Over the last 20 years, neuroimaging and electrophysiology techniques have become central to understanding the mechanisms that accompany loss and recovery of consciousness. Much of this research is performed in the context of healthy individuals with neurotypical brain dynamics. Yet, a true understanding of how consciousness emerges from the joint action of neurons has to account for how severely pathological brains, often showing phenotypes typical of unconsciousness, can nonetheless generate a subjective viewpoint. In this presentation, I will start from the context of Disorders of Consciousness and will discuss recent work aimed at finding generalizable signatures of consciousness that are reliable across a spectrum of brain electrophysiological phenotypes focusing in particular on the notion of edge-of-chaos criticality.

Efficient cognition requires flexible interactions between distributed neural networks in the human brain. These networks adapt to challenges by flexibly recruiting different regions and connections. In this talk, I will discuss how we study functional network plasticity and reorganization with combined neurostimulation and neuroimaging across the adult life span. I will argue that short-term plasticity enables flexible adaptation to challenges, via functional reorganization. My key hypothesis is that disruption of higher-level cognitive functions such as language can be compensated for by the recruitment of domain-general networks in our brain. Examples from healthy young brains illustrate how neurostimulation can be used to temporarily interfere with efficient processing, probing short-term network plasticity at the systems level. Examples from people with dyslexia help to better understand network disorders in the language domain and outline the potential of facilitatory neurostimulation for treatment. I will also discuss examples from aging brains where plasticity helps to compensate for loss of function. Finally, examples from lesioned brains after stroke provide insight into the brain’s potential for long-term reorganization and recovery of function. Collectively, these results challenge the view of a modular organization of the human brain and argue for a flexible redistribution of function via systems plasticity.

Microglia are the resident CNS macrophages of the brain parenchyma. They have many and opposing roles in health and disease, ranging from inflammatory to anti-inflammatory and protective functions, depending on the developmental stage and the disease context. In Multiple Sclerosis, microglia are involved to important hallmarks of the disease, such as inflammation, demyelination, axonal damage and remyelination, however the exact mechanisms controlling their transformation towards a protective or devastating phenotype during the disease progression remains largely unknown until now. We wish to understand how brain microglia respond to demyelinating insults and how their behaviour changes in recovery. To do so we developed a novel histopathological analysis approach in 3D and a cell-based analysis tool that when applied in the cuprizone model of demyelination revealed region- and disease- dependent changes in microglial dynamics in the brain grey matter during demyelination and remyelination. We now use similar approaches with the aim to unravel sensitive changes in microglial dynamics during neuroinflammation in the EAE model. Furthermore, we employ constitutive knockout and tamoxifen-inducible gene-targeting approaches, immunological techniques, genetics and bioinformatics and currently seek to clarify the specific role of the brain resident microglial NF-κB molecular pathway versus other tissue macrophages in EAE.

Waiting is one of healthcare’s core experiences. It is there in the time it takes to access services; through the days, weeks, months or years needed for diagnoses; in the time that treatment takes; and in the elongated time-frames of recovery, relapse, remission and dying.Funded by the Wellcome Trust, our project opens up what it means to wait in and for healthcare by examining lived experiences, representations and histories of delayed and impeded time.In an era in which time is lived at increasingly different and complex tempos, Waiting Times looks to understand both the difficulties and vital significance of waiting for practices of care, offering a fundamental re-conceptualisation of the relation between time and care in contemporary thinking about health, illness, and wellbeing.

Cancer-related fatigue is a prominent and debilitating side effect of cancer and its treatment. It can develop prior to diagnosis, generally peaks during cancer treatment, and can persist long after treatment completion. Its mechanisms are multifactorial, and its expression is highly variable. Unfortunately, treatment options are limited. Our research uses syngeneic murine models of cancer and cisplatin-based chemotherapy to better understand these mechanisms. Our data indicate that both peripherally and centrally processes may contribute to the developmental of fatigue. These processes include metabolic alterations, mitochondrial dysfunction, pre-cachexia, and inflammation. However, our data has revealed that behavioral fatigue can persist even after the toxicity associated with cancer and its treatment recover. For example, running during cancer treatment attenuates kidney toxicity while also delaying recovery from fatigue-like behavior. Additionally, administration of anesthetics known to disrupt memory consolidation at the time treatment can promote recovery, and treatment-related cues can re-instate fatigue after recovery. Cancer-related fatigue can also promote habitual behavioral patterns, as observed using a devaluation task. We interpret this data to suggest that limit metabolic resources during cancer promote the utilization of habit-based behavioral strategies that serve to maintain fatigue behavior into survivorship. This line of work is exciting as it points us toward novel interventional targets for the treatment of persistent cancer-related fatigue.

We will explore how neuropsychology can be leveraged to directly test cognitive neuroscience theories using the case of frontotemporal dementias affecting the language network. Specifically, we will focus on pathological, neuroimaging, and cognitive data from primary progressive aphasia. We will see how they can help us investigate the reading network, semantic knowledge organisation, and grammatical categories processing. Time permitting, the end of the talk will cover the temporal dynamics of semantic dimensions recovery and the role played by the task.

In the lecture Yvette Roke and Jamie Hoefakker will discuss the positive and negative effects of daily stress on mental health. They will also highlight which characteristics are likely to cause more stress related issues, and why recovery time is very important. They will give an understanding of autism spectrum disorder (ASD) in relation to daily stress and they will discuss the app, SAM the stress autism mate, developed and investigated (SCED design) in co-creation with their patients with ASD.

Our goal is to understand why myelin repair fails in multiple sclerosis and to develop regenerative medicines for the nervous system. A central obstacle for progress in this area has been the complex biology underlying the response to CNS injury. Acute CNS damage is followed by a multicellular response that encompasses different cell types and spans different scales. Currently, we do not understand which factors determines lesion recovery. Failure of inflammation to resolve is a key underlying reason of poor regeneration, and one focus is therefore on the biology of microglia during de- and remyelination, and their cross talk to other cells, in particular oligodendrocytes and the progenitor cells. In addition, we are exploring the link between lipid metabolism and inflammation, and its role in the regulation of regeneration. I will report about our recent progress in our understanding of how microglia promote regeneration in the CNS.

Thoughts and emotions can impact physiology. This connection is evident by the emergence of disease following stress, psychosomatic disorders, or recovery in response to placebo treatment. Nevertheless, this fundamental aspect of physiology remains largely unexplored. In this talk, I will focus on the brain’s involvement in regulating the peripheral immune response and explore the question of how the brain evaluates and represents the state of the immune system it regulates.

The cerebellum resembles a feedforward, three-layer network of neurons in which the “hidden layer” consists of Purkinje cells (P-cells) and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron is a prediction that is compared with the actual observation, resulting in an error signal that originates in the inferior olive. Efficient learning requires that the error signal reach the DCN neurons, as well as the P-cells that project onto them. However, this basic rule of learning is violated in the cerebellum: the olivary projections to the DCN are weak, particularly in adulthood. Instead, an extraordinarily strong signal is sent from the olive to the P-cells, producing complex spikes. Curiously, P-cells are grouped into small populations that converge onto single DCN neurons. Why are the P-cells organized in this way, and what is the membership criterion of each population? Here, I apply elementary mathematics from machine learning and consider the fact that P-cells that form a population exhibit a special property: they can synchronize their complex spikes, which in turn suppress activity of DCN neuron they project to. Thus complex spikes cannot only act as a teaching signal for a P-cell, but through complex spike synchrony, a P-cell population may act as a surrogate teacher for the DCN neuron that produced the erroneous output. It appears that grouping of P-cells into small populations that share a preference for error satisfies a critical requirement of efficient learning: providing error information to the output layer neuron (DCN) that was responsible for the error, as well as the hidden layer neurons (P-cells) that contributed to it. This population coding may account for several remarkable features of behavior during learning, including multiple timescales, protection from erasure, and spontaneous recovery of memory.

Neuronal plasticity has for a long time been considered important for the recovery from depression and for the antidepressant drug action, but how the drug action is translated to plasticity has remained unclear. Brain-derived neurotrophic factor (BDNF) and its receptor TRKB are critical regulators of neuronal plasticity and have been implicated in the antidepressant action. We have recently found that many, if not all, different antidepressants, including serotonin selective SSRIs, tricyclic as well as fast-acting ketamine, directly bind to TRKB, thereby promoting TRKB translocation to synaptic membranes, which increases BDNF signaling. We have previously shown that antidepressant treatment induces a juvenile-like state of activity in the cortex that facilitates beneficial rewiring of abnormal networks. We recently showed that activation of TRKB receptors in parvalbumin-containing interneurons orchestrates cortical activation states and is both necessary and sufficient for the antidepressantinduced cortical plasticity. Our findings open a new framework how the action of antidepressants act: rather than regulating brain monoamine concentrations, antidepressants directly bind to TRKB and allosterically promote BDNF signaling, thereby inducing a state of plasticity that allows re-wiring of abnormal networks for better functionality.

Large-scale recordings of neural activity are providing new opportunities to study network-level dynamics with unprecedented detail. However, the sheer volume of data and its dynamical complexity are major barriers to uncovering and interpreting these dynamics. I will present machine learning frameworks that enable inference of dynamics from neuronal population spiking activity on single trials and millisecond timescales, from diverse brain areas, and without regard to behavior. I will then demonstrate extensions that allow recovery of dynamics from two-photon calcium imaging data with surprising precision. Finally, I will discuss our efforts to facilitate comparisons within our field by curating datasets and standardizing model evaluation, including a currently active modeling challenge, the 2021 Neural Latents Benchmark [neurallatents.github.io].

Spike-driven adaptation involves intracellular mechanisms that are initiated by neural firing and lead to the subsequent reduction of spiking rate followed by a recovery back to baseline. We report on long (>0.5 second) recovery times from adaptation in a thalamic-like structure in weakly electric fish. This adaptation process is shown via modeling and experiment to encode in a spatially invariant manner the time intervals between event encounters, e.g. with landmarks as the animal learns the location of food. These cells also come in two varieties, ones that care only about the time since the last encounter, and others that care about the history of encounters. We discuss how the two populations can share in the task of representing sequences of events, supporting path integration and converting from ego-to-allocentric representations. The heterogeneity of the population parameters enables the representation and Bayesian decoding of time sequences of events which may be put to good use in path integration and hilus neuron function in hippocampus. Finally we discuss how all the cells of this gateway to the pallium exhibit mixed selectivity of social features of their environment. The data and computational modeling further reveal that, in contrast to a long-held belief, these gymnotiform fish are endowed with a corollary discharge, albeit only for social signalling.

Spike-driven adaptation involves intracellular mechanisms that are initiated by spiking and lead to the subsequent reduction of spiking rate. One of its consequences is the temporal patterning of spike trains, as it imparts serial correlations between interspike intervals in baseline activity. Surprisingly the hidden adaptation states that lead to these correlations themselves exhibit quasi-independence. This talk will first discuss recent findings about the role of such adaptation in suppressing noise and extending sensory detection to weak stimuli that leave the firing rate unchanged. Further, a matching of the post-synaptic responses to the pre-synaptic adaptation time scale enables a recovery of the quasi-independence property, and can explain observations of correlations between post-synaptic EPSPs and behavioural detection thresholds. We then consider the involvement of spike-driven adaptation in the representation of intervals between sensory events. We discuss the possible link of this time-stamping mechanism to the conversion of egocentric to allocentric coordinates. The heterogeneity of the population parameters enables the representation and Bayesian decoding of time sequences of events which may be put to good use in path integration and hilus neuron function in hippocampus.

A brain-machine interface (BMI) is a system that enables users to interact with computers and robots through the voluntary modulation of their brain activity. Such a BMI is particularly relevant as an aid for patients with severe neuromuscular disabilities, although it also opens up new possibilities in human-machine interaction for able-bodied people. Real-time signal processing and decoding of brain signals are certainly at the heart of a BMI. Yet, this does not suffice for subjects to operate a brain-controlled device. In the first part of my talk I will review some of our recent studies, most involving participants with severe motor disabilities, that illustrate additional principles of a reliable BMI that enable users to operate different devices. In particular, I will show how an exclusive focus on machine learning is not necessarily the solution as it may not promote subject learning. This highlights the need for a comprehensive mutual learning methodology that foster learning at the three critical levels of the machine, subject and application. To further illustrate that BMI is more than just decoding, I will discuss how to enhance subject learning and BMI performance through appropriate feedback modalities. Finally, I will show how these principles translate to motor rehabilitation, where in a controlled trial chronic stroke patients achieved a significant functional recovery after the intervention, which was retained 6-12 months after the end of therapy.

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

There are currently no approved therapies to slow down the accumulation of neurological disability that occurs independently of relapses in multiple sclerosis (MS). International agencies are engaging to expedite the development of novel strategies capable of modifying disease progression, abrogating persistent CNS inflammation, and support degenerating axons in people with progressive MS. Understanding why regeneration fails in the progressive MS brain and developing new regenerative approaches is a key priority for the Pluchino Lab. In particular, we aim to elucidate how the immune system, in particular its cells called myeloid cells, affects brain structure and function under normal healthy conditions and in disease. Our objective is to find how myeloid cells communicate with the central nervous system and affect tissue healing and functional recovery by stimulating mechanisms of brain plasticity mechanisms such as the generation of new nerve cells and the reduction of scar formation. Applying combination of state-of-the-art omic technologies, and molecular approaches to study murine and human disease models of inflammation and neurodegeneration, we aim to develop experimental molecular medicines, including those with stem cells and gene therapy vectors, which slow down the accumulation of irreversible disabilities and improve functional recovery after progressive multiple sclerosis, stroke and traumatic injuries. By understanding the mechanisms of intercellular (neuro-immune) signalling, diseases of the brain and spinal cord may be treated more effectively, and significant neuroprotection may be achieved with new tailored molecular therapeutics.

Scientists work in laboratories; comfortable spaces which we equip and configure to be ideal for our needs. The scientific paradigm has been adopted by clinicians, who run diagnostic tests and treatments in fully equipped hospital facilities. Yet advances in technology mean that that increasingly many functions of a laboratory can be compressed into miniature devices, or even into a smartphone app. This has the potential to be transformative for healthcare in developing nations, allowing complex tests and interventions to be made available in every village. In this talk, I will give two examples of this approach from my recent work. In the field of stroke rehabilitation, I will present basic research which we have conducted in animals over the last decade. This reveals new ways to intervene and strengthen surviving pathways, which can be deployed in cheap electronic devices to enhance functional recovery. In degenerative disease, we have used Bayesian statistical methods to improve an algorithm to measure how rapidly a subject can stop an action. We then implemented this on a portable device and on a smartphone app. The measurement obtained can act as a useful screen for Parkinson’s Disease. I conclude with an outlook for the future of this approach, and an invitation to those who would be interesting in collaborating in rolling it out to in African settings.

The Group Iterative Multiple Model Estimation (GIMME) framework has emerged as a promising method for characterizing connections between brain regions in functional neuroimaging data. Two of the most appealing features of this framework are its ability to estimate the directionality of connections between network nodes and its ability to determine whether those connections apply to everyone in a sample (group-level) or just to one person (individual-level). However, there are outstanding questions about the validity and stability of these estimates, including: 1) how recovery of connection directionality is affected by features of data sets such as scan length and autoregressive effects, which may be strong in some imaging modalities (resting state fMRI, fNIRS) but weaker in others (task fMRI); and 2) whether inferences about directionality at the group and individual levels are stable across time. This talk will provide an overview of the GIMME framework and describe relevant results from a large-scale simulation study that assesses directionality recovery under various conditions and a separate project that investigates the temporal stability of GIMME’s inferences in the Human Connectome Project data set. Analyses from these projects demonstrate that estimates of directionality are most precise when autoregressive and cross-lagged relations in the data are relatively strong, and that inferences about the directionality of group-level connections, specifically, appear to be stable across time. Implications of these findings for the interpretation of directional connectivity estimates in different types of neuroimaging data will be discussed.

Using Systems Neuroscience Approaches to Understand Motor Learning & Recovery Post-Stroke

General anesthesia is a drug-induced, reversible condition comprised of five behavioral states: unconsciousness, amnesia (loss of memory), antinociception (loss of pain sensation), akinesia (immobility), and hemodynamic stability with control of the stress response. Our work shows that a primary mechanism through which anesthetics create these altered states of arousal is by initiating and maintaining highly structured oscillations. These oscillations impair communication among brain regions. We illustrate this effect by presenting findings from our human studies of general anesthesia using high-density EEG recordings and intracranial recordings. These studies have allowed us to give a detailed characterization of the neurophysiology of loss and recovery of consciousness due to propofol. We show how these dynamics change systematically with different anesthetic classes and with age. As a consequence, we have developed a principled, neuroscience-based paradigm for using the EEG to monitor the brain states of patients receiving general anesthesia. We demonstrate that the state of general anesthesia can be rapidly reversed by activating specific brain circuits. Finally, we demonstrate that the state of general anesthesia can be controlled using closed loop feedback control systems. The success of our research has depended critically on tight coupling of experiments, signal processing research and mathematical modeling.

After an injury in the adult mammalian central nervous system, lesioned axons fail to regenerate. This failure to regenerate contrasts with the remarkable potential of axons to grow during embryonic development and after an injury in the peripheral nervous system. Peripheral sensory neurons with cell soma in dorsal root ganglia (DRG) switch to a regenerative state after nerve injury to enable axon regeneration and functional recovery. Decades of research have focused on the signaling pathways elicited by injury in sensory neurons and in Schwann cells that insulate axons as central mechanisms regulating nerve repair. However, neuronal microenvironment is far more complex and is composed of multiple cell types including endothelial, immune and glial cells. Whether the microenvironment surrounding neuronal soma contribute to the poor regenerative outcomes following central injuries remains largely unexplored. To answer this question, we performed a single cell transcriptional profiling of the DRG neuronal microenvironment response to peripheral and central injuries. In dissecting the roles of the microenvironment contribution, we have focused on a poorly studied population of Satellite Glial Cells (SGC) surrounding the neuronal cell soma. This study has uncovered a previously unknown role for SGC in nerve regeneration and defined SGC as transcriptionally distinct from Schwann cells while sharing similarities with astrocytes. Upon a peripheral injury, SGC contribute to axon regeneration via Fatty acid synthase (Fasn)-PPARα signaling pathway. Through repurposing fenofibrate, an FDA- approved PPARα agonist used for dyslipidemia treatment, we were able to rescue the impaired regeneration in mice lacking Fasn in SGC. Our analysis reveals that in response to central injuries, SGC do not activate the PPAR signaling pathway. However, induction of this pathway with fenofibrate treatment, rescued axon regeneration following an injury to the central nerves. Collectively, our results uncovered a previously unappreciated role of the neuronal microenvironment differential response in central and peripheral injuries.

Spontaneous firing, observed in many neurons, is often attributed to ion channel or network level noise. Cortical cells during slow wave sleep exhibit transitions between so called Up and Down states. In this sleep state, with limited sensory stimuli, neurons fire in the Up state. Spontaneous firing is also observed in slices of cholinergic interneurons, cerebellar Purkinje cells and even brainstem inspiratory neurons. In such in vitro preparations, where the functional relevance is long lost, neurons continue to display a rich repertoire of firing properties. It is perplexing that these neurons, instead of saving their energy during information downtime and functional irrelevance, are eager to fire. We propose that spontaneous firing is not a chance event but instead, a vital activity for the well-being of a neuron. We postulate that neurons, in anticipation of synaptic inputs, keep their ATP levels at maximum. As recovery from inputs requires most of the energy resources, neurons are ATP surplus and ADP scarce during synaptic quiescence. With ADP as the rate-limiting step, ATP production stalls in the mitochondria when ADP is low. This leads to toxic Reactive Oxygen Species (ROS) formation, which are known to disrupt many cellular processes. We hypothesize that spontaneous firing occurs at these conditions - as a release valve to spend energy and to restore ATP production, shielding the neuron against ROS. By linking a mitochondrial metabolism model to a conductance-based neuron model, we show that spontaneous firing depends on baseline ATP usage and on ATP-cost-per-spike. From our model, emerges a mitochondrial mediated homeostatic mechanism that provides a recipe for different firing patterns. Our findings, though mostly affecting intracellular dynamics, may have large knock-on effects on the nature of neural coding. Hitherto it has been thought that the neural code is optimised for energy minimisation, but this may be true only when neurons do not experience synaptic quiescence.

Drug addiction is a chronically relapsing disorder characterized by compulsive drug use despite catastrophic personal consequences (e.g., loss of family, job) and even when the substance is no longer perceived as pleasurable. In this talk, I will present results of human neuroimaging studies, utilizing a multimodal approach (neuropsychology, functional magnetic resonance imaging, event-related potentials recordings), to explore the neurobiology underlying the core psychological impairments in drug addiction (impulsivity, drive/motivation, insight/awareness) as associated with its clinical symptomatology (intoxication, craving, bingeing, withdrawal). The focus of this talk is on understanding the role of the dopaminergic mesocorticolimbic circuit, and especially the prefrontal cortex, in higher-order executive dysfunction (e.g., disadvantageous decision-making such as trading a car for a couple of cocaine hits) in drug addicted individuals. The theoretical model that guides the presented research is called iRISA (Impaired Response Inhibition and Salience Attribution), postulating that abnormalities in the orbitofrontal cortex and anterior cingulate cortex, as related to dopaminergic dysfunction, contribute to the core clinical symptoms in drug addiction. Specifically, our multi-modality program of research is guided by the underlying working hypothesis that drug addicted individuals disproportionately attribute reward value to their drug of choice at the expense of other potentially but no-longer-rewarding stimuli, with a concomitant decrease in the ability to inhibit maladaptive drug use. In this talk I will also explore whether treatment (as usual) and 6-month abstinence enhance recovery in these brain-behavior compromises in treatment seeking cocaine addicted individuals. Promising neuroimaging studies, which combine pharmacological (i.e., oral methylphenidate, or RitalinTM) and salient cognitive tasks or functional connectivity during resting-state, will be discussed as examples for using neuroimaging for empirically guiding the development of effective neurorehabilitation strategies (encompassing cognitive reappraisal and transcranial direct current stimulation) in drug addiction.