Although stress can be considered as an ongoing process that helps an organism to cope with present and future challenges, when it is too intense or uncontrollable, it can lead to adverse consequences for physical and mental health. Social stress specifically, is a highly prevalent traumatic experience, present in multiple contexts, such as war, bullying and interpersonal violence, and it has been linked with increased risk for major depression and anxiety disorders. Nevertheless, not all individuals exposed to strong stressful events develop psychopathology, with the mechanisms of resilience and vulnerability being still under investigation. During this talk, I will identify key gaps in our knowledge about stress vulnerability and I will present our recent data from our contextual fear learning protocol based on social defeat stress in mice.

The Geffen laboratory at the University of Pennsylvania has multiple postdoctoral positions open in systems neuroscience with the broad goal of understanding the neuronal circuits for auditory perception and learning. We are looking for energetic and talented scientists interested in studying the function of the brain. The postdoctoral fellow will have the opportunity to learn and apply a host of systems neuroscience techniques, including two-photon imaging of population activity, optogenetic manipulations, large-scale electrophysiology and behavior in mice. Prior experience with some of these methods is preferred, but not required. Depending on the candidate’s interests, all projects provide an opportunity to learn and apply advanced computational methods, including dynamic systems analysis of neuronal population activity; Bayesian approaches for understanding the relation between neuronal activity and behavior; machine learning methods to understand large-scale neuronal activity. We currently have openings for postdoctoral fellows for three projects: (1) Neuronal mechanisms for predictive coding: Auditory perception relies on predicting statistics of incoming signals, be it identifying the speech of a conversation partner in a crowded room or recognizing the sound of a babbling brook in a forest. The human brain detects statistical regularities in sounds as a fundamental aspect of prediction, evidenced by reduced responses to repeated sound patterns and enhanced responses to unexpected sounds. Multiple studies demonstrate that the neuronal responses to regular signals are reduced through adaptation, which can contribute to prediction. However, adaptation alone is not sufficient to account for prediction and studies at cellular and neuronal population levels in animals thus far lend only partial support to existing theories of predictive coding. The goal of the project is to close this gap in knowledge and to determine the circuits that predict signals and detect statistical regularity and its violation in auditory behavior. Funded by NIH NIDCD. (2) Neuronal circuits for learning-driven changes in auditory perception: Everyday auditory behavior depends critically on learning-driven changes in auditory perception that rely on neuronal plasticity within the auditory pathway. By combining state-of-the-art optogenetic, electrophysiological, behavioral and computational approaches, the project seeks to identify the function of specific circuit elements in auditory learning. Funded by NIH NIDCD. (3) Neuronal mechanisms for hearing under uncertainty: In everyday life, because both sensory signals and neuronal responses are noisy, important cognitive tasks, such as auditory categorization, are based on uncertain information. To overcome this limitation, listeners incorporate other types of signals, such as the statistics of sounds over short and long time scales and signals from other sensory modalities into their categorization decision processes. This project will identify the contribution of specific cell types to categorization and the neuronal mechanisms for how contextual signals bias auditory categorization. In collaboration with Dr. Yale Cohen and Dr. Konrad Kording, funded by NIH BRAIN Initiative. Our laboratory is a close community of fun-loving scientists, striving to help each other while exploring the mysteries of the brain. Our trainees have won numerous awards and have been awarded government and private foundation grants. We value diversity and promote equity in the scientific community and beyond. The systems neuroscience community at the University of Pennsylvania is top-notch and highly collaborative, and postdoctoral fellows will have opportunities to engage in interdepartmental initiatives, including MindCore, MINS and CNI. Penn has a gorgeous campus and offers many cultural activities. Philadelphia is a beautiful city with world-class music, food and entertainment. To apply, please email Dr. Geffen at mgeffen@pennmedicine.upenn.edu : a cover letter (summarize your prior research experience, why you are interested in the position, and your future plans) and your CV.

We are pleased to announce the opening of a PhD position at INMED (Aix-Marseille University) through the SCHADOC program, focused on the neural coding of social interactions and memory in the cortex of behaving mice. The project will investigate how social behaviors essential for cooperation, mating, and group dynamics are encoded in the brain, and how these processes are disrupted in neurodevelopmental disorders such as autism. This project uses longitudinal calcium imaging and population-level data analysis to study how cortical circuits encode social interactions in mice. Recordings from mPFC and S1 in wild-type and Neurod2 KO mice will be used to extract neural representations of social memory. The candidate will develop and apply computational models of neural dynamics and representational geometry to uncover how these codes evolve over time and are disrupted in social amnesia.

When we attend a demanding task, our performance is poor at low arousal (when drowsy) or high arousal (when anxious), but we achieve optimal performance at intermediate arousal. This celebrated Yerkes-Dodson inverted-U law relating performance and arousal is colloquially referred to as being "in the zone." In this talk, I will elucidate the behavioral and neural mechanisms linking arousal and performance under the Yerkes-Dodson law in a mouse model. During decision-making tasks, mice express an array of discrete strategies, whereby the optimal strategy occurs at intermediate arousal, measured by pupil, consistent with the inverted-U law. Population recordings from the auditory cortex (A1) further revealed that sound encoding is optimal at intermediate arousal. To explain the computational principle underlying this inverted-U law, we modeled the A1 circuit as a spiking network with excitatory/inhibitory clusters, based on the observed functional clusters in A1. Arousal induced a transition from a multi-attractor (low arousal) to a single attractor phase (high arousal), and performance is optimized at the transition point. The model also predicts stimulus- and arousal-induced modulations of neural variability, which we confirmed in the data. Our theory suggests that a single unifying dynamical principle, phase transitions in metastable dynamics, underlies both the inverted-U law of optimal performance and state-dependent modulations of neural variability.

Memories of emotionally-salient events are long-lasting, guiding behavior from minutes to years after learning. The prelimbic cortex (PL) is required for fear memory retrieval across time and is densely interconnected with many subcortical and cortical areas involved in recent and remote memory recall, including the temporal association area (TeA). While the behavioral expression of a memory may remain constant over time, the neural activity mediating memory-guided behavior is dynamic. In PL, different neurons underlie recent and remote memory retrieval and remote memory-encoding neurons have preferential functional connectivity with cortical association areas, including TeA. TeA plays a preferential role in remote compared to recent memory retrieval, yet how TeA circuits drive remote memory retrieval remains poorly understood. Here we used a combination of activity-dependent neuronal tagging, viral circuit mapping and miniscope imaging to investigate the role of the PL-TeA circuit in fear memory retrieval across time in mice. We show that PL memory ensembles recruit PL-TeA neurons across time, and that PL-TeA neurons have enhanced encoding of salient cues and behaviors at remote timepoints. This recruitment depends upon ongoing synaptic activity in the learning-activated PL ensemble. Our results reveal a novel circuit encoding remote memory and provide insight into the principles of memory circuit reorganization across time.

I’m interested in structure-function relationships in neural circuits and behavior, with a focus on motor and somatosensory areas of the mouse’s cortex involved in controlling forelimb movements. In one line of investigation, we take a bottom-up, cellularly oriented approach and use optogenetics, electrophysiology, and related slice-based methods to dissect cell-type-specific circuits of corticospinal and other neurons in forelimb motor cortex. In another, we take a top-down ethologically oriented approach and analyze the kinematics and cortical correlates of “oromanual” dexterity as mice handle food. I'll discuss recent progress on both fronts.

Every movement we make requires us to precisely coordinate muscle activity across our body in space and time. In this talk I will describe our efforts to understand how the brain generates flexible, coordinated movement. We have taken a behavior-centric approach to this problem, starting with the development of quantitative frameworks for mouse locomotion (LocoMouse; Machado et al., eLife 2015, 2020) and locomotor learning, in which mice adapt their locomotor symmetry in response to environmental perturbations (Darmohray et al., Neuron 2019). Combined with genetic circuit dissection, these studies reveal specific, cerebellum-dependent features of these complex, whole-body behaviors. This provides a key entry point for understanding how neural computations within the highly stereotyped cerebellar circuit support the precise coordination of muscle activity in space and time. Finally, I will present recent unpublished data that provide surprising insights into how cerebellar circuits flexibly coordinate whole-body movements in dynamic environments.

Psychedelics like psilocybin have shown rapid and long-lasting efficacy on depressive and anxiety symptoms. Other psychedelics with shorter half-lives, such as DMT and 5-MeO-DMT, have also shown promising preliminary outcomes in major depression, making them interesting candidates for clinical practice. Despite several promising clinical studies, the influence of the context on therapeutic responses or adverse effects remains poorly documented. To address this, we conducted preclinical studies evaluating the psychopharmacological profile of 5-MeO-DMT in contexts previously validated in mice as either pleasant (positive setting) or aversive (negative setting). Healthy C57BL/6J male mice received a single intraperitoneal (i.p.) injection of 5-MeO-DMT at doses of 0.5, 5, and 10 mg/kg, with assessments at 2 hours, 24 hours, and one week post-administration. In a corticosterone (CORT) mouse model of depression, 5-MeO-DMT was administered in different settings, and behavioral tests mimicking core symptoms of depression and anxiety were conducted. In CORT-exposed mice, an acute dose of 0.5 mg/kg administered in a neutral setting produced antidepressant-like effects at 24 hours, as observed by reduced immobility time in the Tail Suspension Test (TST). In a positive setting, the drug also reduced latency to first immobility and total immobility time in the TST. However, these beneficial effects were negated in a negative setting, where 5-MeO-DMT failed to produce antidepressant-like effects and instead elicited an anxiogenic response in the Elevated Plus Maze (EPM).Our results indicate a strong influence of setting on the psychopharmacological profile of 5-MeO-DMT. Future experiments will examine cortical markers of pre- and post-synaptic density to correlate neuroplasticity changes with the behavioral effects of 5-MeO-DMT in different settings.

This webinar will showcase new approaches for electrophysiological recordings using our silicon neural probes and surface arrays combined with diverse optical methods such as wide-field or 2-photon imaging, fiber photometry, and optogenetic perturbations in awake, behaving mice. Multi-modal recording of single units and local field potentials across cortex, hippocampus and thalamus alongside calcium activity via GCaMP6F in cortical neurons in triple-transgenic animals or in hippocampal astrocytes via viral transduction are brought to bear to reveal hitherto inaccessible and under-appreciated aspects of coordinated dynamics in the brain.

Inhibition has long been thought to stabilize the activity of cortical networks at low rates, and to shape significantly their response to sensory inputs. In this talk, I will describe three recent collaborative projects that shed light on these issues. (1) I will show how optogenetic excitation of inhibition neurons is consistent with cortex being inhibition stabilized even in the absence of sensory inputs, and how this data can constrain the coupling strengths of E-I cortical network models. (2) Recent analysis of the effects of optogenetic excitation of pyramidal cells in V1 of mice and monkeys shows that in some cases this optogenetic input reshuffles the firing rates of neurons of the network, leaving the distribution of rates unaffected. I will show how this surprising effect can be reproduced in sufficiently strongly coupled E-I networks. (3) Another puzzle has been to understand the respective roles of different inhibitory subtypes in network stabilization. Recent data reveal a novel, state dependent, paradoxical effect of weakening AMPAR mediated synaptic currents onto SST cells. Mathematical analysis of a network model with multiple inhibitory cell types shows that this effect tells us in which conditions SST cells are required for network stabilization.

A core aspect of clinical epileptology revolves around relating epileptic field potentials to underlying neural sources (e.g. an “epileptogenic focus”). Yet still, how neural population activity relates to epileptic field potentials and ultimately clinical phenomenology, remains far from being understood. After a brief overview on this topic, this seminar will focus on unpublished work, with an emphasis on seizure-related focal spreading depression. The presented results will include hippocampal and neocortical chronic in vivo two-photon population imaging and local field potential recordings of epileptic micronetworks in mice, in the context of viral encephalitis or optogenetic stimulation. The findings are corroborated by invasive depth electrode recordings (macroelectrodes and BF microwires) in epilepsy patients during pre-surgical evaluation. The presented work carries general implications for clinical epileptology, and basic epilepsy research.

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal, and abstract relationships that can be used to shape thought, planning, and behavior. Cognitive maps have been observed in the hippocampus, but their algorithmic form and the processes by which they are learned remain obscure. Here, we employed large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different versions of linear tracks in virtual reality. The results provide a detailed view of the formation of a cognitive map in the hippocampus. Throughout learning, both the animal behavior and hippocampal neural activity progressed through multiple intermediate stages, gradually revealing improved task representation that mirrored improved behavioral efficiency. The learning process led to progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent struture of the task. We show that a Hidden Markov Model (HMM) and a biologically plausible recurrent neural network trained using Hebbian learning can both capture core aspects of the learning dynamics and the orthogonalized representational structure in neural activity. In contrast, we show that gradient-based learning of sequence models such as Long Short-Term Memory networks (LSTMs) and Transformers do not naturally produce such orthogonalized representations. We further demonstrate that mice exhibited adaptive behavior in novel task settings, with neural activity reflecting flexible deployment of the state machine. These findings shed light on the mathematical form of cognitive maps, the learning rules that sculpt them, and the algorithms that promote adaptive behavior in animals. The work thus charts a course toward a deeper understanding of biological intelligence and offers insights toward developing more robust learning algorithms in artificial intelligence.

Understanding how macroscale brain dynamics are shaped by microscale mechanisms is crucial in neuroscience. We investigate this relationship in animal models by directly manipulating cellular properties and measuring whole-brain responses using resting-state fMRI. Specifically, we explore the impact of chemogenetically neuromodulating D1 medium spiny neurons in the dorsomedial caudate putamen (CPdm) on BOLD dynamics within a striato-thalamo-cortical circuit in mice. Our findings indicate that CPdm neuromodulation alters BOLD dynamics in thalamic subregions projecting to the dorsomedial striatum, influencing both local and inter-regional connectivity in cortical areas. This study contributes to understanding structure–function relationships in shaping inter-regional communication between subcortical and cortical levels.

The field of human biology faces three major technological challenges. Firstly, the causation problem is difficult to address in humans compared to model animals. Secondly, the complexity problem arises due to the lack of a comprehensive cell atlas for the human body, despite its cellular composition. Lastly, the heterogeneity problem arises from significant variations in both genetic and environmental factors among individuals. To tackle these challenges, we have developed innovative approaches. These include 1) mammalian next-generation genetics, such as Triple CRISPR for knockout (KO) mice and ES mice for knock-in (KI) mice, which enables causation studies without traditional breeding methods; 2) whole-body/brain cell profiling techniques, such as CUBIC, to unravel the complexity of cellular composition; and 3) accurate and user-friendly technologies for measuring sleep and awake states, exemplified by ACCEL, to facilitate the monitoring of fundamental brain states in real-world settings and thus address heterogeneity in human.

Subsecond timing underlies nearly all sensory and motor activities across species and is critical to survival. While subsecond temporal information has been found across cortical and subcortical regions, it is unclear if it is generated locally and intrinsically or if it is a read out of a centralized clock-like mechanism. Indeed, mechanisms of subsecond timing at the circuit level are largely obscure. Primary sensory areas are well-suited to address these question as they have early access to sensory information and provide minimal processing to it: if temporal information is found in these regions, it is likely to be generated intrinsically and locally. We test this hypothesis by training mice to perform an audio-visual temporal pattern sensory discrimination task as we use 2-photon calcium imaging, a technique capable of recording population level activity at single cell resolution, to record activity in primary visual cortex (V1). We have found significant changes in network dynamics through mice’s learning of the task from naive to middle to expert levels. Changes in network dynamics and behavioral performance are well accounted for by an intrinsic model of timing in which the trajectory of q network through high dimensional state space represents temporal sensory information. Conversely, while we found evidence of other temporal encoding models, such as oscillatory activity, we did not find that they accounted for increased performance but were in fact correlated with the intrinsic model itself. These results provide insight into how subsecond temporal information is encoded mechanistically at the circuit level.

Working memory (WM) is a cognitive function that allows the short-term maintenance and manipulation of information when no longer accessible to the senses. It relies on temporarily storing stimulus features in the activity of neuronal populations. To preserve these dynamics from distraction it has been proposed that pre and post-distraction population activity decomposes into orthogonal subspaces. If orthogonalization is necessary to avoid WM distraction, it should emerge as performance in the task improves. We sought evidence of WM orthogonalization learning and the underlying mechanisms by analyzing calcium imaging data from the prelimbic (PrL) and anterior cingulate (ACC) cortices of mice as they learned to perform an olfactory dual task. The dual task combines an outer Delayed Paired-Association task (DPA) with an inner Go-NoGo task. We examined how neuronal activity reflected the process of protecting the DPA sample information against Go/NoGo distractors. As mice learned the task, we measured the overlap between the neural activity onto the low-dimensional subspaces that encode sample or distractor odors. Early in the training, pre-distraction activity overlapped with both sample and distractor subspaces. Later in the training, pre-distraction activity was strictly confined to the sample subspace, resulting in a more robust sample code. To gain mechanistic insight into how these low-dimensional WM representations evolve with learning we built a recurrent spiking network model of excitatory and inhibitory neurons with low-rank connections. The model links learning to (1) the orthogonalization of sample and distractor WM subspaces and (2) the orthogonalization of each subspace with irrelevant inputs. We validated (1) by measuring the angular distance between the sample and distractor subspaces through learning in the data. Prediction (2) was validated in PrL through the photoinhibition of ACC to PrL inputs, which induced early-training neural dynamics in well-trained animals. In the model, learning drives the network from a double-well attractor toward a more continuous ring attractor regime. We tested signatures for this dynamical evolution in the experimental data by estimating the energy landscape of the dynamics on a one-dimensional ring. In sum, our study defines network dynamics underlying the process of learning to shield WM representations from distracting tasks.

When experts are immersed in a task, a natural assumption is that their brains prioritize task-related activity. Accordingly, most efforts to understand neural activity during well-learned tasks focus on cognitive computations and task-related movements. Surprisingly, we observed that during decision-making, the cortex-wide activity of multiple cell types is dominated by movements, especially “uninstructed movements”, that are spontaneously expressed. These observations argue that animals execute expert decisions while performing richly varied, uninstructed movements that profoundly shape neural activity. To understand the relationship between these movements and decision-making, we examined the movements more closely. We tested whether the magnitude or the timing of the movements was correlated with decision-making performance. To do this, we partitioned movements into two groups: task-aligned movements that were well predicted by task events (such as the onset of the sensory stimulus or choice) and task independent movement (TIM) that occurred independently of task events. TIM had a reliable, inverse correlation with performance in head-restrained mice and freely moving rats. This hinted that the timing of spontaneous movements could indicate periods of disengagement. To confirm this, we compared TIM to the latent behavioral states recovered by a hidden Markov model with Bernoulli generalized linear model observations (GLM-HMM) and found these, again, to be inversely correlated. Finally, we examined the impact of these behavioral states on neural activity. Surprisingly, we found that the same movement impacts neural activity more strongly when animals are disengaged. An intriguing possibility is that these larger movement signals disrupt cognitive computations, leading to poor decision-making performance. Taken together, these observations argue that movements and cognitionare closely intertwined, even during expert decision-making.

Myopia is predicted to affect 50% of all people worldwide by 2050, and is a risk factor for significant, potentially blinding ocular pathologies, such as retinal detachment and glaucoma. Thus, there is significant motivation to better understand the process of myopigenesis and to develop effective anti-myopigenic treatments. In nearly all cases of human myopia, scleral remodeling is an obligate step in the axial elongation that characterizes the condition. Here I will describe the development of a biomechanical assay based on transient unconfined compression of scleral samples. By treating the scleral as a poroelastic material, one can determine scleral biomechanical properties from extremely small samples, such as obtained from the mouse eye. These properties provide proxy measures of scleral remodeling, and have allowed us to identify all-trans retinoic acid (atRA) as a myopigenic stimulus in mice. I will also describe nascent collaborative work on modeling the transport of atRA in the eye.

The size and structure of the dendritic arbor play important roles in determining how synaptic inputs of neurons are converted to action potential output and how neurons are integrated in the surrounding neuronal network. Accordingly, neurons with aberrant morphology have been associated with neurological disorders. Dysmorphic, enlarged neurons are, for example, a hallmark of focal epileptogenic lesions like focal cortical dysplasia (FCDIIb) and gangliogliomas (GG). However, the regulatory mechanisms governing the development of dendrites are insufficiently understood. The evolutionary conserved Ste20/Hippo kinase pathway has been proposed to play an important role in regulating the formation and maintenance of dendritic architecture. A key element of this pathway, Ste20-like kinase (SLK), regulates cytoskeletal dynamics in non-neuronal cells and is strongly expressed throughout neuronal development. Nevertheless, its function in neurons is unknown. We found that during development of mouse cortical neurons, SLK has a surprisingly specific role for proper elaboration of higher, ≥ 3rd, order dendrites both in cultured neurons and living mice. Moreover, SLK is required to maintain excitation-inhibition balance. Specifically, SLK knockdown causes a selective loss of inhibitory synapses and functional inhibition after postnatal day 15, while excitatory neurotransmission is unaffected. This mechanism may be relevant for human disease, as dysmorphic neurons within human cortical malformations exhibit significant loss of SLK expression. To uncover the signaling cascades underlying the action of SLK, we combined phosphoproteomics, protein interaction screens and single cell RNA seq. Overall, our data identifies SLK as a key regulator of both dendritic complexity during development and of inhibitory synapse maintenance.

Zoe has developed an open-source tool PhaSER, which allows her to perform real-time oscillatory phase estimation and apply optogenetic manipulations at precise phases of hippocampal theta during high-density electrophysiological recordings in head-fixed mice while they navigate a virtual environment. The precise timing of single-unit spiking relative to network-wide oscillations (i.e., phase locking) has long been thought to maintain excitatory-inhibitory homeostasis and coordinate cognitive processes, but due to intense experimental demands, the causal influence of this phenomenon has never been determined. Thus, we developed PhaSER (Phase-locked Stimulation to Endogenous Rhythms), a tool which allows the user to explore the temporal relationship between single-unit spiking and ongoing oscillatory activity.

The hippocampus is a key player in learning and memory. Research into this brain structure has long emphasized its plasticity and flexibility, though recent reports have come to appreciate its remarkably stable firing patterns. How novel information incorporates itself into networks that maintain their ongoing dynamics remains an open question, largely due to a lack of experimental access points into network stability. Development may provide one such access point. To explore this hypothesis, we birthdated CA1 pyramidal neurons using in-utero electroporation and examined their functional features in freely moving, adult mice. We show that CA1 pyramidal neurons of the same embryonic birthdate exhibit prominent cofiring across different brain states, including behavior in the form of overlapping place fields. Spatial representations remapped across different environments in a manner that preserves the biased correlation patterns between same birthdate neurons. These features of CA1 activity could partially be explained by structured connectivity between pyramidal cells and local interneurons. These observations suggest the existence of developmentally installed circuit motifs that impose powerful constraints on the statistics of hippocampal output.

Despite the profound effects of nutrition and physical activity on human health, our understanding of the molecules mediating the salutary effects of specific foods or activities remains remarkably limited. Here, we share our ongoing studies that use unbiased and high-resolution metabolomics technologies to uncover the molecules and molecular effectors of diet and exercise. We describe how exercise stimulates the production of Lac-Phe, a blood-borne signaling metabolite that suppresses feeding and obesity. Ablation of Lac-Phe biosynthesis in mice increases food intake and obesity after exercise. We also describe the discovery of an orphan metabolite, BHB-Phe. Ketosis-inducible BHB-Phe is a congener of exercise-inducible Lac-Phe, produced in CNDP2+ cells when levels of BHB are high, and functions to lower body weight and adiposity in ketosis. Our data uncover an unexpected and underappreciated signaling role for metabolic fuel derivatives in mediating the cardiometabolic benefits of diet and exercise. These data also suggest that diet and exercise may mediate their physiologic effects on energy balance via a common family of molecules and overlapping signaling pathways.

Hallucinations are a core symptom of psychotic disorders and have traditionally been difficult to study biologically. We developed a new behavioral computational approach to measure hallucinations-like perception in humans and mice alike. Using targeted neural circuit manipulations, we identified a causal role for striatal dopamine in mediating hallucination-like perception. Building on this, we currently investigate the neural and immunological upstream regulators of these dopaminergic circuits with the goal to identify new biological treatment targets for psychosis

Neurons in the rodent hippocampus appear to encode the position of the animal in physical space during movement. Individual ``place cells'' fire in restricted sub-regions of an environment, a feature often taken as evidence that the hippocampus encodes a map of space that subserves navigation. But these same neurons exhibit complex responses to many other variables that defy explanation by position alone, and the hippocampus is known to be more broadly critical for memory formation. Here we elaborate and test a theory of hippocampal coding which produces place cells as a general consequence of efficient memory coding. We constructed neural networks that actively exploit the correlations between memories in order to learn compressed representations of experience. Place cells readily emerged in the trained model, due to the correlations in sensory input between experiences at nearby locations. Notably, these properties were highly sensitive to the compressibility of the sensory environment, with place field size and population coding level in dynamic opposition to optimally encode the correlations between experiences. The effects of learning were also strongly biphasic: nearby locations are represented more similarly following training, while locations with intermediate similarity become increasingly decorrelated, both distance-dependent effects that scaled with the compressibility of the input features. Using virtual reality and 2-photon functional calcium imaging in head-fixed mice, we recorded the simultaneous activity of thousands of hippocampal neurons during virtual exploration to test these predictions. Varying the compressibility of sensory information in the environment produced systematic changes in place cell properties that reflected the changing input statistics, consistent with the theory. We similarly identified representational plasticity during learning, which produced a distance-dependent exchange between compression and pattern separation. These results motivate a more domain-general interpretation of hippocampal computation, one that is naturally compatible with earlier theories on the circuit's importance for episodic memory formation. Work done in collaboration with James Priestley, Lorenzo Posani, Marcus Benna, Attila Losonczy.

During navigation, animals estimate their position using path integration and landmarks. In a series of two studies, we used virtual reality and electrophysiology to dissect how these inputs combine to generate the brain’s spatial representations. In the first study (Campbell et al., 2018), we focused on the medial entorhinal cortex (MEC) and its set of navigationally-relevant cell types, including grid cells, border cells, and speed cells. We discovered that attractor dynamics could explain an array of initially puzzling MEC responses to virtual reality manipulations. This theoretical framework successfully predicted both MEC grid cell responses to additional virtual reality manipulations, as well as mouse behavior in a virtual path integration task. In the second study (Campbell*, Attinger* et al., 2021), we asked whether these principles generalize to other navigationally-relevant brain regions. We used Neuropixels probes to record thousands of neurons from MEC, primary visual cortex (V1), and retrosplenial cortex (RSC). In contrast to the prevailing view that “everything is everywhere all at once,” we identified a unique population of MEC neurons, overlapping with grid cells, that became active with striking spatial periodicity while head-fixed mice ran on a treadmill in darkness. These neurons exhibited unique cue-integration properties compared to other MEC, V1, or RSC neurons: they remapped more readily in response to conflicts between path integration and landmarks; they coded position prospectively as opposed to retrospectively; they upweighted path integration relative to landmarks in conditions of low visual contrast; and as a population, they exhibited a lower-dimensional activity structure. Based on these results, our current view is that MEC attractor dynamics play a privileged role in resolving conflicts between path integration and landmarks during navigation. Future work should include carefully designed causal manipulations to rigorously test this idea, and expand the theoretical framework to incorporate notions of uncertainty and optimality.

Memory impairment is a common cognitive deficit in temporal lobe epilepsy (TLE). The hippocampus is severely altered in TLE exhibiting multiple anatomical changes that lead to a hyperexcitable network capable of generating frequent epileptic discharges and seizures. In this study we investigated whether hippocampal involvement in epileptic activity drives working memory deficits using bilateral LFP recordings from CA1 during task performance. We discovered that epileptic mice experienced focal rhythmic discharges (FRDs) while they performed the spatial working memory task. Spatial correlation analysis revealed that FRDs were often spatially stable on the maze and were most common around reward zones (25 ‰) and delay zones (50 ‰). Memory performance was correlated with stability of FRDs, suggesting that spatially unstable FRDs interfere with working memory codes in real time.

Although bradykinesia, tremor, and rigidity are hallmark motor defects in Parkinson’s disease (PD) patients, they also experience motor learning impairments and non-motor symptoms such as depression. The neural basis for these different PD symptoms are not well understood. While current treatments are effective for locomotion deficits in PD, therapeutic strategies targeting motor learning deficits and non-motor symptoms are lacking. We found that distinct parafascicular (PF) thalamic subpopulations project to caudate putamen (CPu), subthalamic nucleus (STN), and nucleus accumbens (NAc). While PF-->CPu and PF-->STN circuits are critical for locomotion and motor learning respectively, inhibition of the PF-->NAc circuit induced a depression-like state. While chemogenetically manipulating CPu-projecting PF neurons led to a long-term restoration of locomotion, optogenetic long-term potentiation at PF-->STN synapses restored motor learning behavior in PD model mice. Furthermore, activation of NAc-projecting PF neurons rescued depression-like PD phenotypes. Importantly, we identified nicotinic acetylcholine receptors capable of modulating PF circuits to rescue different PD phenotypes. Thus, targeting PF thalamic circuits may be an effective strategy for treating motor and non-motor deficits in PD.

Many animals live in complex social groups. To survive, it is essential to know who to avoid and who to interact. Although naïve mice are naturally attracted to any adult conspecifics, a single defeat experience could elicit social avoidance towards the aggressor for days. The neural mechanisms underlying the behavior switch from social approach to social avoidance remains incompletely understood. Here, we identify oxytocin neurons in the retrochiasmatic supraoptic nucleus (SOROXT) and oxytocin receptor (OXTR) expressing cells in the anterior subdivision of ventromedial hypothalamus, ventrolateral part (aVMHvlOXTR) as a key circuit motif for defeat-induced social avoidance learning. After defeat, aVMHvlOXTR cells drastically increase their responses to aggressor cues. This response change is functionally important as optogenetic activation of aVMHvlOXTR cells elicits time-locked social avoidance towards a benign social target whereas inactivating the cells suppresses defeat-induced social avoidance. Furthermore, OXTR in the aVMHvl is itself essential for the behavior change. Knocking out OXTR in the aVMHvl or antagonizing the receptor during defeat, but not during post-defeat social interaction, impairs defeat-induced social avoidance. aVMHvlOXTR receives its private supply of oxytocin from SOROXT cells. SOROXT is highly activated by the noxious somatosensory inputs associated with defeat. Oxytocin released from SOROXT depolarizes aVMHvlOXTR cells and facilitates their synaptic potentiation, and hence, increases aVMHvlOXTR cell responses to aggressor cues. Ablating SOROXT cells impairs defeat-induced social avoidance learning whereas activating the cells promotes social avoidance after a subthreshold defeat experience. Altogether, our study reveals an essential role of SOROXT-aVMHvlOXTR circuit in defeat-induced social learning and highlights the importance of hypothalamic oxytocin system in social ranking and its plasticity.

Programmed axon death is a widespread and completely preventable mechanism in injury and disease. Mouse and Drosophila studies define a molecular pathway involving activation of SARM1 NA Dase and its prevention by NAD synthesising enzyme NMNAT2 . Loss of axonal NMNAT2 causes its substrate, NMN , to accumulate and activate SARM1 , driving loss of NAD and changes in ATP , ROS and calcium. Animal models caused by genetic mutation, toxins, viruses or metabolic defects can be alleviated by blocking programmed axon death, for example models of CMT1B , chemotherapy-induced peripheral neuropathy (CIPN), rabies and diabetic peripheral neuropathy (DPN). The perinatal lethality of NMNAT2 null mice is completely rescued, restoring a normal, healthy lifespan. Animal models lack the genetic and environmental diversity present in human populations and this is problematic for modelling gene-environment combinations, for example in CIPN and DPN , and identifying rare, pathogenic mutations. Instead, by testing human gene variants in WGS datasets for loss- and gain-of-function, we identified enrichment of rare SARM1 gain-of-function variants in sporadic ALS , despite previous negative findings in SOD1 transgenic mice. We have shown in mice that heterozygous SARM1 loss-of-function is protective from a range of axonal stresses and that naturally-occurring SARM1 loss-of-function alleles are present in human populations. This enables new approaches to identify disorders where blocking SARM1 may be therapeutically useful, and the existence of two dominant negative human variants in healthy adults is some of the best evidence available that drugs blocking SARM1 are likely to be safe. Further loss- and gain-of-function variants in SARM1 and NMNAT2 are being identified and used to extend and strengthen the evidence of association with neurological disorders. We aim to identify diseases, and specific patients, in whom SARM1 -blocking drugs are most likely to be effective.

The impact of apolipoprotein E ε4 (APOE4), the strongest genetic risk factor for Alzheimer’s disease (AD), on human brain cellular function remains unclear. Here, we investigated the effects of APOE4 on brain cell types derived from population and isogenic human induced pluripotent stem cells, post-mortem brain, and APOE targeted replacement mice. Population and isogenic models demonstrate that APOE4 local haplotype, rather than a single risk allele, contributes to risk. Global transcriptomic analyses reveal human-specific, APOE4-driven lipid metabolic dysregulation in astrocytes and microglia. APOE4 enhances de novo cholesterol synthesis despite elevated intracellular cholesterol due to lysosomal cholesterol sequestration in astrocytes. Further, matrisome dysregulation is associated with upregulated chemotaxis, glial activation, and lipid biosynthesis in astrocytes co-cultured with neurons, which recapitulates altered astrocyte matrisome signaling in human brain. Thus, APOE4 initiates glia-specific cell and non-cell autonomous dysregulation that may contribute to increased AD risk." https://doi.org/10.1016/j.cell.2022.05.017

Talk(1): Fetal and maternal NLRP3 signaling is required for preterm labor and birth. (DOI: 10.1172/jci.insight.158238) Talk(2): Maternal IL-33 critically regulates tissue remodeling and type 2 immune responses in the uterus during early pregnancy in mice (DOI: 10.1073/pnas.2123267119)

We provide protocols for the social transfer of pain and analgesia in mice. We describe the steps to induce pain or analgesia (pain relief) in bystander mice with a 1-h social interaction with a partner injected with CFA (complete Freund’s adjuvant) or CFA and morphine, respectively. We detail behavioral tests to assess pain or analgesia in the untreated bystander mice. This protocol has been validated in mice and rats and can be used for investigating mechanisms of empathy. Highlights • A protocol for the rapid social transfer of pain in rodents • Detailed requirements for handling and housing conditions • Procedures for habituation, social interaction, and pain induction and assessment • Adaptable for social transfer of analgesia and may be used to study empathy in rodents https://doi.org/10.1016/j.xpro.2022.101756

Here, we present a protocol for using a versatile treadmill system to measure locomotion and neural activity at high temporal resolution in head-fixed mice. We first describe the assembly of the treadmill system. We then detail surgical implantation of the headplate on the mouse skull, followed by habituation of mice to locomotion on the treadmill system. The system is compact, movable, and simple to synchronize with other data streams, making it ideal for monitoring brain activity in diverse behavioral frameworks. https://dx.doi.org/10.1016/j.xpro.2022.101701

The rules governing behavior often vary with behavioral contexts. As a result, an action rewarded in one context may be discouraged in another. Animals and humans are capable of switching between behavioral strategies under different contexts and acting adaptively according to the variable rules, a flexibility that is thought to be mediated by the prefrontal cortex (PFC). However, how the PFC orchestrates the context-dependent switch of strategies remains unclear. Here we show that pathway-specific projection neurons in the medial PFC (mPFC) differentially contribute to context-instructed strategy selection. In mice trained in a decision-making task in which a previously established rule and a newly learned rule are associated with distinct contexts, the activity of mPFC neurons projecting to the dorsomedial striatum (mPFC-DMS) encodes the contexts and further represents decision strategies conforming to the old and new rules. Moreover, mPFC-DMS neuron activity is required for the context-instructed strategy selection. In contrast, the activity of mPFC neurons projecting to the ventral midline thalamus (mPFC-VMT) does not discriminate between the contexts, and represents the old rule even if mice have adopted the new one. Furthermore, these neurons act to prevent the strategy switch under the new rule. Our results suggest that mPFC-DMS neurons promote flexible strategy selection guided by contexts, whereas mPFC-VMT neurons favor fixed strategy selection by preserving old rules.

The circadian release of endogenous glucocorticoids is essential in preparing and synchronizing the body’s daily physiological needs. Disruption in the rhythmic activity of glucocorticoids has been observed in individuals with a variety of cancer types, and blunting of this rhythm has been shown to predict cancer mortality and declines in quality of life. This suggests that a disrupted glucocorticoid rhythm is potentially a shared phenotype across cancers. However, where this phenomenon is driven by the cancer itself, and the causal mechanisms that link glucocorticoid rhythm dysfunction and cancer outcomes remain preliminary at best. The regulation of daily glucocorticoid activity has been well-characterized and is maintained, in part, by the coordinated response of the hypothalamic-pituitary-adrenal (HPA) axis, consisting of the suprachiasmatic nucleus (SCN) and corticotropin-releasing hormone-expressing neurons of the paraventricular nucleus of the hypothalamus (PVNCRH). Consequently, we set out to examine if cancer-induced glucocorticoid dysfunction is regulated by disruptions within these hypothalamic nuclei. In comparison to their tumor-free baseline, mammary tumor-bearing mice exhibited a blunting of glucocorticoid rhythms across multiple timepoints throughout the day, as measured by the overall levels and the slope of fecal corticosterone rhythms, during tumor progression. We further examined how peripheral tumors shape hypothalamic activity within the brain. Serial two-photon tomography for whole-brain cFos imaging suggests a disrupted activation of the PVN in mice with tumors. Additionally, we found GFP labeled CRH+ neurons within the PVN after injection of pseudorabies virus expressing GFP into the tumor, pointing to the PVN as a primary target disrupted by mammary tumors. Preliminary in vivo fiber photometry data show that PVNCRH neurons exhibit enhanced calcium activity during tumor progression, as compared to baseline (no tumor) activity. Taken together, this suggests that there may be an overactive HPA response during tumor progression, which in turn, may result in a subsequent negative feedback on glucocorticoid rhythms. Current studies are examining whether tumor progression modulates SCN calcium activity, how the transcriptional profile of PVNCRH neurons is changed, and test if manipulation of the neurocircuitry surrounding glucocorticoid rhythmicity alters tumor characteristics.

GABAergic interneurons are chiefly responsible for controlling the activity of local circuits in the cortex. Chandelier cells (ChCs) are a type of GABAergic interneuron that control the output of hundreds of neighbouring pyramidal cells through axo-axonic synapses which target the axon initial segment (AIS). Despite their importance in modulating circuit activity, our knowledge of the development and function of axo-axonic synapses remains elusive. We have investigated the emergence and plasticity of axo-axonic synapses in layer 2/3 of the somatosensory cortex (S1) and found that ChCs follow what appear to be homeostatic rules when forming synapses with pyramidal neurons. We are currently implementing in vivo techniques to image the process of axo-axonic synapse formation during development and uncover the dynamics of synaptogenesis and pruning at the AIS. In addition, we are using an all-optical approach to both activate and measure the activity of chandelier cells and their postsynaptic partners in the primary visual cortex (V1) and somatosensory cortex (S1) in mice, also during development. We aim to provide a structural and functional description of the emergence and plasticity of a GABAergic synapse type in the cortex.

New tasks are often similar in structure to old ones. Animals that take advantage of such conserved or “abstract” task structures can master new tasks with minimal training. To understand the neural basis of this abstraction, we developed a novel behavioural paradigm for mice: the “ABCD” task, and recorded from their medial frontal neurons as they learned. Animals learned multiple tasks where they had to visit 4 rewarded locations on a spatial maze in sequence, which defined a sequence of four “task states” (ABCD). Tasks shared the same circular transition structure (… ABCDABCD …) but differed in the spatial arrangement of rewards. As well as improving across tasks, mice inferred that A followed D (i.e. completed the loop) on the very first trial of a new task. This “zero-shot inference” is only possible if animals had learned the abstract structure of the task. Across tasks, individual medial Frontal Cortex (mFC) neurons maintained their tuning to the phase of an animal’s trajectory between rewards but not their tuning to task states, even in the absence of spatial tuning. Intriguingly, groups of mFC neurons formed modules of coherently remapping neurons that maintained their tuning relationships across tasks. Such tuning relationships were expressed as replay/preplay during sleep, consistent with an internal organisation of activity into multiple, task-matched ring attractors. Remarkably, these modules were anchored to spatial locations: neurons were tuned to specific task space “distances” from a particular spatial location. These newly discovered “Spatially Anchored Task clocks” (SATs), suggest a novel algorithm for solving abstraction tasks. Using computational modelling, we show that SATs can perform zero-shot inference on new tasks in the absence of plasticity and guide optimal policy in the absence of continual planning. These findings provide novel insights into the Frontal mechanisms mediating abstraction and flexible behaviour.

Due to the unsuitability of benchtop imaging for tasks that require unrestrained movement, investigators have tried, for almost two decades, to develop miniature 2P microscopes-2P miniscopes–that can be carried on the head of freely moving animals. In this talk, I would first briefly review the development history of this technique, and then report our latest progress on developing the new generation of 2P miniscopes, MINI2P, that overcomes the limits of previous versions by both meeting requirements for fatigue-free exploratory behavior during extended recording periods and satisfying demands for further increasing the cell yield by an order of magnitude, to thousands of neurons. The performance and reliability of MINI2P are validated by recordings of spatially tuned neurons in three brain regions and in three behavioral assays. All information about MINI2P is open access, with instruction videos, code, and manuals on public repositories, and workshops will be organized to help new users getting started. MINI2P permits large-scale and high-resolution calcium imaging in freely-moving mice, and opens the door to investigating brain functions during unconstrained natural behaviors.

Cells preferentially responding to visual motion in a particular direction are said to be direction-selective, and these were first identified in the primary visual cortex. Since then, direction-selective responses have been observed in the retina of several species, including mice, indicating motion analysis begins at the earliest stage of the visual hierarchy. Yet little is known about how retinal direction selectivity contributes to motion processing in the visual cortex. In this talk, I will present our experimental efforts to narrow this gap in our knowledge. To this end, we used genetic approaches to disrupt direction selectivity in the retina and mapped neuronal responses to visual motion in the visual cortex of mice using intrinsic signal optical imaging and two-photon calcium imaging. In essence, our work demonstrates that direction selectivity computed at the level of the retina causally serves to establish specialized motion responses in distinct areas of the mouse visual cortex. This finding thus compels us to revisit our notions of how the brain builds complex visual representations and underscores the importance of the processing performed in the periphery of sensory systems.

Sex-based modulation of cognitive processes could set the stage for individual differences in vulnerability to neuropsychiatric disorders. While value-based decision making processes in particular have been proposed to be influenced by sex differences, the overall correct performance in decision making tasks often show variable or minimal differences across sexes. Computational tools allow us to uncover latent variables that define different decision making approaches, even in animals with similar correct performance. Here, we quantify sex differences in mice in the latent variables underlying behavior in a classic value-based decision making task: a restless two-armed bandit. While male and female mice had similar accuracy, they achieved this performance via different patterns of exploration. Male mice tended to make more exploratory choices overall, largely because they appeared to get ‘stuck’ in exploration once they had started. Female mice tended to explore less but learned more quickly during exploration. Together, these results suggest that sex exerts stronger influences on decision making during periods of learning and exploration than during stable choices. Exploration during decision making is altered in people diagnosed with addictions, depression, and neurodevelopmental disabilities, pinpointing the neural mechanisms of exploration as a highly translational avenue for conferring sex-modulated vulnerability to neuropsychiatric diagnoses.

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in head-fixed mice facilitates the use of imaging, electrophysiological, and optogenetic methods. Here, a low-cost behavioral platform (~$1000) was developed that allows tracking of associative learning in head-fixed mice that locomote freely on a running wheel. The platform incorporates two common associative learning paradigms: eyeblink conditioning and delayed tactile startle conditioning. Behavior is tracked using a camera and the wheel movement by a detector. We describe the components and setup and provide a detailed protocol for training and data analysis. This platform allows the incorporation of optogenetic stimulation and fluorescence imaging. The design allows a single host computer to control multiple platforms for training multiple animals simultaneously.

Brain aging leads to cognitive decline and is the main risk factor for sporadic forms of neurodegenerative diseases including Alzheimer’s disease. While brain cell- and tissue-intrinsic factors are likely key determinants of the aging process recent studies document a remarkable susceptibility of the brain to circulatory factors. Thus, blood borne factors from young mice or humans are sufficient to slow aspects of brain aging and improve cognitive function in old mice and, vice versa, factors from old mice are detrimental for young mice and impair cognition. We found evidence that the cerebrovasculature is an important target of circulatory factors and that brain endothelial cells show prominent age-related transcriptional changes in response to plasma. Furthermore, plasma proteins are taken up broadly into the young brain through receptor mediated transport which declines with aging. At the same time, brain derived proteins are detectable in plasma allowing us to measure physiological changes linked to brain aging in plasma. We are exploring the relevance of these findings for neurodegeneration and potential applications towards therapies.

Nuo Li (Baylor College of Medicine, USA) shares novel insights into coordinated interhemispheric large-scale neural network activity underpinning short-term memory in mice. Relevant techniques covered include: simultaneous multi-regional recordings using multiple 64-channel H probes during head-fixed behavior in mice. simultaneous optogenetics and population recording. analysis of population recordings to infer interactions between brain regions. Reference: Chen G, Kang B, Lindsey J, Druckmann S, Li N, (2021). Modularity and robustness of frontal cortex networks. Cell, 184(14):3717-3730.

Who says you can't reinvent the wheel?! This running wheel for head-fixed mice allows 3D reconstruction of body kinematics using a single camera and DeepLabCut (or similar) software. A lightweight, transparent polycarbonate floor and a mirror mounted on the inside allow two views to be captured simultaneously. All parts are commercially available or laser cut

Neural computations occurring simultaneously in multiple cerebral cortical regions are critical for mediating behaviors. Progress has been made in understanding how neural activity in specific cortical regions contributes to behavior. However, there is a lack of tools that allow simultaneous monitoring and perturbing neural activity from multiple cortical regions. We have engineered a suite of technologies to enable easy, robust access to much of the dorsal cortex of mice for optical and electrophysiological recordings. First, I will describe microsurgery robots that can programmed to perform delicate microsurgical procedures such as large bilateral craniotomies across the cortex and skull thinning in a semi-automated fashion. Next, I will describe digitally designed, morphologically realistic, transparent polymer skulls that allow long-term (+300 days) optical access. These polymer skulls allow mesoscopic imaging, as well as cellular and subcellular resolution two-photon imaging of neural structures up to 600 µm deep. We next engineered a widefield, miniaturized, head-mounted fluorescence microscope that is compatible with transparent polymer skull preparations. With a field of view of 8 × 10 mm2 and weighing less than 4 g, the ‘mini-mScope’ can image most of the mouse dorsal cortex with resolutions ranging from 39 to 56 µm. We used the mini-mScope to record mesoscale calcium activity across the dorsal cortex during sensory-evoked stimuli, open field behaviors, social interactions and transitions from wakefulness to sleep.

The overall aim of my research is to understand how the organisation of the synapse, with particular reference to the postsynaptic proteome (PSP) of excitatory synapses in the brain, informs the fundamental mechanisms of learning, memory and behaviour and how these mechanisms go awry in neurological dysfunction. The PSP indeed bears a remarkable burden of disease, with components being disrupted in disorders (synaptopathies) including schizophrenia, depression, autism and intellectual disability. Our work has been fundamental in revealing and then characterising the unprecedented complexity (>1000 highly conserved proteins) of the PSP in terms of the subsynaptic architecture of postsynaptic proteins such as PSD95 and how these proteins assemble into complexes and supercomplexes in different neurons and regions of the brain. Characterising the PSPs in multiple species, including human and mouse, has revealed differences in key sets of functionally important proteins, correlates with brain imaging and connectome data, and a differential distribution of disease-relevant proteins and pathways. Such studies have also provided important insight into synapse evolution, establishing that vertebrate behavioural complexity is a product of the evolutionary expansion in synapse proteomes that occurred ~500 million years ago. My lab has identified many mutations causing cognitive impairments in mice before they were found to cause human disorders. Our proteomic studies revealed that >130 brain diseases are caused by mutations affecting postsynaptic proteins. We uncovered mechanisms that explain the polygenic basis and age of onset of schizophrenia, with postsynaptic proteins, including PSD95 supercomplexes, carrying much of the polygenic burden. We discovered the “Genetic Lifespan Calendar”, a genomic programme controlling when genes are regulated. We showed that this could explain how schizophrenia susceptibility genes are timed to exert their effects in young adults. The Genes to Cognition programme is the largest genetic study so far undertaken into the synaptic molecular mechanisms underlying behaviour and physiology. We made important conceptual advances that inform how the repertoire of both innate and learned behaviours is built from unique combinations of postsynaptic proteins that either amplify or attenuate the behavioural response. This constitutes a key advance in understanding how the brain decodes information inherent in patterns of nerve impulses, and provides insight into why the PSP has evolved to be so complex, and consequently why the phenotypes of synaptopathies are so diverse. Our most recent work has opened a new phase, and scale, in understanding synapses with the first synaptome maps of the brain. We have developed next-generation methods (SYNMAP) that enable single-synapse resolution molecular mapping across the whole mouse brain and extensive regions of the human brain, revealing the molecular and morphological features of a billion synapses. This has already uncovered unprecedented spatiotemporal synapse diversity organised into an architecture that correlates with the structural and functional connectomes, and shown how mutations that cause cognitive disorders reorganise these synaptome maps; for example, by detecting vulnerable synapse subtypes and synapse loss in Alzheimer’s disease. This innovative synaptome mapping technology has huge potential to help characterise how the brain changes during normal development, including in specific cell types, and with degeneration, facilitating novel pathways to diagnosis and therapy.

Episodic Ataxia type 2 (EA2), caused by mutations in the CACNA1A gene, results in a loss-of-function of the P/Q type calcium channel, which leads to baseline ataxia, and attacks of dyskinesia, that can last a few hours to a few days. Attacks are brought on by consumption of caffeine, alcohol, and physical or emotional stress. Interestingly, caffeine and stress are common triggers among other episodic channelopathies, as well as causing tremor or shaking in otherwise healthy adults. The mechanism underlying stress and caffeine induced motor impairment remains poorly understood. Utilizing behavior, and in vivo and in vitro electrophysiology in the tottering mouse, a well characterized mouse model of EA2, or WT mice, we first sought to elucidate the mechanism underlying stress-induced motor impairment. We found stress induces attacks in EA2 though the activation of cerebellar alpha 1 adrenergic receptors by norepinephrine (NE) through casein kinase 2 (CK2) dependent phosphorylation. This decreases SK2 channel activity, causing increased Purkinje cell irregularity and motor impairment. Knocking down or blocking CK2 with an FDA approved drug CX-4945 prevented PC irregularity and stress-induced attacks. We next hypothesized caffeine, which has been shown to increase NE levels, could induce attacks through the same alpha 1 adrenergic mechanism in EA2. We found caffeine increases PC irregularity and induces attacks through the same CK2 pathway. Block of alpha 1 adrenergic receptors, however, failed to prevent caffeine-induced attacks. Caffeine instead induces attacks through the block of cerebellar A1 adenosine receptors. This increases the release of glutamate, which interacts with mGluR1 receptors on PC, resulting in erratic firing and motor attacks. Finally, we show a novel direct interaction between mGluR1 and CK2, and inhibition of mGluR1 prior to initiation of attack, prevents the caffeine-induced increase in phosphorylation. These data elucidate the mechanism underlying stress and caffeine-induced motor impairment. Furthermore, given the success of CX-4945 to prevent stress and caffeine induced attacks, it establishes ground-work for the development of therapeutics for the treatment of caffeine and stress induced attacks in EA2 patients and possibly other episodic channelopathies.

The neural correlates of decision-making have been investigated extensively, and recent work aims to identify under what conditions cortex is actually necessary for making accurate decisions. We discovered that mice with distinct cognitive experiences, beyond sensory and motor learning, use different cortical areas and neural activity patterns to solve the same task, revealing past learning as a critical determinant of whether cortex is necessary for decision tasks. We used optogenetics and calcium imaging to study the necessity and neural activity of multiple cortical areas in mice with different training histories. Posterior parietal cortex and retrosplenial cortex were mostly dispensable for accurate performance of a simple navigation-based visual discrimination task. In contrast, these areas were essential for the same simple task when mice were previously trained on complex tasks with delay periods or association switches. Multi-area calcium imaging showed that, in mice with complex-task experience, single-neuron activity had higher selectivity and neuron-neuron correlations were weaker, leading to codes with higher task information. Therefore, past experience is a key factor in determining whether cortical areas have a causal role in decision tasks.

The arousal construct underlies a spectrum of behaviors that include sleep, exploration, feeding, sexual activity and adaptive stress. Pathological arousal conditions include stress, anxiety disorders, and addiction. The dynamics between arousal state transitions are modulated by norepinephrine neurons in the locus coeruleus, histaminergic neurons in the hypothalamus, dopaminergic neurons in the mesencephalon and cholinergic neurons in the basal forebrain. The hypocretin/orexin system in the lateral hypothalamus I will also present a new mechanism underlying sleep fragmentation during aging. Hcrt neurons are hyperexcitable in aged mice. We identify a potassium conductance known as the M-current, as a critical player in maintaining excitability of Hcrt neurons. Genetic disruption of KCNQ channels in Hcrt neurons of young animals results in sleep fragmentation. In contrast, treatment of aged animals with a KCNQ channel opener restores sleep/wake architecture. These data point to multiple circuits modulating sleep integrity across lifespan.

Inter-individual variability refers to differences in the expression of behaviors between members of a population. For instance, some individuals take greater risks, are more attracted to immediate gains or are more susceptible to drugs of abuse than others. To probe the neural bases of inter-individual variability  we study reward seeking and decision-making in mice, and dissect the specific role of dopamine in the modulation of these behaviors. Using a spatial version of the multi-armed bandit task, in which mice are faced with consecutive binary choices, we could link modifications of midbrain dopamine cell dynamics with modulation of exploratory behaviors, a major component of individual characteristics in mice. By analyzing mouse behaviors in semi-naturalistic environments, we then explored the role of social relationships in the shaping of dopamine activity and associated beahviors. I will present recent data from the laboratory suggesting that changes in the activity of dopaminergic networks link social influences with variations in the expression of non-social behaviors: by acting on the dopamine system, the social context may indeed affect the capacity of individuals to make decisions, as well as their vulnerability to drugs of abuse, in particular nicotine.

In most animals including in humans, emotions occur together with changes in the body, such as variations in breathing or heart rate, sweaty palms, or facial expressions. It has been suggested that this interoceptive information acts as a feedback signal to the brain, enabling adaptive modulation of emotions that is essential for survival. As such, fear, one of our basic emotions, must be kept in a functional balance to minimize risk-taking while allowing for the pursuit of essential needs. However, the neural mechanisms underlying this adaptive modulation of fear remain poorly understood. In this talk, I want to present and discuss the data from my PhD work where we uncover a crucial role for the interoceptive insular cortex in detecting changes in heart rate to maintain an equilibrium between the extinction and maintenance of fear memories in mice.

Accumulating data indicate that the brain can affect immunity, as evidenced, for example, by the effects of stress, stroke, and reward system activity on the peripheral immune system. However, our understanding of this neuroimmune interaction is still limited. Importantly, we do not know how the brain evaluates and represents the state of the immune system. In this talk, I will present our latest study from our lab, designed to test the existence of immune-related information in the brain and determine its relevance to immune regulation. We hypothesized that the InsCtx, specifically the posterior InsCtx (as a primary cortical site of interoception in the brain), is especially suited to contain such a representation of the immune system. Using activity-dependent cell labeling in mice (FosTRAP), we captured neuronal ensembles in the InsCtx that were active under two different inflammatory conditions (dextran sulfate sodium [DSS]-induced colitis and zymosan-induced peritonitis). Chemogenetic reactivation of these neuronal ensembles was sufficient to broadly retrieve the inflammatory state under which these neurons were captured. Moreover, using retrograde neuronal tracing, we found an anatomical efferent pathway linking these InsCtx neurons to the inflamed peripheral sites. Taken together, we show that the brain can store and retrieve specific immune responses, extending the classical concept of immunological memory to neuronal representations of inflammatory information.

Information processing is energetically expensive. In the mammalian brain, it is unclear how information coding and energy usage are regulated during food scarcity. I addressed this in the visual cortex of awake mice using whole-cell recordings and two-photon imaging to monitor layer 2/3 neuronal activity and ATP usage. I found that food restriction reduced synaptic ATP usage by 29% through a decrease in AMPA receptor conductance. Neuronal excitability was nonetheless preserved by a compensatory increase in input resistance and a depolarized resting membrane potential. Consequently, neurons spiked at similar rates as controls, but spent less ATP on underlying excitatory currents. This energy-saving strategy had a cost since it amplified the variability of visually-evoked subthreshold responses, leading to a 32% broadening in orientation tuning and impaired fine visual discrimination. This reduction in coding precision was associated with reduced levels of the fat mass-regulated hormone leptin and was restored by exogenous leptin supplementation. These findings reveal novel mechanisms that dynamically regulate energy usage and coding precision in neocortex.

Human movement disorders and pharmacological studies have long suggested molecular dopamine modulates the pace of the internal clock. But how does the endogenous dopaminergic system influence the timing of our movements? We examined the relationship between dopaminergic signaling and the timing of reward-related, self-timed movements in mice. Animals were trained to initiate licking after a self-timed interval following a start cue; reward was delivered if the animal’s first lick fell within a rewarded window (3.3-7 s). The first-lick timing distributions exhibited the scalar property, and we leveraged the considerable variability in these distributions to determine how the activity of the dopaminergic system related to the animals’ timing. Surprisingly, dopaminergic signals ramped-up over seconds between the start-timing cue and the self-timed movement, with variable dynamics that predicted the movement/reward time, even on single trials. Steeply rising signals preceded early initiation, whereas slowly rising signals preceded later initiation. Higher baseline signals also predicted earlier self-timed movement. Optogenetic activation of dopamine neurons during self-timing did not trigger immediate movements, but rather caused systematic early-shifting of the timing distribution, whereas inhibition caused late-shifting, as if dopaminergic manipulation modulated the moment-to-moment probability of unleashing the planned movement. Consistent with this view, the dynamics of the endogenous dopaminergic signals quantitatively predicted the moment-by-moment probability of movement initiation. We conclude that ramping dopaminergic signals, potentially encoding dynamic reward expectation, probabilistically modulate the moment-by-moment decision of when to move. (Based on work from Hamilos et al., eLife, 2021).

The gradual accumulation of hyperphosphorylated forms of the Tau protein (pTau) in the human brain correlate with cognitive dysfunction and neurodegeneration. I will present our recent findings on the consequences of human pTau aggregation in the hippocampal formation of a mouse tauopathy model. We show that pTau preferentially accumulates in deep-layer pyramidal neurons, leading to their neurodegeneration. In aged but not younger mice, pTau spreads to oligodendrocytes. During ‘goal-directed’ navigation, we detect fewer high-firing pyramidal cells, but coupling to network oscillations is maintained in the remaining cells. The firing patterns of individually recorded and labelled pyramidal and GABAergic neurons are similar in transgenic and non-transgenic mice, as are network oscillations, suggesting intact neuronal coordination. This is consistent with a lack of pTau in subcortical brain areas that provide rhythmic input to the cortex. Spatial memory tests reveal a reduction in short-term familiarity of spatial cues but unimpaired spatial working and reference memory. These results suggest that preserved subcortical network mechanisms compensate for the widespread pTau aggregation in the hippocampal formation. I will also briefly discuss ideas on the subcortical origins of spatial memory and the concept of the cortex as a monitoring device.