortical silent period, paired-pulse TMS measures of intracortical inhibition and facilitation, and plasticity metrics after repetitive TMS. We then discuss the biomarker applications of TMS in a few representative neurodevelopmental disorders including autism spectrum disorder, fragile X syndrome, attention-deficit hyperactivity disorder, Tourette syndrome, and developmental stuttering.

Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices

The number of neurons in mammalian cortex varies by multiple orders of magnitude across different species. In contrast, the ratio of excitatory to inhibitory neurons (E:I ratio) varies in a much smaller range, from 3:1 to 9:1 and remains roughly constant for different sensory areas within a species. Despite this structure being important for understanding the function of neural circuits, the reason for this consistency is not yet understood. While recent models of vision based on the efficient coding hypothesis show that increasing the number of both excitatory and inhibitory cells improves stimulus representation, the two cannot increase simultaneously due to constraints on brain volume. In this work, we implement an efficient coding model of vision under a constraint on the volume (using number of neurons as a surrogate) while varying the E:I ratio. We show that the performance of the model is optimal at biologically observed E:I ratios under several metrics. We argue that this happens due to trade-offs between the computational accuracy and the representation capacity for natural stimuli. Further, we make experimentally testable predictions that 1) the optimal E:I ratio should be higher for species with a higher sparsity in the neural activity and 2) the character of inhibitory synaptic distributions and firing rates should change depending on E:I ratio. Our findings, which are supported by our new preliminary analyses of publicly available data, provide the first quantitative and testable hypothesis based on optimal coding models for the distribution of excitatory and inhibitory neural types in the mammalian sensory cortices.

Neurons in the brain come in two main types: excitatory and inhibitory. The interplay between them shapes neural computation. Despite brain sizes varying by several orders of magnitude across species, the ratio of excitatory and inhibitory sub-populations (E:I ratio) remains relatively constant, and we don’t know why. Simulations of theoretical models of the brain can help answer such questions, especially when experiments are prohibitive or impossible. Here we placed one such theoretical model of sensory coding (’sparse coding’ that minimizes the simultaneously active neurons) under a biophysical ‘volume’ constraint that fixes the total number of neurons available. We vary the E:I ratio in the model (which cannot be done in experiments), and reveal an optimal E:I ratio where the representation of sensory stimulus and energy consumption within the circuit are concurrently optimal. We also show that varying the population sparsity changes the optimal E:I ratio, spanning the relatively narrow ranges observed in biology. Crucially, this minimally parameterized theoretical model makes predictions about structure (recurrent connectivity) and activity (population sparsity) in neural circuits with different E:I ratios (i.e., different species), of which we verify the latter in a first-of-its-kind inter-species comparison using newly publicly available data.

The role of plasticity in the recovery of consciousness

Disorders of consciousness (DOCs), i.e., coma, vegetative state, and minimally conscious state are the consequences of a severe brain injury that disrupts the brain ability to generate consciousness. Recovery from DOCs requires functional and structural changes in the brain. The sites where these plastic changes take place vary according to the pathophysiology of the DOC. The ascending reticular activating system of the brainstem and its complex connections with the thalamus and cortex are involved in the pathophysiology of coma. Subcortical structures, such as the striatum and globus pallidus, together with thalamocortical and corticothalamic projections, the basal forebrain, and several networks among different cortical areas are probably involved in vegetative and minimally conscious states. Some mechanisms of plasticity that allegedly operate in each of these sites to promote recovery of consciousness will be discussed in this chapter. While some mechanisms of plasticity work at a local level, others produce functional changes in complex neuronal networks, for example by entraining neuronal oscillations. The specific mechanisms of brain plasticity represent potential targets for future treatments aiming to restore consciousness in patients with severe DOCs.

Reliable Sensory Processing in Mouse Visual Cortex through Cooperative Interactions between Somatostatin and Parvalbumin Interneurons

Intrinsic neuronal variability significantly limits information encoding in the primary visual cortex (V1). However, under certain conditions, neurons can respond reliably with highly precise responses to the same visual stimuli from trial to trial. This suggests that there exists intrinsic neural circuit mechanisms that dynamically modulate the intertrial variability of visual cortical neurons. Here, we sought to elucidate the role of different inhibitory interneurons (INs) in reliable coding in mouse V1. To study the interactions between somatostatin-expressing interneurons (SST-INs) and parvalbumin-expressing interneurons (PV-INs), we used a dual-color calcium imaging technique that allowed us to simultaneously monitor these two neural ensembles while awake mice, of both sexes, passively viewed natural movies. SST neurons were more active during epochs of reliable pyramidal neuron firing, whereas PV neurons were more active during epochs of unreliable firing. SST neuron activity lagged that of PV neurons, consistent with a feedback inhibitory SST→PV circuit. To dissect the role of this circuit in pyramidal neuron activity, we used temporally limited optogenetic activation and inactivation of SST and PV interneurons during periods of reliable and unreliable pyramidal cell firing. Transient firing of SST neurons increased pyramidal neuron reliability by actively suppressing PV neurons, a proposal that was supported by a rate-based model of V1 neurons. These results identify a cooperative functional role for the SST→PV circuit in modulating the reliability of pyramidal neuron activity.SIGNIFICANCE STATEMENT Cortical neurons often respond to identical sensory stimuli with large variability. However, under certain conditions, the same neurons can also respond highly reliably. The circuit mechanisms that contribute to this modulation remain unknown. Here, we used novel dual-wavelength calcium imaging and temporally selective optical perturbation to identify an inhibitory neural circuit in visual cortex that can modulate the reliability of pyramidal neurons to naturalistic visual stimuli. Our results, supported by computational models, suggest that somatostatin interneurons increase pyramidal neuron reliability by suppressing parvalbumin interneurons via the inhibitory SST→PV circuit. These findings reveal a novel role of the SST→PV circuit in modulating the fidelity of neural coding critical for visual perception.

State transitions through inhibitory interneurons in a cortical network model

Inhibitory interneurons shape the spiking characteristics and computational properties of cortical networks. Interneuron subtypes can precisely regulate cortical function but the roles of interneuron subtypes for promoting different regimes of cortical activity remains unclear. Therefore, we investigated the impact of fast spiking and non-fast spiking interneuron subtypes on cortical activity using a network model with connectivity and synaptic properties constrained by experimental data. We found that network properties were more sensitive to modulation of the fast spiking population, with reductions of fast spiking excitability generating strong spike correlations and network oscillations. Paradoxically, reduced fast spiking excitability produced a reduction of global excitation-inhibition balance and features of an inhibition stabilised network, in which firing rates were driven by the activity of excitatory neurons within the network. Further analysis revealed that the synaptic interactions and biophysical features associated with fast spiking interneurons, in particular their rapid intrinsic response properties and short synaptic latency, enabled this state transition by enhancing gain within the excitatory population. Therefore, fast spiking interneurons may be uniquely positioned to control the strength of recurrent excitatory connectivity and the transition to an inhibition stabilised regime. Overall, our results suggest that interneuron subtypes can exert selective control over excitatory gain allowing for differential modulation of global network state.

Inhibitory interneurons comprise a significant proportion of all cortical neurons and play a crucial role in sustaining normal neural activity in the brain. Although it is well established that there exist distinct subtypes of interneurons, the impact of different interneuron subtypes upon cortical function remains unclear. In this work, we explore the role of interneuron subtypes for modulating neural activity using a network model containing two of the most common interneuron subtypes. We find that one interneuron subtype, known as fast spiking interneurons, preferentially control the strength of activity between excitatory neurons to regulate changes in network state. These findings suggest that interneuron subtypes may selectively modulate cortical activity to promote different computational capabilities.

Probing mechanisms of visual spatial attention in mice

The role of spatial attention for visual perception has been thoroughly studied in primates, but less so in mice. Several behavioral tasks in mice reveal spatial attentional effects, with similarities to observations in primates. Pairing these tasks with large-scale, cell-type-specific techniques could enable deeper access to underlying mechanisms, and help define the utility and limitations of resolving attentional effects on visual perception and neural activity in mice. In this Review, we evaluate behavioral and neural evidence for visual spatial attention in mice; assess how specializations of the mouse visual system and behavioral repertoire impact interpretation of spatial attentional effects; and outline how several measurement and manipulation techniques in mice could precisely test and refine models of attentional modulation across scales.

A dynamic neural field model of continuous input integration

The ability of neural systems to turn transient inputs into persistent changes in activity is thought to be a fundamental requirement for higher cognitive functions. In continuous attractor networks frequently used to model working memory or decision making tasks, the persistent activity settles to a stable pattern with the stereotyped shape of a "bump" independent of integration time or input strength. Here, we investigate a new bump attractor model in which the bump width and amplitude not only reflect qualitative and quantitative characteristics of a preceding input but also the continuous integration of evidence over longer timescales. The model is formalized by two coupled dynamic field equations of Amari-type which combine recurrent interactions mediated by a Mexican-hat connectivity with local feedback mechanisms that balance excitation and inhibition. We analyze the existence, stability and bifurcation structure of single and multi-bump solutions and discuss the relevance of their input dependence to modeling cognitive functions. We then systematically compare the pattern formation process of the two-field model with the classical Amari model. The results reveal that the balanced local feedback mechanisms facilitate the encoding and maintenance of multi-item memories. The existence of stable subthreshold bumps suggests that different to the Amari model, the suppression effect of neighboring bumps in the range of lateral competition may not lead to a complete loss of information. Moreover, bumps with larger amplitude are less vulnerable to noise-induced drifts and distance-dependent interaction effects resulting in more faithful memory representations over time.

Global, Low-Amplitude Cortical State Predicts Response Outcomes in a Selective Detection Task in Mice

Spontaneous neuronal activity strongly impacts stimulus encoding and behavioral responses. We sought to determine the effects of neocortical prestimulus activity on stimulus detection. We trained mice in a selective whisker detection task, in which they learned to respond (lick) to target stimuli in one whisker field and ignore distractor stimuli in the contralateral whisker field. During expert task performance, we used widefield Ca2+ imaging to assess prestimulus and post-stimulus neuronal activity broadly across frontal and parietal cortices. We found that lower prestimulus activity correlated with enhanced stimulus detection: lower prestimulus activity predicted response versus no response outcomes and faster reaction times. The activity predictive of trial outcome was distributed through dorsal neocortex, rather than being restricted to whisker or licking regions. Using principal component analysis, we demonstrate that response trials are associated with a distinct and less variable prestimulus neuronal subspace. For single units, prestimulus choice probability was weak yet distributed broadly, with lower than chance choice probability correlating with stronger sensory and motor encoding. These findings support low amplitude and low variability as an optimal prestimulus cortical state for stimulus detection that presents globally and predicts response outcomes for both target and distractor stimuli.

Neuronal Switching between Single- and Dual-Network Activity via Modulation of Intrinsic Membrane Properties

Oscillatory networks underlie rhythmic behaviors (e.g., walking, chewing) and complex behaviors (e.g., memory formation, decision-making). Flexibility of oscillatory networks includes neurons switching between single- and dual-network participation, even generating oscillations at two distinct frequencies. Modulation of synaptic strength can underlie this neuronal switching. Here we ask whether switching into dual-frequency oscillations can also result from modulation of intrinsic neuronal properties. The isolated stomatogastric nervous system of male Cancer borealis crabs contains two well-characterized rhythmic feeding-related networks (pyloric, ∼1 Hz; gastric mill, ∼0.1 Hz). The identified modulatory projection neuron MCN5 causes the pyloric-only lateral posterior gastric (LPG) neuron to switch to dual pyloric/gastric mill bursting. Bath applying the MCN5 neuropeptide transmitter Gly¹-SIFamide only partly mimics the LPG switch to dual activity because of continued LP neuron inhibition of LPG. Here, we find that MCN5 uses a cotransmitter, glutamate, to inhibit LP, unlike Gly¹-SIFamide excitation of LP. Thus, we modeled the MCN5-elicited LPG switching with Gly¹-SIFamide application and LP photoinactivation. Using hyperpolarization of pyloric pacemaker neurons and gastric mill network neurons, we found that LPG pyloric-timed oscillations require rhythmic electrical synaptic input. However, LPG gastric mill-timed oscillations do not require any pyloric/gastric mill synaptic input and are voltage-dependent. Thus, we identify modulation of intrinsic properties as an additional mechanism for switching a neuron into dual-frequency activity. Instead of synaptic modulation switching a neuron into a second network as a passive follower, modulation of intrinsic properties could enable a switching neuron to become an active contributor to rhythm generation in the second network.SIGNIFICANCE STATEMENT Neuromodulation of oscillatory networks can enable network neurons to switch from single- to dual-network participation, even when two networks oscillate at distinct frequencies. We used small, well-characterized networks to determine whether modulation of synaptic strength, an identified mechanism for switching, is necessary for dual-network recruitment. We demonstrate that rhythmic electrical synaptic input is required for continued linkage with a "home" network, whereas modulation of intrinsic properties enables a neuron to generate oscillations at a second frequency. Neuromodulator-induced switches in neuronal participation between networks occur in motor, cognitive, and sensory networks. Our study highlights the importance of considering intrinsic properties as a pivotal target for enabling parallel participation of a neuron in two oscillatory networks.

Reduced Effect of Anticonvulsants on AMPA Receptor Palmitoylation-Deficient Mice

AMPA receptors are responsible for fast excitatory synaptic transmission in the mammalian brain. Post-translational protein S-palmitoylation of AMPA receptor subunits GluA1-4 reversibly regulates synaptic AMPA receptor expression, resulting in long-lasting changes in excitatory synaptic strengths. Our previous studies have shown that GluA1 C-terminal palmitoylation-deficient (GluA1C811S) mice exhibited hyperexcitability in the cerebrum and elevated seizure susceptibility without affecting brain structure or basal synaptic transmission. Moreover, some inhibitory GABAergic synapses-targeting anticonvulsants, such as valproic acid, phenobarbital, and diazepam, had less effect on these AMPA receptor palmitoylation-deficient mutant mice. This work explores pharmacological effect of voltage-gated ion channel-targeted anticonvulsants, phenytoin and trimethadione, on GluA1C811S mice. Similar to GABAergic synapses-targeting anticonvulsants, anticonvulsive effects were also reduced for both sodium channel- and calcium channel-blocking anticonvulsants, which suppress excess excitation. These data strongly suggest that the GluA1C811S mice generally underlie the excessive excitability in response to seizure-inducing stimulation. AMPA receptor palmitoylation site could be a novel target to develop unprecedented type of anticonvulsants and GluA1C811S mice are suitable as a model animal for broadly evaluating pharmacological effectiveness of antiepileptic drugs.

Septotemporal variation in beta-adrenergic modulation of short-term dynamics in the hippocampus

Recent evidence shows a greater facilitating effect of beta-adrenergic receptors (β-ARs) on long-term synaptic plasticity in the ventral versus the dorsal hippocampus. Here, using field potentials from the CA1 area and a ten-pulse stimulation train of varying frequency we show that activation of β-ARs by isoproterenol preferentially facilitates the output from the dorsal hippocampus at the frequency range of 3–40 Hz without affecting short-term synaptic plasticity. Furthermore, isoproterenol increases basal synaptic transmission in the dorsal hippocampus only and enhances basal neuronal excitation more in the dorsal than the ventral hippocampus. These results suggest that β-AR-modulation of short-term neuronal dynamics differs along the longitudinal axis of the hippocampus, thereby contributing to functional specialization along the same axis.

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We studied the effects of isoproterenol (ISO) in dorsal (DH) and ventral (VH) hippocampus.

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ISO increased synaptic transmission and population spike more in DH than VH.

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ISO modulated short-term changes of population spike in the dorsal hippocampus only.

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ISO did not affect short-term changes of synaptic transmission in DH or VH.

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β adrenergic receptors modulate short-term changes in excitation in DH only.

The Structural E/I Balance Constrains the Early Development of Cortical Network Activity

Neocortical networks have a characteristic constant ratio in the number of glutamatergic projection neurons (PN) and GABAergic interneurons (IN), and deviations in this ratio are often associated with developmental neuropathologies. Cultured networks with defined cellular content allowed us to ask if initial PN/IN ratios change the developmental population dynamics, and how different ratios impact the physiological excitatory/inhibitory (E/I) balance and the network activity development. During the first week in vitro, the IN content modulated PN numbers, increasing their proliferation in networks with higher IN proportions. The proportion of INs in each network set remained similar to the initial plating ratio during the 4 weeks cultivation period. Results from additional networks generated with more diverse cellular composition, including early-born GABA neurons, suggest that a GABA-dependent mechanism may decrease the survival of additional INs. A large variation of the PN/IN ratio did not change the balance between isolated spontaneous glutamatergic and GABAergic postsynaptic currents charge transfer (E/I balance) measured in PNs or INs. In contrast, the E/I balance of multisynaptic bursts reflected differences in IN content. Additionally, the spontaneous activity recorded by calcium imaging showed that higher IN ratios were associated with increased frequency of network bursts combined with a decrease of participating neurons per event. In the 4th week in vitro, bursting activity was stereotypically synchronized in networks with very few INs but was more desynchronized in networks with higher IN proportions. These results suggest that the E/I balance of isolated postsynaptic currents in single cells may be regulated independently of PN/IN proportions, but the network bursts E/I balance and the maturation of spontaneous network activity critically depends upon the structural PN/IN ratio.

Epileptic Mechanisms Shared by Alzheimer's Disease: Viewed via the Unique Lens of Genetic Epilepsy

Our recent work on genetic epilepsy (GE) has identified common mechanisms between GE and neurodegenerative diseases including Alzheimer's disease (AD). Although both disorders are seemingly unrelated and occur at opposite ends of the age spectrum, it is likely there are shared mechanisms and studies on GE could provide unique insights into AD pathogenesis. Neurodegenerative diseases are typically late-onset disorders, but the underlying pathology may have already occurred long before the clinical symptoms emerge. Pathophysiology in the early phase of these diseases is understudied but critical for developing mechanism-based treatment. In AD, increased seizure susceptibility and silent epileptiform activity due to disrupted excitatory/inhibitory (E/I) balance has been identified much earlier than cognition deficit. Increased epileptiform activity is likely a main pathology in the early phase that directly contributes to impaired cognition. It is an enormous challenge to model the early phase of pathology with conventional AD mouse models due to the chronic disease course, let alone the complex interplay between subclinical nonconvulsive epileptiform activity, AD pathology, and cognition deficit. We have extensively studied GE, especially with gene mutations that affect the GABA pathway such as mutations in GABA_A receptors and GABA transporter 1. We believe that some mouse models developed for studying GE and insights gained from GE could provide unique opportunity to understand AD. These include the pathology in early phase of AD, endoplasmic reticulum (ER) stress, and E/I imbalance as well as the contribution to cognitive deficit. In this review, we will focus on the overlapping mechanisms between GE and AD, the insights from mutations affecting GABA_A receptors, and GABA transporter 1. We will detail mechanisms of E/I imbalance and the toxic epileptiform generation in AD, and the complex interplay between ER stress, impaired membrane protein trafficking, and synaptic physiology in both GE and AD.

State-Dependent Regulation of Cortical Processing Speed via Gain Modulation

To thrive in dynamic environments, animals must be capable of rapidly and flexibly adapting behavioral responses to a changing context and internal state. Examples of behavioral flexibility include faster stimulus responses when attentive and slower responses when distracted. Contextual or state-dependent modulations may occur early in the cortical hierarchy and may be implemented via top-down projections from corticocortical or neuromodulatory pathways. However, the computational mechanisms mediating the effects of such projections are not known. Here, we introduce a theoretical framework to classify the effects of cell type-specific top-down perturbations on the information processing speed of cortical circuits. Our theory demonstrates that perturbation effects on stimulus processing can be predicted by intrinsic gain modulation, which controls the timescale of the circuit dynamics. Our theory leads to counterintuitive effects, such as improved performance with increased input variance. We tested the model predictions using large-scale electrophysiological recordings from the visual hierarchy in freely running mice, where we found that a decrease in single-cell intrinsic gain during locomotion led to an acceleration of visual processing. Our results establish a novel theory of cell type-specific perturbations, applicable to top-down modulation as well as optogenetic and pharmacological manipulations. Our theory links connectivity, dynamics, and information processing via gain modulation.SIGNIFICANCE STATEMENT To thrive in dynamic environments, animals adapt their behavior to changing circumstances and different internal states. Examples of behavioral flexibility include faster responses to sensory stimuli when attentive and slower responses when distracted. Previous work suggested that contextual modulations may be implemented via top-down inputs to sensory cortex coming from higher brain areas or neuromodulatory pathways. Here, we introduce a theory explaining how the speed at which sensory cortex processes incoming information is adjusted by changes in these top-down projections, which control the timescale of neural activity. We tested our model predictions in freely running mice, revealing that locomotion accelerates visual processing. Our theory is applicable to internal modulation as well as optogenetic and pharmacological manipulations and links circuit connectivity, dynamics, and information processing.

Interneuron development and dysfunction

Understanding excitation and inhibition balance in the brain begins with the tale of two basic types of neurons, glutamatergic projection neurons and GABAergic interneurons. The diversity of cortical interneurons is contributed by multiple origins in the ventral forebrain, various tangential migration routes, and complicated regulations of intrinsic factors, extrinsic signals, and activities. Abnormalities of interneuron development lead to dysfunction of interneurons and inhibitory circuits, which are highly associated with neurodevelopmental disorders including schizophrenia, autism spectrum disorders, and intellectual disability. In this review, we mainly discuss recent findings on the development of cortical interneuron and on neurodevelopmental disorders related to interneuron dysfunction.

A Subpopulation of Prefrontal Cortical Neurons Is Required for Social Memory

BACKGROUND

The medial prefrontal cortex (mPFC) is essential for social behaviors, yet whether and how it encodes social memory remains unclear.

METHODS

We combined whole-cell patch recording, morphological analysis, optogenetic/chemogenetic manipulation, and the TRAP (targeted recombination in active populations) transgenic mouse tool to study the social-associated neural populations in the mPFC.

RESULTS

Fos-TRAPed prefrontal social-associated neurons are excitatory pyramidal neurons with relatively small soma sizes and thin-tufted apical dendrite. These cells exhibit intrinsic firing features of dopamine D₁ receptor-like neurons, show persisting firing pattern after social investigation, and project dense axons to nucleus accumbens. In behaving TRAP mice, selective inhibition of prefrontal social-associated neurons does not affect social investigation but does impair subsequent social recognition, whereas optogenetic reactivation of their projections to the nucleus accumbens enables recall of a previously encountered but "forgotten" mouse. Moreover, chemogenetic activation of mPFC-to-nucleus accumbens projections ameliorates MK-801-induced social memory impairments.

CONCLUSIONS

Our results characterize the electrophysiological and morphological features of social-associated neurons in the mPFC and indicate that these Fos-labeled, social-activated prefrontal neurons are necessary and sufficient for social memory.

Amyloid-β Protein Precursor Deficiency Changes Neuronal Electrical Activity and Levels of Mitochondrial Proteins in the Medial Prefrontal Cortex

BACKGROUND

Neuropathological features of Alzheimer's disease are characterized by the deposition of amyloid-β (Aβ) plaques and impairments in synaptic activity and memory. However, we know little about the physiological role of amyloid-β protein precursor (AβPP) from which Aβ derives.

OBJECTIVE

Evaluate APP deficiency induced alterations in neuronal electrical activity and mitochondrial protein expression.

METHODS

Utilizing electrophysiological, biochemical, pharmacological, and behavioral tests, we revealed aberrant local field potential (LFP), extracellular neuronal firing and levels of mitochondrial proteins.

RESULT

We show that APP knockout (APP-/-) leads to increased gamma oscillations in the medial prefrontal cortex (mPFC) at 1-2 months old, which can be restored by baclofen (Bac), a γ-aminobutyric acid type B receptor (GABABR) agonist. A higher dose and longer exposure time is required for Bac to suppress neuronal firing in APP-/- mice than in wild type animals, indicating enhanced GABABR mediated activity in the mPFC of APP-/- mice. In line with increased GABABR function, the glutamine synthetase inhibitor, L-methionine sulfonate, significantly increases GABABR levels in the mPFC of APP-/- mice and this is associated with a significantly lower incidence of death. The results suggest that APP-/- mice developed stronger GABABR mediated inhibition. Using HEK 293 as an expression system, we uncover that AβPP functions to suppress GABABR expression. Furthermore, APP-/- mice show abnormal expression of several mitochondrial proteins.

CONCLUSION

APP deficiency leads to both abnormal network activity involving defected GABABR and mitochondrial dysfunction, suggesting critical role of AβPP in synaptic and network function.

Dynorphin/Kappa-Opioid Receptor System Modulation of Cortical Circuitry

Cortical circuits control a plethora of behaviors, from sensation to cognition. The cortex is enriched with neuropeptides and receptors that play a role in information processing, including opioid peptides and their cognate receptors. The dynorphin (DYN)/kappa-opioid receptor (KOR) system has been implicated in the processing of sensory and motivationally-charged emotional information and is highly expressed in cortical circuits. This is important as dysregulation of DYN/KOR signaling in limbic and cortical circuits has been implicated in promoting negative affect and cognitive deficits in various neuropsychiatric disorders. However, research investigating the role of this system in controlling cortical circuits and computations therein is limited. Here, we review the (1) basic anatomy of cortical circuits, (2) anatomical architecture of the cortical DYN/KOR system, (3) functional regulation of cortical synaptic transmission and microcircuit function by the DYN/KOR system, (4) regulation of behavior by the cortical DYN/KOR system, (5) implications for the DYN/KOR system for human health and disease, and (6) future directions and unanswered questions for the field. Further work elucidating the role of the DYN/KOR system in controlling cortical information processing and associated behaviors will be of importance to increasing our understanding of principles underlying neuropeptide modulation of cortical circuits, mechanisms underlying sensation and perception, motivated and emotional behavior, and cognition. Increased emphasis in this area of study will also aid in the identification of novel ways to target the DYN/KOR system to treat neuropsychiatric disorders.

Neuromodulation of the mind-wandering brain state: the interaction between neuromodulatory tone, sharp wave-ripples and spontaneous thought

Mind-wandering has become a captivating topic for cognitive neuroscientists. By now, it is reasonably well described in terms of its phenomenology and the large-scale neural networks that support it. However, we know very little about what neurobiological mechanisms trigger a mind-wandering episode and sustain the mind-wandering brain state. Here, we focus on the role of ascending neuromodulatory systems (i.e. acetylcholine, noradrenaline, serotonin and dopamine) in shaping mind-wandering. We advance the hypothesis that the hippocampal sharp wave-ripple (SWR) is a compelling candidate for a brain state that can trigger mind-wandering episodes. This hippocampal rhythm, which occurs spontaneously in quiescent behavioural states, is capable of propagating widespread activity in the default network and is functionally associated with recollective, associative, imagination and simulation processes. The occurrence of the SWR is heavily dependent on hippocampal neuromodulatory tone. We describe how the interplay of neuromodulators may promote the hippocampal SWR and trigger mind-wandering episodes. We then identify the global neuromodulatory signatures that shape the evolution of the mind-wandering brain state. Under our proposed framework, mind-wandering emerges due to the interplay between neuromodulatory systems that influence the transitions between brain states, which either facilitate, or impede, a wandering mind. This article is part of the theme issue 'Offline perception: voluntary and spontaneous perceptual experiences without matching external stimulation'.

Inhibitory stabilization and cortical computation

Neuronal networks with strong recurrent connectivity provide the brain with a powerful means to perform complex computational tasks. However, high-gain excitatory networks are susceptible to instability, which can lead to runaway activity, as manifested in pathological regimes such as epilepsy. Inhibitory stabilization offers a dynamic, fast and flexible compensatory mechanism to balance otherwise unstable networks, thus enabling the brain to operate in its most efficient regimes. Here we review recent experimental evidence for the presence of such inhibition-stabilized dynamics in the brain and discuss their consequences for cortical computation. We show how the study of inhibition-stabilized networks in the brain has been facilitated by recent advances in the technological toolbox and perturbative techniques, as well as a concomitant development of biologically realistic computational models. By outlining future avenues, we suggest that inhibitory stabilization can offer an exemplary case of how experimental neuroscience can progress in tandem with technology and theory to advance our understanding of the brain.