Methods to Enhance Information Extraction from Microelectrode Array Measurements of Neuronal Networks

For the last couple of decades, hand-in-hand progresses in stem-cell technologies and culturing neuronal cells together with advances in microelectrode array (MEA) technology have enabled more efficient biological models. Thus, understanding the neuronal behavior in general or realizing some particular disease model by studying neuronal responses to pharmacological or neurotoxical assays has become more achievable.Moreover, with the widespread practical usage of MEA technology, a vast amount of new types of data has been collected to be analyzed. Conventionally, MEA data from neuronal networks have been analyzed, e.g., with the methods using predefined parameters and suitable for analyzing only specific neuronal behaviors or by considering only a portion of the data such as extracted extracellular action potentials (EAPs). Therefore, in addition to the current analysis methods, novel methods and newly acquired measures are needed to understand the new models. In fact, we hypothesized that existing measurement data carry a lot more information than is considered at present. In this thesis, we proposed novel methods and measures to increase the information which we can extract from MEA recordings; thus, we hope these to contribute to better understanding of neuronal behaviors and interactions.Firstly, to analyze firing properties of neuronal ensembles, we developed a method which identifies bursts based on spiking behavior of recordings; thus, the method is feasible for the cultures with variable firing dynamics. The developed method was also designed to process a large amount of data automatically for statistical justification. Therefore, we increased the analysis power in the subsequent analyses in comparison to the existing burst detection methods which are using pre-defined and strict definitions.Subsequently, we proposed novel metrics to evaluate and quantify the information content of the bursts. Entropy-based measures were employed for quantifying bursts according to their self-similarity and spectral uniformity. We showed that different types of bursts can be distinguished using entropy-based measures. Also, the joint analysis of bursts and action potential waveforms were proposed to obtain a novel type of information, i.e., spike type compositions of bursts. We presented that the spike type compositions of bursts would change under different pharmacological applications.In addition, we developed a novel method to calculate synchronization between neuronal ensembles by evaluating their time variant spectral distributions: For that, we assessed correlations of the spectral entropy (CorSE). We showed that CorSE was able to estimate synchronicity by studying both local field potentials (LFPs) and extracellular action potentials (EAPs); thus, we could contribute to understanding synchronicity between neuronal ensembles which also don’t exhibit detectable EAPs.In conclusion, motivated by the recent popularity of MEA usage in the neuroscience field, we developed novel and enhanced methods to derive new types of information. We showed that by using our developed methods one could extract additional information from MEA recordings. As a result, the proposed methods and metrics would enhance the analysis efficiency of the microelectrode array measurement based studies and provide different viewpoints for the analyses. The derived novel information would contribute to interpreting neuronal signals recorded from a single or multiple recording locations. Consequently, methods presented in this thesis are important complements to the existing methods to understand neuronal behavior and population-wise neuronal interactions

Functional characterization of human pluripotent stem cell-derived cortical networks differentiated on laminin-521 substrate : comparison to rat cortical cultures

Human pluripotent stem cell (hPSC)-derived neurons provide exciting opportunities for in vitro modeling of neurological diseases and for advancing drug development and neurotoxicological studies. However, generating electrophysiologically mature neuronal networks from hPSCs has been challenging. Here, we report the differentiation of functionally active hPSC-derived cortical networks on defined laminin-521 substrate. We apply microelectrode array (MEA) measurements to assess network events and compare the activity development of hPSC-derived networks to that of widely used rat embryonic cortical cultures. In both of these networks, activity developed through a similar sequence of stages and time frames; however, the hPSC-derived networks showed unique patterns of bursting activity. The hPSC-derived networks developed synchronous activity, which involved glutamatergic and GABAergic inputs, recapitulating the classical cortical activity also observed in rodent counterparts. Principal component analysis (PCA) based on spike rates, network synchronization and burst features revealed the segregation of hPSC-derived and rat network recordings into different clusters, reflecting the species-specific and maturation state differences between the two networks. Overall, hPSC-derived neural cultures produced with a defined protocol generate cortical type network activity, which validates their applicability as a human-specific model for pharmacological studies and modeling network dysfunctions.Peer reviewe

PRINCIPLES OF INFORMATION PROCESSING IN NEURONAL AVALANCHES

How the brain processes information is poorly understood. It has been suggested that the imbalance of excitation and inhibition (E/I) can significantly affect information processing in the brain. Neuronal avalanches, a type of spontaneous activity recently discovered, have been ubiquitously observed in vitro and in vivo when the cortical network is in the E/I balanced state. In this dissertation, I experimentally demonstrate that several properties regarding information processing in the cortex, i.e. the entropy of spontaneous activity, the information transmission between stimulus and response, the diversity of synchronized states and the discrimination of external stimuli, are optimized when the cortical network is in the E/I balanced state, exhibiting neuronal avalanche dynamics. These experimental studies not only support the hypothesis that the cortex operates in the critical state, but also suggest that criticality is a potential principle of information processing in the cortex. Further, we study the interaction structure in population neuronal dynamics, and discovered a special structure of higher order interactions that are inherent in the neuronal dynamics

Building Neural in vitro Models with Human Pluripotent Stem Cells : Neuronal Functionality and the Role of Astrocytes in the Networks

Ymmärryksemme ihmisaivojen kehityksestä ja toiminnasta on edelleen puutteellista. Valitettavasti neurotieteiden alalta puuttuu edustavia ihmisspesifisiä malleja, jotka täydentäisivät eläinmalleilla tehtäviä tutkimuksia. Ihmisaivokudoksen saatavuus tutkimustarkoituksiin on rajoitettua, mikä kannustaa uusien lähestymistapojen, kuten ihmisen monikykyisistä kantasoluista johdettujen hermosolujen hyödyntämistä. Muutaman viime vuosikymmenen aikana ihmisen monikykyisiin kantasoluihin liittyvä tutkimusala on laajentunut valtavasti. Kasvavat odotukset kohdistuvat kantasolujen ja niiden johdannaisten soveltamiseen regeneratiivisessa lääketieteessä, lääkkeiden seulonnassa ja tautien mallinnuksessa. Tämän tutkimuksen pääpainona oli arvioida ihmisen monikykyisistä kantasoluista johdettujen hermosoluviljelmien potentiaalia jäljitellä tiettyjä keskushermoston kehityksen ja toiminnallisuuden tunnusmerkkejä. Tulosten on tarkoitus auttaa validoimaan kantasolumallien hyödyllisyys soveltavan neurotieteen käyttötarkoituksissa. Tätä varten verrattiin kahdella yleisesti käytetyllä erilaistusmenetelmällä tuotettujen ihmisen monikykyisistä kantasoluista johdettujen hermosolujen erilaistumiskykyä. Hermosolujen toiminnallista kypsymistä arvioitiin sekä pidennetyn erilaistusajan jälkeen, että viljelyolosuhteiden optimoinnin seurauksena verkostotason analyyseillä. Tuloksia täydennettiin vertailemalla näiden verkostojen toiminnallisuutta laajalti käytettyyn jyrsijäperäiseen solumalliin. Koska astrosyytit ovat hermosoluja ympäröiviä soluja ja ne tukevat keskushermoston toiminnallisuutta, erityistä huomiota kiinnitettiin myös astrosyyttien rooliin sekä normaaleissa että tulehduksellisissa olosuhteissa, joista jälkimmäinen on tyypillinen tila keskushermoston vaurioissa. Tämän väitöskirjan tulokset viittaavat siihen, että ihmisen monikykyisistä kantasoluista johdetut hermosoluviljelmät toistavat useita in vivo olosuhteissa tapahtuvia keskushermoston kehityksen vaiheita. Erityinen kemiallinen induktio tehosti hermosoluerilaistusta johtaen korkeaan solupuhtauteen ja saantoon. Erilaistusajan pidennys lisäsi endogeenisesti muodostuvien astrosyyttien osuutta viljelmässä ja edisti hermosolujen toiminnallisuudessa havaittua kypsää aktiivisuustyyppiä. Lisäksi valitsemalla viljelypinnoitteeksi tietty laminiini-isoformi saavutettiin vahva, pitkäkestoinen hermosoluaktiivisuus muodostuneissa verkostoissa. Tämän kehitystyön ansiosta kantasoluista johdetut hermoverkot osoittivat samankaltaista ajallista ja vaiheittaista toiminnallisuuden kehitystä, kuin vastaavat jyrsijöistä eristetyt hermosolut. Jyrsijä- ja ihmisverkostojen toiminnallisuuden muodoissa havaittiin kuitenkin myös huomattavia eroavaisuuksia, mikä saattaa liittyä eroihin niiden kypsyysasteissa tai lajien välisiin eroavaisuuksiin. Lopuksi ihmisen monikykyisistä kantasoluista johdetut astrosyytit altistettiin tietyille tulehduksellisille tekijöille. Niiden vaste osoitti keskushermoston sairauksissa tyypillisesti havaitun astroglioosin erityispiirteitä, ja tutkitut hermosoluihin kohdistuvat vaikutukset viittasivat polarisaatioon hermosoluja tukevaksi fenotyyppiksi. Tutkimuksessa luodut kontrolloidut ihmisen hermosolujen ja astrosyyttien yhteisviljelmät tarjoavat vaihtoehtoisen kantasolupohjaisen alustan solujen vuorovaikutusten mallintamiseksi sekä terveessä että sairauskonteksteissa. Yhteenvetona voidaan todeta, että tämän väitöskirjan tulokset edistävät toiminnallisten ihmisen monikykyisistä kantasoluista johdettujen hermosoluverkkojen kehittämistä, ne vahvistavat astrosyyttien roolia merkittävinä kumppaneina näissä verkostoissa sekä rohkaisevat kantasolujen soveltamiseen ihmisspesifisinä malleina neurotieteen tutkimuksissa.Our understanding of human brain development and function is still incomplete. Unfortunately, the field of neuroscience lacks representative human-specific models to accompany the animal studies. Access to human brain tissue for research purposes is limited, encouraging the utilization of novel approaches such as human pluripotent stem cell (hPSC)-derived neurons. Within the last few decades, research on hPSCs has undergone enormous expansion. Growing expectations are aimed at the application of stem cells and their derivatives in regenerative medicine, drug screening and disease modeling. The main focus of this thesis was to evaluate the potential of hPSC-derived neural cultures in mimicking certain characteristics of central nervous system (CNS) development and functionality. The results were intended to help validate the utility of stem cell models for translational neuroscience applications. For this purpose, the differentiation capacities of hPSC-derived neuronal cells generated with two generally used differentiation methodologies were compared. The functional maturation of neurons following a prolonged differentiation time and optimization of culture conditions was assessed in network-level analyses. The results were complemented with a functional comparison to the widely used rodent in vitro model. Since astrocytes are the cells surrounding neurons and supporting neuronal functionality in the CNS, special focus was also placed on their role in both normal and neuroinflammatory conditions, the latter of which is typical of CNS insults. The results of this thesis suggest that hPSC-derived neuronal cultures recapitulate many of the characteristics of CNS development in vivo. Specific chemical induction accelerated neural differentiation, leading to high cell purity and yield. Prolongation of the differentiation time increased the proportion of endogenously formed astrocytes and promoted the functionally mature activity type of neurons. Furthermore, the emergence of robust neuronal activity and the long-term maintenance of functional networks were achieved with the selection of defined laminin isoform as a culture substrate. With this improvement, the hPSC-derived networks exhibited time frames and stages of activity development similar to those of their rodent counterparts. However, marked variability was detected in the activity patterns between the rodent and human networks, which could relate to differences in their maturation stage or interspecies dissimilarities. Finally, hPSC-derived astrocytes were exposed to specific inflammatory stimuli. Their response showed distinct characteristics of astrogliosis observed in CNS diseases, and the studied neuronal effects suggested polarization into a neurosupportive phenotype. Established controlled co-cultures with human neurons and astrocytes provide an alternative hPSC-based platform for modeling cell interactions in the context of health and disease. In conclusion, the work presented in this thesis advances the development of functional hPSC-derived neuronal networks, confirms the role of astrocytes as significant partners in these networks, and encourages their translation into human- specific models for neuroscience research

In vitro neuronal cultures on MEA: an engineering approach to study physiological and pathological brain networks

Reti neuronali accoppiate a matrici di microelettrodi: un metodo ingegneristico per studiare reti cerebrali in situazioni fisiologiche e patologich

Astrocytes Exhibit a Protective Role in Neuronal Firing Patterns under Chemically Induced Seizures in Neuron-Astrocyte Co-Cultures.

Astrocytes and neurons respond to each other by releasing transmitters, such as γ-aminobutyric acid (GABA) and glutamate, that modulate the synaptic transmission and electrochemical behavior of both cell types. Astrocytes also maintain neuronal homeostasis by clearing neurotransmitters from the extracellular space. These astrocytic actions are altered in diseases involving malfunction of neurons, e.g., in epilepsy, Alzheimer's disease, and Parkinson's disease. Convulsant drugs such as 4-aminopyridine (4-AP) and gabazine are commonly used to study epilepsy in vitro. In this study, we aim to assess the modulatory roles of astrocytes during epileptic-like conditions and in compensating drug-elicited hyperactivity. We plated rat cortical neurons and astrocytes with different ratios on microelectrode arrays, induced seizures with 4-AP and gabazine, and recorded the evoked neuronal activity. Our results indicated that astrocytes effectively counteracted the effect of 4-AP during stimulation. Gabazine, instead, induced neuronal hyperactivity and synchronicity in all cultures. Furthermore, our results showed that the response time to the drugs increased with an increasing number of astrocytes in the co-cultures. To the best of our knowledge, our study is the first that shows the critical modulatory role of astrocytes in 4-AP and gabazine-induced discharges and highlights the importance of considering different proportions of cells in the cultures

Connectivity Measures for In Vitro Neuronal Cell Networks

In this thesis, different connectivity measures are reviewed in detail in order to investigate what kind of information they provide, what are the advantages and limitations of them. Based on the literature review comparison, we selected three methods; Phase Lock Value (PLV), generalized Partial Directed Coherence (gPDC) and Transfer Entropy (TE). The selected methods were tested and evaluated with the data from human embryonic stem cell derived neuronal cell (hESC) networks which are cultured on MEAs. The analysis is divided into two parts: simulated connectivity signal studies and real MEA data analysis.The simulation study indicates that PLV method correctly recognized the connections, while gPDC provided unreliable results. TE provided the most detailed results only with few inaccuracies. Based on the simulation results, TE and PLV seem potential for further research on MEA signals. However, incoherent results were obtained in real MEA data analysis. For example, PLV claimed connections between signals measured from different wells. Based on the results, further research is needed in order to assess whether the incoherencies are influenced by the measurement environment, the methods themselves, or by the quality problem of signals in 6-well MEA

Connectivity Influences on Nonlinear Dynamics in Weakly-Synchronized Networks: Insights from Rössler Systems, Electronic Chaotic Oscillators, Model and Biological Neurons

Natural and engineered networks, such as interconnected neurons, ecological and social networks, coupled oscillators, wireless terminals and power loads, are characterized by an appreciable heterogeneity in the local connectivity around each node. For instance, in both elementary structures such as stars and complex graphs having scale-free topology, a minority of elements are linked to the rest of the network disproportionately strongly. While the effect of the arrangement of structural connections on the emergent synchronization pattern has been studied extensively, considerably less is known about its influence on the temporal dynamics unfolding within each node. Here, we present a comprehensive investigation across diverse simulated and experimental systems, encompassing star and complex networks of Rössler systems, coupled hysteresis-based electronic oscillators, microcircuits of leaky integrate-and-fire model neurons, and finally recordings from in-vitro cultures of spontaneously-growing neuronal networks. We systematically consider a range of dynamical measures, including the correlation dimension, nonlinear prediction error, permutation entropy, and other information-theoretical indices. The empirical evidence gathered reveals that under situations of weak synchronization, wherein rather than a collective behavior one observes significantly differentiated dynamics, denser connectivity tends to locally promote the emergence of stronger signatures of nonlinear dynamics. In deterministic systems, transition to chaos and generation of higher-dimensional signals were observed; however, when the coupling is stronger, this relationship may be lost or even inverted. In systems with a strong stochastic component, the generation of more temporally-organized activity could be induced. These observations have many potential implications across diverse fields of basic and applied science, for example, in the design of distributed sensing systems based on wireless coupled oscillators, in network identification and control, as well as in the interpretation of neuroscientific and other dynamical data

Development of statistical and computational methods to estimate functional connectivity and topology in large-scale neuronal assemblies

One of the most fundamental features of a neural circuit is its connectivity since the single neuron activity is not due only to its intrinsic properties but especially to the direct or indirect influence of other neurons1. It is fundamental to elaborate research strategies aimed at a comprehensive structural description of neuronal interconnections as well as the networks\u2019 elements forming the human connectome. The connectome will significantly increase our understanding of how functional brain states emerge from their underlying structural substrate, and will provide new mechanistic insights into how brain function is affected if this structural substrate is disrupted. The connectome is characterized by three different types of connectivity: structural, functional and effective connectivity. It is evident that the final goal of a connectivity analysis is the reconstruction of the human connectome, thus, the application of statistical measures to the in vivo model in both physiological and pathological states. Since the system under study (i.e. brain areas, cell assemblies) is highly complex, to achieve the purpose described above, it is useful to adopt a reductionist approach. During my PhD work, I focused on a reduced and simplified model, represented by neural networks chronically coupled to Micro Electrodes Arrays (MEAs). Large networks of cortical neurons developing in vitro and chronically coupled to MEAs2 represent a well-established experimental model for studying the neuronal dynamics at the network level3, and for understanding the basic principles of information coding4 learning and memory5. Thus, during my PhD work, I developed and optimized statistical methods to infer functional connectivity from spike train data. In particular, I worked on correlation-based methods: cross-correlation and partial correlation, and information-theory based methods: Transfer Entropy (TE) and Joint Entropy (JE). More in detail, my PhD\u2019s aim has been applying functional connectivity methods to neural networks coupled to high density resolution system, like the 3Brain active pixel sensor array with 4096 electrodes6. To fulfill such an aim, I re-adapted the computational logic operations of the aforementioned connectivity methods. Moreover, I worked on a new method based on the cross-correlogram, able to detect both inhibitory and excitatory links. I called such an algorithm Filtered Normalized Cross-Correlation Histogram (FNCCH). The FNCCH shows a very high precision in detecting both inhibitory and excitatory functional links when applied to our developed in silico model. I worked also on a temporal and pattern extension of the TE algorithm. In this way, I developed a Delayed TE (DTE) and a Delayed High Order TE (DHOTE) version of the TE algorithm. These two extension of the TE algorithm are able to consider different temporal bins at different temporal delays for the pattern recognition with respect to the basic TE. I worked also on algorithm for the JE computation. Starting from the mathematical definition in7, I developed a customized version of JE capable to detect the delay associated to a functional link, together with a dedicated shuffling based thresholding approach. Finally, I embedded all of these connectivity methods into a user-friendly open source software named SPICODYN8. SPICODYN allows the user to perform a complete analysis on data acquired from any acquisition system. I used a standard format for the input data, providing the user with the possibility to perform a complete set of operations on the input data, including: raw data viewing, spike and burst detection and analysis, functional connectivity analysis, graph theory and topological analysis. SPICODYN inherits the backbone structure from TOOLCONNECT, a previously published software that allowed to perform a functional connectivity analysis on spike trains dat