Scaling Up Bioimaging with Microfluidic Chips

Explore how microfluidic chips can enhance your imaging experiments by increasing control, throughput, or flexibility. In this remote, personalized workshop, participants will receive expert guidance, support and chips to run tests on their own microscopes.

Rejuvenating the Alzheimer’s brain: Challenges & Opportunities

Unlocking the Secrets of Microglia in Neurodegenerative diseases: Mechanisms of resilience to AD pathologies

Digital Minds: Brain Development in the Age of Technology

Digital Minds: Brain Development in the Age of Technology examines how our increasingly connected world shapes mental and cognitive health. From screen time and social media to virtual interactions, this seminar delves into the latest research on how technology influences brain development, relationships, and emotional well-being. Join us to explore strategies for harnessing technology's benefits while mitigating its potential challenges, empowering you to thrive in a digital age.

Analyzing Network-Level Brain Processing and Plasticity Using Molecular Neuroimaging

Behavior and cognition depend on the integrated action of neural structures and populations distributed throughout the brain. We recently developed a set of molecular imaging tools that enable multiregional processing and plasticity in neural networks to be studied at a brain-wide scale in rodents and nonhuman primates. Here we will describe how a novel genetically encoded activity reporter enables information flow in virally labeled neural circuitry to be monitored by fMRI. Using the reporter to perform functional imaging of synaptically defined neural populations in the rat somatosensory system, we show how activity is transformed within brain regions to yield characteristics specific to distinct output projections. We also show how this approach enables regional activity to be modeled in terms of inputs, in a paradigm that we are extending to address circuit-level origins of functional specialization in marmoset brains. In the second part of the talk, we will discuss how another genetic tool for MRI enables systematic studies of the relationship between anatomical and functional connectivity in the mouse brain. We show that variations in physical and functional connectivity can be dissociated both across individual subjects and over experience. We also use the tool to examine brain-wide relationships between plasticity and activity during an opioid treatment. This work demonstrates the possibility of studying diverse brain-wide processing phenomena using molecular neuroimaging.

Mouse Motor Cortex Circuits and Roles in Oromanual Behavior

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.

Trackoscope: A low-cost, open, autonomous tracking microscope for long-term observations of microscale organisms

Cells and microorganisms are motile, yet the stationary nature of conventional microscopes impedes comprehensive, long-term behavioral and biomechanical analysis. The limitations are twofold: a narrow focus permits high-resolution imaging but sacrifices the broader context of organism behavior, while a wider focus compromises microscopic detail. This trade-off is especially problematic when investigating rapidly motile ciliates, which often have to be confined to small volumes between coverslips affecting their natural behavior. To address this challenge, we introduce Trackoscope, an 2-axis autonomous tracking microscope designed to follow swimming organisms ranging from 10μm to 2mm across a 325 square centimeter area for extended durations—ranging from hours to days—at high resolution. Utilizing Trackoscope, we captured a diverse array of behaviors, from the air-water swimming locomotion of Amoeba to bacterial hunting dynamics in Actinosphaerium, walking gait in Tardigrada, and binary fission in motile Blepharisma. Trackoscope is a cost-effective solution well-suited for diverse settings, from high school labs to resource-constrained research environments. Its capability to capture diverse behaviors in larger, more realistic ecosystems extends our understanding of the physics of living systems. The low-cost, open architecture democratizes scientific discovery, offering a dynamic window into the lives of previously inaccessible small aquatic organisms.

Navigating semantic spaces: recycling the brain GPS for higher-level cognition

Humans share with other animals a complex neuronal machinery that evolved to support navigation in the physical space and that supports wayfinding and path integration. In my talk I will present a series of recent neuroimaging studies in humans performed in my Lab aimed at investigating the idea that this same neural navigation system (the “brain GPS”) is also used to organize and navigate concepts and memories, and that abstract and spatial representations rely on a common neural fabric. I will argue that this might represent a novel example of “cortical recycling”, where the neuronal machinery that primarily evolved, in lower level animals, to represent relationships between spatial locations and navigate space, in humans are reused to encode relationships between concepts in an internal abstract representational space of meaning.

The Role of Cognitive Appraisal in the Relationship between Personality and Emotional Reactivity

Emotion is defined as a rapid psychological process involving experiential, expressive and physiological responses. These emerge following an appraisal process that involves cognitive evaluations of the environment assessing its relevance, implication, coping potential, and normative significance. It has been suggested that changes in appraisal processes lead to changes in the resulting emotional nature. Simultaneously, it was demonstrated that personality can be seen as a predisposition to feel more frequently certain emotions, but the personality-appraisal-emotional response chain is rarely fully investigated. The present project thus sought to investigate the extent to which personality traits influence certain appraisals, which in turn influence the subsequent emotional reactions via a systematic analysis of the link between personality traits of different current models, specific appraisals, and emotional response patterns at the experiential, expressive, and physiological levels. Major results include the coherence of emotion components clustering, and the centrality of the pleasantness, coping potential and consequences appraisals, in context; and the differentiated mediating role of cognitive appraisal in the relation between personality and the intensity and duration of an emotional state, and autonomic arousal, such as Extraversion-pleasantness-experience, and Neuroticism-powerlessness-arousal. Elucidating these relationships deepens our understanding of individual differences in emotional reactivity and spot routes of action on appraisal processes to modify upcoming adverse emotional responses, with a broader societal impact on clinical and non-clinical populations.

The quest for brain identification

In the 17th century, physician Marcello Malpighi observed the existence of distinctive patterns of ridges and sweat glands on fingertips. This was a major breakthrough, and originated a long and continuing quest for ways to uniquely identify individuals based on fingerprints, a technique massively used until today. It is only in the past few years that technologies and methodologies have achieved high-quality measures of an individual’s brain to the extent that personality traits and behavior can be characterized. The concept of “fingerprints of the brain” is very novel and has been boosted thanks to a seminal publication by Finn et al. in 2015. They were among the firsts to show that an individual’s functional brain connectivity profile is both unique and reliable, similarly to a fingerprint, and that it is possible to identify an individual among a large group of subjects solely on the basis of her or his connectivity profile. Yet, the discovery of brain fingerprints opened up a plethora of new questions. In particular, what exactly is the information encoded in brain connectivity patterns that ultimately leads to correctly differentiating someone’s connectome from anybody else’s? In other words, what makes our brains unique? In this talk I am going to partially address these open questions while keeping a personal viewpoint on the subject. I will outline the main findings, discuss potential issues, and propose future directions in the quest for identifiability of human brain networks.

Learning produces a hippocampal cognitive map in the form of an orthogonalized state machine

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.

Neuromodulation of striatal D1 cells shapes BOLD fluctuations in anatomically connected thalamic and cortical regions

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.

Perceptions of responsiveness and rejection in romantic relationships. What are the implications for individuals and relationship functioning?

From birth, human beings need to be embedded into social ties to function best, because other individuals can provide us with a sense of belonging, which is a fundamental human need. One of the closest bonds we build throughout our life is with our intimate partners. When the relationship involves intimacy and when both partners accept and support each other’s needs and goals (through perceived responsiveness) individuals experience an increase in relationship satisfaction as well as physical and mental well-being. However, feeling rejected by a partner may impair the feeling of connectedness and belonging, and affect emotional and behavioural responses. When we perceive our partner to be responsive to our needs or desires, in turn we naturally strive to respond positively and adequately to our partner’s needs and desires. This implies that individuals are interdependent, and changes in one partner prompt changes in the other. Evidence suggests that partners regulate themselves and co-regulate each other in their emotional, psychological, and physiological responses. However, such processes may threaten the relationship when partners face stressful situations or interactions, like the transition to parenthood or rejection. Therefore, in this presentation, I will provide evidence for the role of perceptions of being accepted or rejected by a significant other on individual and relationship functioning, while considering the contextual settings. The three studies presented here explore romantic relationships, and how perceptions of rejection and responsiveness from the partner impact both individuals, their physiological and their emotional responses, as well as their relationship dynamics.

Neuromodulation of subjective experience

Many psychoactive substances are used with the aim of altering experience, e.g. as analgesics, antidepressants or antipsychotics. These drugs act on specific receptor systems in the brain, including the opioid, serotonergic and dopaminergic systems. In this talk, I will summarise human drug studies targeting opioid receptors and their role for human experience, with focus on the experience of pain, stress, mood, and social connection. Opioids are only indicated for analgesia, due to their potential to cause addiction. When these regulations occurred, other known effects were relegated to side effects. This may be the cause of the prevalent myth that opioids are the most potent painkillers, despite evidence from head-to-head trials, Cochrane reviews and network meta-analyses that opioids are not superior to non-opioid analgesics in the treatment of acute or chronic non-cancer pain. However, due to the variability and diversity of opioid effects across contexts and experiences, some people under some circumstances may indeed benefit from prolonged treatment. I will present data on individual differences in opioid effects due to participant sex and stress induction. Understanding the effects of these commonly used medications on other aspects of the human experience is important to ensure correct use and to prevent unnecessary pain and addiction risk.

Mathematical and computational modelling of ocular hemodynamics: from theory to applications

Changes in ocular hemodynamics may be indicative of pathological conditions in the eye (e.g. glaucoma, age-related macular degeneration), but also elsewhere in the body (e.g. systemic hypertension, diabetes, neurodegenerative disorders). Thanks to its transparent fluids and structures that allow the light to go through, the eye offers a unique window on the circulation from large to small vessels, and from arteries to veins. Deciphering the causes that lead to changes in ocular hemodynamics in a specific individual could help prevent vision loss as well as aid in the diagnosis and management of diseases beyond the eye. In this talk, we will discuss how mathematical and computational modelling can help in this regard. We will focus on two main factors, namely blood pressure (BP), which drives the blood flow through the vessels, and intraocular pressure (IOP), which compresses the vessels and may impede the flow. Mechanism-driven models translates fundamental principles of physics and physiology into computable equations that allow for identification of cause-to-effect relationships among interplaying factors (e.g. BP, IOP, blood flow). While invaluable for causality, mechanism-driven models are often based on simplifying assumptions to make them tractable for analysis and simulation; however, this often brings into question their relevance beyond theoretical explorations. Data-driven models offer a natural remedy to address these short-comings. Data-driven methods may be supervised (based on labelled training data) or unsupervised (clustering and other data analytics) and they include models based on statistics, machine learning, deep learning and neural networks. Data-driven models naturally thrive on large datasets, making them scalable to a plethora of applications. While invaluable for scalability, data-driven models are often perceived as black- boxes, as their outcomes are difficult to explain in terms of fundamental principles of physics and physiology and this limits the delivery of actionable insights. The combination of mechanism-driven and data-driven models allows us to harness the advantages of both, as mechanism-driven models excel at interpretability but suffer from a lack of scalability, while data-driven models are excellent at scale but suffer in terms of generalizability and insights for hypothesis generation. This combined, integrative approach represents the pillar of the interdisciplinary approach to data science that will be discussed in this talk, with application to ocular hemodynamics and specific examples in glaucoma research.

Use of brain imaging data to improve prescriptions of psychotropic drugs - Examples of ketamine in depression and antipsychotics in schizophrenia

The use of molecular imaging, particularly PET and SPECT, has significantly transformed the treatment of schizophrenia with antipsychotic drugs since the late 1980s. It has offered insights into the links between drug target engagement, clinical effects, and side effects. A therapeutic window for receptor occupancy is established for antipsychotics, yet there is a divergence of opinions regarding the importance of blood levels, with many downplaying their significance. As a result, the role of therapeutic drug monitoring (TDM) as a personalized therapy tool is often underrated. Since molecular imaging of antipsychotics has focused almost entirely on D2-like dopamine receptors and their potential to control positive symptoms, negative symptoms and cognitive deficits are hardly or not at all investigated. Alternative methods have been introduced, i.e. to investigate the correlation between approximated receptor occupancies from blood levels and cognitive measures. Within the domain of antidepressants, and specifically regarding ketamine's efficacy in depression treatment, there is limited comprehension of the association between plasma concentrations and target engagement. The measurement of AMPA receptors in the human brain has added a new level of comprehension regarding ketamine's antidepressant effects. To ensure precise prescription of psychotropic drugs, it is vital to have a nuanced understanding of how molecular and clinical effects interact. Clinician scientists are assigned with the task of integrating these indispensable pharmacological insights into practice, thereby ensuring a rational and effective approach to the treatment of mental health disorders, signaling a new era of personalized drug therapy mechanisms that promote neuronal plasticity not only under pathological conditions, but also in the healthy aging brain.

Location, time and type of epileptic activity influence how sleep modulates epilepsy

Sleep and epilepsy are tightly interconnected: On the one hand disturbed sleep is known to negatively affect epilepsy, whereas on the other hand epilepsy negatively impacts sleep. In this talk, we leverage on the unique opportunity provided by simultaneous stereo-EEG and sleep recordings to disentangle these relationships. We will discuss latest evidence on if anatomy (temporal vs. extratemporal), time (early vs. late sleep), and type of epileptic activity (ictal vs. interictal) influence how epileptic activity is modulated by sleep. After this talk, attendees will have a more nuanced understanding of the contributions of location, time and type of epileptic activity in the relationship between sleep and epilepsy.

Touch in romantic relationships

Responsive behavior is crucial to relationship quality and well-being across a variety of interpersonal domains. In this talk I will share research from studies in which we investigate how responsiveness is conveyed nonverbally in the context of male friendships and in heterosexual romantic relationships, largely focusing on affectionate touch as a nonverbal signal of understanding, validation, and care

Diverse applications of artificial intelligence and mathematical approaches in ophthalmology

Ophthalmology is ideally placed to benefit from recent advances in artificial intelligence. It is a highly image-based specialty and provides unique access to the microvascular circulation and the central nervous system. This talk will demonstrate diverse applications of machine learning and deep learning techniques in ophthalmology, including in age-related macular degeneration (AMD), the leading cause of blindness in industrialized countries, and cataract, the leading cause of blindness worldwide. This will include deep learning approaches to automated diagnosis, quantitative severity classification, and prognostic prediction of disease progression, both from images alone and accompanied by demographic and genetic information. The approaches discussed will include deep feature extraction, label transfer, and multi-modal, multi-task training. Cluster analysis, an unsupervised machine learning approach to data classification, will be demonstrated by its application to geographic atrophy in AMD, including exploration of genotype-phenotype relationships. Finally, mediation analysis will be discussed, with the aim of dissecting complex relationships between AMD disease features, genotype, and progression.

Identification of dendritic cell-T cell interactions driving immune responses to food

Convergence of scene perception and visuospatial memory in posterior cerebral cortex

Prosody in the voice, face, and hands changes which words you hear

Speech may be characterized as conveying both segmental information (i.e., about vowels and consonants) as well as suprasegmental information - cued through pitch, intensity, and duration - also known as the prosody of speech. In this contribution, I will argue that prosody shapes low-level speech perception, changing which speech sounds we hear. Perhaps the most notable example of how prosody guides word recognition is the phenomenon of lexical stress, whereby suprasegmental F0, intensity, and duration cues can distinguish otherwise segmentally identical words, such as "PLAto" vs. "plaTEAU" in Dutch. Work from our group showcases the vast variability in how different talkers produce stressed vs. unstressed syllables, while also unveiling the remarkable flexibility with which listeners can learn to handle this between-talker variability. It also emphasizes that lexical stress is a multimodal linguistic phenomenon, with the voice, lips, and even hands conveying stress in concert. In turn, human listeners actively weigh these multisensory cues to stress depending on the listening conditions at hand. Finally, lexical stress is presented as having a robust and lasting impact on low-level speech perception, even down to changing vowel perception. Thus, prosody - in all its multisensory forms - is a potent factor in speech perception, determining what speech sounds we hear.

Epigenetic rewiring in Schinzel-Giedion syndrome

During life, a variety of specialized cells arise to grant the right and timely corrected functions of tissues and organs. Regulation of chromatin in defining specialized genomic regions (e.g. enhancers) plays a key role in developmental transitions from progenitors into cell lineages. These enhancers, properly topologically positioned in 3D space, ultimately guide the transcriptional programs. It is becoming clear that several pathologies converge in differential enhancer usage with respect to physiological situations. However, why some regulatory regions are physiologically preferred, while some others can emerge in certain conditions, including other fate decisions or diseases, remains obscure. Schinzel-Giedion syndrome (SGS) is a rare disease with symptoms such as severe developmental delay, congenital malformations, progressive brain atrophy, intractable seizures, and infantile death. SGS is caused by mutations in the SETBP1 gene that results in its accumulation further leading to the downstream accumulation of SET. The oncoprotein SET has been found as part of the histone chaperone complex INHAT that blocks the activity of histone acetyltransferases suggesting that SGS may (i) represent a natural model of alternative chromatin regulation and (ii) offer chances to study downstream (mal)adaptive mechanisms. I will present our work on the characterization of SGS in appropriate experimental models including iPSC-derived cultures and mouse.

How Children Design by Analogy: The Role of Spatial Thinking

Analogical reasoning is a common reasoning tool for learning and problem-solving. Existing research has extensively studied children’s reasoning when comparing, or choosing from ready-made analogies. Relatively less is known about how children come up with analogies in authentic learning environments. Design education provides a suitable context to investigate how children generate analogies for creative learning purposes. Meanwhile, the frequent use of visual analogies in design provides an additional opportunity to understand the role of spatial reasoning in design-by-analogy. Spatial reasoning is one of the most studied human cognitive factors and is critical to the learning of science, technology, engineering, arts, and mathematics (STEAM). There is growing interest in exploring the interplay between analogical reasoning and spatial reasoning. In this talk, I will share qualitative findings from a case study, where a class of 11-to-12-year-olds in the Netherlands participated in a biomimicry design project. These findings illustrate (1) practical ways to support children’s analogical reasoning in the ideation process and (2) the potential role of spatial reasoning as seen in children mapping form-function relationships in nature analogically and adaptively to those in human designs.

Central place foraging: how insects anchor spatial information

Many insect species maintain a nest around which their foraging behaviour is centered, and can use path integration to maintain an accurate estimate of their distance and direction (a vector) to their nest. Some species, such as bees and ants, can also store the vector information for multiple salient locations in the world, such as food sources, in a common coordinate system. They can also use remembered views of the terrain around salient locations or along travelled routes to guide return. Recent modelling of these abilities shows convergence on a small set of algorithms and assumptions that appear sufficient to account for a wide range of behavioural data, and which can be mapped to specific insect brain circuits. Notably, this does not include any significant topological knowledge: the insect does not need to recover the information (implicit in their vector memory) about the relationships between salient places; nor to maintain any connectedness or ordering information between view memories; nor to form any associations between views and vectors. However, there remains some experimental evidence not fully explained by these algorithms that may point towards the existence of a more complex or integrated mental map in insects.

Research Data Management in neuroimaging

This set of short webinars will provide neuroscience researchers working in a neuroimaging setting with practical tips on strengthening credibility at different stages of the research project. Each webinar will be hosted by Cassandra Gould Van Praag from the Wellcome Centre for Integrative Neuroimaging.

Cell-type specific alterations underpinning convergent ASD phenotypes in PACS1 neurodevelopmental disorder

Exploring the Potential of High-Density Data for Neuropsychological Testing with Coregraph

Coregraph is a tool under development that allows us to collect high-density data patterns during the administration of classic neuropsychological tests such as the Trail Making Test and Clock Drawing Test. These tests are widely used to evaluate cognitive function and screen for neurodegenerative disorders, but traditional methods of data collection only yield sparse information, such as test completion time or error types. By contrast, the high-density data collected with Coregraph may contribute to a better understanding of the cognitive processes involved in executing these tests. In addition, Coregraph may potentially revolutionize the field of cognitive evaluation by aiding in the prediction of cognitive deficits and in the identification of early signs of neurodegenerative disorders such as Alzheimer's dementia. By analyzing high-density graphomotor data through techniques like manual feature engineering and machine learning, we can uncover patterns and relationships that would be otherwise hidden with traditional methods of data analysis. We are currently in the process of determining the most effective methods of feature extraction and feature analysis to develop Coregraph to its full potential.

Data privacy for neuroimaging

This set of short webinars will provide neuroscience researchers working in a neuroimaging setting with practical tips on strengthening credibility at different stages of the research project. Each webinar will be hosted by Cassandra Gould Van Praag from the Wellcome Centre for Integrative Neuroimaging.

Preregistration in neuroimaging

This set of short webinars will provide neuroscience researchers working in a neuroimaging setting with practical tips on strengthening credibility at different stages of the research project. Each webinar will be hosted by Cassandra Gould Van Praag from the Wellcome Centre for Integrative Neuroimaging.

Does subjective time interact with the heart rate?

Decades of research have investigated the relationship between perception of time and heart rate with often mixed results. In search of such a relationship, I will present my far journey between two projects: from time perception in the realistic VR experience of crowded subway trips in the order of minutes (project 1); to the perceived duration of sub-second white noise tones (project 2). Heart rate had multiple concurrent relationships with subjective temporal distortions for the sub-second tones, while the effects were lacking or weak for the supra-minute subway trips. What does the heart have to do with sub-second time perception? We addressed this question with a cardiac drift-diffusion model, demonstrating the sensory accumulation of temporal evidence as a function of heart rate.

Bridging the gap between artificial models and cortical circuits

Artificial neural networks simplify complex biological circuits into tractable models for computational exploration and experimentation. However, the simplification of artificial models also undermines their applicability to real brain dynamics. Typical efforts to address this mismatch add complexity to increasingly unwieldy models. Here, we take a different approach; by reducing the complexity of a biological cortical culture, we aim to distil the essential factors of neuronal dynamics and plasticity. We leverage recent advances in growing neurons from human induced pluripotent stem cells (hiPSCs) to analyse ex vivo cortical cultures with only two distinct excitatory and inhibitory neuron populations. Over 6 weeks of development, we record from thousands of neurons using high-density microelectrode arrays (HD-MEAs) that allow access to individual neurons and the broader population dynamics. We compare these dynamics to two-population artificial networks of single-compartment neurons with random sparse connections and show that they produce similar dynamics. Specifically, our model captures the firing and bursting statistics of the cultures. Moreover, tightly integrating models and cultures allows us to evaluate the impact of changing architectures over weeks of development, with and without external stimuli. Broadly, the use of simplified cortical cultures enables us to use the repertoire of theoretical neuroscience techniques established over the past decades on artificial network models. Our approach of deriving neural networks from human cells also allows us, for the first time, to directly compare neural dynamics of disease and control. We found that cultures e.g. from epilepsy patients tended to have increasingly more avalanches of synchronous activity over weeks of development, in contrast to the control cultures. Next, we will test possible interventions, in silico and in vitro, in a drive for personalised approaches to medical care. This work starts bridging an important theoretical-experimental neuroscience gap for advancing our understanding of mammalian neuron dynamics.

Intrinsic Geometry of a Combinatorial Sensory Neural Code for Birdsong

Understanding the nature of neural representation is a central challenge of neuroscience. One common approach to this challenge is to compute receptive fields by correlating neural activity with external variables drawn from sensory signals. But these receptive fields are only meaningful to the experimenter, not the organism, because only the experimenter has access to both the neural activity and knowledge of the external variables. To understand neural representation more directly, recent methodological advances have sought to capture the intrinsic geometry of sensory driven neural responses without external reference. To date, this approach has largely been restricted to low-dimensional stimuli as in spatial navigation. In this talk, I will discuss recent work from my lab examining the intrinsic geometry of sensory representations in a model vocal communication system, songbirds. From the assumption that sensory systems capture invariant relationships among stimulus features, we conceptualized the space of natural birdsongs to lie on the surface of an n-dimensional hypersphere. We computed composite receptive field models for large populations of simultaneously recorded single neurons in the auditory forebrain and show that solutions to these models define convex regions of response probability in the spherical stimulus space. We then define a combinatorial code over the set of receptive fields, realized in the moment-to-moment spiking and non-spiking patterns across the population, and show that this code can be used to reconstruct high-fidelity spectrographic representations of natural songs from evoked neural responses. Notably, we find that topological relationships among combinatorial codewords directly mirror acoustic relationships among songs in the spherical stimulus space. That is, the time-varying pattern of co-activity across the neural population expresses an intrinsic representational geometry that mirrors the natural, extrinsic stimulus space.  Combinatorial patterns across this intrinsic space directly represent complex vocal communication signals, do not require computation of receptive fields, and are in a form, spike time coincidences, amenable to biophysical mechanisms of neural information propagation.

Navigating Increasing Levels of Relational Complexity: Perceptual, Analogical, and System Mappings

Relational thinking involves comparing abstract relationships between mental representations that vary in complexity; however, this complexity is rarely made explicit during everyday comparisons. This study explored how people naturally navigate relational complexity and interference using a novel relational match-to-sample (RMTS) task with both minimal and relationally directed instruction to observe changes in performance across three levels of relational complexity: perceptual, analogy, and system mappings. Individual working memory and relational abilities were examined to understand RMTS performance and susceptibility to interfering relational structures. Trials were presented without practice across four blocks and participants received feedback after each attempt to guide learning. Experiment 1 instructed participants to select the target that best matched the sample, while Experiment 2 additionally directed participants’ attention to same and different relations. Participants in Experiment 2 demonstrated improved performance when solving analogical mappings, suggesting that directing attention to relational characteristics affected behavior. Higher performing participants—those above chance performance on the final block of system mappings—solved more analogical RMTS problems and had greater visuospatial working memory, abstraction, verbal analogy, and scene analogy scores compared to lower performers. Lower performers were less dynamic in their performance across blocks and demonstrated negative relationships between analogy and system mapping accuracy, suggesting increased interference between these relational structures. Participant performance on RMTS problems did not change monotonically with relational complexity, suggesting that increases in relational complexity places nonlinear demands on working memory. We argue that competing relational information causes additional interference, especially in individuals with lower executive function abilities.

Internally Organized Abstract Task Maps in the Mouse Medial Frontal 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.

Linking GWAS to pharmacological treatments for psychiatric disorders

Genome-wide association studies (GWAS) have identified multiple disease-associated genetic variations across different psychiatric disorders raising the question of how these genetic variants relate to the corresponding pharmacological treatments. In this talk, I will outline our work investigating whether functional information from a range of open bioinformatics datasets such as protein interaction network (PPI), brain eQTL, and gene expression pattern across the brain can uncover the relationship between GWAS-identified genetic variation and the genes targeted by current drugs for psychiatric disorders. Focusing on four psychiatric disorders---ADHD, bipolar disorder, schizophrenia, and major depressive disorder---we assess relationships between the gene targets of drug treatments and GWAS hits and show that while incorporating information derived from functional bioinformatics data, such as the PPI network and spatial gene expression, can reveal links for bipolar disorder, the overall correspondence between treatment targets and GWAS-implicated genes in psychiatric disorders rarely exceeds null expectations. This relatively low degree of correspondence across modalities suggests that the genetic mechanisms driving the risk for psychiatric disorders may be distinct from the pathophysiological mechanisms used for targeting symptom manifestations through pharmacological treatments and that novel approaches for understanding and treating psychiatric disorders may be required.

Investigating activity-dependent processes in cerebral cortex development and disease

The cerebral cortex contains an extraordinary diversity of excitatory projection neuron (PN) and inhibitory interneurons (IN), wired together to form complex circuits. Spatiotemporally coordinated execution of intrinsic molecular programs by PNs and INs and activity-dependent processes, contribute to cortical development and cortical microcircuits formation. Alterations of these delicate processes have often been associated to neurological/neurodevelopmental disorders. However, despite the groundbreaking discovery that spontaneous activity in the embryonic brain can shape regional identities of distinct cortical territories, it is still unclear whether this early activity contributes to define subtype-specific neuronal fate as well as circuit assembly. In this study, we combined in utero genetic perturbations via CRISPR/Cas9 system and pharmacological inhibition of selected ion channels with RNA-sequencing and live imaging technologies to identify the activity-regulated processes controlling the development of different cortical PN classes, their wiring and the acquisition of subtype specific features. Moreover, we generated human induced pluripotent stem cells (iPSCs) form patients affected by a severe, rare and untreatable form of developmental epileptic encephalopathy. By differentiating cortical organoids form patient-derived iPSCs we create human models of early electrical alterations for studying molecular, structural and functional consequences of the genetic mutations during cortical development. Our ultimate goal is to define the activity-conditioned processes that physiologically occur during the development of cortical circuits, to identify novel therapeutical paths to address the pathological consequences of neonatal epilepsies.

A Game Theoretical Framework for Quantifying​ Causes in Neural Networks

Which nodes in a brain network causally influence one another, and how do such interactions utilize the underlying structural connectivity? One of the fundamental goals of neuroscience is to pinpoint such causal relations. Conventionally, these relationships are established by manipulating a node while tracking changes in another node. A causal role is then assigned to the first node if this intervention led to a significant change in the state of the tracked node. In this presentation, I use a series of intuitive thought experiments to demonstrate the methodological shortcomings of the current ‘causation via manipulation’ framework. Namely, a node might causally influence another node, but how much and through which mechanistic interactions? Therefore, establishing a causal relationship, however reliable, does not provide the proper causal understanding of the system, because there often exists a wide range of causal influences that require to be adequately decomposed. To do so, I introduce a game-theoretical framework called Multi-perturbation Shapley value Analysis (MSA). Then, I present our work in which we employed MSA on an Echo State Network (ESN), quantified how much its nodes were influencing each other, and compared these measures with the underlying synaptic strength. We found that: 1. Even though the network itself was sparse, every node could causally influence other nodes. In this case, a mere elucidation of causal relationships did not provide any useful information. 2. Additionally, the full knowledge of the structural connectome did not provide a complete causal picture of the system either, since nodes frequently influenced each other indirectly, that is, via other intermediate nodes. Our results show that just elucidating causal contributions in complex networks such as the brain is not sufficient to draw mechanistic conclusions. Moreover, quantifying causal interactions requires a systematic and extensive manipulation framework. The framework put forward here benefits from employing neural network models, and in turn, provides explainability for them.

The 15th David Smith Lecture in Anatomical Neuropharmacology: Professor Tim Bliss, "Memories of long term potentiation

The David Smith Lectures in Anatomical Neuropharmacology, Part of the 'Pharmacology, Anatomical Neuropharmacology and Drug Discovery Seminars Series', Department of Pharmacology, University of Oxford. The 15th David Smith Award Lecture in Anatomical Neuropharmacology will be delivered by Professor Tim Bliss, Visiting Professor at UCL and the Frontier Institutes of Science and Technology, Xi’an Jiaotong University, China, and is hosted by Professor Nigel Emptage. This award lecture was set up to celebrate the vision of Professor A David Smith, namely, that explanations of the action of drugs on the brain requires the definition of neuronal circuits, the location and interactions of molecules. Tim Bliss gained his PhD at McGill University in Canada. He joined the MRC National Institute for Medical Research in Mill Hill, London in 1967, where he remained throughout his career. His work with Terje Lømo in the late 1960’s established the phenomenon of long-term potentiation (LTP) as the dominant synaptic model of how the mammalian brain stores memories. He was elected as a Fellow of the Royal Society in 1994 and is a founding fellow of the Academy of Medical Sciences. He shared the Bristol Myers Squibb award for Neuroscience with Eric Kandel in 1991, the Ipsen Prize for Neural Plasticity with Richard Morris and Yadin Dudai in 2013. In May 2012 he gave the annual Croonian Lecture at the Royal Society on ‘The Mechanics of Memory’. In 2016 Tim, with Graham Collingridge and Richard Morris shared the Brain Prize, one of the world's most coveted science prizes. Abstract: In 1966 there appeared in Acta Physiologica Scandinavica an abstract of a talk given by Terje Lømo, a PhD student in Per Andersen’s laboratory at the University of Oslo. In it Lømo described the long-lasting potentiation of synaptic responses in the dentate gyrus of the anaesthetised rabbit that followed repeated episodes of 10-20Hz stimulation of the perforant path. Thus, heralded and almost entirely unnoticed, one of the most consequential discoveries of 20th century neuroscience was ushered into the world. Two years later I arrived in Oslo as a visiting post-doc from the National Institute for Medical Research in Mill Hill, London. In this talk I recall the events that led us to embark on a systematic reinvestigation of the phenomenon now known as long-term potentiation (LTP) and will then go on to describe the discoveries and controversies that enlivened the early decades of research into synaptic plasticity in the mammalian brain. I will end with an observer’s view of the current state of research in the field, and what we might expect from it in the future.

Multimodal tracking of motor activity, sleep and mood

This talk will (1) describe patterns and correlates of objectively assessed motor activity (2) present findings on the inter-relationships among motor activity, sleep and circadian rhythms and mood disorders; (3) describe potential of cross species studies of motor activity and related systems to inform human chronobiology research

The neural basis of flexible semantic cognition (BACN Mid-career Prize Lecture 2022)

Semantic cognition brings meaning to our world – it allows us to make sense of what we see and hear, and to produce adaptive thoughts and behaviour. Since we have a wealth of information about any given concept, our store of knowledge is not sufficient for successful semantic cognition; we also need mechanisms that can steer the information that we retrieve so it suits the context or our current goals. This talk traces the neural networks that underpin this flexibility in semantic cognition. It draws on evidence from multiple methods (neuropsychology, neuroimaging, neural stimulation) to show that two interacting heteromodal networks underpin different aspects of flexibility. Regions including anterior temporal cortex and left angular gyrus respond more strongly when semantic retrieval follows highly-related concepts or multiple convergent cues; the multivariate responses in these regions correspond to context-dependent aspects of meaning. A second network centred on left inferior frontal gyrus and left posterior middle temporal gyrus is associated with controlled semantic retrieval, responding more strongly when weak associations are required or there is more competition between concepts. This semantic control network is linked to creativity and also captures context-dependent aspects of meaning; however, this network specifically shows more similar multivariate responses across trials when association strength is weak, reflecting a common controlled retrieval state when more unusual associations are the focus. Evidence from neuropsychology, fMRI and TMS suggests that this semantic control network is distinct from multiple-demand cortex which supports executive control across domains, although challenging semantic tasks recruit both networks. The semantic control network is juxtaposed between regions of default mode network that might be sufficient for the retrieval of strong semantic relationships and multiple-demand regions in the left hemisphere, suggesting that the large-scale organisation of flexible semantic cognition can be understood in terms of cortical gradients that capture systematic functional transitions that are repeated in temporal, parietal and frontal cortex.

Brain and behavioural impacts of early life adversity

Abuse, neglect, and other forms of uncontrollable stress during childhood and early adolescence can lead to adverse outcomes later in life, including especially perturbations in the regulation of mood and emotional states, and specifically anxiety disorders and depression. However, stress experiences vary from one individual to the next, meaning that causal relationships and mechanistic accounts are often difficult to establish in humans. This interdisciplinary talk considers the value of research in experimental animals where stressor experiences can be tightly controlled and detailed investigations of molecular, cellular, and circuit-level mechanisms can be carried out. The talk will focus on the widely used repeated maternal separation procedure in rats where rat offspring are repeatedly separated from maternal care during early postnatal life. This early life stress has remarkably persistent effects on behaviour with a general recognition that maternally-deprived animals are susceptible to depressive-like phenotypes. The validity of this conclusion will be critically appraised with convergent insights from a recent longitudinal study in maternally separated rats involving translational brain imaging, transcriptomics, and behavioural assessment.

Emotions and Partner Phubbing: The Role of Understanding and Validation in Predicting Anger and Loneliness

Interactions between romantic partners may be disturbed by problematic mobile phone use, i.e., phubbing. Research shows that phubbing reduces the ability to be responsive, but emotional aspects of phubbing, such as experiences of anger and loneliness, have not been explored. Anger has been linked to partner blame in negative social interactions, whereas loneliness has been associated with low social acceptance. Moreover, two aspects of partner responsiveness, understanding and validation, refer to the ability to recognize partner’s perspective and convey acceptance of their point of view, respectively. High understanding and validation by partner have been found to prevent from negative affect during social interaction. The impact of understanding and validation on emotions has not been investigated in the context of phubbing, therefore we posit the following exploratory hypotheses. (1) Participants will report higher levels of anger and loneliness on days with phubbing by partner, compared to days without; (2) understanding and validation will moderate the relationship between phubbing intensity and levels of anger and loneliness. We conducted a daily diary study over seven days. Based on a sample of 133 participants in intimate relationships and living with their partners, we analyzed the nested within and between-person data using multilevel models. Participants reported higher levels of anger and loneliness on days they experienced phubbing. Both, understanding and validation, buffer the relationship between phubbing intensity and negative experiences, and the interaction effects indicate certain nuances between the two constructs. Our research provides a unique insight into how specific mechanisms related to couple interactions may explain experiences of anger and loneliness.

Human stem cell models of Alzheimer’s disease and frontotemporal dementia

The development of human induced pluripotent stem cells (iPSC) and their subsequent differentiation into neurons has provided new opportunities for the generation of physiologically-relevant, in vitro disease models. I will present our work using iPSC to modal familial Alzheimer's Disease (fAD) and Frontotemporal Dementia (FTD). We have investigated the mutation-specific effects of APP and PSEN1 mutations on Abeta generation in neurons generated from individuals with fAD, revealing distinct mechanisms that may contribute to clinical heterogeneity in disease. I will also discuss our work to understand the developmental and pathological changes to tau that occur in iPSC-neurons, particularly the challenges of understanding tau pathology in a developmental system, tau proteostasis and how iPSC-neurons may help us identify early signatures of tau pathology in disease.

Astroglial modulation of the antidepressant action of deep brain and bright light stimulation

Even if major depression is now the most common of psychiatric disorders, successful antidepressant treatments are still difficult to achieve. Therefore, a better understanding of the mechanisms of action of current antidepressant treatments is needed to ultimately identify new targets and enhance beneficial effects. Given the intimate relationships between astrocytes and neurons at synapses and the ability of astrocytes to "sense" neuronal communication and release gliotransmitters, an attractive hypothesis is emerging stating that the effects of antidepressants on brain function could be, at least in part, modulated by direct influences of astrocytes on neuronal networks. We will present two preclinical studies revealing a permissive role of glia in the antidepressant response: i) Control of the antidepressant-like effects of rat prefrontal cortex Deep Brain Stimulation (DBS) by astroglia, ii) Modulation of antidepressant efficacy of Bright Light Stimulation (BLS) by lateral habenula astroglia. Therefore, it is proposed that an unaltered neuronal-glial system constitutes a major prerequisite to optimize antidepressant efficacy of DBS or BLS. Collectively, these results pave also the way to the development of safer and more effective antidepressant strategies.

Inter-individual variability in reward seeking and decision making: role of social life and consequence for vulnerability to nicotine

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.

2nd In-Vitro 2D & 3D Neuronal Networks Summit

The event is open to everyone interested in Neuroscience, Cell Biology, Drug Discovery, Disease Modeling, and Bio/Neuroengineering! This meeting is a platform bringing scientists from all over the world together and fostering scientific exchange and collaboration.

2nd In-Vitro 2D & 3D Neuronal Networks Summit

The event is open to everyone interested in Neuroscience, Cell Biology, Drug Discovery, Disease Modeling, and Bio/Neuroengineering! This meeting is a platform bringing scientists from all over the world together and fostering scientific exchange and collaboration.

Multi-modal biomarkers improve prediction of memory function in cognitively unimpaired older adults

Identifying biomarkers that predict current and future cognition may improve estimates of Alzheimer’s disease risk among cognitively unimpaired older adults (CU). In vivo measures of amyloid and tau protein burden and task-based functional MRI measures of core memory mechanisms, such as the strength of cortical reinstatement during remembering, have each been linked to individual differences in memory in CU. This study assesses whether combining CSF biomarkers with fMRI indices of cortical reinstatement improves estimation of memory function in CU, assayed using three unique tests of hippocampal-dependent memory. Participants were 158 CU (90F, aged 60-88 years, CDR=0) enrolled in the Stanford Aging and Memory Study (SAMS). Cortical reinstatement was quantified using multivoxel pattern analysis of fMRI data collected during completion of a paired associate cued recall task. Memory was assayed by associative cued recall, a delayed recall composite, and a mnemonic discrimination task that involved discrimination between studied ‘target’ objects, novel ‘foil’ objects, and perceptually similar ‘lure’ objects. CSF Aβ42, Aβ40, and p-tau181 were measured with the automated Lumipulse G system (N=115). Regression analyses examined cross-sectional relationships between memory performance in each task and a) the strength of cortical reinstatement in the Default Network (comprised of posterior medial, medial frontal, and lateral parietal regions) during associative cued recall and b) CSF Aβ42/Aβ40 and p-tau181, controlling for age, sex, and education. For mnemonic discrimination, linear mixed effects models were used to examine the relationship between discrimination (d’) and each predictor as a function of target-lure similarity. Stronger cortical reinstatement was associated with better performance across all three memory assays. Age and higher CSF p-tau181 were each associated with poorer associative memory and a diminished improvement in mnemonic discrimination as target-lure similarity decreased. When combined in a single model, CSF p-tau181 and Default Network reinstatement strength, but not age, explained unique variance in associative memory and mnemonic discrimination performance, outperforming the single-modality models. Combining fMRI measures of core memory functions with protein biomarkers of Alzheimer’s disease significantly improved prediction of individual differences in memory performance in CU. Leveraging multimodal biomarkers may enhance future prediction of risk for cognitive decline.

How evidence synthesis can boost in vivo credibility

As part of the BNA's ongoing Credibility in Neuroscience work, this series of three short webinars will provide neuroscience researchers working in an in vivo setting with tips on how to improve the credibility of their work. Each webinar will be hosted by Emily Sena, member of the BNA's Credibility Advisory Board, with the opportunity for questions.

Embracing variation to boost reproducibility

As part of the BNA's ongoing Credibility in Neuroscience work, this series of three short webinars will provide neuroscience researchers working in an in vivo setting with tips on how to improve the credibility of their work. Each webinar will be hosted by Emily Sena, member of the BNA's Credibility Advisory Board, with the opportunity for questions.

Improving reliability through design and reporting

As part of the BNA's ongoing Credibility in Neuroscience work, this series of three short webinars will provide neuroscience researchers working in an in vivo setting with tips on how to improve the credibility of their work. Each webinar will be hosted by Emily Sena, member of the BNA's Credibility Advisory Board, with the opportunity for questions.

Emerging Treatment Options in Psychiatry

The World Health Organization (WHO) estimates that untreated mental disorders accountfor 13% of the total global burden of disease, and by 2030, depression alone will be the leadingcause of disability around the world – outpacing heart disease, cancer, and HIV. This grim pictureis further compounded by the mental health burden delivered by the coronavirus pandemic.The lack of novel treatment options in psychiatry is restricted by a limited understanding in theneuroscience basis of mental disorders, availability of relevant biomarkers, poor predictability inanimal models, and high failure rates in psychiatric drug development. However, theannouncement in 2019 from the Federal Drug Administration (FDA) for approvals of newinterventions for treatment-resistant depression (intranasal esketamine) and postpartumdepression (i.v. brexanolone), demand critical attention. Novel public-private partnerships indrug discovery, new translational data on co-morbid biology, in particular the ascendance ofpsycho-immunology, have highlighted the arrival of a new frontier in biological psychiatryresearch for depressive disorders.

Commonly used face cognition tests yield low reliability and inconsistent performance: Implications for test design, analysis, and interpretation of individual differences data

Unfamiliar face processing (face cognition) ability varies considerably in the general population. However, the means of its assessment are not standardised, and selected laboratory tests vary between studies. It is also unclear whether 1) the most commonly employed tests are reliable, 2) participants show a degree of consistency in their performance, 3) and the face cognition tests broadly measure one underlying ability, akin to general intelligence. In this study, we asked participants to perform eight tests frequently employed in the individual differences literature. We examined the reliability of these tests, relationships between them, consistency in participants’ performance, and used data driven approaches to determine factors underpinning performance. Overall, our findings suggest that the reliability of these tests is poor to moderate, the correlations between them are weak, the consistency in participant performance across tasks is low and that performance can be broadly split into two factors: telling faces together, and telling faces apart. We recommend that future studies adjust analyses to account for stimuli (face images) and participants as random factors, routinely assess reliability, and that newly developed tests of face cognition are examined in the context of convergent validity with other commonly used measures of face cognition ability.

Inferring informational structures in neural recordings of drosophila with epsilon-machines

Measuring the degree of consciousness an organism possesses has remained a longstanding challenge in Neuroscience. In part, this is due to the difficulty of finding the appropriate mathematical tools for describing such a subjective phenomenon. Current methods relate the level of consciousness to the complexity of neural activity, i.e., using the information contained in a stream of recorded signals they can tell whether the subject might be awake, asleep, or anaesthetised. Usually, the signals stemming from a complex system are correlated in time; the behaviour of the future depends on the patterns in the neural activity of the past. However these past-future relationships remain either hidden to, or not taken into account in the current measures of consciousness. These past-future correlations are likely to contain more information and thus can reveal a richer understanding about the behaviour of complex systems like a brain. Our work employs the "epsilon-machines” framework to account for the time correlations in neural recordings. In a nutshell, epsilon-machines reveal how much of the past neural activity is needed in order to accurately predict how the activity in the future will behave, and this is summarised in a single number called "statistical complexity". If a lot of past neural activity is required to predict the future behaviour, then can we say that the brain was more “awake" at the time of recording? Furthermore, if we read the recordings in reverse, does the difference between forward and reverse-time statistical complexity allow us to quantify the level of time asymmetry in the brain? Neuroscience predicts that there should be a degree of time asymmetry in the brain. However, this has never been measured. To test this, we used neural recordings measured from the brains of fruit flies and inferred the epsilon-machines. We found that the nature of the past and future correlations of neural activity in the brain, drastically changes depending on whether the fly was awake or anaesthetised. Not only does our study find that wakeful and anaesthetised fly brains are distinguished by how statistically complex they are, but that the amount of correlations in wakeful fly brains was much more sensitive to whether the neural recordings were read forward vs. backwards in time, compared to anaesthetised brains. In other words, wakeful fly brains were more complex, and time asymmetric than anaesthetised ones.

Monash Biomedical Imaging highlights from 2021 and looking ahead to 2022

Despite the challenges COVID-19 has continued to present, Monash Biomedical Imaging (MBI) has had another outstanding year in terms of publications and scientific output. In this webinar, Professor Gary Egan, Director of MBI, will present an overview of MBI’s achievements during 2021 and outline the biomedical imaging research programs and partnerships in 2022. His presentation will cover: • MBI operational and research achievements during 2021 • Biomedical imaging technology developments and research outcomes during 2021 • Linked laboratories and research teams at MBI • Progress on the development of a cyclotron and precision radiopharmaceutical facility at Clayton • Emerging research opportunities at the Monash Heart Hospital in cardiology and cardiovascular disease. Professor Gary Egan is Director of Monash Biomedical Imaging, Director of the ARC Centre of Excellence for Integrative Brain Function and a Distinguished Professor at the Turner Institute for Brain and Mental Health, Monash University. He is also lead investigator of the Victorian Biomedical Imaging Capability, and Deputy Director of the Australian National Imaging Facility. His substantive body of published work has made a significant impact on the neuroimaging and neuroscience fields. He has sustained success in obtaining significant grants to support his own research and the development of facilities to advance biomedical imaging.

NMC4 Short Talk: Multiscale and extended retrieval of associative memory structures in a cortical model of local-global inhibition balance

Inhibitory neurons take on many forms and functions. How this diversity contributes to memory function is not completely known. Previous formal studies indicate inhibition differentiated by local and global connectivity in associative memory networks functions to rescale the level of retrieval of excitatory assemblies. However, such studies lack biological details such as a distinction between types of neurons (excitatory and inhibitory), unrealistic connection schemas, and non-sparse assemblies. In this study, we present a rate-based cortical model where neurons are distinguished (as excitatory, local inhibitory, or global inhibitory), connected more realistically, and where memory items correspond to sparse excitatory assemblies. We use this model to study how local-global inhibition balance can alter memory retrieval in associative memory structures, including naturalistic and artificial structures. Experimental studies have reported inhibitory neurons and their sub-types uniquely respond to specific stimuli and can form sophisticated, joint excitatory-inhibitory assemblies. Our model suggests such joint assemblies, as well as a distribution and rebalancing of overall inhibition between two inhibitory sub-populations – one connected to excitatory assemblies locally and the other connected globally – can quadruple the range of retrieval across related memories. We identify a possible functional role for local-global inhibitory balance to, in the context of choice or preference of relationships, permit and maintain a broader range of memory items when local inhibition is dominant and conversely consolidate and strengthen a smaller range of memory items when global inhibition is dominant. This model therefore highlights a biologically-plausible and behaviourally-useful function of inhibitory diversity in memory.

A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations

Bernstein Conference 2024

Structure-function relationships and extended critical region in modular spiking model

Bernstein Conference 2024

Human iPSC-derived cell grafts promote functional recovery by molecular interaction with stroke-injured brain

FENS Forum 2024

Uncovering relationships between neural network activation changes and parameter dynamics during learning

COSYNE 2023

Altered autophagy in KANSL1 haploinsufficient iPSC-derived astrocytes

FENS Forum 2024

Analysis of the impact of MnCl2 present in atmospheric particulates on synaptic development using brain models based on hiPSCs derived neurons

FENS Forum 2024

Characterisation of Magi-family synaptic scaffolding proteins in human iPSC-derived neurons

FENS Forum 2024

Characterization of the autophagic-lysosomal pathway in Parkinson’s disease using patient iPSC-derived dopaminergic neurons containing a LRRK2 G2019S mutation

FENS Forum 2024

Development of iPSC-derived neural progenitor cells with enhanced migration to stroke tissue and inducible ablation systems

FENS Forum 2024

Development of a microfluidic device to mimic the blood-brain barrier using human iPSC-differentiated cells

FENS Forum 2024

A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations

FENS Forum 2024

Criticality explains structure-function relationships in the human brain

FENS Forum 2024

Decoding retinitis pigmentosa: Unveiling PRPF31 mutation effects on human iPSC-derived retinal organoids in vitro models

FENS Forum 2024

Digital light processing of 3D structures for improved connectivity of hiPSC-derived neurons

FENS Forum 2024

The effect of second-generation antipsychotics on mitochondria and the development of metabolic syndrome

FENS Forum 2024

ESCRT proteins as targets for novel antipsychotic drugs

FENS Forum 2024

Establishment of an in vitro patient-derived hiPSC-based blood-brain barrier model of SYNGAP1 disorder

FENS Forum 2024

Exploring the function of the synaptic adaptor protein p140Cap in human excitatory neurons derived from iPSCs

FENS Forum 2024

Exploring the impact of chemical and electrical stimulation on human-iPSCs-derived neural networks coupled to high-density arrays

FENS Forum 2024

Exploring the maturation of the GABA shift as a diverging mechanism in SCN1A-related epilepsy using patient iPSC-derived neurons

FENS Forum 2024

Exploring relationships between inflammatory mediators and the kynurenine pathway

FENS Forum 2024

Exploring the role of the primary cilium in homeostatic plasticity in hiPSC-derived neuronal networks

FENS Forum 2024

Functional characterization of healthy and Alzheimer’s disease-related 3D neurospheres formed using human iPSC-derived glutamatergic neurons, GABAergic neurons, and astrocytes

FENS Forum 2024

hiPSC-derived dopaminergic and glutamatergic neurons of schizophrenia patients show neuronal aberrations in a co-culture model

FENS Forum 2024

Human iPSC-derived neurogenin 2 (NGN2) cortical neurons develop functional connectivity and small-world network topology in vitro

FENS Forum 2024

Human iPSC-derived neurons to investigate subtype-specific alterations in neurodevelopmental disorders: Our progress on SSADH deficiency

FENS Forum 2024

The human-specific nicotinic receptor subunit CHRFAM7A (dupα7) reduces α7 nAChR function in human iPSC-derived and transgenic mouse neurons

FENS Forum 2024

The importance of high-density microelectrode arrays for recording multi-scale extracellular potential and label-free characterization of network dynamics in iPSC-derived neurons

FENS Forum 2024

Integrating network activity with transcriptomic profiling in hiPSCs-derived neuronal networks to understand the molecular drivers of functional heterogeneity in the context of neurodevelopmental disorders

FENS Forum 2024

Interrogating CDKL5 deficiency disorder using human iPSCs-derived cerebral organoids

FENS Forum 2024

Investigating Parkinson's disease using patient-derived iPSCs transplanted in a human-mouse chimera model

FENS Forum 2024

Investigating synaptic dysfunction caused by AMPA receptor trafficking to lysosomes in familial Alzheimer’s disease iPSC-derived neurons

FENS Forum 2024

Investigation of blood-brain barrier transporter dysfunction in sporadic Alzheimer's disease: Insights from patient iPSC-derived models

FENS Forum 2024

Label-free functional analysis for the characterization of iPSC-derived neural organoid development and maturation

FENS Forum 2024

Modelling Dravet syndrome using human induced pluripotent stem cell (hiPSC)-derived neural circuits

FENS Forum 2024

Mutant huntingtin disrupts global DNA methylation in human iPSC-derived cerebral organoids

FENS Forum 2024

Patient-derived iPSC modeling of Prader-Willi syndrome

FENS Forum 2024

Physiological measurements of activity and microtubule health in human iPSC-derived neurons using fluorescence and second harmonic microscopy

FENS Forum 2024

Plasticity in iPSC-derived 2D cortical neuronal networks

FENS Forum 2024

Prediction of antipsychotic-induced extrapyramidal symptoms in schizophrenia using machine learning

FENS Forum 2024