Neuroscience seminars

Dysregulation of emotional processing and its integration with cognitive functions are central features of many mental/emotional disorders associated both with externalizing problems (aggressive, antisocial behaviors) and internalizing problems (anxiety, depression). As Dr. Joseph LeDoux, our invited speaker of this program, wrote in his famous book “Synaptic self: How Our Brains Become Who We Are”—the brain’s synapses—are the channels through which we think, act, imagine, feel, and remember. Synapses encode the essence of personality, enabling each of us to function as a distinctive, integrated individual from moment to moment. Thus, exploring the functioning of synapses leads to the understanding of the mechanism of (patho)physiological function of our brain. In this context, we have investigated the pathophysiology of psychiatric disorders, with particular emphasis on the synaptic function of model mice of various psychiatric disorders such as schizophrenia, autism, depression, and PTSD. Our current interest is how synaptic inputs are integrated to generate the action potential. Because the spatiotemporal organization of neuronal firing is crucial for information processing, but how thousands of inputs to the dendritic spines drive the firing remains a central question in neuroscience. We identified a distinct pattern of synaptic integration in the disease-related models, in which extra-large (XL) spines generate NMDA spikes within these spines, which was sufficient to drive neuronal firing. We experimentally and theoretically observed that XL spines negatively correlated with working memory. Our work offers a whole new concept for dendritic computation and network dynamics, and the understanding of psychiatric research will be greatly reconsidered. The second half of my talk is the development of a novel synaptic tool. Because, no matter how beautifully we can illuminate the spine morphology and how accurately we can quantify the synaptic integration, the links between synapse and brain function remain correlational. In order to challenge the causal relationship between synapse and brain function, we established AS-PaRac1, which is unique not only because it can specifically label and manipulate the recently potentiated dendritic spine (Hayashi-Takagi et al, 2015, Nature). With use of AS-PaRac1, we developed an activity-dependent simultaneous labeling of the presynaptic bouton and the potentiated spines to establish “functional connectomics” in a synaptic resolution. When we apply this new imaging method for PTSD model mice, we identified a completely new functional neural circuit of brain region A→B→C with a very strong S/N in the PTSD model mice. This novel tool of “functional connectomics” and its photo-manipulation could open up new areas of emotional/psychiatric research, and by extension, shed light on the neural networks that determine who we are.

Many of the collaborations we encounter in our scientific careers are centered on a common idea that can be associated with certain resources, such as a dataset, an algorithm, or a model. All partners in a collaboration need to develop a common understanding of these resources, and need to be able to access them in a simple and unambiguous manner in order to avoid incorrect conclusions especially in highly cross-disciplinary contexts. While digital computers have entered to assist scientific workflows in experiment and simulation for many decades, the high degree of heterogeneity in the field had led to a scattered landscape of highly customized, lab-internal solutions to organizing and managing the resources on a project-by-project basis. Only with the availability of modern technologies such as the semantic web, platforms for collaborative coding or the development of data standards overarching different disciplines, we have tools at our disposal to make resources increasingly more accessible, understandable, and usable. However, without overarching standardization efforts and adaptation of such technologies to the workflows and needs of individual researchers, their adoption by the neuroscience community will be impeded. From the perspective of computational neuroscience, which is inherently dependent on leveraging data and methods across the field of neuroscience for inspiration and validation, I will outline my view on past and present developments towards a more rigorous use of digital resources and how they improved collaboration, and introduce emerging initiatives to support this process in the future (e.g., EBRAINS http://ebrains.eu, NFDI-Neuro http://www.nfdi-neuro.de).

Understanding how the brain uses information is a fundamental goal of neuroscience. Several human disorders (ranging from autism spectrum disorder to PTSD to Alzheimer’s disease) may stem from disrupted information processing. Therefore, this basic knowledge is not only critical for understanding normal brain function, but also vital for the development of new treatment strategies for these disorders. Memory may be defined as the retention over time of internal representations gained through experience, and the capacity to reconstruct these representations at later times. Long-lasting physical brain changes (‘engrams’) are thought to encode these internal representations. The concept of a physical memory trace likely originated in ancient Greece, although it wasn’t until 1904 that Richard Semon first coined the term ‘engram’. Despite its long history, finding a specific engram has been challenging, likely because an engram is encoded at multiple levels (epigenetic, synaptic, cell assembly). My lab is interested in understanding how specific neurons are recruited or allocated to an engram, and how neuronal membership in an engram may change over time or with new experience. Here I will describe both older and new unpublished data in our efforts to understand memories in mice.

At the foundation of human conscious experience lie basic embodied experiences of selfhood – experiences of simply ‘being alive’. In this talk, I will make the case that this central feature of human existence is underpinned by predictive regulation of the interior of the body, using the framework of predictive processing, or active inference. I start by showing how conscious experiences of the world around us can be understood in terms of perceptual predictions, drawing on examples from psychophysics and virtual reality. Then, turning the lens inwards, we will see how the experience of being an ‘embodied self’ rests on control-oriented predictive (allostatic) regulation of the body’s physiological condition. This approach implies a deep connection between mind and life, and provides a new way to understand the subjective nature of consciousness as emerging from systems that care intrinsically about their own existence. Contrary to the old doctrine of Descartes, we are conscious because we are beast machines.

Human society operates on large-scale cooperation and shared norms of fairness. However, individual differences in cooperation and incentives to free-riding on others’ cooperation make large-scale cooperation fragile and can lead to reduced social-welfare. Deciphering the neural codes representing potential rewards/costs for self and others is crucial for understanding social decision-making and cooperation. I will first talk about how we integrate computational modeling with functional magnetic resonance imaging to investigate the neural representation of social value and the modulation by oxytocin, a nine-amino acid neuropeptide, in participants evaluating monetary allocations to self and other (self-other allocations). Then I will introduce our recent studies examining the neurobiological mechanisms underlying intergroup decision-making using hyper-scanning, and share with you how we alter intergroup decisions using psychological manipulations and pharmacological challenge. Finally, I will share with you our on-going project that reveals how individual cooperation spreads through human social networks. Our results help to better understand the neurocomputational mechanism underlying interpersonal and intergroup decision-making.

Neuromodulators control information processing in cortical microcircuits by regulating the cellular and synaptic physiology of neurons. Computational models and detailed simulations of neocortical microcircuitry offer a unifying framework to analyze the role of neuromodulators on network activity. In the present study, to get a deeper insight in the organization of the cortical neuropil for modeling purposes, we quantify the fiber length per cortical volume and the density of varicosities for catecholaminergic, serotonergic and cholinergic systems using immunocytochemical staining and stereological techniques. The data obtained are integrated into a biologically detailed digital reconstruction of the rodent neocortex (Markram et al, 2015) in order to model the influence of modulatory systems on the activity of the somatosensory cortex neocortical column. Simulations of ascending modulation of network activity in our model predict the effects of increasing levels of neuromodulators on diverse neuron types and synapses and reveal a spectrum of activity states. Low levels of neuromodulation drive microcircuit activity into slow oscillations and network synchrony, whereas high neuromodulator concentrations govern fast oscillations and network asynchrony. The models and simulations thus provide a unifying in silico framework to study the role of neuromodulators in reconfiguring network activity.

The vagus nerve is a major pathway by which the brain and the body communicate. Electrical stimulation of the vagus nerve (VNS) is widely used as a therapeutic intervention for epilepsy and there is compelling evidence that it can enhance recovery following stroke. Our work demonstrates that VNS exerts a robust excitatory effect on the brain. First, we establish that VNS triggers an increase in arousal state as measured by behavioral state change. This behavioral state change is linked to an increase in excitatory activity within the cortex. We also show that cholinergic and noradrenergic neuromodulatory pathways are activated by VNS, providing a potential mechanism by which VNS may trigger cortical activation. Importantly, the effect of VNS on neuromodulation and cortical excitation persists in anesthetized mice, demonstrating that VNS-induced cortical activation cannot be fully explained by associated behavioral changes.

Two-photon (2P) in vivo functional imaging of genetically encoded fluorescent Ca2+indicators (GECIs) for neuronal activity has become a broadly applied standard tool in modern neuroscience, because it allows simultaneous imaging of the activity of many neurons at high spatial resolution within living animals. Unfortunately, the most commonly used light-sources – tunable femtosecond pulsed ti:sapphire lasers – can be prohibitively expensive for many labs and fall short of delivering sufficient powers for some new ultra-fast 2P microscopy modalities. Inexpensive homebuilt or industrial light sources such as Ytterbium fiber lasers (YbFLs) show great promise to overcome these limitations as they are becoming widely available at costs orders of magnitude lower and power outputs of up to many times higher than conventional ti:sapphire lasers. However, these lasers are typically bound to emitting a single wavelength (i.e., not tunable) centered around 1020-1060 nm, which fails to efficiently excite state of the art green GECIs such as jGCaMP7 or 8. To this end, we designed and characterized spectral variants (yellow CaMP = YCaMP) of the ultrasensitive genetically encoded calcium indicator jGCaMP7, that allows for efficient 2P-excitation at wavelengths above 1010nm. In this talk I will give a brief overview over some of the reasons why using a fiber laser for 2P excitation might be right for you. I will talk about the development of jYCaMP and some exciting new experimental avenues that it has opened while touching on the prospect that shifting biosensors yellow could have for the 2P imaging community. Please join me for an interesting and fun discussion on whether “yellow is the new green” after the talk!

Waves and patterns in the brain are well known to subserve natural computation. Much attention in the theoretical neuroscience community has been devoted to analysing networks of relatively simple spiking neurons (IF type) or firing rate models (Wilson-Cowan type) and to great effect! Indeed, the understanding of how spatio-temporal patterns of neural activity may arise in the cortex has advanced significantly with the development and analysis of such models. To replicate this success for sub-cortical tissues requires an extension to include relevant ionic currents that can further shape firing response. Here I will advocate for two complementary approaches: i) that augments the approach for IF networks to include piecewise linear caricatures of gating dynamics for nonlinear ionic current models, ii) firing rate reductions for systems where the nonlinear ionic currents are slow. By way of illustration, I will show how to construct spatially periodic waves and patterns in i) a simple spiking tissue model of medial enthorinal cortex (with an I_h current), ii) a firing rate model of thalamus (with an I_T current). The biological commonality between these two models is that both express local 'rebound' currents that can usefully shape global tissue response. The mathematical commonality is the use of tools from non-smooth dynamical systems theory to make analytical progress in determining patterns and their stability. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2021-06-30. Recording duration: 00:40:23.

Organisms face challenges to survival throughout life. When we freeze or flee in danger, we often feel fear. Tracing the deep history of danger gives a different perspective. The first cells living billions of years ago had to detect and respond to danger in order to survive. Life is about not being dead, and behavior is a major way that organisms hold death off. Although behavior does not require a nervous system, complex organisms have brain circuits for detecting and responding to danger, the deep roots of which go back to the first cells. But these circuits do not make fear, and fear is not the cause of why we freeze or flee. Fear a human invention; a construct we use to account for what happens in our minds when we become aware that we are in harm’s way. This requires a brain that can personally know that it existed in the past, that it is the entity that might be harmed in the present, and that it will cease to exist it the future. If other animals have conscious experiences, they cannot have the kinds of conscious experiences we have because they do not have the kinds of brains we have. This is not meant as a denial of animal consciousness; it is simply a statement about the fact that every species has a different brain. Nor is it a declaration about the wonders of the human brain, since we have done some wonderful, but also horrific, things with our brains. In fact, we are on the way to a climatic disaster that will not, as some suggest, destroy the Earth. But it will make it inhabitable for our kind, and other organisms with high energy demands. Bacteria have made it for billions of years and will likely be fine. The rest is up for grabs, and, in a very real sense, up to us.

In order to successfully navigate through the environment, animals must accurately estimate the status of their motion with respect to the surrounding scene and objects. In this talk, I will present our recent work on how retrosplenial cortical (RSC) neurons combine vestibular and visual signals to reliably encode the direction and speed of head turns during passive motion and active navigation. I will discuss these data in the context of RSC long-range connectivity and further show our ongoing work on building population-level models of motion representation across cortical and subcortical networks.

For many animals, the sense of olfaction plays a major role in controlling sexual behaviors. Olfaction helps animals to detect mates, discriminate their status, and ultimately, decide on their behavioral output such as courtship behavior or aggression. Specific pheromone cues and receptors have provided a useful model to study how sensory inputs are converted into certain behavioral outputs. With the aid of recent advances in tools to record and manipulate genetically defined neurons, our understanding of the neural basis of sexual and social behavior has expanded substantially. I will discuss the current understanding of the neural processing of sex pheromones and the neural circuitry which controls sexual and social behaviors and ultimately reproduction, by focusing on rodent studies, mainly in mice, and the vomeronasal sensory system.

Interoception, the sense of internal bodily signals, is essential for physiological homeostasis, cognition, and emotions. While human insular cortex (InsCtx) is implicated in interoception, the cellular and circuit mechanisms remain unclear. I will describe our recent work imaging mouse InsCtx neurons during two physiological deficiency states – hunger and thirst. InsCtx ongoing activity patterns reliably tracked the gradual return to homeostasis, but not changes in behavior. Accordingly, while artificial induction of hunger/thirst in sated mice via activation of specific hypothalamic neurons (AgRP/SFOGLUT) restored cue-evoked food/water-seeking, InsCtx ongoing activity continued to reflect physiological satiety. During natural hunger/thirst, food/water cues rapidly and transiently shifted InsCtx population activity to the future satiety-related pattern. During artificial hunger/thirst, food/water cues further shifted activity beyond the current satiety-related pattern. Together with circuit-mapping experiments, these findings suggest that InsCtx integrates visceral-sensory inputs regarding current physiological state with hypothalamus-gated amygdala inputs signaling upcoming ingestion of food/water, to compute a prediction of future physiological state.

Transient changes in dopamine activity in response to reward and punishment have been known to regulate reward-related learning. However, the cellular basis that detects the transient dopamine signaling has long been unclear. Using two-photon microscopy and optogenetics, I have shown that transient increases and decreases of dopamine modulate plasticity of dopamine D1 and D2 receptor-expressing cells in the nucleus accumbens, respectively. At the behavioral level, I characterized that these D1 and D2 cells cooperatively tune learning by generalization and discrimination learning. Interestingly, disturbance of the dopamine signaling impaired D2 cell plasticity and discrimination learning, which was analogous to salience misattribution seen in subjects with schizophrenia.

The last decade in the field of neuroscience has been marked by intense debate on the meaning of the term fear. Whereas some have argued that fear (as well as other emotions) relies on cognitive capacities that are unique to humans, others view it as a negative state constructed from essential building blocks. This latter definition posits that fear states are associated with varying readouts that one could consider to be parallel processes or serial events tied to a specific hierarchy. Within this framework, innate defensive behaviors are considered to be common displays of fear states that lie under the control of hard-wired brain circuits. As a general rule, these defensive behaviors can be classified as either reactive or cognitive based on a thread imminence continuum. However, while evidence of the neuronal circuits that lead to these divergent behavioral strategies has accrued over the last decades, most literature has considered these responses in isolation. As a result, important misconceptions have arisen regarding how fear circuits are distributed in the brain and the contribution of specific nodes within these circuits to defensive behaviors. To mitigate the status quo, I will conduct a systematic comparison of brain circuits driving the expression of freezing and active avoidance behavior, which I will use as well-studied proxies of reactive and cognitive fear, respectively. In addition, I propose that by integrating associative information with interoceptive and exteroceptive signals the central nucleus of the amygdala plays a crucial role in biasing the selection of defensive behaviors.

There is a surprising consistency in the images we remember and forget – across observers, certain images are intrinsically more memorable than others in spite of our diverse individual experiences. The perception of images at different memorability levels also results in stereotyped patterns in visual and mnemonic regions in the brain, regardless of an individual’s actual memory for that item. In this talk, Dr. Bainbridge will discuss our current neuroscientific understanding of how memorability is represented in patterns in the brain, potentially serving as a signal for how stimulus information is prioritized for eventual memory encoding.

This talk will focus on two areas. I will firstly discuss some new data, starting with insights from rare human genetic variants, which helps to clarify the role of the central melanocortin system in the acquisition of nutrients and their disposition into growth, the acquisition of lean mass and sexual maturation . I will then discuss some aspects of the emerging biology of GDF15; a sentinel hormone conveying information regarding a range of somatic stresses to the brain.

Adult hippocampal neurogenesis is implicated in memory formation and mood regulation. The Thuret lab investigates environmental and molecular mechanisms controlling the production of these adult-born neurons and how they impact mental health. We study neurogenesis in healthy ageing as well as in the context of diseases such as Alzheimer’s and depression. By approaching neurogenesis in health and disease, the strategy is two folds: (i) Validating the neurogenic process as a target for prevention and pharmacological interventions. (ii) Developing neurogenesis as a biomarker of disease prediction and progression. In this talk, I will focus on presenting some recent human studies demonstrating how hippocampal neural stem cells fate can be used as biomarkers of cognitive aging and dementia.