Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin

The Impact of Mild Traumatic Brain injury on Neuronal Networks and Neurobehavior

Despite its enormous incidence, mild traumatic brain injury is not well understood. One aspect that needs more definition is how the mechanical energy during injury affects neural circuit function. Recent developments in cellular imaging probes provide an opportunity to assess the dynamic state of neural networks with single-cell resolution. In this dissertation, we developed imaging methods to assess the state of dissociated cortical networks exposed to mild injury. We probed the microarchitecture of an injured cortical circuit subject to two different injury levels, mild stretch (10% peak) and mild/moderate (35%). We found that mild injury produced a transient increase in calcium activity that dissipated within 1 h after injury. Alternatively, mild/moderate mechanical injury produced immediate disruption in network synchrony, loss in excitatory tone, and increased modular topology, suggesting a threshold for repair and degradation. The more significant changes in network behavior at moderate stretch are influenced by NMDA receptor activation and subsequent proteolytic changes in the neuronal populations. With the ability to analyze individual neurons in a circuit before and after injury, we identified several biomarkers that confer increased risk or protection from mechanical injury. We found that pre-injury connectivity and NMDA receptor subtype composition (NR2A and NR2B content) are important predictors of node loss and remodeling. Mechanistically, stretch injury caused a reduction in voltage-dependent Mg2+ block of the NR2B-cotaning NMDA receptors, resulting in increased uncorrelated activity both at the single channel and network level. The reduced coincidence detection of the NMDA receptor and overactivation of these receptors further impaired network function and plasticity. Given the demonstrated link between NR2B-NMDARs and mitochondrial dysfunction, we discovered that neuronal de-integration from the network is mediated through mitochondrial signaling. Finally, we bridged these network level studies with an investigation of changes in neurobehavior following blast-induced traumatic brain injury (bTBI), a form of mild TBI. We first developed and validated an open-source toolbox for automating the scoring of several common behavior tasks to study the deficits that occur following bTBI. We then specifically evaluated the role of neuronal transcription factor Elk-1 in mediating deficits following blast by exposing Elk-1 knockout mouse to equivalent blast pressure loading. Our systems-level behavior analysis showed that bTBI creates a complex change in behavior, with an increase in anxiety and loss of habituation in object recognition. Moreover, we found these behavioral deficits were eliminated in Elk-1 knockout animals exposed to blast loading. Together, we merged information from different perspectives (in silico, in vitro, and in vivo) and length scales (single channels, single-cells, networks, and animals) to study the impact of mild traumatic brain injury on neuronal networks and neurobehavior

Dynamics and Effective Connectivity in Bi- and Three–dimensional Neuronal Cultures: from Self–organization to Engineering

[eng] This thesis was part of the European consortium MESOBRAIN, a team of 5 organizations that joined efforts in nanofabrication, cell culturing, imaging and data analysis to build tailored human 3D networks. The thesis timing was limited to 3 years, and several of the resources needed for its development were built from scratch. The main objective of this Ph.D. thesis was to explore complex characteristics of cortical neuronal cultures in terms of effective connectivity and exhaustive network analyses. This objective comprised four research lines: (i) The evaluation of neuronal network resilience and emerging plasticity mechanisms, (ii) the characterization of functional development to underline crucial timepoints in healthy neuronal networks, (iii) the study of 3D network interactions of neurons embedded inside an ECM--like environment, and (iv) the design, construction and viability inspection of neurons seeded on tiny 3D nanoprinted solid scaffold structures as a first step towards recreating cortical columns in vitro. For these multiple lines, we used either primary rat cultures (i,iii,iv) or human--derived neurons (ii). The former group corresponds to cultures with long established protocols that have been thoroughly studied in the field. The latter group corresponds to human neurons derived from iPSCs, a relatively novel model with promising and thrilling applications in regenerative medicine. Despite the increasing use of stem cells in neuroscience, complex systems and medicine, they still lack a thorough exploration in terms of neuronal and circuit formation as well as the properties of the emergent activity patterns. With either primary or stem cells, we explored the formation of neuronal circuits in 2D and 3D, characterized the effective connectivity and rendered a number of network traits. This Thesis combines experiments of highly difficult implementation with detailed data analysis. It was necessary to develop brand new protocols for culturing 3D neuronal networks and for human-derived neurons, the use of different microscopy setups the programming of object detection and tracking software and advance the analysis toolbox of calcium fluorescence data. First, resilience experiments on primary clustered neuronal cultures consisted on progressive perturbations through chemical receptor antagonists. This study represents an inspiring numerical--experimental model to comprehend the impact of plasticity mechanisms in the spontaneous activity of neuronal circuits. The results showed that, upon progressive connectivity blockade through chemical receptors' antagonists, only--excitatory neuronal networks displayed a surprising hyper--efficiency (HE) state for early--onset doses. As plasticity mechanisms influence the response of effective connectivity in the presence of perturbations, these compensatory mechanisms, usually disregarded, must be included in biological modeling as accurately as possible. Otherwise, episodes of functional rewiring and synaptic strengthening could mask important phenomena during experiments that alter channel communication. A simple algorithm that hypothesized an effective synaptic scaling was able to capture the hyper--efficiency state seen in experimental data, while percolation models wrongly predicted a progressive decay. The second research line was a sum of engineering efforts within the MESOBRAIN consortium, the European adventure to build 3D neuronal cultures embedded in hydrogels and with the presence of scaffolds. After several months of biomaterials testing, the candidate D--Clear resulted suitable for the construction of scaffolds, both with primary rat cells and hiPSCs, due to its good optical properties, manageability and biocompatibility. To our knowledge, D--Clear was never used before outside the orthodontic field and could provide a new catalogue of interesting designs for support and guidance of neuronal assemblies. Using this material, we developed a series of designs to offer support and guidance to cortical neurons in a 3D platform. The third research line focused on the study of neuronal development and cell-to-cell interactions in a semi-synthetic hydrogel that resembles the extracellular matrix of the brain. These hydrogel cultures keep the advantages of in vitro models while achieving an effective connectivity and architecture closer to in vivo. Finally, the fourth line of research applied cortical neurons from human-derived pluripotent stem cells to study key developmental stages and characterize the healthy maturation of these cells in vitro. As this technology has tremendous potential for regenerative medicine and to model neuronal diseases, it is urgent to consolidate the capacity of these human neuronal networks to reproduce efficient activity patterns of healthy patients, and explore the differences against the results obtained with animal models.[spa] La presente tesis doctoral se enmarca en el contexto de la Física de la Materia Condensada, la Biofísica y la Neurociencia. Principalmente, se centra en el estudio de la conectividad funcional en cultivos neuronales bidimensionales (2D) y tridimensionales (3D). El trabajo se ha desarrollado en el Laboratorio del director de tesis Dr. Jordi Soriano, en la Facultad de Física de la Universitat de Barcelona, junto con el codirector Dr. Daniel Tornero, en el Hospital Clínic de Barcelona. Esta tesis forma parte del proyecto europeo MESO-BRAIN, del programa Future and Emergent Technologies (FET) de la Comisión Europea, Horizon2020. El trabajo de investigación combina experimentos con cultivos neuronales (de rata embrionaria o células humanas pluripotentes) y un análisis detallado en el contexto de teoría de redes y sistemas complejos. Los principales núcleos del trabajo realizado son los siguientes: (i) Actividad funcional en cultivos de redes neuronales y los mecanismos homeostáticos que emergen en presencia de perturbaciones; (ii) el desarrollo de herramientas de neuroingeniería para preparar cultivos ad hoc con conectividad dirigida mediante scaffolds; (iii) el análisis exhaustivo de los procesos de formación y madurez de redes funcionales humanas obtenidas de células madre pluripotentes inducidas, una nueva tecnología que promete revolucionar el campo de la medicina regenerativa; y (iv) la caracterización de cultivos neuronales 3D en estructuras que imitan la matriz extracelular natural de su entorno. Entre las diversas técnicas para la realización de cultivos tridimensionales, destacan los hidrogeles semi-sintéticos, constituidos en base a polímeros altamente hidratados con alta biocompatibilidad y cuyas propiedades mecánicas pueden ser manipuladas para obtener la estructura óptima según el tipo de tejido. En conjunto, los resultados de la presente tesis muestran la gran versatilidad de los cultivos neuronales y aportan avances relevantes en el estudio de plasticidad en redes neuronales, madurez y desarrollo tanto en 2D como en 3D, con sus correspondientes diferencias, incluyendo el uso de neuronas humanas derivadas de células madre inducidas. En el futuro, estos estudios nos permitirán incrementar nuestro conocimiento sobre el funcionamiento global del cerebro y avanzar en la investigación de diferentes enfermedades neurodegenerativas

Etude expérimentale des dynamiques temporelles du comportement normal et pathologique chez le rat et la souris

155 p.Modern neuroscience highlights the need for designing sophisticated behavioral readout of internal cognitive states. From a thorough analysis of classical behavioral test, my results supports the hypothesis that sensory ypersensitivity might be the cause of other behavioural deficits, and confirm the potassium channel BKCa as a potentially relevant molecular target for the development of drug medication against Fragile X Syndrome/Autism Spectrum Disorders. I have also used an innovative device, based on pressure sensors that can non-invasively detect the slightest animal movement with unprecedented sensitivity and time resolution, during spontaneous behaviour. Analysing this signal with sophisticated computational tools, I could demonstrate the outstanding potential of this methodology for behavioural phenotyping in general, and more specifically for the investigation of pain, fear or locomotion in normal mice and models of neurodevelopmental and neurodegenerative disorders

Dynamic and Thermodynamic Models of Adaptation

The concept of biological adaptation was closely connected to some mathematical, engineering and physical ideas from the very beginning. Cannon in his "The wisdom of the body" (1932) used the engineering vision of regulation. In 1938, Selye enriched this approach by the notion of adaptation energy. This term causes much debate when one takes it literally, i.e. as a sort of energy. Selye did not use the language of mathematics, but the formalization of his phenomenological theory in the spirit of thermodynamics was simple and led to verifiable predictions. In 1980s, the dynamics of correlation and variance in systems under adaptation to a load of environmental factors were studied and the universal effect in ensembles of systems under a load of similar factors was discovered: in a crisis, as a rule, even before the onset of obvious symptoms of stress, the correlation increases together with variance (and volatility). During 30 years, this effect has been supported by many observations of groups of humans, mice, trees, grassy plants, and on financial time series. In the last ten years, these results were supplemented by many new experiments, from gene networks in cardiology and oncology to dynamics of depression and clinical psychotherapy. Several systems of models were developed: the thermodynamic-like theory of adaptation of ensembles and several families of models of individual adaptation. Historically, the first group of models was based on Selye's concept of adaptation energy and used fitness estimates. Two other groups of models are based on the idea of hidden attractor bifurcation and on the advection--diffusion model for distribution of population in the space of physiological attributes. We explore this world of models and experiments, starting with classic works, with particular attention to the results of the last ten years and open questions.Comment: Review paper, 48 pages, 29 figures, 183 bibliography, the final version accepted in Phys Life Re

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Feature selection and modelling methods for microarray data from acute coronary syndrome

Acute coronary syndrome (ACS) represents a leading cause of mortality and morbidity worldwide. Providing better diagnostic solutions and developing therapeutic strategies customized to the individual patient represent societal and economical urgencies. Progressive improvement in diagnosis and treatment procedures require a thorough understanding of the underlying genetic mechanisms of the disease. Recent advances in microarray technologies together with the decreasing costs of the specialized equipment enabled affordable harvesting of time-course gene expression data. The high-dimensional data generated demands for computational tools able to extract the underlying biological knowledge. This thesis is concerned with developing new methods for analysing time-course gene expression data, focused on identifying differentially expressed genes, deconvolving heterogeneous gene expression measurements and inferring dynamic gene regulatory interactions. The main contributions include: a novel multi-stage feature selection method, a new deconvolution approach for estimating cell-type specific signatures and quantifying the contribution of each cell type to the variance of the gene expression patters, a novel approach to identify the cellular sources of differential gene expression, a new approach to model gene expression dynamics using sums of exponentials and a novel method to estimate stable linear dynamical systems from noisy and unequally spaced time series data. The performance of the proposed methods was demonstrated on a time-course dataset consisting of microarray gene expression levels collected from the blood samples of patients with ACS and associated blood count measurements. The results of the feature selection study are of significant biological relevance. For the first time is was reported high diagnostic performance of the ACS subtypes up to three months after hospital admission. The deconvolution study exposed features of within and between groups variation in expression measurements and identified potential cell type markers and cellular sources of differential gene expression. It was shown that the dynamics of post-admission gene expression data can be accurately modelled using sums of exponentials, suggesting that gene expression levels undergo a transient response to the ACS events before returning to equilibrium. The linear dynamical models capturing the gene regulatory interactions exhibit high predictive performance and can serve as platforms for system-level analysis, numerical simulations and intervention studies

Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review.

Background: Parkinson’s disease (PD) affects 7–10 million people worldwide. Its diagnosis is clinical and can be supported by image-based tests, which are expensive and not always accessible. Electroencephalograms (EEG) are non-invasive, widely accessible, low-cost tests. However, the signals obtained are difficult to analyze visually, so advanced techniques, such as Machine Learning (ML), need to be used. In this article, we review those studies that consider ML techniques to study the EEG of patients with PD. Methods: The review process was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which are used to provide quality standards for the objective evaluation of various studies. All publications before February 2022 were included, and their main characteristics and results were evaluated and documented through three key points associated with the development of ML techniques: dataset quality, data preprocessing, and model evaluation. Results: 59 studies were included. The predominating models were Support Vector Machine (SVM) and Artificial Neural Networks (ANNs). In total, 31 articles diagnosed PD with a mean accuracy of 97.35 ± 3.46%. There was no standard cleaning protocol for EEG and a great heterogeneity in EEG characteristics was shown, although spectral features predominated by 88.37%. Conclusions: Neither the cleaning protocol nor the number of EEG channels influenced the classification results. A baseline value was provided for the PD diagnostic problem, although recent studies focus on the identification of cognitive impairment.post-print1392 K

From Neurons to Nucleic Acids: Spatio-temporal Emergent Behaviors of Complex Biological Systems

Biological systems, from the molecular to the organismal level, demonstrate emergent behaviors that form fundamental characteristics of the system. Many biological phenomena are difficult to observe experimentally because of technical limitations. Computational models are a useful tool for interpretation of behaviors of complex biological systems. This dissertation examines models for two different types of emergent behaviors: cortical state and RNA structure. In Chapter 2, I use a computational neural model to understand the effects of neurons with long-range projections and propagation delays. I find that propagation delays cause a local network to exhibit a variety of metastable network states. Application of transcranial alternating current stimulation enables the switching of a network to a different metastable state. These emergent behaviors of a network of modeled neurons are a simplified version of neocortical states, and the results provide a foundation for future research on the effects of stimulation on cortical behavior. In Chapter 3, I examine the structure of the 5′ UTR of the human tumor suppressor gene RB1 using an experimentally-directed RNA structural model. The 5′ UTR adopts three distinct structures with similar frequencies. Two disease-associated mutations each collapse the structural ensemble into a single structure, and also affect translation efficiency. By creating structural models of two homologous UTRs, I find that the ability to adopt multiple conformations is a conserved feature of this UTR and that RNA structure regulates this transcript. In Chapter 4, I model RNA structure in Sindbis virus (SINV). SINV is a single-stranded RNA virus, with known functional elements within its RNA genome. I created experimentally-directed structural models for highly structured portions of the genome. By disrupting these structures through systematic mutational design, I identified regulatory RNA elements within the genome. Most structures within the genome are not conserved in related species of virus, indicating that this virus is highly structurally divergent and utilizes its evolutionary space to create new structures. These three projects present three different ways of using computational models to characterize complex biological systems. Informed by biological data, computational models provide further insight into the role of these emergent behaviors within a system.Doctor of Philosoph