Dendritic spines are ubiquitous morpho-functional elements of excitatory synaptic transmission and plasticity. Whether axo-spinous communication exists in the thalamus is presently unclear. Here we find that layer 5 (L5) corticothalamic terminals arising from frontal cortices selectively target thalamic spines with classical head and neck dimensions. The synapses of the L5 axo-spinous contacts were enriched in GluR1 subunit of AMPA receptor and their size correlated with their spine head volume. Synaptic stimulation of thalamic spines resulted in compartmentalized Ca2+ responses similar to that of hippocampal spines. Optogenetic activation of the frontal L5-thalamus pathway had strong impact on thalamic spiking even at high stimulation frequencies and could selectively recruit a subnetwork of thalamic cells in a behavioral state dependent manner. Optogenetic interference with the small L5-thalamic pathway perturbed motor learning. Our study reveals a hitherto unrecognized, effective top-down communication channel between the frontal cortex and thalamus that involves highly variable axo-spinous contacts

A single exposure to a stressful event can result in enduring changes in behaviour. Long-term modifications in neuronal networks induced by stress are well explored but the initial steps leading to these alterations remain incompletely understood. In this study, we found that acute stress exposure triggers an immediate increase in the firing activity of calretinin-positive neurons in the paraventricular thalamic nucleus (PVT/CR+) that persists for several days in mice. This increase in activity had a causal role in stress-induced changes in spontaneous behaviour. Attenuating PVT/CR+ neuronal activity for only 1 h after the stress event rescued both the protracted increase in PVT/CR+ firing rate and the stress-induced behavioural alterations. Activation of the key forebrain targets (basolateral amygdala, prelimbic cortex, and nucleus accumbens) that mediate defensive behaviour has also been reduced by this post-stress inhibition. Reduction of PVT/CR+ cell activity 5 days later remained still effective in ameliorating stress-induced changes in spontaneous behaviour. The results demonstrate a critical role of the prolonged, post-stress changes in firing activity of PVT/CR+ neurons in shaping the behavioural changes associated with stress. Our data proposes a therapeutic window for intervention in acute stress-related disorders, offering potential avenues for targeted treatment strategies.

COVID-19 is associated with diverse neurological abnormalities, but the underlying mechanisms are unclear. We hypothesized that microglia, the resident immune cells of the brain, are centrally involved in this process. To study this, we developed an autopsy platform allowing the integration of molecular anatomy, protein and mRNA datasets in postmortem mirror blocks of brain and peripheral organ samples from cases of COVID-19. We observed focal loss of microglial P2Y12R, CX3CR1-CX3CL1 axis deficits and metabolic failure at sites of virus-associated vascular inflammation in severely affected medullary autonomic nuclei and other brain areas. Microglial dysfunction is linked to mitochondrial injury at sites of excessive synapse and myelin phagocytosis and loss of glutamatergic terminals, in line with proteomic changes of synapse assembly, metabolism and neuronal injury. Furthermore, regionally heterogeneous microglial changes are associated with viral load and central and systemic inflammation related to interleukin (IL)-1 or IL-6 via virus-sensing pattern recognition receptors and inflammasomes. Thus, SARS-CoV-2-induced inflammation might lead to a primarily gliovascular failure in the brain, which could be a common contributor to diverse COVID-19-related neuropathologies.

Movement and locomotion are controlled by large neuronal circuits like the cortex-basal ganglia (BG)-thalamus loop. Besides the inhibitory thalamic output, the BG directly control movement via specialized connections with the brainstem. Whether other parallel loops with similar logic exist is presently unclear. Here, we demonstrate that the secondary motor and cingulate cortices (M2/Cg) target and strongly control the activity of glycine transporter 2-positive (GlyT2+) cells in the pontine reticular formation (PRF). In turn, PRF/GlyT2+ cells project to and powerfully inhibit the intralaminar/parafascicular nuclei of the thalamus (IL/Pf). M2/Cg cells co-innervate PRF/GlyT2+ cells and the IL/Pf. Thalamus-projecting PRF/GlyT2+ cells target ipsilateral subcortical regions distinct from BG targets. Activation of the thalamus-projecting PRF/GlyT2+ cells leads to contralateral turning. These results demonstrate that the PRF is part of a cortico-subcortical loop that regulates motor activity parallel to BG circuits. The cortico-PRF-thalamus loop can control turning synergistically with the BG loops via distinct descending pathways.

The paraventricular thalamic nucleus (PVT) integrates subcortical signals related to arousal, stress, addiction, and anxiety with top-down cortical influences. Increases or decreases in PVT activity exert profound, long-lasting effects on behavior related to motivation, addiction, and homeostasis. Yet the sources of its subcortical excitatory and inhibitory afferents, their distribution within the PVT, and their integration with layer-specific cortical inputs remain unclear. Using transgenic male and female mice selective for GABAergic and glutamatergic neurons, or for different cortical layers, we found that the input organization of PVT is unique among thalamic nuclei. PVT received subcortical GABAergic and glutamatergic inputs from multiple, distinct hypothalamic and brainstem regions. Most regions provided either excitatory or inhibitory afferents; however, subcortical inputs with dual components have also been found. Most of these subcortical inputs selectively targeted the core region of the PVT that contained large number of densely packed calretinin-positive (CR+) neurons. Cortical afferents to PVT displayed layer-specific segregation. Layer 5 neurons of the medial prefrontal cortex preferentially innervated the CR+ core, whereas layer 6 input was more abundant in the transition zone between PVT and the mediodorsal nucleus. These findings demonstrate extensive convergence of excitatory and inhibitory inputs from diverse subcortical sources, selectively, in a sharply delineated CR+ core region of PVT which is also under strong top-down control from layer 5. This unique organization may explain why the CR+ PVT core serves as a critical bottleneck in the subcortex-cortex communication involved in affective behavior.

The enzyme dUTPase has an essential role in maintaining genomic integrity. In mouse, nuclear and mitochondrial isoforms of the enzyme have been described. Here we present the isoform-specific mRNA expression levels in different murine organs during development using RT-qPCR. In this study, we analyzed organs of 14.5-day embryos and of postnatal 2-, 4-, 10-week- and 13-month-old mice. We demonstrate organ-, sex- and developmental stage-specific differences in the mRNA expression levels of both isoforms. We found high mRNA expression level of the nuclear isoform in the embryo brain, and the expression level remained relatively high in the adult brain as well. This was surprising, since dUTPase is known to play an important role in proliferating cells, and mass production of neural cells is completed by adulthood. Thus, we investigated the pattern of the dUTPase protein expression specifically in the adult brain with immunostaining and found that dUTPase is present in the germinative zones, the subventricular and the subgranular zones, where neurogenesis occurs and in the rostral migratory stream where neuroblasts migrate to the olfactory bulb. These novel findings suggest that dUTPase may have a role in cell differentiation and indicate that accurate dTTP biosynthesis can be vital, especially in neurogenesis.

Unsupervised segmentation in biological and non-biological images is only partially resolved. Segmentation either requires arbitrary thresholds or large teaching datasets. Here, we propose a spatial autocorrelation method based on Local Moran’s I coefficient to differentiate signal, background, and noise in any type of image. The method, originally described for geoinformatics, does not require a predefined intensity threshold or teaching algorithm for image segmentation and allows quantitative comparison of samples obtained in different conditions. It utilizes relative intensity as well as spatial information of neighboring elements to select spatially contiguous groups of pixels. We demonstrate that Moran’s method outperforms threshold-based method in both artificially generated as well as in natural images especially when background noise is substantial. This superior performance can be attributed to the exclusion of false positive pixels resulting from isolated, high intensity pixels in high noise conditions. To test the method’s power in real situation, we used high power confocal images of the somatosensory thalamus immunostained for Kv4.2 and Kv4.3 (A-type) voltage-gated potassium channels in mice. Moran’s method identified high-intensity Kv4.2 and Kv4.3 ion channel clusters in the thalamic neuropil. Spatial distribution of these clusters displayed strong correlation with large sensory axon terminals of subcortical origin. The unique association of the special presynaptic terminals and a postsynaptic voltage-gated ion channel cluster was confirmed with electron microscopy. These data demonstrate that Moran’s method is a rapid, simple image segmentation method optimal for variable and high noise conditions.

Widespread cortical accumulation of misfolded pathological tau proteins (ptau) in the form of paired helical filaments is a major hallmark of Alzheimer’s disease. Subcellular localization of ptau at various stages of disease progression is likely to be informative of the cellular mechanisms involving its spread. Here, we found that the density of ptau within several distinct rostral thalamic nuclei in post-mortem human tissue (n = 25 cases) increased with the disease stage, with the anterodorsal nucleus (ADn) consistently being the most affected. In the ADn, ptau-positive elements were present already in the pre-cortical (Braak 0) stage. Tau pathology preferentially affected the calretinin-expressing subpopulation of glutamatergic neurons in the ADn. At the subcellular level, we detected ptau immunoreactivity in ADn cell bodies, dendrites, and in a specialized type of presynaptic terminal that expresses vesicular glutamate transporter 2 (vGLUT2) and likely originates from the mammillary body. The ptau-containing terminals displayed signs of degeneration, including endosomal/lysosomal organelles. In contrast, corticothalamic axon terminals lacked ptau. The data demonstrate the involvement of a specific cell population in ADn at the onset of the disease. The presence of ptau in subcortical glutamatergic presynaptic terminals supports hypotheses about the transsynaptic spread of tau selectively affecting specialized axonal pathways.

The Papez circuit comprises several interconnected brain areas important for spatial navigation and orientation. An early symptom of dementia is disorientation, suggesting that brain regions responsible for providing a sense of direction are adversely affected. We examined post-mortem human tissue from cases with no cognitive impairment, mild cognitive impairment, and Alzheimer’s disease. A key part of the Papez circuit, the anterodorsal thalamic nucleus (ADn), contained a high density of misfolded pathological Tau (pTau) at all disease stages, including in control cases. Moreover, pTau preferentially accumulated in calretinin-expressing neurons. At the subcellular level, we detected pTau filaments in ADn cell bodies, dendrites, and in specialized presynaptic terminals. Large vesicular-glutamate-transporter-2-containing terminals from the lateral mammillary nucleus, rather than corticothalamic terminals, preferentially contained pTau, suggesting that Tau crosses specific synapses within the Papez circuit. As the ADn contains a high density of head direction cells, pTau may degrade the processing of orientation signals, explaining why people become disorientated years-to-decades before memory deficits emerge.

COVID-19 is associated with diverse neurological abnormalities, which predict poor outcome in patients. However, the mechanisms whereby infection-induced inflammation could affect complex neuropathologies in COVID-19 are unclear. We hypothesized that microglia, the resident immune cells of brain, are centrally involved in this process. To study this, we developed an autopsy platform allowing the integration of molecular anatomy-, protein- and mRNA data sets in post-mortem mirror blocks of brain and peripheral organ samples from COVID-19 cases. Nanoscale microscopy, single-cell RNA sequencing and analysis of inflammatory and metabolic signatures revealed distinct mechanisms of microglial dysfunction associated with cerebral SARS-CoV-2 infection. We observed focal loss of microglial P2Y12R at sites of virus-associated vascular inflammation together with dysregulated microglia-vascular-astrocyte interactions, Cx3Cr1-fractalkine axis deficits and mitochondrial failure in severely affected medullary autonomic nuclei and other brain areas. Microglial dysfunction occurs at sites of excessive synapse- and myelin phagocytosis and loss of glutamatergic terminals. While central and systemic viral load is strongly linked in individual patients, the regionally heterogenous microglial reactivity in the brain correlated with the extent of central and systemic inflammation related to IL-1 / IL-6 via virus-sensing pattern recognition receptors (PRRs) and inflammasome activation pathways. Thus, SARS-CoV-2-induced central and systemic inflammation might lead to a primarily glio-vascular failure in the brain, which could be a common contributor to diverse COVID-19-related neuropathologies.

Corticothalamic pathways, responsible for the top-down control of the thalamus, have a canonical organization such that every cortical region sends output from both layer 6 (L6) and layer 5 (L5) to the thalamus. Here we demonstrate a qualitative, region-specific difference in the organization of mouse corticothalamic pathways. Specifically, L5 pyramidal cells of the frontal cortex, but not other cortical regions, establish monosynaptic connections with the inhibitory thalamic reticular nucleus (TRN). The frontal L5-TRN pathway parallels the L6-TRN projection but has distinct morphological and physiological features. The exact spike output of the L5-contacted TRN cells correlated with the level of cortical synchrony. Optogenetic perturbation of the L5-TRN connection disrupted the tight link between cortical and TRN activity. L5-driven TRN cells innervated thalamic nuclei involved in the control of frontal cortex activity. Our data show that frontal cortex functions require a highly specialized cortical control over intrathalamic inhibitory processes.

Abstract Unlike cortical neurons, thalamocortical neurons are not connected to each other via a rich recurrant collateral system. As a consequence, thalamic computation is dominated by the integration of extrinsic inputs, not by the interactions among intrinsic elements. The thalamus represents a critical bottleneck in the communication between the cortex and the rest of the brain. The cortex provides by far the most numerous inputs to the thalamus but a variety of other afferents arise from almost the entire neuraxis. Axon terminals in the thalamus display substantial morphological heterogeneity and associated variability in short-term dynamics of neurotransmission. Axon terminals having different origins, transmitters, and synapse numbers can be integrated in many distinct ways in different thalamic territories. Thus there is no canonical input integration in the thalamus. Instead, a rich complexity of uni- and multisynaptic terminals in the thalamus suggests that a core feature of thalamic afferents is the heterogeneous dynamics of synaptic transmission associated with inputs having different number of synapses.

An organism can be aroused in many different manners. Here, Wang el al.1 demonstrate that a multisensory thalamic region can mediate spontaneous, sensory, and defensive arousal via its widespread projection, which indicates a non-canonical function of this area.

Full text Figures and data Side by side Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Oscillations of extracellular voltage, reflecting synchronous, rhythmic activity in large populations of neurons, are a ubiquitous feature in the mammalian brain, and are thought to subserve important, if not fully understood roles in normal and abnormal brain function. Oscillations at different frequency bands are hallmarks of specific brain and behavioral states. At the higher end of the spectrum, 150-200 Hz ripples occur in the hippocampus during slow-wave sleep, and ultrafast (400-600 Hz) oscillations arise in the somatosensory cortices of humans and several other mammalian species in response to peripheral nerve stimulation or punctate sensory stimuli. Here we report that brief optogenetic activation of thalamocortical axons, in brain slices from mouse somatosensory (barrel) cortex, elicited in the thalamorecipient layer local field potential (LFP) oscillations which we dubbed "ripplets". Ripplets originated in the postsynaptic cortical network and consisted of a precisely repeating sequence of 2‑5 negative transients, closely resembling hippocampal ripples but, at ~400 Hz, over twice as fast. Fast-spiking (FS) inhibitory interneurons fired highly synchronous 400 Hz spike bursts entrained to the LFP oscillation, while regular-spiking (RS), excitatory neurons typically fired only 1-2 spikes per ripplet, in antiphase to FS spikes, and received synchronous sequences of alternating excitatory and inhibitory inputs. We suggest that ripplets are an intrinsically generated cortical response to a strong, synchronous thalamocortical volley, and could provide increased bandwidth for encoding and transmitting sensory information. Importantly, optogenetically induced ripplets are a uniquely accessible model system for studying synaptic mechanisms of fast and ultrafast cortical and hippocampal oscillations. Editor's evaluation This study examines the potential neuronal basis for generating ultrafast oscillations (250-600Hz) in the cortex evoked by optogenetic stimulation of thalamocortical afferents in ex vivo brain slices. The authors proposed that these oscillations correlate with sensory stimulation and may be relevant for the perception of relevant sensory inputs and they combined ex-vivo whole-cell patch-clamp recordings, local field potential (LFP) recordings, and optogenetic activation of thalamic afferents to generate ripple-like extracellular waveforms in the cortex, referred to as "ripplets." The authors described the sequences of RS and FS neuron discharge and how they phase-locked to the ripplet, providing a model for the cellular mechanism generating the ripplet. The authors also cited the literature about ultrafast oscillations and carefully compared the novel ripplets to the well-known hippocampal ripples. https://doi.org/10.7554/eLife.82412.sa0 Decision letter Reviews on Sciety eLife's review process Introduction A ubiquitous feature of neuronal networks, in the brains of multiple mammalian species throughout the evolutionary tree, is their propensity to engage in large-scale, synchronous and rhythmic patterns of electrical activity, reflected as oscillations in the local field potential (LFP) (Buzsáki et al., 2013). LFP oscillations occur at frequencies spanning four or more orders of magnitude, with oscillations at different frequency bands often nested together and co-modulating each other (Steriade, 2006). Different oscillatory patterns are hallmarks of specific brain and behavioral states; for example, theta rhythms (4–9 Hz) are typical of exploratory behavior, sleep spindles (~10 Hz) occur during certain sleep stages, and gamma oscillations (30–90 Hz) are the hallmark of cognitive activity and sensory processing (Lopes da Silva, 2013; Singer, 2018). Even faster, transient 150–200 Hz 'ripples' in the pyramidal cell layer of the hippocampus occur during quiet immobility and slow-wave sleep (Bragin et al., 1999a; Bragin et al., 1999b; Csicsvari et al., 1999a), and are thought to be critical for memory consolidation (Girardeau and Lopes-Dos-Santos, 2021). Extracellular voltage signals, including oscillations, are driven by currents entering (sinks) or leaving (sources) the intracellular brain compartment, and reflect both synaptic activity and action potential firing (Mitzdorf, 1985; Liebe et al., 2011; Buzsáki et al., 2012; Schomburg et al., 2012; Reimann et al., 2013; Pesaran et al., 2018). Indeed, excitatory and inhibitory neurons in the hippocampus fire at precise phases of theta, gamma, and ripple oscillations in a subtype-specific manner (Klausberger et al., 2003; Klausberger and Somogyi, 2008; Cardin, 2018), and synaptic interactions between excitatory and inhibitory cells are implicated in the generation of oscillations across the spectrum, although the details may vary between frequency bands, between different brain areas, and even between cortical layers (Kopell et al., 2010; Wang, 2010; Whittington et al., 2018). At the high end of the frequency range, ultrafast, 250–600 Hz oscillations have been observed in both hippocampus and neocortex. In the hippocampus, such oscillations (often called 'fast ripples') are almost exclusively paroxysmal (reviewed in Gulyás and Freund, 2015; Lévesque and Avoli, 2019), although their underlying cellular and network mechanisms remain under debate (Dzhala and Staley, 2004; Foffani et al., 2007; Engel et al., 2009). In the neocortex, however, non-paroxysmal 600 Hz wavelets have been repeatedly observed in scalp recordings from human subjects in response to peripheral nerve stimulation (reviewed in Curio, 2000). Similar 600 Hz LFP oscillations in response to peripheral nerve stimulation were also observed in monkeys and piglets (Ikeda et al., 2002; Baker et al., 2003). Notably, LFP oscillations in the 300–500 Hz range were also consistently observed in rats in response to whisker deflections or to auditory clicks (Jones and Barth, 1999; Jones et al., 2000; Jones and Barth, 2002); however, their underlying mechanisms and functional significance remain largely unexplored. We recorded ex vivo extracellular and intracellular activity evoked by optogenetic activation of channelrhodopsin (ChR2)-expressing thalamocortical axons in thalamorecipient layers of mouse somatosensory (barrel) cortex. We report here that brief light pulses (as short as 1 ms) elicited a transient (<25 ms), highly reproducible LFP oscillation or 'ripplet,' which closely resembled hippocampal ripples but, at ~400 Hz, was more than twice as fast. Paired whole-cell recordings from fast-spiking (FS) inhibitory interneurons and regular-spiking (RS) excitatory cells revealed precise phase relationships between FS spikes, RS spikes and ripplets, and between FS spikes and sequences of alternating EPSCs and IPSCs in RS cells, suggesting that phasic RS→RS and RS→FS excitation underlies ripplet generation. Ripplets may be a stereotypical cortical response to strong, punctate stimuli that activate thalamocortical afferents with a high degree of synchrony and could be used by the cortex to encode specific features of a sensory event. Importantly, optogenetically evoked ex vivo ripplets are a uniquely accessible model for studying the synaptic basis of fast and ultrafast network oscillations, including hippocampal ripples. Results We recorded extracellular and intracellular responses in layer 4 (L4) of barrel cortex brain slices, evoked by widefield optogenetic activation of thalamocortical synapses. Slices were prepared from brains of 3–7-postnatal-week-old mice of both sexes expressing ChR2 in the somatosensory thalamus (see 'Materials and methods'). We applied brief light pulses through the epi-illumination path of the microscope, likely activating most of the thalamocortical axons and terminals within the surrounding L4 barrel. To verify that ChR2 in L4 was expressed by thalamocortical and not intracortical axons, we imaged and quantified the extent of tdTomato reporter expression in the cortex (Figure 1—figure supplement 1). Within the barrel cortex, there was a sparse population of reporter-expressing cell bodies in L2/3 and L5, but virtually none within L4 (Figure 1—source data 1). Since the thalamus and L4 itself are the two main sources of excitatory input to L4 neurons (Feldmeyer et al., 1999; Petersen and Sakmann, 2000; Lefort et al., 2009), we conclude that the network activity we observed was predominantly evoked by thalamocortical activation, although we cannot rule out that a small number of corticothalamic neurons in L6, which send axonal collaterals to L4 (White and Keller, 1987; Kumar and Ohana, 2008), also expressed ChR2 and provided a minor excitatory contribution. Optogenetic activation of thalamocortical axons evoked ripple-like extracellular wavelets A brief light pulse (1–10 ms, typically 2–5 ms) evoked a stereotypical field potential wavelet in L4 (Figure 1A). In this and all other electrophysiological traces, the light-evoked response is represented by a 25-ms-long record beginning 1 ms before light onset, and the timing of the light pulse is indicated by a cyan bar above or below the trace. At all stimulus durations, the LFP waveform consisted of an initial low-amplitude negative component (solid arrowhead), followed by 2–5 (typically 3–4) larger negative transients (hollow arrowheads) riding on a slow, positive-going envelope. In the example slice in Figure 1A, the fourth transient was barely discernible in response to a 1 ms light pulse (left), but became noticeably larger with a 5 ms pulse (right). A low-amplitude negative shoulder or 'hump' (arrow) was often observed on the rising phase of the first large transient. These optogenetically evoked waveforms were reminiscent of hippocampus ripples, LFP wavelets recorded from the cell body layer of areas CA1 or CA3; we therefore named them RIPPle-Like Extracellular Transients or 'ripplets'. Figure 1 with 2 supplements see all Download asset Open asset Local field potential oscillations (ripplets) induced by optogenetic thalamocortical activation. (A) Averaged ripplets evoked in an example slice; light pulses (1 and 5 ms duration for left and right traces, respectively) represented by cyan bars above traces, and apply to (A–C). Solid arrowhead indicates the early presynaptic TC volley; hollow arrowheads indicate the peaks of the postsynaptic components; the arrow points to the hump on the rising phase of the first postsynaptic transient. (B) The iGluR-independent (i.e. presynaptic) component: same stimuli as in (A) but in the presence of CNQX + APV. Arrowheads indicate a single presynaptic volley in response to a 1 ms stimulus and three volleys (of decreasing amplitude and coherence) with a 5 ms stimulus. (C) The difference between (A) and (B), revealing the purely postsynaptic component of the ripplet. Symbols as in (A). (D) Superposition of the presynaptic (blue, left) and postsynaptic (red, right) components of the ripplets at three stimulus durations. (E) Superposition of presynaptic components (blue, left) and ripplets (red, right) from slices from four different animals, normalized to the same maximal amplitudes to facilitate comparison of their time course. (F) Histogram of peak times (from light onset) of presynaptic volleys (left) and of ripplet transients (right) in slices from 8 and 11 animals, respectively (5 animals are included in both plots). Presynaptic volleys peaked at 2.3 ± 0.07, 6.0 ± 0.22, and 10.8 ± 0.24 ms from light onset. Ripplet transients peaked at 4.4 ± 0.11, 6.5 ± 0.26, 8.9 ± 0.30, and 11.9 ± 0.38 ms. (G) The histograms from (F) modeled as normal distributions, with the same mean, SD and integral as the corresponding component. Left, presynaptic volleys; right, ripplet transients (red) with presynaptic volleys overlaid as gray outlines for comparison. Figure 1—source data 1 Counts of Cre-expressing neurons in the KN282 mouse. Four male mice (4–6 weeks old) were deeply anesthetized with 2.5% Avertin and transcardially perfused with saline followed by 50 ml 4% paraformaldehyde (PFA). Brains were removed, post-fixed in 4% PFA at room temperature for 4 hr, stored for at least 72 hr in 30% sucrose solution in phosphate buffered saline (PBS) at 4°C, and sectioned coronally on a freezing stage sliding microtome. For imaging, five 60 μm sections were sampled per brain at 240 μm intervals, in total extending 800–2000 μm posterior to bregma, as determined by comparison with the Paxinos and Franklin atlas (Paxinos and Franklin, The Mouse Brain in Stereotaxic Coordinates, 4th edition, Academic Press 2012). Confocal image stacks were taken with a ×10 objective at 2.5 μm Z-steps on a Zeiss LSM 710 or a Nikon A1R confocal microscope. Labeled cells were counted by visual inspection of the full stack and summed per area over the five sections from each animal; counts thus represent the number of cells in a composite 300 μm section. Areal boundaries follow the designations in the Paxinos and Franklin atlas. https://cdn.elifesciences.org/articles/82412/elife-82412-fig1-data1-v2.xlsx Download elife-82412-fig1-data1-v2.xlsx Synchronous spike volleys in thalamocortical terminals generate relatively large current sinks in layer 4, and these are discernible as negative transients in the LFP (Morin and Steriade, 1981; Agmon and Connors, 1991; Swadlow et al., 2002; Bruno et al., 2003). To reveal any presynaptic thalamocortical components in the ripplets, we blocked fast ionotropic glutamate receptors (iGluRs) pharmacologically with CNQX + APV. This eliminated the large negative transients but left unchanged the early negative component (Figure 1B, left panel, solid arrowhead), which therefore reflected the initial thalamocortical volley. Interestingly, the presynaptic response to 5 ms or longer light pulses revealed one or two additional presynaptic transients of decreasing amplitudes (Figure 1B, right panel, solid arrowheads), suggesting that thalamocortical axons fired additional volleys of decreasing magnitude and coherence. Subtracting the waveforms in CNQX + APV from those in control ACSF isolated the postsynaptic component of the response (Figure 1C). Superposition of the isolated presynaptic (Figure 1D, left) and postsynaptic (Figure 1D, right) contributions at 1, 5, and 10 ms stimulus durations demonstrated that prolonging the light pulse added late components to the response waveform but did not affect the timing of the earlier peaks. To confirm that the iGluR-independent signals reflected action potentials, we added TTX to the bath, which blocked all remaining responses except for a small square waveform (Figure 1—figure supplement 2). The latter was coincident with the light pulse and was most likely an electrical or optoelectrical artifact, and is subtracted from all traces in Figure 1. Ripplet waveforms, as well as their isolated presynaptic components, were remarkably consistent between animals in their temporal structure, as illustrated in Figure 1E by the superposition of responses in slices from four different animals, drawn at different vertical scales to facilitate time course comparison. To quantify the variability in the temporal structure of ripplets between animals, we measured the peak times (relative to light onset) of the isolated presynaptic volleys (Figure 1F, left; eight slices from eight animals tested in CNQX + APV) and of the postsynaptic transients (Figure 1F, right; 11 slices from 11 animals with ripplet amplitudes ≥0.5 mV). Standard deviations (SDs) of the presynaptic volleys ranged from 0.2 to 0.6 ms, and SDs of ripplet peaks ranged from 0.3 to 1.0 ms. Calculated over the three volleys, the presynaptic axons appeared to fire at an average frequency ( = 1/average ISI) of 239 ± 6 Hz. This is consistent with the observed burst frequency in thalamocortical neurons of the same mouse genotype when activated optogenetically ex vivo, which rarely exceeds 300 Hz (Hu and Agmon, 2016, their Figure 4E). Calculated over the four ripplet peaks, ripplet frequency averaged 408 ± 15 Hz, that is, nearly twice as fast as the apparent presynaptic volleys. To facilitate comparison of presynaptic components and ripplets, we modeled the histogram peaks in Figure 1F as Gaussians with the same mean, SD, and integral as that of the respective event (Figure 1G). Overlaying the Gaussians (Figure 1G, right) highlighted the lack of temporal coherence between the presynaptic volleys and ripplets, indicating that ripplets were not a direct, 1:1 response to bursts in thalamocortical axons but were likely generate de novo within the L4 network. Optogenetic stimulation of thalamocortical axons evoked precisely timed FS spike bursts Since ripplets required intact glutamatergic neurotransmission, they most likely reflected population activity within L4; but in which cells? To answer this question, we examined light-induced whole-cell responses in inhibitory FS interneurons and excitatory, RS cells, two subclasses known to receive direct thalamocortical inputs (Agmon and Connors, 1992; Gibson et al., 1999; Porter et al., 2001; Bruno and Simons, 2002; Gabernet et al., 2005; Sun et al., 2006; Cruikshank et al., 2007; Shigematsu et al., 2019). These two subtypes are readily identifiable in slice recordings by their firing patterns and also fall into distinct, nonoverlapping clusters in post hoc analysis of their electrophysiological parameters (Figure 2—figure supplement 1, Figure 2—source data 1). A brief (typically 2–5 ms) light pulse at 60–90% maximal intensity elicited a strong (10–30 mV) depolarization in L4 FS cells, which, in 80% of cells (N = 44 cells from 35 animals), gave rise to a stereotypical burst of 2–7 (typically 3–5) spikes. Representative bursts in an example FS cell, evoked by 2 and 5 ms light pulses, are illustrated in Figure 2A and B. Spiking almost never persisted beyond the illustrated 25 ms time window, although the underlying subthreshold depolarization often lasted for 50 ms or more. We performed detailed analysis on all FS cells that consistently fired bursts of four or more spikes (25 cells from 20 animals). Bursts in this dataset had three distinguishing properties: Very high frequency: Averaged over these 25 cells, the first three interspike intervals (ISIs) were 2.4 ± 0.05, 2.2 ± 0.08, and 2.7 ± 0.13 ms (mean ± SEM), respectively, for an average burst frequency ( = 1/average ISI) of 418 ± 9.5 Hz, not significantly different from the extracellular ripplet frequency (p=0.56). Very high temporal precision: Bursts in a given cell were precisely reproducible, as illustrated in Figure 2A and B by the near-perfect registration of consecutive sweeps, the alignment of spikes in the raster plots and the very low jitter in spike times indicated above the raster plot. In the full dataset, spike jitter averaged 23 ± 4, 63 ± 10, 110 ± 12, and 183 ± 21 μs for the four spikes, which translates to coefficients of variation (CV = SD/mean) of 0.7 ± 0.1%, 1.1 ± 0.2%, 1.4 ± 0.1% and 1.8 ± 0.2%, respectively. This low jitter is also illustrated in Figure 2H and I, in which the vertical extent of the boxed regions indicates 10–90th percentiles of spike time jitter and CV, respectively. High animal-to-animal reproducibility: Across slices from different animals, the distributions of peak spike times for each of the four spikes in the burst was very narrow: 3.1 ± 0.06, 5.5 ± 0.08, 7.7 ± 0.12, and 10.3 ± 0.14 ms after light onset. This is also illustrated in Figure 2H and I, in which the horizontal extent of the boxed regions indicates 10–90th percentile of spike times. Figure 2 with 2 supplements see all Download asset Open asset Precise light-induced spike bursts in fast-spiking (FS) interneurons. (A) Upper panel: spike bursts evoked in an example L4 FS interneuron by 5 ms (blue traces) and 2 ms (red traces) light pulses (cyan bars) at 90% intensity; 10 consecutive sweeps repeated at 8 s intervals at each duration. Arrowhead indicates a late, low-reliability spike evoked by the longer light pulse. Lower panel: raster plots of 20 consecutive bursts from the same neuron, evoked by 2 and 5 ms pulses (red and blue symbols, respectively). Spike time jitters (SD of spike peak times), in μs, are indicated above the raster, in the respective colors. (B) As in (A) but for bursts evoked by 5ms light pulses at 20 and 90% intensities (red and blue traces and symbols, respectively). (C) Average number of spikes/stimulus in seven FS interneurons tested with both 2 and 5 ms light pulses. Medians indicated by horizontal black lines. *p=0.016, sign test. (D) The number of spikes/stimulus fired by 14 FS interneurons tested at 4–5 different light intensities. Error bars indicate SEM. ***p<0.0001; *p=0.02. (E) Jitter of the second spike in the burst, and the first interspike interval (ISI), compared between 5 and 2 ms light pulses, from the dataset of panel (C); n.s., not significant. (F) Same as (E), but comparing the lowest and highest light intensities from the dataset of panel (D). Note significant difference in spike jitter (***p<0.0001) but not ISI. (G) Another example cell stimulated at 90% intensity (upper trace, five superimposed sweeps) and at 20% intensity (lower traces, three single traces and eight superimposed sweeps). Note that at 20% intensity, spikes drop out sporadically and reveal subthreshold excitatory postsynaptic currents (EPSPs) (arrows), with little change in the temporal structure of the burst (vertical guidelines are aligned with the spikes at 90% intensity). (H) Spike times in all 25 FS cells with each spike order in a different color, plotted by its mean time measured from light onset (X axis) and by its jitter (Y axis); crosses indicate medians, boxes indicate 10–90th percentile range. (I) As in (H) but plotted along the Y axis by CV (jitter/mean spike time). (J) Histogram of all average spike times using the same color scheme as in (H); each peak in the histogram is overlaid by a Gaussian with the same mean, SD, and integral (blue curves). (K) The Gaussians from panel (J), representing FS spike bursts, superimposed with the Gaussian from Figure 1G representing thalamocortical spike volleys (blue and red curves, respectively). (L) The Gaussians representing FS spike bursts (blue) and those from Figure 1G representing ripplets (red) are superimposed; note the antiphase relationship between FS spikes and ripplets. Figure 2—source data 1 Electrophysiological parameters of fast-spiking (FS) and regular-spiking (RS) neurons. Analysis was done on a subset of 32 FS interneurons and 25 RS cells in slices from 25 and 22 animals, respectively, 3–6.5 weeks old (14 animals were common to both subsets). A total of eight electrophysiological parameters were analyzed per cell. Single-spike parameters were measured at rheobase (minimal current evoking an action potential). All current steps were 600 ms long. Electrophysiological parameters definitions: Vrest: resting potential upon break-in, with no holding current applied. Vthreshold: the voltage where dv/dt = 5 V/s. Spike height: Spike peak-Vthreshold. Spike width at half-height (SWHH): spike width measured half-way between Vthreshold and spike peak. After Hyperpolarization (AHP): Vthreshold -spike trough. Input Resistance (Rin): the slope of the I-V plot, calculated from 4 to 6 positive and negative subthreshold current steps, at membrane potentials up to ±15 mV from rest. Imax: the maximal current step applied before a noticeable reduction in spike height. Fmax: the maximal steady-state firing frequency, computed as the reciprocal of the average of the last five interspike intervals (ISIs) in a spike train elicited by Imax. https://cdn.elifesciences.org/articles/82412/elife-82412-fig2-data1-v2.xlsx Download elife-82412-fig2-data1-v2.xlsx Since thalamocortical axons also form a lower tier of terminations in L5B (Frost and Caviness, 1980; Herkenham, 1980; Agmon et al., 1993; Meyer et al., 2010), we also tested for occurrence of light-evoked spike bursts in infragranular FS cells. Brief light pulses evoked in L5B FS interneurons spike bursts that were indistinguishable, in spike number and frequency, from those in L4; of 13 cells stimulated with 2 ms light pulses, 11 cells from nine animals fired consistently four spikes/stimulus, at an average frequency of 405 ± 13 Hz (Figure 2—figure supplement 2). In the remainder of this study, we focus on L4. To examine if burst patterns were dependent on stimulus parameters (light duration and intensity), we first compared bursts elicited by 2 and 5 ms light pulses. In the example cell in Figure 2A, a 2 ms stimulus elicited a precisely reproducible four-spike burst (10 superimposed red traces); increasing stimulus duration to 5 ms left the timing and jitter on these four spikes nearly unchanged but occasionally elicited a fifth, long-latency spike (arrowhead) with considerably higher jitter compared to the first four spikes (blue traces; note increased jitter in the raster plot in the lower panel). We refer to such spikes, with higher jitter and occasional failures, as 'low-reliability spikes.' All seven FS cells tested at both stimulus durations fired more spikes in response to a 5 ms compared with 2 ms stimulus, with a median increase of one spike/stimulus (Figure 2C) but with no appreciable change in timing and precision of the earlier spikes, as seen by the identical median values of the second spike jitter and the first ISI at the two stimulus durations (Figure 2E). We then compared the number and precision of spikes in response to different light intensities. Bursts elicited by 20 and 90% intensity, in the same example cell from Figure 2A, are illustrated in Figure 2B. At the lower intensity, there was again one less spike, but in addition the jitter of all spikes except the first was >3× higher. In a subsample of 14 cells tested with 5 ms light pulses at five different intensities (10, 20, 40, 60, and 90% of maximal intensity), the average number of spikes/trial increased from 2.9 at 10% to 4.8 at 90% intensity (Figure 2D). As seen by comparing the second spike jitter and first ISI between at 20 and 90% light pulses (Figure 2F), overall spike timing did not differ between the two intensities (p=0.38) but at lower intensities spikes were much less precise (second spike jitter: 106 ± 13 vs. 43 ± 6 μs, p<0.0001). That ISIs were largely independent of stimulation intensity suggested that the burst pattern was not intrinsically generated in the FS cell but was imposed on it by extrinsic inputs. Consistent with this conclusion, reducing the light level resulted in some cells in spikes dropping out sporadically, revealing apparent subthreshold EPSPs that remained aligned with the temporal structure of the burst (Figure 2G, guidelines). That EPSPs elicit FS spikes with such high precision is consistent with the properties of excitatory synaptic inputs onto FS interneurons in neocortex and hippocampus (Geiger et al., 1997; Galarreta and Hestrin, 2001). What were the temporal relationships between FS spike bursts, thalamocortical volleys, and ripplets? We plotted all FS spike times in our dataset as a histogram and then modeled each spike order as a Gaussian (Figure 2J). Overlaying Gaussians representing FS spikes with those representing thalamocortical volleys from Figure 1G (Figure 2K) revealed a close temporal relationship between the first FS spike and the initial thalamocortical volley, with the postsynaptic spike following the presynaptic volley at a latency of 0.8 ms, consistent with a monosynaptic response. Later FS spikes, however, actually preceded the closest presynaptic volley, consistent with the conclusion above that the additional thalamocortical volleys did not drive the postsynaptic bursts in a 1:1 manner. In contrast, overlaying the distributions of FS spikes and LFP transients (Figure 2L) revealed a 1:1 relationship between these events, however, with the two oscillations almost exactly out of phase: each ripplet transient lagged behind the corresponding FS spike peak by 0.44–0.54 of a cycle, translating to 1.0–1.3 ms. FS cells synchronized their firing with near-zero lag and submillisecond precision Given the precise temporal relationship between FS spikes and the population activity reflected as ripplets, one would expect different FS cells in the same barrel to fire in tight synchrony with each other. We therefore examined intra-barrel FS-FS temporal relationships by simultaneous paired recordings. Indeed, when stimulated by a light pulse, simultaneously recorded FS-FS pairs exhibited highly precise spike synchrony. In the two example pairs shown in Figure 3A and B, the peaks of the first three spikes in each burst aligned to within 0.1–0.3 ms between the two cells. To quantify the magnitude and precision of pairwise synchrony, we used the Jitter-Based Synchrony Index (JBSI), which is a normalized measure of spike-spike synchrony in excess of that expected by chance (Agmon, 2012; see 'Materials and methods'). Synchrony is defined as the fraction of spikes co-occurring within a predetermined 'synchrony window' (SW), and chance synchrony is the average synchrony remaining after shifting each spike in one train by a random jitter ≤J, J = 2·SW. When calculated for decreasing SW values, the JBSI typically increases to a maximum and then precipitously drops off to zero, as J falls below the intrinsic precision of the system and the applied jitter no longer disrupts the pairwise synchrony. We defined 'pairwise precision' as the smallest SW value with JBSI > 0.5. Since neurons can maintain a precise temporal relationship even when they fire a

SummaryCorticothalamic pathways, responsible for the top-down control of the thalamus display a classical, canonical organization in that every cortical region sends dual, layer 6 (L6) and layer 5 (L5) output to the thalamus. Here we demonstrate a qualitative, region-specific difference in the organization of corticothalamic pathways. We show that L5 pyramidal cells of the frontal, but not other cortical regions establish monosynaptic connection with the inhibitory thalamic reticular nucleus (TRN). The frontal L5-TRN pathway paralleled the L6-TRN projection but displayed distinct morphological and physiological features. The exact spike output of the L5 contacted TRN cells correlated with the level of cortical synchrony. Optogenetic perturbation of the L5-TRN connection disrupted the tight link between cortical and TRN activity. L5-driven TRN cells innervated all thalamic nuclei involved in the control of frontal cortical activity. Our data show that frontal cortical functions require a highly specialized cortical control over intrathalamic inhibitory processes.

The unconventional N-methyl-D-aspartate (NMDA) receptor subunits GluN3A and GluN3B can, when associated with the other glycine-binding subunit GluN1, generate excitatory conductances purely activated by glycine. However, functional GluN1/GluN3 receptors have not been identified in native adult tissues. We discovered that GluN1/GluN3A receptors are operational in neurons of the mouse adult medial habenula (MHb), an epithalamic area controlling aversive physiological states. In the absence of glycinergic neuronal specializations in the MHb, glial cells tuned neuronal activity via GluN1/GluN3A receptors. Reducing GluN1/GluN3A receptor levels in the MHb prevented place-aversion conditioning. Our study extends the physiological and behavioral implications of glycine by demonstrating its control of negatively valued emotional associations via excitatory glycinergic NMDA receptors.

Recurring bursts of thalamocortical cells were thought to be indispensable in driving absence seizures. A new study demonstrates that bursts from inhibitory thalamic reticular neurons are crucial instead. Reticular bursts are driven by cortical inputs and govern precise timing of thalamocortical cell activity during seizures.