Day 1 and 2. System-level understanding of the brain and its emulation in advanced technology.
How can one build and simulate basal ganglia microcircuits in a data-driven, bottom-up manner.
Simulating large-scale networks of neurons is an important approach when interpreting and synthesizing different types of experimental data from the healthy as well as from the diseased brain. Thanks to both the rapid development of simulation software and the accumulation of quantitative data of different neuronal types, it starts to become possible to predict both computational as well as dynamical properties of local microcircuits in a ‘bottom-up’ manner. Simulated data from these models can then be compared with experiments as well as with ‘top-down’ modeling approaches, successively helping in the bridging of scales. I will describe an open source pipeline for predicting microcircuit connectivity and for setting up simulations in a reproducible way. This model building pipeline has been used to set up a first version of a large-scale cellular level model of the mouse striatum [1, 2]. This work also demonstrates the feasibility of using sparse data to generate a detailed model of the striatal microcircuitry.[1] Hjorth JJJ et al. (2000) The microcircuits of striatum in silico. Proc. Natl. Acad. Sci. U. S. A. 117, 9554–9565; https://doi.org/10.1073/pnas.2
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Dr. Alexander Kozlov is at the KTH Royal Institute of Technology and his research interest focuses on the use of mathematical modeling to understand the neural mechanisms underlying information processing, rhythm generation, and learning in motor systems. Of specific interest are the neuronal networks that generate locomotion.
Multi-neuronal sequences in the hippocampus during waking and sleep.
During different network states accompanied by distinct neural oscillations, populations of hippocampal neurons fire in sequential patterns. Based on studies in behaving rodents, I will discuss relationship between the hippocampal cognitive map for space and the neuronal firing patterns observed during sharp-wave ripples and theta oscillations . Then, I will present a quantitative framework for evaluating how running speed affects the hippocampal theta phase code at the single cell and population levels and demonstrate that a behavior-dependent map for space accounts for a wide variety of experimental observations. Finally, I will present findings from long-duration extracellular recordings that indicate a role for neuronal replay during sleep in the consolidation of memory.
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Prof. Kamran Diba at the University of Michigan Medical School, is interested in how neurons function in the intact brain, embedded in dynamic circuits. In his lab, he performs large-scale neuronal recordings from the cortex of freely behaving rats to study sequential activity patterns in the hippocampus. His group is also investigating how neural activity in the brain encodes and stores memories using precise temporal relationships at multiple timescales, and how the underlying neural circuits generate these firing patterns.
Neural circuits underlying
I will introduce a motor skill learning
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Probing the midbrain mechanisms for visual search: from circuits to behavior
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Prof. Andreas Kardamakis is at the Karolinska Institute and one of his main research objectives is to obtain a mechanistic understanding of how neural circuits implement this computation by linking patterned visual input onto selected motor behaviour at: (A) the level of the individual neuron and (B) their synaptic interactions within visuomotor loops involving the retina, superior colliculus, frontocortical networks and substantia nigra. Our expertise lies in microcircuit and systems approaches that involve a combination of electrophysiology, optogenetics, viral-based approaches, behaviour, and computation.
Directional Dynamics in the Motor Cortex
Movement direction is strongly encoded in the firing rate of neurons throughout the nervous system. This robust signaling, recorded from the motor cortex, can be used directly to control prosthetic devices for those suffering from paralysis. In addition, this signaling can be used as a foundation for analytical techniques used to describe the way information propagates through biological networks. In our applied studies, we have shown how firing rates recorded in parallel from populations of motor cortical neurons can be decoded to control a prosthetic arm and hand with 10 degrees-of- freedom. Using this technology, paralyzed human subjects can operate the prosthetic device to perform sophisticated, natural movements allowing them to carry out a variety of daily-living
tasks. Recently, dynamical system theory has been applied to motor cortical neuronal activity. Of particular interest is the idea that current activity in the motor cortex generates future structured patterns. A simple instantiation of this concept is that of oscillators that are specified before movement begins and then ‘released’ to generate muscle contraction and arm movement. In its basic, form this theory dictates that control takes place autonomously, in the absence of
ongoing input. We are exploring this concept with basic research using behaving monkeys. Our results show how direction signaling drives correlational structure in neuronal populations. Using dimensionality reduction, this structure can be decomposed into independent components. During reaching, the primary components occur sequentially in a way that is linked to features of the behavior. Matching the results of dynamical systems advocates, phase plots of these components show curved trajectories. However, instead of interpreting these ‘rotational dynamics’, as a signature of underlying oscillation, we show they are likely due to correlation induced by directional signaling. These results are consistent with the concept of directional signaling acting as a latent driver to neuronal populations.
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Andrew Schwartz is a Prof. at the University of Pittsburgh and his research is centered on two aspects of motor control cerebral mechanisms of volitional arm movement and cortical control of neural prosthetics. We use electrode arrays to record action potentials from populations of individual neurons in motor cortical areas while monkeys perform tasks related to reaching and drawing. A number of signal-processing and statistical analyses are performed on these data to extract movement-related information from the recorded activity.
Day 2 and 3. Brain function in health and disease, from the lab to the clinic.
Technologies and Neural Interfaces as means to facilitate Neuroplasticity
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Dr (MD) Franco Molteni is the director of the Unità Operativa Complessa Recupero e Riabilitazione Funzionale at the Hospital Villa Beretta in Italy. Dr. Molteni’s clinical activities focus on severe brain injuries and cognitive disorders, movement disorders, respiratory rehabilitation.
How to improve stroke rehabilitation: from biomarkers to predictive models of recovery.
Despite enormous advances in acute stroke management during the last 2 decades, the only existing treatment during the subacute and chronic phases of the disease is neurorehabilitation to improve the functional status of the patient. Most clinical guidelines highlight the need of designing rehabilitation treatments considering starting time, intensity or frequency, according to the patient’s tolerance. However, there are no homogeneous protocols and the underlying molecules and mechanisms associated with functional/motor improvement during rehabilitation are poorly understood. By identifying pathways and bio-molecules involved, we could predict recovery and personalization of rehabilitation treatments after stroke could be possible and should be our target.
Angiogenesis and vascular remodeling are mechanisms activated early after stroke and tightly coupled to neurogenesis and oligodendrogenesis as part of the endogenous neurorepair response. Moreover, angiogenesis has been associated with a neurological improvement in animal models of stroke and modulated by exercise. We have been working in identifying some molecules modulated during rehabilitation of stroke patients which could serve as potential biomarkers of functional/motor outcome related to their participation in plasticity mechanisms during neurorepair. Another important question in neurorehabilitation is about which variables best predict patients’ potential to benefit from intensive rehabilitation. Given the importance of identifying appropriate patients for admission to post-acute intensive rehabilitation after stroke, numerous studies have attempted to identify variables that may be useful to predict functional outcomes after inpatient rehabilitation.
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Susana Rodriguez is an MD at the Hospital Val D’Hebron, Barcelona, ES, in the Neurological Rehabilitation and Brain Injury Unit. Her clinical activity includes caring for patients with stroke, cranioencephalic trauma, and neurodegenerative and neuromuscular diseases. Her scientific activity is focused on translational research in the field of stroke rehabilitation, the treatment of spasticity, and the use of virtual reality and new technologies in the recovery of upper limb deficits after stroke.
Title to be announced
Reinout Wiers University of Amsterdam, NL
Prof. Wiers is full professor of developmental psychopathology at the University of Amsterdam. He is internationally known for his work on assessing and changing implicit cognitive processes in addiction. He published over 250 international papers and many books and book-chapters. He received the prestigious VIDI (2002) and VICI (2008) research grants from the Dutch National Science Foundation (N.W.O.) for research on implicit cognition and addiction.
Data analytics in modern medicine: dangers and opportunities
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Ton Coolen is a professor of physics of machine learning and complex systems at the Department of Biophysics at Radboud University. His main research interests are the development and application of statistical inference and machine learning methods for the predictive analysis of high-dimensional data in medicine, the development of mathematical and statistical methods for the modelling and analysis of complex many-variable systems (with applications in physics, biomedical sciences, computer science, and economics), and the mathematical analysis of random networks and graphs.
Adaptive rehabilitation after stroke
Dr (MD) Per Hamid Ghatan is the senior physician and development Manager at Bragée Kliniker in Uppsala. Since the late 1980s, Dr. Ghatan has specialized in the rehabilitation of patients with brain injuries and he welcomes innovations that could reduce impacts causing diffuse axonal injuries. He recognises the need for the adoption of technologies to protect against brain injuries and possibly recover function after trauma .
