Predictable Fluctuations in Excitatory Synaptic Strength Due to Natural Variation in Presynaptic Firing Rate.

Many controlled in vitro studies have demonstrated how postsynaptic responses to presynaptic spikes are not constant but depend on short-term synaptic plasticity (STP) and the detailed timing of presynaptic spikes. However, the effects of short-term plasticity (depression and facilitation) are not limited to short, subsecond timescales. The effects of STP appear on long timescales as changes in presynaptic firing rates lead to changes in steady-state synaptic transmission. Here, we examine the relationship between natural variations in the presynaptic firing rates and spike transmission in vivo Using large-scale spike recordings in awake male and female mice from the Allen Institute Neuropixels dataset, we first detect putative excitatory synaptic connections based on cross-correlations between the spike trains of millions of pairs of neurons. For the subset of pairs where a transient, excitatory effect was detected, we use a model-based approach to track fluctuations in synaptic efficacy and find that efficacy varies substantially on slow (∼1 min) timescales over the course of these recordings. For many connections, the efficacy fluctuations are correlated with fluctuations in the presynaptic firing rate. To understand the potential mechanisms underlying this relationship, we then model the detailed probability of postsynaptic spiking on a millisecond timescale, including both slow changes in postsynaptic excitability and monosynaptic inputs with short-term plasticity. The detailed model reproduces the slow efficacy fluctuations observed with many putative excitatory connections, suggesting that these fluctuations can be both directly predicted based on the time-varying presynaptic firing rate and, at least partly, explained by the cumulative effects of STP.SIGNIFICANCE STATEMENT The firing rates of individual neurons naturally vary because of stimuli, movement, and brain state. Models of synaptic transmission predict that these variations in firing rates should be accompanied by slow fluctuations in synaptic strength because of short-term depression and facilitation. Here, we characterize the magnitude and predictability of fluctuations in synaptic strength in vivo using large-scale spike recordings. For putative excitatory connections from a wide range of brain areas, we find that typical synaptic efficacy varies as much as ∼70%, and in many cases the fluctuations are well described by models of short-term synaptic plasticity. These results highlight the dynamic nature of in vivo synaptic transmission and the interplay between synaptic strength and firing rates in awake animals.

Modeling the Short-Term Dynamics of in Vivo Excitatory Spike Transmission.

Information transmission in neural networks is influenced by both short-term synaptic plasticity (STP) as well as nonsynaptic factors, such as after-hyperpolarization currents and changes in excitability. Although these effects have been widely characterized in vitro using intracellular recordings, how they interact in vivo is unclear. Here, we develop a statistical model of the short-term dynamics of spike transmission that aims to disentangle the contributions of synaptic and nonsynaptic effects based only on observed presynaptic and postsynaptic spiking. The model includes a dynamic functional connection with short-term plasticity as well as effects due to the recent history of postsynaptic spiking and slow changes in postsynaptic excitability. Using paired spike recordings, we find that the model accurately describes the short-term dynamics of in vivo spike transmission at a diverse set of identified and putative excitatory synapses, including a pair of connected neurons within thalamus in mouse, a thalamocortical connection in a female rabbit, and an auditory brainstem synapse in a female gerbil. We illustrate the utility of this modeling approach by showing how the spike transmission patterns captured by the model may be sufficient to account for stimulus-dependent differences in spike transmission in the auditory brainstem (endbulb of Held). Finally, we apply this model to large-scale multielectrode recordings to illustrate how such an approach has the potential to reveal cell type-specific differences in spike transmission in vivo Although STP parameters estimated from ongoing presynaptic and postsynaptic spiking are highly uncertain, our results are partially consistent with previous intracellular observations in these synapses.SIGNIFICANCE STATEMENT Although synaptic dynamics have been extensively studied and modeled using intracellular recordings of postsynaptic currents and potentials, inferring synaptic effects from extracellular spiking is challenging. Whether or not a synaptic current contributes to postsynaptic spiking depends not only on the amplitude of the current, but also on many other factors, including the activity of other, typically unobserved, synapses, the overall excitability of the postsynaptic neuron, and how recently the postsynaptic neuron has spiked. Here, we developed a model that, using only observations of presynaptic and postsynaptic spiking, aims to describe the dynamics of in vivo spike transmission by modeling both short-term synaptic plasticity (STP) and nonsynaptic effects. This approach may provide a novel description of fast, structured changes in spike transmission.

Model-based detection of putative synaptic connections from spike recordings with latency and type constraints.

Detecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. Although previous methods often treat the detection of each putative connection as a separate hypothesis test, here we develop a modeling approach that infers synaptic connections while incorporating circuit properties learned from the whole network. We use an extension of the generalized linear model framework to describe the cross-correlograms between pairs of neurons and separate correlograms into two parts: a slowly varying effect due to background fluctuations and a fast, transient effect due to the synapse. We then use the observations from all putative connections in the recording to estimate two network properties: the presynaptic neuron type (excitatory or inhibitory) and the relationship between synaptic latency and distance between neurons. Constraining the presynaptic neuron's type, synaptic latencies, and time constants improves synapse detection. In data from simulated networks, this model outperforms two previously developed synapse detection methods, especially on the weak connections. We also apply our model to in vitro multielectrode array recordings from the mouse somatosensory cortex. Here, our model automatically recovers plausible connections from hundreds of neurons, and the properties of the putative connections are largely consistent with previous research.NEW & NOTEWORTHY Detecting synaptic connections using large-scale extracellular spike recordings is a difficult statistical problem. Here, we develop an extension of a generalized linear model that explicitly separates fast synaptic effects and slow background fluctuations in cross-correlograms between pairs of neurons while incorporating circuit properties learned from the whole network. This model outperforms two previously developed synapse detection methods in the simulated networks and recovers plausible connections from hundreds of neurons in in vitro multielectrode array data.

Intracerebral Hemorrhage-Induced Brain Injury: the Role of Lysosomal-Associated Transmembrane Protein 5.

Intracerebral hemorrhage (ICH) is a lethal stroke with high mortality or disability. However, effective therapy for ICH damage is generally lacking. Previous investigations have suggested that lysosomal protein transmembrane 5 (LAPTM5) is involved in various pathological processes, including autophagy, apoptosis, and inflammation. In this study, we aimed to identify the expression and functions of LAPTM5 in collagenase-induced ICH mouse models and hemoglobin-induced cell models. We found that LAPTM5 was highly expressed in brain tissues around the hematoma, and double immunostaining studies showed that LAPTM5 was co-expressed with microglia cells, neurons, and astrocytes. Following ICH, the mice presented increased brain edema, blood-brain barrier permeability, and neurological deficits, while pathological symptoms were alleviated after the LAPTM5 knockdown. Adeno-associated virus 9-mediated downregulation of LAPTM5 also improves ICH-induced secondary cerebral damage, including neuronal degeneration, the polarization of M1-like microglia, and inflammatory cascades. Furthermore, LAPTM5 promoted activation of the nuclear factor kappa-B (NF-κB) pathway in response to neuroinflammation. Further investigations indicated that brain injury improved by LAPTM5 knockdown was further exacerbated after the overexpression of receptor-interacting protein kinase 1 (RIP1), which is revealed to trigger the NF-κB pathway. In vitro experiments demonstrated that LAPTM5 silencing inhibited hemoglobin-induced cell function and confirmed regulation between RIP1 and LAPTM5. In conclusion, the present study indicates that LAPTM5 may act as a positive regulator in the context of ICH by modulating the RIP1/NF-κB pathway. Thus, it may be a candidate gene for further study of molecular or therapeutic targets.

Perceptual integration and the composite face effect.

The composite face paradigm is widely used to investigate holistic perception of faces. In the paradigm, parts from different faces (usually the top and bottom halves) are recombined. The principal criterion for holistic perception is that responses involving the component parts of composites in which the parts are aligned into a face-like configuration are disrupted compared with the same parts in a misaligned (not face-like) format. This is often taken as evidence that seeing a whole face in the aligned condition interferes with perceiving its separate parts, but the extent to which the effect is perceptually driven remains unclear. We used salient perceptual categories of gender (male or female) and race (Asian or Caucasian appearance) to create composite stimuli from parts of faces that varied orthogonally on these characteristics. In Experiment 1, participants categorised the gender of the parts of aligned composite and misaligned images created from parts with the same (congruent) or different (incongruent) gender and the same (congruent) or different (incongruent) race. In Experiment 2, the same stimuli were used but the task changed to categorising race. In both experiments, there was a strong influence of the task-relevant manipulation on the composite effect, with slower responses to aligned stimuli with incongruent gender in Experiment 1 and incongruent race in Experiment 2. In contrast, the task-irrelevant variable (race in Experiment 1, gender in Experiment 2) did not exert much influence on the composite effect in either experiment. These findings show that although holistic integration of salient visual properties makes a strong contribution to the composite face effect, it clearly also involves targeted processing of an attended visual characteristic.

Interaction between social categories in the composite face paradigm.

The composite face paradigm (Young, Hellawell, & Hay, 1987) is widely used to demonstrate holistic perception of faces (Rossion, 2013). In the paradigm, parts from different faces (usually the top and bottom halves) are recombined. The principal criterion for holistic perception is that responses involving the component parts of composites in which the parts are aligned into a face-like configuration are slower and less accurate than responses to the same parts in a misaligned (not face-like) format. This is often taken as evidence that seeing a whole face in the aligned condition interferes with perceiving its separate parts, but it remains unclear to what extent the composite face effect also reflects contributions from other potential sources of interference. We present a new variant of the paradigm involving composites created from top and bottom parts of familiar faces drawn from orthogonal social categories of gender and occupation. This allows us to examine the contributions of differences in relatively visual properties (gender) or relatively semantic properties (occupation) to composite interference and to measure whether variation in a task-irrelevant category (e.g., differences in gender across the parts of the composite when the task is to categorize the occupation of one of the parts) will influence the size of the composite effect. Our findings show that the composite face effect can be modulated by task-irrelevant social categories and that this interference is primarily visual in nature because the influence of face gender is more direct and more consistent than the influence of occupation. (PsycINFO Database Record

Processing of Individual Items during Ensemble Coding of Facial Expressions.

There is growing evidence that human observers are able to extract the mean emotion or other type of information from a set of faces. The most intriguing aspect of this phenomenon is that observers often fail to identify or form a representation for individual faces in a face set. However, most of these results were based on judgments under limited processing resource. We examined a wider range of exposure time and observed how the relationship between the extraction of a mean and representation of individual facial expressions would change. The results showed that with an exposure time of 50 ms for the faces, observers were more sensitive to mean representation over individual representation, replicating the typical findings in the literature. With longer exposure time, however, observers were able to extract both individual and mean representation more accurately. Furthermore, diffusion model analysis revealed that the mean representation is also more prone to suffer from the noise accumulated in redundant processing time and leads to a more conservative decision bias, whereas individual representations seem more resistant to this noise. Results suggest that the encoding of emotional information from multiple faces may take two forms: single face processing and crowd face processing.

Facial expression at retrieval affects recognition of facial identity.

It is well known that memory can be modulated by emotional stimuli at the time of encoding and consolidation. For example, happy faces create better identity recognition than faces with certain other expressions. However, the influence of facial expression at the time of retrieval remains unknown in the literature. To separate the potential influence of expression at retrieval from its effects at earlier stages, we had participants learn neutral faces but manipulated facial expression at the time of memory retrieval in a standard old/new recognition task. The results showed a clear effect of facial expression, where happy test faces were identified more successfully than angry test faces. This effect is unlikely due to greater image similarity between the neural training face and the happy test face, because image analysis showed that the happy test faces are in fact less similar to the neutral training faces relative to the angry test faces. In the second experiment, we investigated whether this emotional effect is affected by the expression at the time of learning. We employed angry or happy faces as learning stimuli, and angry, happy, and neutral faces as test stimuli. The results showed that the emotional effect at retrieval is robust across different encoding conditions with happy or angry expressions. These findings indicate that emotional expressions do not only affect the stages of encoding and consolidation, but also the retrieval process in identity recognition.