Fast periodic visual stimulation (FPVS) has emerged as a promising tool for assessing cognitive function in individuals with dementia. This technique leverages electroencephalography (EEG) to measure brain responses to rapidly presented visual stimuli, offering a non-invasive and objective method for evaluating a range of cognitive functions. Unlike traditional cognitive assessments, FPVS does not rely on behavioural responses, making it particularly suitable for individuals with cognitive impairment. In this talk I will highlight a series of studies that have demonstrated its ability to detect subtle deficits in recognition memory, visual processing and attention in dementia patients using EEG in the lab, at home and in clinic. The method is quick, cost-effective, and scalable, utilizing widely available EEG technology. FPVS holds significant potential as a functional biomarker for early diagnosis and monitoring of dementia, paving the way for timely interventions and improved patient outcomes.

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

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

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.

Post traumatic stress disorder (PTSD) is a prevalent and disabling condition that affects larger numbers of children and adolescents worldwide. Until recently, we have understood little about the nature of PTSD reactions in our youngest children (aged under 8 years old). This talk describes our work over the last 15 years working with this very young age group. It overviews how we need a markedly different PTSD diagnosis for very young children, data on the prevalence of this new diagnostic algorithm, and the development of a psychological intervention and its evaluation in a clinical trial.

Accurate segmentation of medical images is a key step in developing Computer-Aided Diagnosis (CAD) and automating various clinical tasks such as image-guided interventions. The success of state-of-the-art methods for medical image segmentation is heavily reliant upon the availability of a sizable amount of labelled data. If the required quantity of labelled data for learning cannot be reached, the technology turns out to be fragile. The principle of consensus tells us that as humans, when we are uncertain how to act in a situation, we tend to look to others to determine how to respond. In this webinar, Dr Mehrtash Harandi will show how to model the principle of consensus to learn to segment medical data with limited labelled data. In doing so, we design multiple segmentation models that collaborate with each other to learn from labelled and unlabelled data collectively.

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.

Networks of stiff biopolymers are an omnipresent structural motif in cells and tissues. A prominent modeling framework for describing biopolymer network mechanics is rigidity percolation theory. This theory describes model networks as nodes joined by randomly placed, springlike bonds. Increasing the amount of bonds in a network results in an abrupt, dramatic increase in elastic moduli above a certain threshold – an example of a mechanical phase transition. While homogeneous networks are well studied, many tissues are made of disparate components and exhibit spatial fluctuations in the concentrations of their constituents. In this talk, I will first discuss recent work in which we explained the structural basis of the shear mechanics of healthy and chemically degraded cartilage by coupling a rigidity percolation framework with a background gel. Our model takes into account collagen concentration, as well as the concentration of peptidoglycans in the surrounding polyelectrolyte gel, to produce a structureproperty relationship that describes the shear mechanics of both sound and diseased cartilage. I will next discuss the introduction of structural correlation in constructing networks, such that sparse and dense patches emerge. I find moderate correlation allows a network to become rigid with fewer bonds, while this benefit is partly erased by excessive correlation. We explain this phenomenon through analysis of the spatial fluctuations in strained networks’ displacement fields. Finally, I will address our work’s implications for non-invasive diagnosis of pathology, as well as rational design of prostheses and novel soft materials.

Many patients presenting to neurology either have primary sleep disorders or suffer from sleep comorbidity. Knowledge on the diagnosis, differential diagnostic considerations, and management of these disorders is therefore mandatory for the general neurologist. This comprehensive course may serve to fulfill part of the preparation requirements for trainees seeking to complete the Royal College Examinations in Neurology. This training course is for R4 and R5 residents in Canadian neurology training programs as well as neurologists.

Menstruation is a normal physiological process in women occurring as a result of changes in two ovarian produced hormones – estrogen and progesterone. As a result of these fluctuations, women experience different symptoms in their bodies – their immune system changes (Sekigawa et al, 2004), there are changes in their cardiovascular and digestive system (Millikan, 2006), as well as skin (Hall and Phillips, 2005). But these hormone fluctuations produce major changes in their behavioral pattern as well causing: anxiety, sadness, heightened irritability and anger (Severino and Moline, 1995) which is usually classified as premenstrual syndrome (PMS). In some cases these symptoms severely impair women’s lives and professional help is required. The official diagnosis according to DSM-5 (2013) is premenstrual dysphoric disorder (PMDD). Despite its ubiquitous presence the origins of PMS and PMDD are poorly understood. Some efforts to understand the underlying brain state during the menstruation cycle were performed by using TMS (Smith et al, 1999; 2002; 2003; Inghilleri et al, 2004; Hausmann et al, 2006). But all of these experiments suffer from major shortcomings - no control groups and small number of subjects. Our plan is to address all of these shortcomings and make this the biggest (to our knowledge) experiment of its kind which will, hopefully, provide us with some much needed answers.

The Hsieh lab focuses on the mechanisms that promote neural stem cell self-renewal and differentiation in embryonic and adult brain. Using mouse models, video-EEG monitoring, viral techniques, and imaging/electrophysiological approaches, we elucidated many of the key transcriptional/epigenetic regulators of adult neurogenesis and showed aberrant new neuron integration in adult rodent hippocampus contribute to circuit disruption and seizure development. Building on this work, I will present our recent studies describing how GABA-mediated Ca2+ activity regulates the production of aberrant adult-born granule cells. In a new direction of my laboratory, we are using human induced pluripotent stem cells and brain organoid models as approaches to understand brain development and disease. Mutations in one gene, Aristaless-related homeobox (ARX), are of considerable interest since they are known to cause a common spectrum of neurodevelopmental disorders including epilepsy, autism, and intellectual disability. We have generated cortical and subpallial organoids from patients with poly-alanine expansion mutations in ARX. To understand the nature of ARX mutations in the organoid system, we are currently performing cellular, molecular, and physiological analyses. I will present these data to gain a comprehensive picture of the effect of ARX mutations in brain development. Since we do not understand how human brain development is affected by ARX mutations that contribute to epilepsy, we believe these studies will allow us to understand the mechanism of pathogenesis of ARX mutations, which has the potential to impact the diagnosis and care of patients.

Spatial neglect is a syndrome that is most frequently associated with damage to the right hemisphere, although damage to the left hemisphere can also result in signs of spatial neglect. It is characterised by absent or deficient awareness of the contralesional side of space. The screening and diagnosis of spatial neglect lacks a universal gold standard, but is usually achieved by using various modes of assessment. Spatial neglect is also difficult to treat, although prism adaptation training (PAT) has in the past reportedly showed some promise. This seminar will include highlights from a series of studies designed to identify knowledge gaps, and will suggest ways in which these can be bridged. The first study was conducted to identify and quantify clinicians’ use of assessment tools for spatial neglect, finding that several different tools are in use, but that there is an emerging consensus and appetite for harmonisation. The second study included PAT, and sought to uncover whether PAT can improve engagement in recommended therapy in order to improve the outcomes of stroke survivors with spatial neglect. The final study, a systematic review and meta-analysis, sought to investigate the scientific efficacy (rather than clinical effectiveness) of PAT, identifying several knowledge gaps in the existing literature and a need for a new approach in the study of PAT in the clinical setting.

This talk will address important aspects of pediatric migraine research, including: 1) Who is affected by pediatric migraine? 2) What does pediatric migraine look like, and what does a clinician need to do to reach a migraine diagnosis in a child? 3) When does pediatric migraine begin, and how might it present clinically before it presents as headache (e.g., infant colic, benign paroxysmal torticollis, cyclic vomiting syndrome etc.) 4) Where does responsibility for decreasing pediatric migraine frequency rest? What is society's role in preventing migraine in young people?

Personalised medicine will be greatly enhanced with the introduction of new radiopharmaceuticals for the diagnosis and treatment of various cancers, as well as cardiovascular disease and brain disorders. The unprecedented interest in developing theranostic radiopharmaceuticals is mainly due to the recent clinical successes of radiometal-based products including: • 177LuDOTA-TATE (trade name Lutathera, FDA approved in 2018), a peptide-based tracer that is used for treating metastatic neuroendocrine tumours • Ga 68 PSMA-11 (FDA approved in 2020), a positron emission tomography agent for imaging prostate-specific membrane antigen positive lesions in men with prostate cancer. In this webinar, Dr Brett Paterson and PhD candidate Mr Cormac Kelderman will present their research on developing the chemistry and radiochemistry to produce new radiometal-based imaging and therapy agents. They will discuss the synthesis of new molecules, the optimisation of the radiochemistry, and results from preclinical evaluations. Dr Brett Paterson is a National Imaging Facility Fellow at Monash Biomedical Imaging and academic group leader in the School of Chemistry, Monash University. His research focuses on the development of radiochemistry and new radiopharmaceuticals. Cormac Kelderman is a PhD candidate under the supervision of Dr Brett Paterson in the School of Chemistry, Monash University. His research focuses on developing new bis(thiosemicarbazone) chelators for technetium-99m SPECT imaging.

Studying the pathophysiology of late stage Parkinson’s disease (PD) – after the patients have experienced severe neuronal loss – has helped develop various symptomatic treatments for PD (e.g., deep brain stimulation). However, it has been of limited use in developing neuroprotective disease-modifying therapies (DMTs), because DMTs require interventions at much earlier stages of PD when vulnerable neurons are still intact. Because PD patients exhibit various non-motor prodromal symptoms (ie, symptoms that predate diagnosis), understanding the pathophysiology underlying these symptom could lead to earlier diagnosis and intervention. In my talk, I will present a recently elucidated example of how PD pathologies alter the channel biophysics of intact vagal motoneurons (known to be selectively vulnerable in PD) to drive dysautonomia that is reminiscent of prodromal PD. I will discuss how elucidating the pathophysiology of prodromal symptoms can lead to earlier diagnosis through the development of physiological biomarkers for PD.

Age-associated dementias are neuropathologically characterized by the identification of hallmark intracellular and extracellular deposition of proteins, i.e., hyperphosphorylated-tau, amyloid-β, and α-synuclein, or cerebrovascular lesions. The neuropathological assessment and staging of these pathologies allows for a diagnosis of a distinct disease, e.g., amyloid-β plaques and hyperphosphorylated tau pathology in Alzheimer's disease. Neuropathological assessment in large scale cohorts, such as the UK’s Brains for Dementia Research (BDR) programme, has made it increasingly clear that the ageing brain is characterized by the presence of multiple age-associated pathologies rather than just the ‘pure’ hallmark lesion as commonly perceived. These additional pathologies can range from low/intermediate levels, that are assumed to have little if any clinical significance, to a full-blown mixed disease where there is the presence of two distinct diseases. In our recent paper (McAleese et al. 2021 Concomitant neurodegenerative pathologies contribute to the transition from mild cognitive impairment to dementia, https://alz-journals.onlinelibrary.wiley.com/doi/full/10.1002/alz.12291, Alzheimer's & Dementia), using the BDR cohort, we investigated the frequency of multimorbidity and specifically investigated the impact of additional low-level pathology on cognition. In this study, of 670 donated post-mortem brains, we found that almost 70% of cases exhibited multimorbidity and only 22% were considered a pure diagnosis. Importantly, no case of Lewy Body dementia or vascular dementia was considered pure. A key finding is that the presence of low levels of additional pathology increased the likelihood of having mild dementia vs mild cognitive impairment by almost 20-fold, indicating low levels of additional pathology do impact the clinical progression of a distinct disease. Given the high prevalence and the potential clinical impact, cerebral multimorbidity should be at the forefront of consideration in dementia research.

Despite the importance of early diagnosis of dementia for prognosis and personalised interventions, we still lack robust tools for predicting individual progression to dementia. We propose a trajectory modelling approach that mines multimodal data from patients at early dementia stages to derive individualised prognostic scores of cognitive decline Our approach has potential to facilitate effective stratification of individuals based on prognostic disease trajectories, reducing patient misclassification with important implications for clinical practice.

Understanding speech in noisy backgrounds requires selective attention to a particular speaker. Humans excel at this challenging task, while current speech recognition technology still struggles when background noise is loud. The neural mechanisms by which we process speech remain, however, poorly understood, not least due to the complexity of natural speech. Here we describe recent progress obtained through applying machine-learning to neuroimaging data of humans listening to speech in different types of background noise. In particular, we develop statistical models to relate characteristic features of speech such as pitch, amplitude fluctuations and linguistic surprisal to neural measurements. We find neural correlates of speech processing both at the subcortical level, related to the pitch, as well as at the cortical level, related to amplitude fluctuations and linguistic structures. We also show that some of these measures allow to diagnose disorders of consciousness. Our findings may be applied in smart hearing aids that automatically adjust speech processing to assist a user, as well as in the diagnosis of brain disorders.

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.

One of the most important scientific and technological frontiers of our time is the interfacing of electronics with the human brain. This endeavour promises to help understand how the brain works and deliver new tools for diagnosis and treatment of pathologies including epilepsy and Parkinson’s disease. Current solutions, however, are limited by the materials that are brought in contact with the tissue and transduce signals across the biotic/abiotic interface. Recent advances in electronics have made available materials with a unique combination of attractive properties, including mechanical flexibility, mixed ionic/electronic conduction, enhanced biocompatibility, and capability for drug delivery. Professor Malliaras will present examples of novel devices for recording and stimulation of neurons and show that organic electronic materials offer tremendous opportunities to study the brain and treat its pathologies.

The European University for Brain and Technology, NeurotechEU, is opening its doors on the 16th of December. From health & healthcare to learning & education, Neuroscience has a key role in addressing some of the most pressing challenges that we face in Europe today. Whether the challenge is the translation of fundamental research to advance the state of the art in prevention, diagnosis or treatment of brain disorders or explaining the complex interactions between the brain, individuals and their environments to design novel practices in cities, schools, hospitals, or companies, brain research is already providing solutions for society at large. There has never been a branch of study that is as inter- and multi-disciplinary as Neuroscience. From the humanities, social sciences and law to natural sciences, engineering and mathematics all traditional disciplines in modern universities have an interest in brain and behaviour as a subject matter. Neuroscience has a great promise to become an applied science, to provide brain-centred or brain-inspired solutions that could benefit the society and kindle a new economy in Europe. The European University of Brain and Technology (NeurotechEU) aims to be the backbone of this new vision by bringing together eight leading universities, 250+ partner research institutions, companies, societal stakeholders, cities, and non-governmental organizations to shape education and training for all segments of society and in all regions of Europe. We will educate students across all levels (bachelor’s, master’s, doctoral as well as life-long learners) and train the next generation multidisciplinary scientists, scholars and graduates, provide them direct access to cutting-edge infrastructure for fundamental, translational and applied research to help Europe address this unmet challenge.

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.

Emotion processing is thought to be impaired in autism and linked to atypical visual exploration and arousal modulation to others faces and gaze, yet evidence is equivocal. We propose that, where observed, atypical socioemotional processing is due to alexithymia, a distinct but frequently co-occurring condition which affects emotional self-awareness and Interoception. In study 1 (N = 80), we tested this hypothesis by studying the spatio-temporal dynamics and entropy of eye-gaze during emotion processing tasks. Evidence from traditional and novel methods revealed that atypical eye-gaze and emotion recognition is best predicted by alexithymia in both autistic and non-autistic individuals. In Study 2 (N = 70), we assessed interoceptive and autonomic signals implicated in socioemotional processing, and found evidence for alexithymia (not autism) driven effects on gaze and arousal modulation to emotions. We also conducted two large-scale studies (N = 1300), using confirmatory factor-analytic and network modelling and found evidence that Alexithymia and Autism are distinct at both a latent level and their intercorrelations. We argue that: 1) models of socioemotional processing in autism should conceptualise difficulties as intrinsic to alexithymia, and 2) assessment of alexithymia is crucial for diagnosis and personalised interventions in autism.

Molecular imaging using Positron Emission Tomography (PET) has become a major biomedical imaging technology. Its application towards characterisation of biochemical processes in disease could enable early detection and diagnosis, development of novel therapies and treatment evaluation. The technology is underpinned by the use of imaging probes radiolabelled with short-lived radioisotopes which can be specific and selective for biological targets in vivo e.g. markers for receptors, protein deposits, enzymes and metabolism. My talk will focus on the increasing development and application of PET imaging to clinical research in neurodegenerative diseases, for which it can be applied to delineate and understand the various pathological components of these disorders.

Non-traumatic brain injury due to stroke, cerebral palsy and HIV often result in serious long-term disability worldwide, affecting more than 150 million persons globally; with the majority of persons living in low and middle income countries. These diseases often result in varying levels of motor and cognitive impairment due to brain injury which then affects the person’s ability to complete activities of daily living and fully participate in society. Increasingly advanced technologies are being used to support identification, diagnosis, assessment, and therapy for patients with brain injury. Specifically, robot and mechatronic systems can provide patients, physicians and rehabilitation clinical providers with additional support to care for and improve the quality of life of children and adults with motor and cognitive impairment. This talk will provide a brief introduction to the area of rehabilitation robotics and, via case studies, illustrate how computer/technology-assisted rehabilitation systems can be developed and used to assess motor and cognitive impairment, detect early evidence of functional impairment, and augment therapy in high and low-resource settings.

Duke VisionFest is a virtual research symposium conceived to bring together the many Duke groups studying aspects of the visual system. Keynote by Dr. Joseph Carroll, Medical College of Wisconsin, and featuring talks ranging from photoreceptor biology to visual system evolution to clinical diagnosis. Pre-registration is required to attend but open to anyone interested.

Electrophysiology of eye and visual pathway is useful tool in ophthalmology and neurology. It covers a few examinations to find out if defect of vision is peripheral or central. Visual evoked potentials (VEP) are most frequently used in neurology and neuroophthalmology. VEP are evoked by flash or pattern stimulations. The combination of these both examinations gives more information about the visual pathway. It is very important to remember that VEP originate in the retina and reflect its function as well. In many cases not only VEP but also electroretinography (ERG) is essential for diagnosis. The seminar presents basic electrophysiological procedures used for diagnosis and follow-up of optic neuropathies and some of central nervous system diseases which affect vision (mostly multiple sclerosis, CNS tumors, stroke, traumas, intracranial hypertension).