This position includes translational research on the treatment of pain using novel devices, as well as brain imaging studies, data analysis, and machine learning to elucidate the working mechanism of the treatments and the condition itself. You will also conduct studies to further improve and develop devices and treatments, with the ultimate goal of relieving people from their chronic and debilitating pain. Information about the department and the research Our group developed a novel treatment for phantom limb pain (PLP) using myoelectric pattern recognition (machine learning) for the decoding of motor volition, and virtual and augmented reality for real-time biofeedback. This treatment is now used worldwide. However, the mechanism underlying PLP is still unknown. This position is related to the translational research involving clinical, behavioral, and brain imaging studies for better understanding of pain due to sensorimotor impairments and it's treatment. The position is within the Center for Bionics and Pain Research (CBPR), a multidisciplinary engineering and medical collaboration between Chalmers University of Technology, Sahlgrenska University Hospital, and the Sahlgrenska Academy at the University of Gothenburg. The mission of CBPR is to develop and clinically implement technologies to eliminate disability and pain due to sensorimotor impairment. The person will be officially employed at the Department of Electrical Engineering at Chalmers, where we conduct internationally renowned research in biomedical engineering, antenna systems, signal processing, image analysis, automatic control, automation, mechatronics, and communication systems. Major responsibilities Your main responsibilites will include: - Design and implementation of clinical trials. - Design and conduct behavioral and brain imagining studies. - Literature reviews on treatments and epidemiology of pain. Contract terms Full-time temporary employment. The position is limited to a maximum of three years (two years initially with a possible extension to three years). We offer Chalmers offers a cultivating and inspiring working environment in the coastal city of Gothenburg. Read more about working at Chalmers and our benefits for employees at https://www.chalmers.se/en/about-chalmers/Working-at-Chalmers/Pages/default.aspx CBPR is located within Sahlgrenska University Hospital in Mölndal, and you can read more about our work and our team at https://cbpr.se/ Chalmers aims to actively improve our gender balance. We work broadly with equality projects, for example the GENIE Initiative on gender equality for excellence. Equality and diversity are substantial foundations in all activities at Chalmers. Application procedure The application should be marked with Ref 20220311 and written in English. The application should be sent electronically and be attached as PDF-files, as below. Maximum size for each file is 40 MB. Please note that the system does not support Zip files. CV: (Please name the document as: CV, Surname, Ref. number) including: • CV, include complete list of publications • Two references that we can contact. Personal letter: (Please name the document as: Personal letter, Family name, Ref. number) 1-3 pages where you: • Introduce yourself • Describe your previous research fields and main research results • Describe how you can contribute to CBPR's research program. Other documents: • Attested copies of completed education, grades and other certificates. <b>How to apply</b> https://www.chalmers.se/en/about-chalmers/Working-at-Chalmers/Vacancies/Pages/default.aspx?rmpage=job&rmjob=10630&rmlang=UK Use the button at the foot of the page to reach the application form. For questions, please contact: Prof. Max Ortiz Catalan, Systems and Control maxo@chalmers, +46 708461065

The Stanford Cognitive and Systems Neuroscience Laboratory (scsnl.stanford.edu) invites applications for a postdoctoral fellowship in computational modeling of human cognitive, behavioral, and brain imaging data. The candidate will be involved in multidisciplinary projects to develop and implement novel neuro-cognitive computational frameworks, using multiple cutting-edge methods that may include computational cognitive modeling, Bayesian inference, dynamic brain circuit analysis, and deep neural networks. These projects will span areas including robust identification of cognitive and neurobiological signatures of psychiatric and neurological disorders, and neurodevelopmental trajectories. Clinical disorders under investigation include autism, ADHD, anxiety and mood disorders, learning disabilities, and schizophrenia. The candidate will have access to multiple large datasets and state-of-the-art computational resources, including HPCs and GPUs. Please include a CV and a statement of research interests and have three letters of reference emailed to Prof. Vinod Menon at scsnl.stanford+postdoc@gmail.com.

The Stanford Cognitive and Systems Neuroscience Laboratory (scsnl.stanford.edu) invites applications for a postdoctoral fellowship in computational modeling of human cognitive, behavioral, and brain imaging data. The candidate will be involved in multidisciplinary projects to develop and implement novel neuro-cognitive computational frameworks, using multiple cutting-edge methods that may include computational cognitive modeling, Bayesian inference, dynamic brain circuit analysis, and deep neural networks. These projects will span areas including robust identification of cognitive and neurobiological signatures of psychiatric and neurological disorders, and neurodevelopmental trajectories. Clinical disorders under investigation include autism, ADHD, anxiety and mood disorders, learning disabilities, and schizophrenia. The candidate will have access to multiple large datasets and state-of-the-art computational resources, including HPCs and GPUs. Please include a CV and a statement of research interests and have three letters of reference emailed to Prof. Vinod Menon at scsnl.stanford+postdoc@gmail.com.

Post-doctoral position in Cognitive Computational Neuroscience at the Adaptive Brain Lab. The role involves combining high field brain imaging (7T fMRI, MR Spectroscopy), electrophysiology (EEG), computational modelling (machine learning, reinforcement learning) and interventions (TMS, tDCS, pharmacology) to understand network dynamics for learning and brain plasticity. The research programme bridges work across scales (local circuits, global networks) and species (humans, rodents) to uncover the neurocomputations that support learning and brain plasticity.

This position will involve collaboration between our laboratory and researchers with expertise in advanced methods of brain imaging (Mark Schnitzer, Stanford), neuroengineering (Duygu Kuzum, UCSD), theoretical neuroscience (Todd Coleman, Stanford), and neurophysiology of visual perception (John Reynolds, Salk Institute for Biological Studies). In collaboration with this multi-disciplinary team, this researcher will apply new signal processing techniques for multisite spatiotemporal data to understand cortical dynamics during visual perception. This project will also involve development of spiking network models to understand the mechanisms underlying observed activity patterns. The project may include intermittent travel between labs to present results and facilitate collaborative work.

Postdoc research fellow wanted to join a new collaborative research project at UCL's Medawar Pain and Somatosensory Labs, UCLH and Birkbeck BabyLab/ToddlerLab, led by Dr. Lorenzo Fabrizi, Dr. Judith Meek and Prof. Emily Jones. Investigate the connection between early sensory experiences and long-term sensory processing in preterm-born neonates and children. Use EEG and novel sensory testing to identify individuals at risk of sensory challenges. Collaborate with experts in preterm brain imaging and neurodevelopment.

The School of Psychology at the University of East Anglia has two lecturer / assistant professor posts available. We welcome applications in all areas of neuroscience – come join our outstanding faculty! We have great resources here at UEA (fNIRS, EEG, MRI, TMS, virtual reality, EyeLink 1000+, Tobii eye-trackers, mobile eye-trackers), including the newly established UEA Wellcome-Wolfson Brain Imaging Centre.

The Cognitive and Behavioral Neuroscience Division at Department of Psychology, University of Miami seeks highly motivated and creative Ph.D. students in our efforts to understand the brain and mind. Applications for entry in the Fall of 2025 are now being accepted, with a deadline of December 1st. For details, including contact information, please visit https://www.psy.miami.edu/graduate/how-to-apply/index.html. The Cognitive and Behavioral Neuroscience Division at Department of Psychology, University of Miami offers a unique program of study spanning neurobiology, behavior, computational and brain imaging research on topics of emotion, mindfulness, learning and memory, mental disorders and health. A listing of faculty affiliated with the division can be found online at https://www.psy.miami.edu/research/faculty-research/index.html and below.

Postdoctoral and graduate research positions are available at Western University (London, ON) and the Fields Lab for Network Science (Toronto, ON). These positions will be supervised by Lyle Muller and involve collaborations with advanced methods of brain imaging (Mark Schnitzer, Stanford), neuroengineering (Duygu Kuzum, UCSD), theoretical neuroscience (Todd Coleman, Stanford), and neurophysiology of visual perception (John Reynolds, Salk Institute for Biological Studies). In collaboration with this multi-disciplinary team, researchers will bring together data science, computational science, and applied mathematics to understand spatiotemporal dynamics and computation in the circuits of neocortex. The project may include intermittent travel between labs to present results and facilitate collaborative work.

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). Title: SwiFT: Swin 4D fMRI Transformer Abstract: Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature extraction risks losing essential information in fMRI scans. To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner. SwiFT achieves this by implementing a 4D window multi-head self-attention mechanism and absolute positional embeddings. We evaluate SwiFT using multiple large-scale resting-state fMRI datasets, including the Human Connectome Project (HCP), Adolescent Brain Cognitive Development (ABCD), and UK Biobank (UKB) datasets, to predict sex, age, and cognitive intelligence. Our experimental outcomes reveal that SwiFT consistently outperforms recent state-of-the-art models. Furthermore, by leveraging its end-to-end learning capability, we show that contrastive loss-based self-supervised pre-training of SwiFT can enhance performance on downstream tasks. Additionally, we employ an explainable AI method to identify the brain regions associated with sex classification. To our knowledge, SwiFT is the first Swin Transformer architecture to process dimensional spatiotemporal brain functional data in an end-to-end fashion. Our work holds substantial potential in facilitating scalable learning of functional brain imaging in neuroscience research by reducing the hurdles associated with applying Transformer models to high-dimensional fMRI. Speaker: Junbeom Kwon is a research associate working in Prof. Jiook Cha’s lab at Seoul National University. Paper link: https://arxiv.org/abs/2307.05916

Advanced noninvasive neuroimaging methods provide valuable information on the brain function, but they have obvious pros and cons in terms of temporal and spatial resolution. Functional magnetic resonance imaging (fMRI) using blood-oxygenation-level-dependent (BOLD) effect provides good spatial resolution in the order of millimeters, but has a poor temporal resolution in the order of seconds due to slow hemodynamic responses to neuronal activation, providing indirect information on neuronal activity. In contrast, electroencephalography (EEG) and magnetoencephalography (MEG) provide excellent temporal resolution in the millisecond range, but spatial information is limited to centimeter scales. Therefore, there has been a longstanding demand for noninvasive brain imaging methods capable of detecting neuronal activity at both high temporal and spatial resolution. In this talk, I will introduce a novel approach that enables Direct Imaging of Neuronal Activity (DIANA) using MRI that can dynamically image neuronal spiking activity in milliseconds precision, achieved by data acquisition scheme of rapid 2D line scan synchronized with periodically applied functional stimuli. DIANA was demonstrated through in vivo mouse brain imaging on a 9.4T animal scanner during electrical whisker-pad stimulation. DIANA with milliseconds temporal resolution had high correlations with neuronal spike activities, which could also be applied in capturing the sequential propagation of neuronal activity along the thalamocortical pathway of brain networks. In terms of the contrast mechanism, DIANA was almost unaffected by hemodynamic responses, but was subject to changes in membrane potential-associated tissue relaxation times such as T2 relaxation time. DIANA is expected to break new ground in brain science by providing an in-depth understanding of the hierarchical functional organization of the brain, including the spatiotemporal dynamics of neural networks.

Sex hormones are powerful neuromodulators of learning and memory. In rodents and nonhuman primates estrogen and progesterone influence the central nervous system across a range of spatiotemporal scales. Yet, their influence on the structural and functional architecture of the human brain is largely unknown. Here, I highlight findings from a series of dense-sampling neuroimaging studies from my laboratory designed to probe the dynamic interplay between the nervous and endocrine systems. Individuals underwent brain imaging and venipuncture every 12-24 hours for 30 consecutive days. These procedures were carried out under freely cycling conditions and again under a pharmacological regimen that chronically suppresses sex hormone production. First, resting state fMRI evidence suggests that transient increases in estrogen drive robust increases in functional connectivity across the brain. Time-lagged methods from dynamical systems analysis further reveals that these transient changes in estrogen enhance within-network integration (i.e. global efficiency) in several large-scale brain networks, particularly Default Mode and Dorsal Attention Networks. Next, using high-resolution hippocampal subfield imaging, we found that intrinsic hormone fluctuations and exogenous hormone manipulations can rapidly and dynamically shape medial temporal lobe morphology. Together, these findings suggest that neuroendocrine factors influence the brain over short and protracted timescales.

It is now widely accepted that openness and transparency are keys to improving the reproducibility of scientific research, but many challenges remain to adoption of these practices. I will discuss the growth of an ecosystem for open science within the field of neuroimaging, focusing on platforms for open data sharing and open source tools for reproducible data analysis. I will also discuss the role of the Brain Imaging Data Structure (BIDS), a community standard for data organization, in enabling this open science ecosystem, and will outline the scientific impacts of these resources.

Flexible computation is a hallmark of intelligent behavior. Yet, little is known about how neural networks contextually reconfigure for different computations. Humans are able to perform a new task without extensive training, presumably through the composition of elementary processes that were previously learned. Cognitive scientists have long hypothesized the possibility of a compositional neural code, where complex neural computations are made up of constituent components; however, the neural substrate underlying this structure remains elusive in biological and artificial neural networks. Here we identified an algorithmic neural substrate for compositional computation through the study of multitasking artificial recurrent neural networks. Dynamical systems analyses of networks revealed learned computational strategies that mirrored the modular subtask structure of the task-set used for training. Dynamical motifs such as attractors, decision boundaries and rotations were reused across different task computations. For example, tasks that required memory of a continuous circular variable repurposed the same ring attractor. We show that dynamical motifs are implemented by clusters of units and are reused across different contexts, allowing for flexibility and generalization of previously learned computation. Lesioning these clusters resulted in modular effects on network performance: a lesion that destroyed one dynamical motif only minimally perturbed the structure of other dynamical motifs. Finally, modular dynamical motifs could be reconfigured for fast transfer learning. After slow initial learning of dynamical motifs, a subsequent faster stage of learning reconfigured motifs to perform novel tasks. This work contributes to a more fundamental understanding of compositional computation underlying flexible general intelligence in neural systems. We present a conceptual framework that establishes dynamical motifs as a fundamental unit of computation, intermediate between the neuron and the network. As more whole brain imaging studies record neural activity from multiple specialized systems simultaneously, the framework of dynamical motifs will guide questions about specialization and generalization across brain regions.

Brains do not fossilise, but as they grow and expand during fetal and infant development, they leave an imprint in the bony braincase. Such imprints of fossilised braincases provide direct evidence of brain evolution, but the underlying biological changes have remained elusive. Combining data from fossil skulls, ancient genomes, brain imaging and gene expression helps shed light on the evolutionary changes shaping the human brain. I will highlight two examples separated by more than 3 million years: the evolution of brain growth in Lucy and her kind, and differences between modern humans and Neanderthals.

Empathy is supposed to play a functional role in prosocial behavior. However, there has been behavioral evidence that people do not empathize everyone equally. I’ll present studies that show brain imaging evidence for racial ingroup bias in empathy for pain. These studies reveal multiple-level neural mechanisms underlying racial ingroup bias in empathy. I’ll also discuss potential intervention of racial ingroup bias in empathy and its social implications.

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

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.

Interest in psychedelic compounds is growing due to their remarkable potential for understanding altered neural states and their breakthrough status to treat various psychiatric disorders. However, there are major knowledge gaps regarding how psychedelics affect the brain. The Computational Neuroscience Laboratory at the Turner Institute for Brain and Mental Health, Monash University, uses multimodal neuroimaging to test hypotheses of the brain’s functional reorganisation under psychedelics, informed by the accounts of hierarchical predictive processing, using dynamic causal modelling (DCM). DCM is a generative modelling technique which allows to infer the directed connectivity among brain regions using functional brain imaging measurements. In this webinar, Associate Professor Adeel Razi and PhD candidate Devon Stoliker will showcase a series of previous and new findings of how changes to synaptic mechanisms, under the control of serotonin receptors, across the brain hierarchy influence sensory and associative brain connectivity. Understanding these neural mechanisms of subjective and therapeutic effects of psychedelics is critical for rational development of novel treatments and for the design and success of future clinical trials. Associate Professor Adeel Razi is a NHMRC Investigator Fellow and CIFAR Azrieli Global Scholar at the Turner Institute of Brain and Mental Health, Monash University. He performs cross-disciplinary research combining engineering, physics, and machine-learning. Devon Stoliker is a PhD candidate at the Turner Institute for Brain and Mental Health, Monash University. His interest in consciousness and psychiatry has led him to investigate the neural mechanisms of classic psychedelic effects in the brain.

In this webinar, Dr Ye Tian and A/Prof Andrew Zalesky will present new research on mapping the functional architecture of the human subcortex. They used 3T and 7T functional MRI from more than 1000 people to map one of the most detailed functional atlases of the human subcortex to date. Comprising four hierarchical scales, the new atlas reveals the complex topographic organisation of the subcortex, which dynamically adapts to changing cognitive demands. The atlas enables whole-brain mapping of connectomes and has been used to optimise targeting of deep brain stimulation. This joint work with Professors Michael Breakspear and Daniel Margulies was recently published in Nature Neuroscience. In the second part of the webinar, Dr Ye Tian will present her current research on the biological ageing of different body systems, including the human brain, in health and degenerative conditions. Conducted in more than 30,000 individuals, this research reveals associations between the biological ageing of different body systems. She will show the impact of lifestyle factors on ageing and how advanced ageing can predict the risk of mortality. Associate Professor Andrew Zalesky is a Principal Researcher with a joint appointment between the Faculties of Engineering and Medicine at The University of Melbourne. He currently holds a NHMRC Senior Research Fellowship and serves as Associate Editor for Brain Topography, Neuroimage Clinical and Network Neuroscience. Dr Zalesky is recognised for the novel tools that he has developed to analyse brain networks and their application to the study of neuropsychiatric disorders. Dr Ye Tian is a postdoctoral researcher at the Department of Psychiatry, University of Melbourne. She received her PhD from the University of Melbourne in 2020, during which she established the Melbourne Subcortex Atlas. Dr Tian is interested in understanding brain organisation and using brain imaging techniques to unveil neuropathology underpinning neuropsychiatric disorders.

The ability to experience pain is old in evolutionary terms. It is an experience shared across species. Acute pain is the body’s alarm system, and as such it is a good thing. Pain that persists beyond normal tissue healing time (3-4 months) is defined as chronic – it is the system gone wrong and it is not a good thing. Chronic pain has recently been classified as both a symptom and disease in its own right. It is one of the largest medical health problems worldwide with one in five adults diagnosed with the condition. The brain is key to the experience of pain and pain relief. This is the place where pain emerges as a perception. So, relating specific brain measures using advanced neuroimaging to the change patients describe in their pain perception induced by peripheral or central sensitization (i.e. amplification), psychological or pharmacological mechanisms has tremendous value. Identifying where amplification or attenuation processes occur along the journey from injury to the brain (i.e. peripheral nerves, spinal cord, brainstem and brain) for an individual and relating these neural mechanisms to specific pain experiences, measures of pain relief, persistence of pain states, degree of injury and the subject's underlying genetics, has neuroscientific and potential diagnostic relevance. This is what neuroimaging has afforded – a better understanding and explanation of why someone’s pain is the way it is. We can go ‘behind the scenes’ of the subjective report to find out what key changes and mechanisms make up an individual’s particular pain experience. A key area of development has been pharmacological imaging where objective evidence of drugs reaching the target and working can be obtained. We even now understand the mechanisms of placebo analgesia – a powerful phenomenon known about for millennia. More recently, researchers have been investigating through brain imaging whether there is a pre-disposing vulnerability in brain networks towards developing chronic pain. So, advanced neuroimaging studies can powerfully aid explanation of a subject’s multidimensional pain experience, pain relief (analgesia) and even what makes them vulnerable to developing chronic pain. The application of this goes beyond the clinic and has relevance in courts of law, and other areas of society, such as in veterinary care. Relatively far less work has been directed at understanding what changes in the brain occur during altered states of consciousness induced either endogenously (e.g. sleep) or exogenously (e.g. anaesthesia). However, that situation is changing rapidly. Our recent multimodal neuroimaging work explores how anaesthetic agents produce altered states of consciousness such that perceptual experiences of pain and awareness are degraded. This is bringing us fascinating insights into the complex phenomenon of anaesthesia, consciousness and even the concept of self-hood. These topics will be discussed in my talk alongside my ‘side-story’ of life as a scientist combining academic leadership roles with doing science and raising a family.

The risks of in utero and early exposure to environmental contaminants, such as heavy metals and persistent organic pollutants, on child neurodevelopment is now established, however our understanding of how these contaminants alter the human brain is very limited. To address this issue, more effort must be made to integrate brain imaging tools with epidemiological studies. In this seminar, I will be presenting EEG and MRI data collected in birth-cohort studies where impairments of cognitive and sensory functions were observed in association with prenatal exposure to mercury, lead, PCB or organophosphate insecticides. Results obtained in children and adolescents suggest that each pollutant might affect different levels of brain processing and that frontal regions are particularly vulnerable.

In this talk I will present recent developments in data sharing, organization, and analyses that allow to build fully reproducible workflows. First, I will present the Brain Imaging Data structure and discuss how this allows to build workflows, showing some new tools to read/import/create studies from EEG data structured that way. Second, I will present several newly developed tools for reproducible pre-processing and statistical analyses. Although it does take some extra effort, I will argue that it largely feasible to make most EEG data analysis fully reproducible.

Alzheimer's disease (AD) is the most common cause of dementia, and its health and socioeconomic burdens are of major concern. Presently, a definite diagnosis of AD is established by examining brain tissue after death. These examinations focus on two major pathological hallmarks of AD in the brain: (i) amyloid plaques consisting of aggregated amyloid beta (Aβ) peptides and (ii) neurofibrillary tangles made of abnormally phosphorylated tau protein. In living individuals, AD diagnosis relies on two main approaches: (i) brain imaging of tau tangles and Aβ plaques using a technique called positron emission tomography (PET) and (ii) measuring biochemical changes in tau (including phosphorylated tau at threonine-181 [p-tau181]) and the Aβ42 peptide metabolized into CSF. Unlike Aβ42, CSF p-tau181 is highly specific for AD but its usability is restricted by the need of a lumbar puncture. Moreover, PET imaging is expensive and only available in specialised medical centres. Due to these shortcomings, a simple blood test that can detect disease-related changes in the brain is a high priority for AD research, clinical care and therapy testing. In this webinar, I will discuss the discovery of p-tau biomarkers in blood and the biochemistry of how these markers differ from those found in CSF. Furthermore, I will critically review the performance of blood p-tau biomarkers across the AD pathological process and how they associate with and predict Aβ and tau pathophysiological and neuropathological changes. Furthermore, I will evaluate the potential advantages, challenges and context of use of blood p-tau in clinical practice, therapeutic trials and population screening.

Cross-sectional and longitudinal multimodal brain imaging studies using positron emission tomography (PET) and magnetic resonance imaging (MRI) have provided detailed insight into the pathophysiological progression of Alzheimer’s disease. It starts at an asymptomatic stage with widespread gradual accumulation of beta-amyloid and spread of pathological tau deposits. Subsequently changes of functional connectivity and glucose metabolism associated with mild cognitive impairment and brain atrophy may develop. However, the rate of progression to a symptomatic stage and ultimately dementia varies considerably between individuals. Mathematical models have been developed to describe disease progression, which may be used to identify markers that determine the current stage and likely rate of progression. Both are very important to improve the efficacy of clinical trials. In this lecture, I will provide an overview on current research and future perspectives in this area.

Microneurography is a method, invented by Ake Vallbo and Karl-Erik Hagbarth in the late 1960, with which we can record the activity from single, identified nerve fibres in awake human participants. In this talk, I will then discuss the method, its advantages and limitations, and some of the key discoveries regarding coding of tactile events in the signalling from receptors in the human skin. An extension of the method is to stimulate single afferents, and record the resulting tactile sensations reported by the participants, so-called microstimulation. The first experiments were done in the 1980s, but the method has recently seen a revival, and is currently being combined with high-resolution brain imaging in the study of the relationship between tactile nerve signals, sensations, and processing of tactile information in the brain.

During histogenesis of the cerebral cortex, a proper laminar placement of defined numbers of specific cellular types is necessary to ensure proper functional connectivity patterns. There is a wide range of cortical malformations causing epilepsy and intellectual disability in humans, characterized with various degrees of neuronal misplacement, aberrant circuit organization or abnormal folding patterns. Although progress in human neurogenetics and brain imaging techniques have considerably advanced the identification of their causative genes, the pathophysiological mechanisms associated with defective cerebral cortex development remain poorly understood. In my presentation, I will outline some of our recent works in rodent models illustrating how misplaced neurons forming grey matter heterotopia, a cortical malformation subtype, interfere with the proper development of cortical circuits, and induce both local and distant circuitry changes associated with the subsequent emergence of epilepsy.