Harnessing Big Data in Neuroscience: From Mapping Brain Connectivity to Predicting Traumatic Brain Injury

Neuroscience is experiencing unprecedented growth in dataset size both within individual brains and across populations. Large-scale, multimodal datasets are transforming our understanding of brain structure and function, creating opportunities to address previously unexplored questions. However, managing this increasing data volume requires new training and technology approaches. Modern data technologies are reshaping neuroscience by enabling researchers to tackle complex questions within a Ph.D. or postdoctoral timeframe. I will discuss cloud-based platforms such as brainlife.io, that provide scalable, reproducible, and accessible computational infrastructure. Modern data technology can democratize neuroscience, accelerate discovery and foster scientific transparency and collaboration. Concrete examples will illustrate how these technologies can be applied to mapping brain connectivity, studying human learning and development, and developing predictive models for traumatic brain injury (TBI). By integrating cloud computing and scalable data-sharing frameworks, neuroscience can become more impactful, inclusive, and data-driven..

Screen Savers : Protecting adolescent mental health in a digital world

In our rapidly evolving digital world, there is increasing concern about the impact of digital technologies and social media on the mental health of young people. Policymakers and the public are nervous. Psychologists are facing mounting pressures to deliver evidence that can inform policies and practices to safeguard both young people and society at large. However, research progress is slow while technological change is accelerating.My talk will reflect on this, both as a question of psychological science and metascience. Digital companies have designed highly popular environments that differ in important ways from traditional offline spaces. By revisiting the foundations of psychology (e.g. development and cognition) and considering digital changes' impact on theories and findings, we gain deeper insights into questions such as the following. (1) How do digital environments exacerbate developmental vulnerabilities that predispose young people to mental health conditions? (2) How do digital designs interact with cognitive and learning processes, formalised through computational approaches such as reinforcement learning or Bayesian modelling?However, we also need to face deeper questions about what it means to do science about new technologies and the challenge of keeping pace with technological advancements. Therefore, I discuss the concept of ‘fast science’, where, during crises, scientists might lower their standards of evidence to come to conclusions quicker. Might psychologists want to take this approach in the face of technological change and looming concerns? The talk concludes with a discussion of such strategies for 21st-century psychology research in the era of digitalization.

Distinctive features of experiential time: Duration, speed and event density

William James’s use of “time in passing” and “stream of thoughts” may be two sides of the same coin that emerge from the brain segmenting the continuous flow of information into discrete events. Departing from that idea, we investigated how the content of a realistic scene impacts two distinct temporal experiences: the felt duration and the speed of the passage of time. I will present you the results from an online study in which we used a well-established experimental paradigm, the temporal bisection task, which we extended to passage of time judgments. 164 participants classified seconds-long videos of naturalistic scenes as short or long (duration), or slow or fast (passage of time). Videos contained a varying number and type of events. We found that a large number of events lengthened subjective duration and accelerated the felt passage of time. Surprisingly, participants were also faster at estimating their felt passage of time compared to duration. The perception of duration heavily depended on objective duration, whereas the felt passage of time scaled with the rate of change. Altogether, our results support a possible dissociation of the mechanisms underlying the two temporal experiences.

Using Adversarial Collaboration to Harness Collective Intelligence

There are many mysteries in the universe. One of the most significant, often considered the final frontier in science, is understanding how our subjective experience, or consciousness, emerges from the collective action of neurons in biological systems. While substantial progress has been made over the past decades, a unified and widely accepted explanation of the neural mechanisms underpinning consciousness remains elusive. The field is rife with theories that frequently provide contradictory explanations of the phenomenon. To accelerate progress, we have adopted a new model of science: adversarial collaboration in team science. Our goal is to test theories of consciousness in an adversarial setting. Adversarial collaboration offers a unique way to bolster creativity and rigor in scientific research by merging the expertise of teams with diverse viewpoints. Ideally, we aim to harness collective intelligence, embracing various perspectives, to expedite the uncovering of scientific truths. In this talk, I will highlight the effectiveness (and challenges) of this approach using selected case studies, showcasing its potential to counter biases, challenge traditional viewpoints, and foster innovative thought. Through the joint design of experiments, teams incorporate a competitive aspect, ensuring comprehensive exploration of problems. This method underscores the importance of structured conflict and diversity in propelling scientific advancement and innovation.

Integration of 3D human stem cell models derived from post-mortem tissue and statistical genomics to guide schizophrenia therapeutic development

Schizophrenia is a neuropsychiatric disorder characterized by positive symptoms (such as hallucinations and delusions), negative symptoms (such as avolition and withdrawal) and cognitive dysfunction1. Schizophrenia is highly heritable, and genetic studies are playing a pivotal role in identifying potential biomarkers and causal disease mechanisms with the hope of informing new treatments. Genome-wide association studies (GWAS) identified nearly 270 loci with a high statistical association with schizophrenia risk; however each locus confers only a small increase in risk therefore it is difficult to translate these findings into understanding disease biology that can lead to treatments. Induced pluripotent stem cell (iPSC) models are a tractable system to translate genetic findings and interrogate mechanisms of pathogenesis. Mounting research with patient-derived iPSCs has proposed several neurodevelopmental pathways altered in SCZ, such as neural progenitor cell (NPC) proliferation, imbalanced differentiation of excitatory and inhibitory cortical neurons. However, it is unclear what exactly these iPS models recapitulate, how potential perturbations of early brain development translates into illness in adults and how iPS models that represent fetal stages can be utilized to further drug development efforts to treat adult illness. I will present the largest transcriptome analysis of post-mortem caudate nucleus in schizophrenia where we discovered that decreased presynaptic DRD2 autoregulation is the causal dopamine risk factor for schizophrenia (Benjamin et al, Nature Neuroscience 2022 https://doi.org/10.1038/s41593-022-01182-7). We developed stem cell models from a subset of the postmortem cohort to better understand the molecular underpinnings of human psychiatric disorders (Sawada et al, Stem Cell Research 2020). We established a method for the differentiation of iPS cells into ventral forebrain organoids and performed single cell RNAseq and cellular phenotyping. To our knowledge, this is the first study to evaluate iPSC models of SZ from the same individuals with postmortem tissue. Our study establishes that striatal neurons in the patients with SCZ carry abnormalities that originated during early brain development. Differentiation of inhibitory neurons is accelerated whereas excitatory neuronal development is delayed, implicating an excitation and inhibition (E-I) imbalance during early brain development in SCZ. We found a significant overlap of genes upregulated in the inhibitory neurons in SCZ organoids with upregulated genes in postmortem caudate tissues from patients with SCZ compared with control individuals, including the donors of our iPS cell cohort. Altogether, we demonstrate that ventral forebrain organoids derived from postmortem tissue of individuals with schizophrenia recapitulate perturbed striatal gene expression dynamics of the donors’ brains (Sawada et al, biorxiv 2022 https://doi.org/10.1101/2022.05.26.493589).

NEW TREATMENTS FOR PAIN: Unmet needs and how to meet them

“Of pain you could wish only one thing: that it should stop. Nothing in the world was so bad as physical pain. In the face of pain there are no heroes.- George Orwell, ‘1984’ " "Neuroscience has revealed the secrets of the brain and nervous system to an extent that was beyond the realm of imagination just 10-20 years ago, let alone in 1949 when Orwell wrote his prophetic novel. Understanding pain, however, presents a unique challenge to academia, industry and medicine, being both a measurable physiological process as well as deeply personal and subjective. Given the millions of people who suffer from pain every day, wishing only, “that it should stop”, the need to find more effective treatments cannot be understated." "‘New treatments for pain’ will bring together approximately 120 people from the commercial, academic, and not-for-profit sectors to share current knowledge, identify future directions, and enable collaboration, providing delegates with meaningful and practical ways to accelerate their own work into developing treatments for pain.

Brian2CUDA: Generating Efficient CUDA Code for Spiking Neural Networks

Graphics processing units (GPUs) are widely available and have been used with great success to accelerate scientific computing in the last decade. These advances, however, are often not available to researchers interested in simulating spiking neural networks, but lacking the technical knowledge to write the necessary low-level code. Writing low-level code is not necessary when using the popular Brian simulator, which provides a framework to generate efficient CPU code from high-level model definitions in Python. Here, we present Brian2CUDA, an open-source software that extends the Brian simulator with a GPU backend. Our implementation generates efficient code for the numerical integration of neuronal states and for the propagation of synaptic events on GPUs, making use of their massively parallel arithmetic capabilities. We benchmark the performance improvements of our software for several model types and find that it can accelerate simulations by up to three orders of magnitude compared to Brian’s CPU backend. Currently, Brian2CUDA is the only package that supports Brian’s full feature set on GPUs, including arbitrary neuron and synapse models, plasticity rules, and heterogeneous delays. When comparing its performance with Brian2GeNN, another GPU-based backend for the Brian simulator with fewer features, we find that Brian2CUDA gives comparable speedups, while being typically slower for small and faster for large networks. By combining the flexibility of the Brian simulator with the simulation speed of GPUs, Brian2CUDA enables researchers to efficiently simulate spiking neural networks with minimal effort and thereby makes the advancements of GPU computing available to a larger audience of neuroscientists.

Root causes and possible solutions to academic bullying in higher education

Academic bullying is a serious issue that affects all disciplines and people of all levels of experience. To create a truly safe, productive, and vibrant environment in academia requires coordinated and collaborative input as well as the action of a variety of stakeholders, including scholarly communities, funding agencies, and institutions. This talk will focus on a framework of integrated responding, in which stakeholders as responsible and response-able parties could proactively collaborate and coordinate to reduce the incidence and consequences of academic bullying while at the same time building constructive academic cultures. The outcome of such a framework would be to create novel entities and actions that accelerate successful responses to academic bullying.

Integrating theory-guided and data-driven approaches for measuring consciousness

Clinical assessment of consciousness is a significant issue, with recent research suggesting some brain-damaged patients who are assessed as unconscious are in fact conscious. Misdiagnosis of consciousness can also be detrimental when it comes to general anaesthesia, causing numerous psychological problems, including post-traumatic stress disorder. Avoiding awareness with overdose of anaesthetics, however, can also lead to cognitive impairment. Currently available objective assessment of consciousness is limited in accuracy or requires expensive equipment with major barriers to translation. In this talk, we will outline our recent theory-guided and data-driven approaches to develop new, optimized consciousness measures that will be robustly evaluated on an unprecedented breadth of high-quality neural data, recorded from the fly model system. We will overcome the subjective-choice problem in data-driven and theory-guided approaches with a comprehensive data analytic framework, which has never been applied to consciousness detection, integrating previously disconnected streams of research in consciousness detection to accelerate the translation of objective consciousness measures into clinical settings.

The vestibular system: a multimodal sense

The vestibular system plays an essential role in everyday life, contributing to a surprising range of functions from reflexes to the highest levels of perception and consciousness. Three orthogonal semicircular canals detect rotational movements of the head and the otolith organs sense translational acceleration, including the gravitational vertical. But, how vestibular signals are encoded by the human brain? We have recently combined innovative methods for eliciting virtual rotation and translation sensations with fMRI to identify brain areas representing vestibular signals. We have identified a bilateral inferior parietal, ventral premotor/anterior insula and prefrontal network and confirmed that these areas reliably possess information about the rotation and translation. We have also investigated how vestibular signals are integrated with other sensory cues to generate our perception of the external environment.

Norse: A library for gradient-based learning in Spiking Neural Networks

We introduce Norse: An open-source library for gradient-based training of spiking neural networks. In contrast to neuron simulators which mainly target computational neuroscientists, our library seamlessly integrates with the existing PyTorch ecosystem using abstractions familiar to the machine learning community. This has immediate benefits in that it provides a familiar interface, hardware accelerator support and, most importantly, the ability to use gradient-based optimization. While many parallel efforts in this direction exist, Norse emphasizes flexibility and usability in three ways. Users can conveniently specify feed-forward (convolutional) architectures, as well as arbitrarily connected recurrent networks. We strictly adhere to a functional and class-based API such that neuron primitives and, for example, plasticity rules composes. Finally, the functional core API ensures compatibility with the PyTorch JIT and ONNX infrastructure. We have made progress to support network execution on the SpiNNaker platform and plan to support other neuromorphic architectures in the future. While the library is useful in its present state, it also has limitations we will address in ongoing work. In particular, we aim to implement event-based gradient computation, using the EventProp algorithm, which will allow us to support sparse event-based data efficiently, as well as work towards support of more complex neuron models. With this library, we hope to contribute to a joint future of computational neuroscience and neuromorphic computing.

Efficient GPU training of SNNs using approximate RTRL

Last year’s SNUFA workshop report concluded “Moving toward neuron numbers comparable with biology and applying these networks to real-world data-sets will require the development of novel algorithms, software libraries, and dedicated hardware accelerators that perform well with the specifics of spiking neural networks” [1]. Taking inspiration from machine learning libraries — where techniques such as parallel batch training minimise latency and maximise GPU occupancy — as well as our previous research on efficiently simulating SNNs on GPUs for computational neuroscience [2,3], we are extending our GeNN SNN simulator to pursue this vision. To explore GeNN’s potential, we use the eProp learning rule [4] — which approximates RTRL — to train SNN classifiers on the Spiking Heidelberg Digits and the Spiking Sequential MNIST datasets. We find that the performance of these classifiers is comparable to those trained using BPTT [5] and verify that the theoretical advantages of neuron models with adaptation dynamics [5] translate to improved classification performance. We then measured execution times and found that training an SNN classifier using GeNN and eProp becomes faster than SpyTorch and BPTT after less than 685 timesteps and much larger models can be trained on the same GPU when using GeNN. Furthermore, we demonstrate that our implementation of parallel batch training improves training performance by over 4⨉ and enables near-perfect scaling across multiple GPUs. Finally, we show that performing inference using a recurrent SNN using GeNN uses less energy and has lower latency than a comparable LSTM simulated with TensorFlow [6].

Evidence for the role of glymphatic dysfunction in the development of Alzheimer’s disease

Glymphatic perivascular exchange is supported by the astroglial water channel aquaporin-4 (AQP4), which localizes to perivascular astrocytic endfeet surrounding the cerebral vasculature. In aging mice, impairment of glymphatic function is associated with reduced perivascular AQP4 localization, yet whether these changes contribute to the development of neurodegenerative disease, such as Alzheimer’s disease (AD), remains unknown. Using post mortem human tissue, we evaluated perivascular AQP4 localization in the frontal cortical gray matter, white matter, and hippocampus of cognitively normal subjects and those with AD. Loss of perivascular and increasing cellular localization of AQP4 in the frontal gray matter was specifically associated with AD status, amyloid β (Aβ) and tau pathology, and cognitive decline in the early stages of disease. Using AAV-PHP.B to drive expression on non-perivascular AQP4 in wild type and Tg2576 (APPSwe, mouse model of Aβ deposition) mice, increased cellular AQP4 localization did not slow glymphatic function or change Aβ deposition. Using the Snta1 knockout line (which lacks perivascular AQP4 localization), we observed that loss AQP4 from perivascular endfeet slowed glymphatic function in wild type mice and accelerated Aβ plaque deposition in Tg2576 mice. These findings demonstrate that loss of perivascular AQP4 localization, and not increased cellular AQP4 localization, slows glymphatic function and promotes the development of AD pathology. To evaluate whether naturally occurring variation in the human AQP4 gene, or the alpha syntrophin (SNTA1), dystrobrevin (DTNA) or dystroglycan (DAG1) genes (whose products maintain perivascular AQP4 localization) confer risk for or protection from AD pathology or clinical progression, we evaluated 56 tag single nucleotide polymorphisms (SNPs) across these genes for association with CSF AD biomarkers, MRI measures of cortical and hippocampal atrophy, and longitudinal cognitive decline in the Alzheimer’s Disease Neuroimaging Initiative I (ADNI I) cohort. We identify 25 different significant associations between AQP4, SNTA1, DTNA, and DAG1 tag SNPs and phenotypic measures of AD pathology and progression. These findings provide complimentary human genetic evidence for the contribution of perivascular glymphatic dysfunction to the development of AD in human populations.

Targeting the brain to improve obesity and type 2 diabetes

The increasing prevalence of obesity and type 2 diabetes (T2D) and associated morbidity and mortality emphasizes the need for a more complete understanding of the mechanisms mediating energy homeostasis to accelerate the identification of new medications. Recent reports indicate that obesity medication, 5-hydroxytryptamine (5-HT, serotonin)2C receptor (5-HT2CR) agonist lorcaserin improves glycemic control in association with weight loss in obese patients with T2D. We examined whether lorcaserin has a direct effect on insulin sensitivity and how this effect is achieved. We clarify that lorcaserin dose-dependently improves glycemic control in a mouse model of T2D without altering body weight. Examining the mechanism of this effect, we reveal a necessary and sufficient neurochemical mediator of lorcaserin’s glucoregulatory effects, via activation of brain pro-opiomelanocortin (POMC) peptides. We observed that lorcaserin reduces hepatic glucose production and improves insulin sensitivity. These data suggest that lorcaserin’s action within the brain represents a mechanistically novel treatment for T2D: findings of significance to a prevalent global disease.

Higher cognitive resources for efficient learning

A central issue in reinforcement learning (RL) is the ‘curse-of-dimensionality’, arising when the degrees-of-freedom are much larger than the number of training samples. In such circumstances, the learning process becomes too slow to be plausible. In the brain, higher cognitive functions (such as abstraction or metacognition) may be part of the solution by generating low dimensional representations on which RL can operate. In this talk I will discuss a series of studies in which we used functional magnetic resonance imaging (fMRI) and computational modeling to investigate the neuro-computational basis of efficient RL. We found that people can learn remarkably complex task structures non-consciously, but also that - intriguingly - metacognition appears tightly coupled to this learning ability. Furthermore, when people use an explicit (conscious) policy to select relevant information, learning is accelerated by abstractions. At the neural level, prefrontal cortex subregions are differentially involved in separate aspects of learning: dorsolateral prefrontal cortex pairs with metacognitive processes, while ventromedial prefrontal cortex with valuation and abstraction. I will discuss the implications of these findings, in particular new questions on the function of metacognition in adaptive behavior and the link with abstraction.

Dynamical Neuromorphic Systems

In this talk, I aim to show that the dynamical properties of emerging nanodevices can accelerate the development of smart, and environmentally friendly chips that inherently learn through their physics. The goal of neuromorphic computing is to draw inspiration from the architecture of the brain to build low-power circuits for artificial intelligence. I will first give a brief overview of the state of the art of neuromorphic computing, highlighting the opportunities offered by emerging nanodevices in this field, and the associated challenges. I will then show that the intrinsic dynamical properties of these nanodevices can be exploited at the device and algorithmic level to assemble systems that infer and learn though their physics. I will illustrate these possibilities with examples from our work on spintronic neural networks that communicate and compute through their microwave oscillations, and on an algorithm called Equilibrium Propagation that minimizes both the error and energy of a dynamical system.

Numbing intraneuronal Tau levels to prevent neurodegeneration in tauopathies

Intraneuronal accumulation of the microtubule associated protein Tau is largely recognized as an important toxic factor linked to neuronal cell death in Alzheimer’s disease and tauopathies. While there has been progress uncovering mechanisms leading to the formation of toxic Tau tangles, less is known about how intraneuronal Tau levels are regulated in health and disease. Here, I will discuss our recent work showing that the intracellular trafficking adaptor protein Numb is critical to control intraneuronal Tau levels. Inactivation of Numb in retinal ganglion cells increases monomeric and oligomeric Tau levels and leads to axonal blebbing in optic nerves, followed by significant neuronal cell loss in old mice. Interestingly, overexpression of the long isoform of Numb (Numb-72) decreases intracellular Tau levels by promoting exocytosis of monomeric Tau. In TauP301S and triple transgenic AD mouse models, expression of Numb-72 in RGCs reduces the number of axonal blebs and prevents neurodegeneration. Finally, inactivation of Numb in TauP301S mice accelerates neurodegeneration in both the retina and spinal cord and leads to precocious paralysis. Taken together, these results uncover Numb as a essential regulator of Tau homeostasis in neurons and as a potential therapeutic agent for AD and tauopathies.

Towards targeted therapies for the treatment of Dravet Syndrome

Dravet syndrome is a severe epileptic encephalopathy that begins during the first year of life and leads to severe cognitive and social interaction deficits. It is mostly caused by heterozygous loss-of-function mutations in the SCN1A gene, which encodes for the alpha-subunit of the voltage-gated sodium channel (Nav1.1) and is responsible mainly of GABAergic interneuron excitability. While different therapies based on the upregulation of the healthy allele of the gene are being developed, the dynamics of reversibility of the pathology are still unclear. In fact, whether and to which extent the pathology is reversible after symptom onset and if it is sufficient to ensure physiological levels of Scn1a during a specific critical period of time are open questions in the field and their answers are required for proper development of effective therapies. We generated a novel Scn1a conditional knock-in mouse model (Scn1aSTOP) in which the endogenous Scn1a gene is silenced by the insertion of a floxed STOP cassette in an intron of Scn1a gene; upon Cre recombinase expression, the STOP cassette is removed, and the mutant allele can be reconstituted as a functional Scn1a allele. In this model we can reactivate the expression of Scn1a exactly in the neuronal subtypes in which it is expressed and at its physiological level. Those aspects are crucial to obtain a final answer on the reversibility of DS after symptom onset. We exploited this model to demonstrate that global brain re-expression of the Scn1a gene when symptoms are already developed (P30) led to a complete rescue of both spontaneous and thermic inducible seizures and amelioration of behavioral abnormalities characteristic of this model. We also highlighted dramatic gene expression alterations associated with astrogliosis and inflammation that, accordingly, were rescued by Scn1a gene expression normalization at P30. Moreover, employing a conditional knock-out mouse model of DS we reported that ensuring physiological levels of Scn1a during the critical period of symptom appearance (until P30) is not sufficient to prevent the DS, conversely, mice start to die of SUDEP and develop spontaneous seizures. These results offer promising insights in the reversibility of DS and can help to accelerate therapeutic translation, providing important information on the timing for gene therapy delivery to Dravet patients.

Bedside to bench and back again, a path to translational pain research?

Pain has both a sensory and emotional component and is driven by activation of sensory neurones called nociceptors that are tuned to detect noxious stimuli in a process called nociception. Although nociception functions as a detect and protect mechanism. and is found in many organisms, this system becomes dysregulated in a number of conditions where chronic pain presents as a key symptom, for example osteoarthritis. Nociceptors do not innervate empty space though and do not act alone. Going beyond the neurone, other cell types, such as fibroblast-like synoviocytes interact with and modify the function of nociceptors, which is likely a key contributor to the chronification of pain. In this talk, I will look at how combining pre-clinical mouse work with human tissue and genetics might provide a way to accelerate new analgesics from bench to bedside, giving examples from our work in joint pain, bowel pain and labour pain.

Recurrent network dynamics lead to interference in sequential learning

Learning in real life is often sequential: A learner first learns task A, then task B. If the tasks are related, the learner may adapt the previously learned representation instead of generating a new one from scratch. Adaptation may ease learning task B but may also decrease the performance on task A. Such interference has been observed in experimental and machine learning studies. In the latter case, it is mediated by correlations between weight updates for the different tasks. In typical applications, like image classification with feed-forward networks, these correlated weight updates can be traced back to input correlations. For many neuroscience tasks, however, networks need to not only transform the input, but also generate substantial internal dynamics. Here we illuminate the role of internal dynamics for interference in recurrent neural networks (RNNs). We analyze RNNs trained sequentially on neuroscience tasks with gradient descent and observe forgetting even for orthogonal tasks. We find that the degree of interference changes systematically with tasks properties, especially with emphasis on input-driven over autonomously generated dynamics. To better understand our numerical observations, we thoroughly analyze a simple model of working memory: For task A, a network is presented with an input pattern and trained to generate a fixed point aligned with this pattern. For task B, the network has to memorize a second, orthogonal pattern. Adapting an existing representation corresponds to the rotation of the fixed point in phase space, as opposed to the emergence of a new one. We show that the two modes of learning – rotation vs. new formation – are directly linked to recurrent vs. input-driven dynamics. We make this notion precise in a further simplified, analytically tractable model, where learning is restricted to a 2x2 matrix. In our analysis of trained RNNs, we also make the surprising observation that, across different tasks, larger random initial connectivity reduces interference. Analyzing the fixed point task reveals the underlying mechanism: The random connectivity strongly accelerates the learning mode of new formation, and has less effect on rotation. The prior thus wins the race to zero loss, and interference is reduced. Altogether, our work offers a new perspective on sequential learning in recurrent networks, and the emphasis on internally generated dynamics allows us to take the history of individual learners into account.

Early constipation predicts faster dementia onset in Parkinson’s disease

Constipation is a common but not a universal feature in early PD, suggesting that gut involvement is heterogeneous and may be part of a distinct PD subtype with prognostic implications. We analysed data from the Parkinson’s Incidence Cohorts Collaboration, composed of incident community-based cohorts of PD patients assessed longitudinally over 8 years. Constipation was assessed with the MDS-UPDRS constipation item or a comparable categorical scale. Primary PD outcomes of interest were dementia, postural instability and death. PD patients were stratified according to constipation severity at diagnosis: none (n=313, 67.3%), minor (n=97, 20.9%) and major (n=55, 11.8%). Clinical progression to all 3 outcomes was more rapid in those with more severe constipation at baseline (Kaplan Meier survival analysis). Cox regression analysis, adjusting for relevant confounders, confirmed a significant relationship between constipation severity and progression to dementia, but not postural instability or death. Early constipation may predict an accelerated progression of neurodegenerative pathology. Conclusions: We show widespread cortical and subcortical grey matter micro-structure associations with schizophrenia PRS. Across all investigated phenotypes NDI, a measure of the density of myelinated axons and dendrites, showed the most robust associations with schizophrenia PRS. We interpret these results as indicative of reduced density of myelinated axons and dendritic arborization in large-scale cortico-subcortical networks mediating the genetic risk for schizophrenia.

Understanding the cellular and molecular landscape of autism spectrum disorders

Large genomic studies of individuals with autism spectrum disorders (ASD) have revealed approximately 100-200 high risk genes. However, whether these genes function in similar or different signaling networks in brain cells (neurons) remains poorly studied. We are using proteomic technology to build an ASD-associated signaling network map as a resource for the Autism research community. This resource can be used to study Autism risk genes and understand how pathways are convergent, and how patient mutations change the interaction profile. In this presentation, we will present how we developed a pipeline using neurons to build protein-protein interaction profiles. We detected previously unknown interactions between different ASD risk genes that have never been linked together before, and for some genes, we identified new signaling pathways that have not been previously reported. This resource will be available to the research community and will foster collaborations between ASD researchers to help accelerate therapeutics for ASD and related disorders.

CURE-ND Neurotechnology Workshop - Innovative models of neurodegenerative diseases

One of the major roadblocks to medical progress in the field of neurodegeneration is the absence of animal models that fully recapitulate features of the human diseases. Unprecedented opportunities to tackle this challenge are emerging e.g. from genome engineering and stem cell technologies, and there are intense efforts to develop models with a high translational value. Simultaneously, single-cell, multi-omics and optogenetics technologies now allow longitudinal, molecular and functional analysis of human disease processes in these models at high resolution. During this workshop, 12 experts will present recent progress in the field and discuss: - What are the most advanced disease models available to date? - Which aspects of the human disease do these accurately models, which ones do they fail to replicate? - How should models be validated? Against which reference, which standards? - What are currently the best methods to analyse these models? - What is the field still missing in terms of modelling, and of technologies to analyse disease models? CURE-ND stands for 'Catalysing a United Response in Europe to Neurodegenerative Diseases'. It is a new alliance between the German Center for Neurodegenerative Diseases (DZNE), the Paris Brain Institute (ICM), Mission Lucidity (ML, a partnership between imec, KU Leuven, UZ Leuven and VIB in Belgium) and the UK Dementia Research Institute (UK DRI). Together, these partners embrace a joint effort to accelerate the pace of scientific discovery and nurture breakthroughs in the field of neurodegenerative diseases. This Neurotechnology Workshop is the first in a series of joint events aiming at exchanging expertise, promoting scientific collaboration and building a strong community of neurodegeneration researchers in Europe and beyond.

The emergence and modulation of time in neural circuits and behavior

Spontaneous behavior in animals and humans shows a striking amount of variability both in the spatial domain (which actions to choose) and temporal domain (when to act). Concatenating actions into sequences and behavioral plans reveals the existence of a hierarchy of timescales ranging from hundreds of milliseconds to minutes. How do multiple timescales emerge from neural circuit dynamics? How do circuits modulate temporal responses to flexibly adapt to changing demands? In this talk, we will present recent results from experiments and theory suggesting a new computational mechanism generating the temporal variability underlying naturalistic behavior and cortical activity. We will show how neural activity from premotor areas unfolds through temporal sequences of attractors, which predict the intention to act. These sequences naturally emerge from recurrent cortical networks, where correlated neural variability plays a crucial role in explaining the observed variability in action timing. We will then discuss how reaction times can be accelerated or slowed down via gain modulation, flexibly induced by neuromodulation or perturbations; and how gain modulation may control response timing in the visual cortex. Finally, we will present a new biologically plausible way to generate a reservoir of multiple timescales in cortical circuits.

Collective Ecophysiology and Physics of Social Insects

Collective behavior of organisms creates environmental micro-niches that buffer them from environmental fluctuations e.g., temperature, humidity, mechanical perturbations, etc., thus coupling organismal physiology, environmental physics, and population ecology. This talk will focus on a combination of biological experiments, theory, and computation to understand how a collective of bees can integrate physical and behavioral cues to attain a non-equilibrium steady state that allows them to resist and respond to environmental fluctuations of forces and flows. We analyze how bee clusters change their shape and connectivity and gain stability by spread-eagling themselves in response to mechanical perturbations. Similarly, we study how bees in a colony respond to environmental thermal perturbations by deploying a fanning strategy at the entrance that they use to create a forced ventilation stream that allows the bees to collectively maintain a constant hive temperature. When combined with quantitative analysis and computations in both systems, we integrate the sensing of the environmental cues (acceleration, temperature, flow) and convert them to behavioral outputs that allow the swarms to achieve a dynamic homeostasis.

The emergence and modulation of time in neural circuits and behavior

Spontaneous behavior in animals and humans shows a striking amount of variability both in the spatial domain (which actions to choose) and temporal domain (when to act). Concatenating actions into sequences and behavioral plans reveals the existence of a hierarchy of timescales ranging from hundreds of milliseconds to minutes. How do multiple timescales emerge from neural circuit dynamics? How do circuits modulate temporal responses to flexibly adapt to changing demands? In this talk, we will present recent results from experiments and theory suggesting a new computational mechanism generating the temporal variability underlying naturalistic behavior. We will show how neural activity from premotor areas unfolds through temporal sequences of attractors, which predict the intention to act. These sequences naturally emerge from recurrent cortical networks, where correlated neural variability plays a crucial role in explaining the observed variability in action timing. We will then discuss how reaction times in these recurrent circuits can be accelerated or slowed down via gain modulation, induced by neuromodulation or perturbations. Finally, we will present a general mechanism producing a reservoir of multiple timescales in recurrent networks.

Exploration of human neural phenotypic diversity through mixed-donor cultures of stem-cell derived NGN2-accelerated progenitors (SNaPs)

CRISPR-based functional genomics in iPSC-based models of brain disease

Human genes associated with brain-related diseases are being discovered at an accelerating pace. A major challenge is an identification of the mechanisms through which these genes act, and of potential therapeutic strategies. To elucidate such mechanisms in human cells, we established a CRISPR-based platform for genetic screening in human iPSC-derived neurons, astrocytes and microglia. Our approach relies on CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa), in which a catalytically dead version of the bacterial Cas9 protein recruits transcriptional repressors or activators, respectively, to endogenous genes to control their expression, as directed by a small guide RNA (sgRNA). Complex libraries of sgRNAs enable us to conduct genome-wide or focused loss-of-function and gain-of-function screens. Such screens uncover molecular players for phenotypes based on survival, stress resistance, fluorescent phenotypes, high-content imaging and single-cell RNA-Seq. To uncover disease mechanisms and therapeutic targets, we are conducting genetic modifier screens for disease-relevant cellular phenotypes in patient-derived neurons and glia with familial mutations and isogenic controls. In a genome-wide screen, we have uncovered genes that modulate the formation of disease-associated aggregates of tau in neurons with a tauopathy-linked mutation (MAPT V337M). CRISPRi/a can also be used to model and functionally evaluate disease-associated changes in gene expression, such as those caused by eQTLs, haploinsufficiency, or disease states of brain cells. We will discuss an application to Alzheimer’s Disease-associated genes in microglia.

Electrical coupling of optic nerve axons - a novel model of gap junctions' involvement in optic nerve function

Axons in the optic nerve are arranged in bundles and conducting action potential with resistance related to their membrane. Optic nerve axons do not form absolutely independent conductive channels. They are directly coupled by gap junctions formed in majority by neuronal Cx45. Coupling of axons, except known transpassing functions, allows to reduce axonal membrane resistance of optic nerve and accelerates transduction of visual signal. This novel finding have substantial implications for understanding of the pathogenesis of various optic neuropathies and identifies a new potential target for a therapeutic approach.

Functional characterization of human iPSC-derived neurons at single-cell resolution

Recent developments in induced pluripotent stem cell (iPSC) technology have enabled easier access to human cells in vitro. With increasing availability of human iPSC-derived neurons, both healthy and disease cell lines, screening compounds for neurodegenerative diseases on human cells can potentially be performed in the earlier stages of drug discovery. To accelerate the functional characterization of iPSC-derived neurons and the effect of compounds, reproducible and relevant results are necessary. In this webinar, the speakers will: Introduce high-resolution functional imaging of human iPSC-derived neurons Showcase how to extract functional features of hundreds of cells in a cell culture sample label-free Discuss electrophysiological parameters for characterizing the differences among several human neuronal cell lines

Accelerating bio-plausible spiking simulations on the Graphcore IPU

Bernstein Conference 2024

Accelerated cognitive decline in obese mouse model of Alzheimer’s disease is linked to sialic acid-driven immune deregulation

Accelerated signal propagation speed in human neocortical microcircuits

cuBNM: GPU-Accelerated Biophysical Network Modeling

Bernstein Conference 2024

Exposing microvascular endothelial cells to low energy accelerated protons and its relevance for hadrontherapy applications

A GPU-Accelerated Deep Reinforcement Learning Pipeline for Simulating Animal Behavior

COSYNE 2025

Conditional deletion of CB1 receptor in the hippocampus accelerates ageing signs in mature mice

Improved hippocampal neurogenesis and cognitive performance in a mouse model of accelerated aging

Longitudinal multielectrode recordings of Purkinje cell activity during accelerated eyeblink conditioning in mice

Human fast-spiking inhibitory neurons in the neocortex accelerate their input-output function with somatic HCN channels

Instrument-free single-cell resolution of transcriptome changes in human stem cells during accelerated neuronal development triggered by knocking out the amyloid precursor protein

Accelerating EEG processing with supercomputers: A case on Independent Component Analysis

FENS Forum 2024

Accelerated epigenetic aging involves Polycomb group proteins in Huntington’s disease

FENS Forum 2024

Acute bouts of exercise in preschool children do not affect working memory capacity but accelerate the execution of the task

FENS Forum 2024

Chronic potentiation of metabotropic glutamate receptor 2 with a nanobody accelerates amyloidogenesis in Alzheimer’s disease

FENS Forum 2024

Exercise accelerates place cell representational drift

FENS Forum 2024

Quick & accurate neuron population quantification: An interactive, deep-learning accelerated method for neuron population quantification in mice brains

FENS Forum 2024

Spiking neural network models of developmental frequency acceleration in the mouse prefrontal cortex

FENS Forum 2024

Sleep and circadian rhythm of senescence-accelerated mice (SAM)P8 and R1

FENS Forum 2024

Response variability can accelerate learning in feedforward-recurrent networks

Bernstein Conference 2024

Temporal looming improves synchronisation: asymmetries in the prediction of accelerating sequences