The misfolding of intrinsically disordered proteins (IDPs) such as tau and α-synuclein (αSyn) has been associated with the on-set and progression of Alzheimer’s (AD) and Parkinson’s (PD) diseases.  A potential strategy to alleviate the aggregation of IDPs is to maintain / stabilize their native functional state by small molecule binding (pharmacological chaperones). However, the targeting of the native state of IDPs by small molecules has been challenging due to their heterogeneous conformational ensembles.

To tackle this challenge, we initially investigated the structural basis of small molecule drugability of IDPs. Two publicly available monomeric conformational ensembles of Tau were analyzed using in silico structure-based fragment mapping, which identified similar number of hot spots and small molecule binding sites on monomeric Tau ensembles as on tertiary folded proteins of similar size (Kiss et. al. ACS Chem Neurosci., Article, 2018, 9, 12, 2997). Similarly, a structural ensemble of αSyn constructed using experimental NMR data and molecular dynamics simulations was analyzed, which identified diverse set of potential small molecule binding sites, some of which were present at an interface involving relatively long-range tertiary contacts (Toth et. al., Plos One, 2014, 9(2):e87133). Next, we applied two distinct high-throughput chemical microarray surface plasmon resonance imaging screen to detect the binding between small molecules and monomeric full-length Tau (Pickhardt et. al., Current Alzheimer Research, 2015 12, 814) and αSyn (Toth et. al. Scientific Reports, 2019, 18;9(1):16947. The screens identified novel set of drug-like fragment and lead-like compounds that bound to either Tau or α Syn.  These results demonstrate that Tau and αSyn are viable receptors of drug-like small molecules.

We verified that selected hit compounds from the screens reduced the aggregation of Tau and α Syn in vitro and in various cell models. Oral administration of selected hit compounds to Drosophila melanogaster, over-expressing full-length wild-type human Tau in their motorneurons, protected them from Tau-induced locomotive impairment by significantly increasing the climbing ability of the flies compared to controls.

Overall, these results support the potential and practical feasibility of the therapeutic strategy to target the monomeric state of IDPs by small molecules to reduce their misfolding and eliminate the formation of potential toxic oligomers. The drug discovery approach presented can be applied to other IDPs linked to other misfolding diseases.

Learning Objectives:

1. Monomeric intrinsically disordered tau and α-synuclein can be viable receptors of drug-like small molecules: structural biology and biophysical perspectives

2. Application of computer-aided structure based and biophysical screening paradigms to identify novel small molecule binders to intrinsically disordered tau and α-synuclein.

3. In vitro and cellular disease models of protein aggregation to characterize the therapeutic effects of discovered novel pharmacological chaperones

Parkinson’s disease (PD) is the second most common neurodegenerative disorder with multiple motor and non-motor symptoms. PD is characterized by the presence on proteinaceous neuronal inclusions (Lewy bodies) which are increasingly observed in multiple brain regions during disease progression. One of the main components in Lewy bodies is misfolded α-synuclein. Studies in animal models and PD patients strongly support α-synuclein contribution in PD progression. Inoculation with extracts from PD patients’ brains induces the development and spread of α-synuclein pathology in rodents, non-human primates, and iPSC-derived human neurons. Thus, α-synuclein has emerged as a promising target for developing disease-modifying therapies, aiming at interfering with initial aggregation by small molecules; enhancing degradation of intracellular α-synuclein by stimulating lysosomal activity; lowering cellular α-synuclein levels by anti-sense oligonucleotides and cell-penetrant nanobodies; inhibiting uptake and targeting transmission of extracellular α-synuclein with active and passive immunization. Pre-formed fibrils (PFFs) able to seed aggregation of endogenous α-synuclein in cultured cells and in vivo have become an essential experimental tool to model and study pathological protein aggregation. PFF model allows investigating the uptake of pathogenic fibrils, their internalization, transport, processing and recruitment of endogenous α-synuclein, formation and maturation of Lewy body-like inclusions and effects of these processes on cell function and survival. With α-synuclein PFFs we can study the mechanism of pathology transmission, motor and behavioral outcomes of accumulation of α-synuclein deposits in different brain structures. While the processes of α-synuclein misfolding and pathology progression are still not fully clear, α-synuclein PFFs have been and will be very instrumental for solving these problems.

Learning Objectives:

1. Define main problems in developing disease-modifying therapies for Parkinson’s disease

2. Identify current therapeutic strategies for Parkinson’s disease treatment

3. Analyze benefits and limitations of modelling alpha-synuclein pathology with pre-formed fibrils

Alzheimer’s and Parkinson’s diseases (AD, PD) are the two most common neurodegenerative disorders. Despite their debilitating effects and increasing prevalence in society, no cures are known. Both diseases are characterized by the presence of aggregates in the brain, which include alpha-synuclein, tau and amyloid beta proteins. These proteins normally have distinct functions in neurons, yet they share a common pathological mechanism – highly flexible monomers aggregate into oligomers and fibrils which form plaques in the brain. These aggregates are associated with neuronal cell death and seed further protein aggregation as disease progresses. To better study the role of these proteins in neurodegenerative disease, we developed consistent, scalable production methods to generate active alpha-synuclein, tau and amyloid beta oligomers and fibrils in vitro. We demonstrate that oligomers are toxic to primary cortical neuron cultures, while fibrils seed AD and PD pathology in a mouse brain. These recombinant protein aggregates have exciting potential for use as molecular tools in neurodegenerative disease models and drug discovery.

Learning Objectives:

1. Define key proteins aggregates present in Alzheimer’s and Parkinson’s diseases.

2. Explain how recombinant protein aggregates can be used to study neurodegenerative disease.

Adolescence is a critical period for the developing brain, and binge drinking during this neuromaturation can result in enduring abnormal brain function and behavior. Repeated adolescent binge ethanol drinking is associated with increased risk of alcohol use disorders, psychopathology, and cognitive dysfunction in adulthood. In rodents, adolescent intermittent ethanol (AIE) exposure causes diminished adult hippocampal neurogenesis and dendritic spine density in the hippocampal dentate gyrus.  Donepezil (Aricept), an anti-cholinesterase with microglial inhibitory actions, has been shown to reverse the effect of AIE on dendritic spine density.  Therefore, we hypothesized donepezil would reverse the AIE-induced decrease of dentate neurogenesis as well as AIE-induced neuroimmune and epigenetic changes that may underlie it.

AIE reduced immunoreactivity for hippocampal dentate gyrus doublecortin (DCX) and increased immunoreactivity for activated caspase-3 and death receptor-3 in adulthood.  Each of these effects was reversed by donepezil treatment in adulthood.  AIE also increased immunoreactivity for the inflammatory signaling molecules HMGB1 and its receptor RAGE, as well as the activated phosphorylated transcription factor pNFkB p65, and the gene silencing marker H3K9me2.  All of these AIE effects were also reversed by donepezil, with the exception of HMGB1. These findings suggest AIE blunts neurogenesis through persistent increases of neuroimmune gene expression that subsequently increase progenitor cell death.

These findings have mechanistic implications as AIE increased immunoreactivity for the RAGE receptor that is known to trigger a cascade that activates pNFkB. AIE increased pNFkB p65+IR, the transcriptionally active form, and H3K9me2, associated with reduced BDNF transcription.   These findings are consistent with AIE causing a persistent shift in proinflammatory/trophic gene expression through RAGE neuroimmune-epigenetic signaling mechanisms.

Learning Objectives:

1. Explain adolescent intermittent ethanol (AIE).

2. Identify important hippocampal neuroimmune and cell death markers and how they are affected by AIE.

3. Define the effective therapy.

Ataxia Telangiectasia, also known as A-T, is a devastating neuropediatric and genetic disorder for which there is no cure. Patients suffer from immune deficiency, cancer predisposition, and a progressive loss of motor capabilities (ataxia). They die within their first 3 decades of life. Our understanding for how the disorder, which is caused by deficiency in the A-T mutated (ATM) protein, has been hampered by a lack of suitable animal model that mimics the most characteristic aspects of the disease (e.g., ataxia). I will detail a new mouse model of A-T that develops many of the key characteristics of A-T, most importantly the progressive ataxia and associated defects and atrophy of the cerebellum. Additionally, I will present promising data on a novel therapeutic that has the potential to restore ATM production in A-T patients whose underlying genetic defect is a nonsense mutation.

Learning Objectives:

1. Ataxia Telangiectasia (A-T), a rare multi system disorder characterized by immune deficiency, cancer predilection, and a severe, progressive loss of motor coordination (ataxia)

2. Readthrough therapies that enable translation of mRNA containing primary nonsense mutations

Over 40 tandem repeats undergo expansion events that lead to neurological disease. This number is likely an underestimate as many repeats are difficult to amplify using existing short read sequencing approaches. Through the use of long-read sequencing, we identified a 69 bp intronic variable number tandem repeat (VNTR) in WD repeat domain 7 (WDR7). The VNTR is expanded in a family with Amyotrophic Lateral Sclerosis (ALS) and exhibits significantly higher repeat copy number in three independent cohorts of individuals with sporadic ALS compared with matched controls, suggesting that it plays a role in modifying susceptibility to ALS.  Each 69 bp repeat motif forms a stem-loop structure with the potential to produce microRNAs, and the repeat RNA can aggregate when expressed in cells – a common hallmark of neurodegenerative disease pathology. We performed multiplexed barcoded PacBio long-read sequencing to resolve the complete internal structure of the WDR7 repeat in 288 geographically diverse individuals. We found striking variability in both repeat length and internal nucleotide composition. Some of the 69 bp repeat motifs are specifically present or absent in certain geographic populations, and we identified common repeat motifs in both Denisovan and Neanderthal genomes. We found that the repeat expands in the 3′-5′ direction, in groups of two repeat units. Extending this analysis to characterize VNTRs genome-wide, we identified several VNTRs that can differ in length (by up to 20 kb) amongst individuals and geographical super-populations and repeats that exhibit substantial diversity in internal sequence composition of the repeat. By characterizing the WDR7 VNTR in ALS, we identify features associated with repeat expansion dynamics, the mechanistic consequences of repeat expansions to ALS susceptibility, and the structure of repeats in geographically diverse populations that can precipitate expansion events.

Learning Objectives:

1. Understand the contribution of genetic risk factors to Amyotrophic Lateral Sclerosis

2. Describe novel methods to identify genetic susceptibility factors in neurodegenerative disease

3. Discuss how resolving the complete sequence of tandem repeats can help us understand patterns of repeat expansions throughout human history

Several age-related neurodegenerative disorders are characterized by the deposition of aberrantly folded proteins. The histopathological hallmark of synucleinopathies is the deposition of ɑ-synuclein (ɑSyn) protein in the central and peripheral nervous system. The functional and pathological role of ɑSyn protein aggregates in these progressive brain disorders, which includes different types of Lewy body diseases (LBDs), such as Parkinson’s disease (PD), multiple system atrophy (MSA), and dementia with Lewy bodies (DLB) have been actively investigated. Even though the pleomorphic nature of these syndromes might suggest different underlying causes, ɑSyn appears to play an important role in this heterogeneous group of diseases. An attractive hypothesis is that different types of ɑSyn protein assemblies have a unique and causative role in distinct synucleinopathies.  The recent research progress will be discussed on ɑSyn assemblies involved in PD, MSA and DLB, their behavior as strains, the role of the host environment and their implication for disease pathogenesis.

Learning Objectives:

1. Define the role of synuclein protein strains in synucleinopathies

2. Explain the importance of the strain environment in disease progression 

3. Understand the relevance of synucleinpathy animal models to human disease

Emerging data from several expansive clinical trials indicates that common chronic vascular risk factors such as diabetes, hypertension, obesity, and age increase the risk of stroke and dementia. This increased risk occurs through direct injury to brain blood vessels resulting from these risk factors and causes brain blood vessel leakage, cerebral edema, and ultimately permanent brain tissue injury. There are currently no clinical biologic assays to directly measure brain blood vessel health. Widely available imaging techniques provide limited quantitative information and only indicate cerebrovascular damage that has already occurred. To develop a blood-based biomarker for early brain vascular injury, we utilized a custom Luminex multiplex assay to measure the serum/plasma levels of six inflammatory molecules in two patient cohorts. In both discovery and validation cohorts (n=298 total), levels of the six defined circulating inflammatory molecules were not only associated with permanent vascular brain injury as measured by T2/FLAIR MRI signal intensity but also with cerebral free water, a diffusion tensor MRI measure of cerebral edema. A population mean-adjusted single composite inflammatory index score resulted in 94% specificity for severe cerebrovascular disease detection. Detection of clinically silent cerebral edema was increased two-fold in subjects with an elevated composite inflammatory index. Circulating cytokines and interleukin molecules present in measurable quantities by high sensitivity multiplex immunoassay can serve as a surrogate biomarker for cerebrovascular disease status that can be utilized to reduce the risk of stroke and dementia.

Learning Objectives:

1. Recognize common chronic vascular risk factors such as diabetes, hypertension, obesity, and age increase the risk of stroke and dementia.

2. Understand how a custom assay multiplexing technology was used in blood based diagnostics for silent brain vascular injury research.

In this preliminary experiment, we used spatial transcriptomics to assess the gene expression profiles of microglia or astrocytes in relation to their distance from plaques. We compared 18-month-old APPKI mice (APPNL-F) which carry 2 mutations for familial Alzheimer’s disease to APPNL-F crossed with mice with the Trem2R47H mutation knocked in (APPTREM2KI; n=4 each genotype). In addition, we included Wild Type (WT) and Trem2R47H KI mice (n=2-3). The APPKI mice start to show amyloid plaques at about 9 months of age and by 18 months have a heavy plaque load. To do spatial transcriptomics with the GeoMx® Mouse Whole Transcriptome Atlas, microglia (Iba1 positive cells) areas of interest (AOIs) were divided into 3 groups: those touching plaques; those within an area of heavy plaque load (periplaque) and those far from plaques. Periplaque astrocytes (S100b positive cells) or away from plaque astrocytes were also analyzed. Several of the “disease-associated microglial” (DAM) genes that do not show significant differences in an RNAseq study showed clear and significant increases in the microglia on plaques, with the differences in some cases dropping off already in the periplaque region, which was rather remarkable. This steep spatial gradient strongly suggests that these genes are upregulated due to contact with the plaque, rather than being involved in the migration of microglia towards plaques. Moreover, we could distinguish which of these genes were influenced by the alteration in Trem2 and its decreased expression. There were also some genes strongly decreased in the vicinity of plaques and microglia and astrocytes that frequently behaved differently.

Learning Objectives: 

1. Understand how spatial transcriptomics can be used to study the whole mouse transcriptome in CNS samples

2. Learn how the Trem2R47H mutation affects the expression profiles of microglia and astrocytes in an Alzheimer's Disease model

3. Understand the relationship between distance from plaques and gene expression for microglia and astrocytes

Technological advances such as single-cell RNA sequencing accelerated our understanding of cellular diversity in tissues. However, the ability to elucidate this cellular heterogeneity while retaining spatial information is crucial for understanding the complex biological networks in health and disease. At Resolve Biosciences we developed Molecular CartographyTM, a highly multiplexed, single-molecule detection technology to measure transcriptomic activity with full spatial context at subcellular resolution. The unparalleled sensitivity and specificity of Molecular CartographyTM helps scientists detect individual transcripts and rare signals to interpret fundamental biology and rapidly advance the understanding of comple x biological questions in neuroscience. In this presentation, we demonstrate applications in mouse and human brain tissue in which we detect millions of individual transcripts originating from a hundred genes in each sample. This allows us to unravel the complex spatial organization of different cell types and their changes in gene expression in response to pathology, for example, in the context of Alzheimer's disease.

Learning Objectives:

1. Method for highly multiplexed and sensitive spatial transcriptomics

2. Spatial analysis of cell phenotypes in response to pathology in the context of Alzheimer’s disease

In 1910, Harrison published the first report of frog embryonic sympathetic ganglia grown in hanging drops of lymph for a few days, where single neurons extended nerve fibers with complex growth cones extending and contracting over time towards target cells. The revolutionary ability to culture embryonic and post-natal neurons for extended time periods demonstrated that developing neurons could form plexi of interconnected nerve fibers. However, over the years it has proven to be exceedingly difficult to consistently culture adult central nervous system (CNS) neurons; while several instances of success in culturing fewer than 10 neurons have been reported, a major unresolved methodological challenge in neuroscience has been the ability to culture adult CNS neurons from any region consistently and in tractable numbers. It has been possible to culture adult dorsal root ganglia neurons for reasons that remain unclear; these neurons also retain an ability to regenerate throughout life. The value of CNS neurons in advancing understanding of intact adult neuronal function, response to injury, and as a screening system for candidate therapeutics is clear. Here we report for the first time the ability to consistently culture adult neurons of specified phenotype in large numbers over extended time periods. We further provide evidence for the utility of these cultures in advancing studies of CNS injury.

Learning Objectives:

1. Successful culture of adult primary neurons from the central nervous system

2. Electrical recordings and stimulation of neural networks in vitro

3. Practical applications for primary adult CNS cultures

Recent advances in machine learning have shown that deep neural networks (DNNs) can provide powerful and flexible models of neural sensory processing. In the auditory system, standard linear-nonlinear (LN) models are unable to account for high-order cortical representations, but thus far it is unclear what additional insights can be provided by DNNs, particularly in the case of single-neuron activity. DNNs can be difficult to fit with relatively small datasets, such as those available from single neurons. In the current study, we developed a population encoding model for a large number of neurons recorded during presentation of a large, fixed set of natural sounds. Leveraging signals from the population substantially improved performance over models fit to individual neurons. We tested a range of DNN architectures on data from primary and non-primary auditory cortex, varying number and size of convolutional layers at the input and dense layers at the output. DNNs performed consistently better than LN models. Moreover, the DNNs were highly generalizable. The output layer of a model pre-fit using one population of neurons could be fit to different single units and/or different stimuli, with performance close to that of neurons in the original fit data. These results indicate that population encoding models capture a general set of computations performed by auditory cortex and can be analyzed to derive a characterization of auditory cortical function. Tools developed for this project as part of the BRAIN Initiative can be applied to a wide range of problems in the auditory and other neural systems.

Learning Objectives:

1. Applying deep learning to neural populations overcomes data limitations that hinder analysis of single-neuron data.

2. Deep learning models consistently outperform traditional linear-nonlinear models of single neurons in auditory cortex.

3. Population-based models generalize well to neurons not included in the original model fit.

Multiscale modeling has arisen as a focus of computational systems biology, with the realization that genome, proteome, connectome, etceteromes, will only become comprehensible once placed in the context of explicit computer simulations. Measurements and activity patterns at one scale must be understood dynamically in the context of patterns at higher and lower scales. Nowhere is this more apparent than in the domain of brain disease, where manipulations at molecular levels (drugs) are used to change, licitly or illicitly, behavior and  thought.  We have begun to study schizophrenia and other brain dysfunctions with an eye towards connecting these levels in order to reveal functional and dysfunctional dependencies. Rather than only thinking hierarchically up the great chain of embeddings (e.g. from molecule to spine to dendrite to cell to circuit to area to behavior), it will be valuable to transform to other representational frames to connect across multiple scales.

Learning Objectives:

1. -Distinguish approaches, objectives, synergies of 1. mechanistic multiscale modeling; 2. phenomenological modeling; 3. AI through deep learning.

2. Appreciate how genomic, molecular, transcriptomic thinking and results can be integrated into functional contexts at higher scales.

3. Assess how datamining and simulation techniques interface with experiment and help integrate experimental measurements across scales.

Cortical neurons process information on multiple timescales, and neurons that remain active during delay periods lasting seconds have been recorded in the parietal and prefrontal cortex.  However, the underlying mechanisms for the emergence of neuronal timescales stable enough to support working memory are unclear. A spiking recurrent neural network (RNN) model was trained on a working memory task and unit activity was compared with single neurons in the primate prefrontal cortex.  The temporal properties of our model and the neural data are remarkably similar. Analysis of the RNN model revealed strong inhibitory-to-inhibitory connections underlying a disinhibitory microcircuit as a critical component for long neuronal timescales and working memory maintenance. Enhancing inhibitory-to-inhibitory connections led to more stable temporal dynamics and improved task performance. The same network can perform other tasks without disrupting its pre-existing timescale architecture, suggesting that strong inhibitory signaling underlies a flexible working memory network.

Learning Objections:

1.  To learn a new approach to understanding brain function by creating networks to perform tasks using machine learning

2.  To learn how how to probe these networks to discover how they solve a task

3.  To learn how these models can be used to formulate experimentally testable hypotheses

Cortical circuits often receive multiple inputs from upstream populations with non-overlapping stimulus tuning. Both the feedforward and recurrent architectures of the receiving cortical layer reflect input tuning diversity. We study how population-wide neuronal variability propagates through a hierarchical cortical network receiving multiple independent tuned inputs. Our new analysis of in vivo neural data from the primate visual system shows that the number of latent variables (dimensions) needed to describe population shared variability is smaller in V4 than in downstream visual area PFC.  We successfully reproduce the dimensionality expansion observed across brain areas using a multi-layer spiking network with structured feedforward projections and recurrent assemblies of multiple, tuned neuron populations. We show that tuning-structured connectivity generates attractor dynamics within the model PFC activity, where attractor competition expands the dimensions of shared variance. Restricting dimensionality analysis to activity from one attractor state recovers the low-dimensional structure inherited from each of our tuned inputs. Our model thus introduces a framework where high-dimensional cortical variability is understood as alternating states of low-dimensional, tuning-specific circuit dynamics.

Learning Objectives:

1. Measure the space in which population-wide neuronal fluctuations inhabit in both visual area V4 and the prefrontal cortex (PFC).

2. Build a circuit based model that captures how neuronal variability is transferred from V4 to PFC.

3. Using our model, build a theoretical framework that predicts how the recurrent circuitry within PFC supports an expansion of the dimensions of variability compared to V4.

Targeted stimulation of the brain has the potential to treat mental illnesses but designing an appropriate protocol requires a multitude of choices.  I will describe an approach to help design the stimulation protocol by identifying electrical dynamics across many brain regions that relate to illness states or behaviors, defined as electrical connectome, or electome, networks.  The observed electrical activity of the brain is modeled as a superposition of activity from the latent electome networks, where the weights on the latent networks predict disease state, behavior, or outcomes.  These electome networks are interpretable in their power and coherence relationships between brain regions, forming an explainable artificial intelligence approach for neural data that facilitates future experimental design.  I will highlight how this approach has been used to successfully design stimulation protocols in the context of an animal model of major depressive disorder and appetitive social behavior.

Learning Objectives:

1. Define an additive model

2. Explain how decompositions of neural activity can be linked with disease states

The intrinsic activity of the brain is organized into networks and motifs that vary over time. To understand how coordinated macroscale patterns of intrinsic activity flow across the brain’s structural network, new analysis approaches that can extract interpretable spatial and temporal features are required.  We demonstrate the use of machine learning to extract prototypical spatiotemporal trajectories of brain activity that provide a sparse and elegant basis of representation. To better interpret the spatiotemporal patterns of brain activity, we incorporate brain network models as constraints, forcing the algorithm to learn parameters that can be measured empirically. This interpretable machine learning-based approach paves the way for multimodal experiments that can validate or disprove the accuracy of different brain network models.

Learning Objectives:

1. Define brain network models and explain their application in the study of macroscale activity in the brain.

2. Identify machine learning approaches to characterizing brain dynamics.

3. Describe the use of brain network models to constrain machine learning techniques and improve interpretation.

We will show how to combine large scale neural recordings and mechanistic neural network models to advance our conceptual understanding of how neural circuits mediate cognitive functions like perception, navigation and semantic cognition.  With additional mathematical analysis, we can also even explain why some neural circuits might be organized the way they are.  First, we will describe state of the art models of the retinal response to natural movies, and use explainable artificial intelligence to understand how they recapitulate over 20 years of retinal physiology experiments.  Second we will describe how the 4 most dominant cell-types in our retina emerge naturally as a consequence of optimal spatiotemporal processing of natural movies.  Third, we will demonstrate how hexagonal grid cell firing fields must obligatorily arise in any biologically plausible neural network that is capable of performing path-integration.   And fourth, time permitting, we will describe how the hierarchical differentiation of concepts that unfolds over time in infant semantic development can be accounted for by simple mechanistic neural models. 

Learning objectives:

1. Describe how to create mechanistic neural models of cognitive phenomena

2. Understand explainable artificial intelligence and how to use it to extract conceptual insights from mechanistic models

3. Learn about optimality principles for explaining why some neural circuits are organized the way they are

4. Learn about specific phenomena in perception, navigation, and semantic cognition
 

Understanding how populations of neurons work together to represent stimuli, build percepts, and generate complex behaviors, is a fundamental challenge in neuroscience. To establish a link between neural activity and behavior, especially in complex behaviors and tasks, we need approaches that allow for an unbiased look into the latent factors that underlie neural activity. In this talk, I will describe ways in which my lab is tackling this objective through the development of novel unsupervised representation learning methods. With new tools to read out information from the brain in a stable and generalizable manner, we can enable rich comparisons across neural recordings spanning different time points in learning, or across aging and development.

Learning Objectives:

1. Audience members will learn about new tools for visualizing and interpreting neural activity

2. Learn about the application of representation learning methods on various neural recordings from macaque, rat, and mouse

One role of theory is in guiding future experiments: What should we aim to measure? Which experimental results should we be surprised about? I will argue here that simple random networks models of neural dynamics explain, to the experimental accuracy, many features of large population recordings in a variety of nervous systems. Observation of such features is thus not surprising in the context of random null models. I will discuss how this affects, or can affect, our interpretation of experiments, our choices of which experiments to do next, and how it raises hopes for building next-level theories of nervous systems, finally leaving the neuron behind.

Learning Objectives:

1. Explain how large simple neural networks can result in complex experimental recordings.

2. Introduce the concept of latent variables dynamics

3. Define coarse-graining modeling approaches

A central challenge in neuroscience is to decipher how cellular mechanisms are coordinated across multiple aspects of the circuits that control whole-animal behavior. We will briefly discuss how modern neuroscience can strive to meet these goals. A key challenge is to unify a top-down approach to brain, computation and behavior, as defined by David Marr and Horace Barlow, with a bottom-up approach that takes inspiration from genes and genetic models, molecules and cell biology. Both of these viewpoints have merit of course, and both find common ground and opportunity for unification at the level of brain circuits. The story behind this talk –too young to be declared a success – concerns a multi-PI NIH U-19 project titled Oxytocin Modulation of Neural Circuit Function and Behavior. My colleagues Robert Froemke (co-PI), Dayu Lin and Gyorgyi Buzsaki and I have focused our labs’ attention on the peptide modulator oxytocin, the neural circuits it affects, and its role in orchestrating mouse social behavior, critical for survival yet still incompletely understood. Oxytocin signaling proves essential for the social transmission of maternal behavior (Carcea, Froemke et al. Nature 2021) and vital for acquisition and storage of information about social rank (Osakada, Lin et al. in preparation). With some striking social behaviors as a starting point we seek clarification of “socio-spatial memory neural circuits” and their operation in disparate brain regions such as auditory cortex, hippocampal area CA2, lateral septum and VMHvl of the hypothalamus. From an evolutionary perspective, work on oxytocin addresses the general question of how neuromodulators can transform circuits by operating at a different level than fast neurotransmitters. It also contributes to a wider effort to comprehend “the molecules, cells, and circuits linking sensory information to the motor outputs of social behavior”, which Park and Insel called “the dark matter” of social neuroscience. In so doing, we cannot help but evaluate the challenges of multilevel, multi-investigator projects, how synergies can be sought and fostered, and indeed how success itself might be defined.

Learning Objectives:

1. Delineate the goals of studies of brain, computation and behavior and how they can be integrated

2. Describe how neuromodulators such as oxytocin transform circuits and operate at a different level than fast neurotransmitters

3. Evaluate the synergies and challenges of multilevel, multi-investigator projects and how success could be defined

Drawing from the latest research and intervention practices, this presentation will address overcoming burn-out, compassion fatigue and vicarious trauma generally, and specifically as related to the COVID-19 era.   The curriculum will present research regarding compassion fatigue and concepts from the new wave of positive psychology.  The talk will also present a reframe of compassion fatigue utilizing concepts from Community Resiliency Model (CRM), a new conceptualization of Post-Traumatic Growth and Mature Gratitude.  The instructional format will utilize videos and anecdotal examples from the work of Safe Harbor- Trauma Recovery Center.  

Learning Objectives:

1. Define burnout, compassion fatigue and secondary trauma and explore signs and symptoms in relation to health providers’ response to the COVID-19 pandemic

2. Explain stress responses and brain neuroplasticity and “rewiring”, and present newly emerging concepts from positive psychology

3. Define resilience and present practices for enhancing resilience and post-traumatic growth

Complex behaviors are often driven by an internal model, which integrates sensory information over time and facilitates long-term planning to reach subjective goals. A fundamental challenge in neuroscience is: how can we use behavior and neural activity to understand this internal model and its dynamic latent variables? Here we interpret behavioral data by assuming an agent behaves rationally — that is, it takes actions that optimize its subjective reward according to its understanding of the task and its relevant causal variables. We apply a new method, Inverse Rational Control (IRC), to learn an agent's internal model and reward function by maximizing the likelihood of its measured sensory observations and actions. This thereby extracts rational and interpretable thoughts of the agent from its behavior. We also provide a framework for interpreting encoding, recoding and decoding of neural data in light of this rational model for behavior. When applied to behavioral and neural data from simulated agents performing suboptimally on a naturalistic foraging task, this method successfully recovers their internal model and reward function, as well as the Markovian computational dynamics within the neural manifold that represents the task. This work lays a foundation for discovering how the brain represents and computes with dynamic latent variables.

Learning Objectives:

1. Distinguish rational behavior from optimal behavior

2. Identify how to use estimates of latent mental variables to predict brain dynamics

Through the COVID-19 pandemic the scientific community has uncovered many aspects of SARS-CoV-2 biology. This includes the multi-organ tropism of SARS-CoV-2 and COVID-19 effects on multiple systems, including the central nervous system (CNS). From early in the pandemic a variety of CNS symptoms have been identified ranging from sensory loss to encephalitis/encephalopathies—with estimates of 30-80% of COVID-19 patients experiencing atleast one of these symptoms. However, there is still a large gap in knowledge in the biological consequences of SARS-CoV-2 infecting neural tissues. In order to investigate this further we used three independent approaches to probe the capacity of SARS-CoV-2 to infect the brain. First, using human brain organoids, we observed clear evidence of infection with accompanying metabolic changes in infected and neighboring neurons. However, no evidence for type I interferon responses was detected. We demonstrate that neuronal infection can be prevented by blocking ACE2 with antibodies or by administering cerebrospinal fluid from a COVID-19 patient. Second, using mice overexpressing human ACE2, we demonstrate SARS-CoV-2 neuroinvasion in vivo. In autopsies from patients who died of COVID-19, we detect SARS-CoV-2 in cortical neurons and note pathological features associated with infection with minimal immune cell infiltrates. Finally, utilizing single-cell RNA sequencing (scRNA-seq) of cerebrospinal fluid (CSF) and blood from individuals with COVID-19 with neurological symptoms, we find compartmentalized, CNS-specific B cell responses.All affected individuals had CSF anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies whose target epitopes diverged from serum antibodies. In an animal model, we find that intrathecal SARS-CoV-2 antibodies are present only during brain infection and not elicited by pulmonary infection.These results provide evidence for the neuroinvasive capacity of SARS-CoV-2 and an unexpected consequence of direct infection of neurons by SARS-CoV-2.

Learning Objectives: 

1. Explore the neuroinvasive potential of SARS-CoV-2

2. Learn about possible consequences of neuroinvasion of SARS-CoV-2

3. Identify neuroimmune responses against SARS-CoV-2

Threshold-linear networks (TLNs) display a wide variety of nonlinear dynamics including multistability, limit cycles, quasiperiodic attractors, and chaos. Over the past few years, we have developed a detailed mathematical theory relating stable and unstable fixed points of TLNs to graph-theoretic properties of the underlying network. These results enable us to design networks that count stimulus pulses, track position, and encode multiple locomotive gaits in a single central pattern generator circuit.

Learning Objectives: 

1. What types of attractors that can be encoded in inhibition-dominated neural networks? Can multiple dynamic attractors coexist in the same network?

2. What neural network functions or computations can be performed by threshold-linear networks? Give two examples.