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A patterned spread of proteinopathy represents a common characteristic of many neurodegenerative diseases. In Parkinson’s disease (PD), misfolded forms of alpha-synuclein proteins accumulate in hallmark pathological inclusions termed Lewy bodies and Lewy neurites. Such protein aggregates seem to affect selectively vulnerable neuronal populations in the substantia nigra and to propagate within interconnected neuronal networks. Research findings suggest that these proteinopathic inclusions are present at very early timepoints in disease development, even before clear behavioural symptoms of dysfunction arise. In this study we investigate the early pathophysiology developing after induced formation of such PDrelated alpha-synuclein inclusions, in a physiologically relevant in vitro setup using engineered human neural networks. We monitor the neural network activity using multielectrode arrays (MEAs) for a period of three weeks following proteinopathy induction to identify associated changes in network function, with a special emphasis on the measure of network criticality. Self-organised criticality represents the critical point between resilience against perturbation and adaptational flexibility, which appears to be a functional trait in self-organising neural networks, both in vitro and in vivo. We show that although developing pathology at early onset is not clearly manifest in standard measurements of network function, it may be discerned by investigating differences in network criticality states. This work was supported by The Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, NTNU; The Liaison Committee for Education, Research and Innovation in Central Norway (Samarbeidsorganet HMN, NTNU); and the SOCRATES project (NFR project agreement 270961). The TEM preparation was performed at the Cellular and Molecular Imaging Core Facility (CMIC, NTNU); The AFM and UV-vis spectroscopy were performed by Birgitte Hjelmeland McDonagh at the Norwegian Micro- and Nano-Fabrication Facility (NorFab), Norwegian University of Science and Technology (NTNU).

Although deep learning has recently increased in popularity, it suffers from various problems including high computational complexity, energy greedy computation, and lack of scalability, to mention a few. In this paper, we investigate an alternative brain-inspired method for data analysis that circumvents the deep learning drawbacks by taking the actual dynamical behavior of biological neural networks into account. For this purpose, we develop a general framework for dynamical systems that can evolve and model a variety of substrates that possess computational capacity. Therefore, dynamical systems can be exploited in the reservoir computing paradigm, i.e., an untrained recurrent nonlinear network with a trained linear readout layer. Moreover, our general framework, called EvoDynamic, is based on an optimized deep neural network library. Hence, generalization and performance can be balanced. The EvoDynamic framework contains three kinds of dynamical systems already implemented, namely cellular automata, random Boolean networks, and echo state networks. The evolution of such systems towards a dynamical behavior, called criticality, is investigated because systems with such behavior may be better suited to do useful computation. The implemented dynamical systems are stochastic and their evolution with genetic algorithm mutates their update rules or network initialization. The obtained results are promising and demonstrate that criticality is achieved. In addition to the presented results, our framework can also be utilized to evolve the dynamical systems connectivity, update and learning rules to improve the quality of the reservoir used for solving computational tasks and physical substrate modeling. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed “neuronal avalanches.” The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other’s functional activity. In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons. Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning. Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings. Finally, information theoretical approaches can tie in more concrete evidence of a network’s computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered. © 2021 Heiney, Huse Ramstad, Fiskum, Christiansen, Sandvig, Nichele and Sandvig. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Alzheimer’s disease (AD) is a debilitating neurodegenerative disease characterized by the accumulation of two proteins in fibrillar form: amyloid-β (Aβ) and tau. Despite decades of intensive research, we cannot yet pinpoint the exact cause of the disease or unequivocally determine the exact mechanism(s) underlying its progression. This confounds early diagnosis and treatment of the disease. Cerebrospinal fluid (CSF) biomarkers, which can reveal ongoing biochemical changes in the brain, can help monitor developing AD pathology prior to clinical diagnosis. Here we review preclinical and clinical investigations of commonly used biomarkers in animals and patients with AD, which can bridge translation from model systems into the clinic. The core AD biomarkers have been found to translate well across species, whereas biomarkers of neuroinflammation translate to a lesser extent. Nevertheless, there is no absolute equivalence between biomarkers in human AD patients and those examined in preclinical models in terms of revealing key pathological hallmarks of the disease. In this review, we provide an overview of current but also novel AD biomarkers and how they relate to key constituents of the pathological cascade, highlighting confounding factors and pitfalls in interpretation, and also provide recommendations for standardized procedures during sample collection to enhance the translational validity of preclinical AD models. Copyright © 2020 Bjorkli, Sandvig and Sandvig. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

In this work, we report the preliminary analysis of the electrophysiological behavior of in vitro neuronal networks to identify when the networks are in a critical state based on the size distribution of network-wide avalanches of activity. The results presented here demonstrate the importance of selecting appropriate parameters in the evaluation of the size distribution and indicate that it is possible to perturb networks showing highly synchronized---or supercritical---behavior into the critical state by increasing the level of inhibition in the network. The classification of critical versus non-critical networks is valuable in identifying networks that can be expected to perform well on computational tasks, as criticality is widely considered to be the state in which a system is best suited for computation. This type of analysis is expected to enable the identification of networks that are well-suited for computation and the classification of networks as perturbed or healthy. This study is part of a larger research project, the overarching aim of which is to develop computational models that are able to reproduce target behaviors observed in in vitro neuronal networks. These models will ultimately be used to aid in the realization of these behaviors in nanomagnet arrays to be used in novel computing hardwares., Comment: 8 pages, 3 figures, submitted to IEEE SSCI2019 ALIFE Symposium. arXiv admin note: substantial text overlap with arXiv:1907.02351

High post-transplantation cell mortality is the main limitation of various approaches that are aimed at improving regeneration of injured neural tissue by an injection of neural stem cells (NSCs) and mesenchymal stromal cells (MStroCs) in and/or around the lesion. Therefore, it is of paramount importance to identify efficient ways to increase cell transplant viability. We have previously proposed the "evolutionary stem cell paradigm," which explains the association between stem cell anaerobic/microaerophilic metabolic set-up and stem cell self-renewal and inhibition of differentiation. Applying these principles, we have identified the main critical point in the collection and preparation of these cells for experimental therapy: exposure of the cells to atmospheric O2, that is, to oxygen concentrations that are several times higher than the physiologically relevant ones. In this way, the primitive anaerobic cells become either inactivated or adapted, through commitment and differentiation, to highly aerobic conditions (20%-21% O2 in atmospheric air). This inadvertently compromises the cells' survival once they are transplanted into normal tissue, especially in the hypoxic/anoxic/ischemic environment, which is typical of central nervous system (CNS) lesions. In addition to the findings suggesting that stem cells can shift to glycolysis and can proliferate in anoxia, recent studies also propose that stem cells may be able to proliferate in completely anaerobic or ischemic conditions by relying on anaerobic mitochondrial respiration. In this systematic review, we propose strategies to enhance the survival of NSCs and MStroCs that are implanted in hypoxic/ischemic neural tissue by harnessing their anaerobic nature and maintaining as well as enhancing their anaerobic properties via appropriate ex vivo conditioning.

In vitro electrophysiological investigation of neural activity at a network level holds tremendous potential for elucidating underlying features of brain function (and dysfunction). In standard neural network modelling systems, however, the fundamental three-dimensional (3D) character of the brain is a largely disregarded feature. This widely applied neuroscientific strategy affects several aspects of the structure–function relationships of the resulting networks, altering network connectivity and topology, ultimately reducing the translatability of the results obtained. As these model systems increase in popularity, it becomes imperative that they capture, as accurately as possible, fundamental features of neural networks in the brain, such as small-worldness. In this report, we combine in vitro neural cell culture with a biologically compatible scaffolding substrate, surface-grafted polymer particles (PPs), to develop neural networks with 3D topology. Furthermore, we investigate their electrophysiological network activity through the use of 3D multielectrode arrays. The resulting neural network activity shows emergent behaviour consistent with maturing neural networks capable of performing computations, i.e. activity patterns suggestive of both information segregation (desynchronized single spikes and local bursts) and information integration (network spikes). Importantly, we demonstrate that the resulting PP-structured neural networks show both structural and functional features consistent with small-world network topology. © 2019 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

Recent advances in cell reprogramming technologies enable the in vitro generation of theoretically unlimited numbers of cells, including cells of neural lineage and specific neuronal subtypes from human, including patient-specific, somatic cells. Similarly, as demonstrated in recent animal studies, by applying morphogenetic neuroengineering principles in situ, it is possible to reprogram resident brain cells to the desired phenotype. These developments open new exciting possibilities for cell replacement therapy in stroke, albeit not without caveats. Main challenges include the successful integration of engineered cells in the ischemic brain to promote functional restoration as well as the fact that the underlying mechanisms of action are not fully understood. In this review, we aim to provide new insights to the above in the context of connectomics of morphogenetically engineered neural networks. Specifically, we discuss the relevance of combining advanced interdisciplinary approaches to: validate the functionality of engineered neurons by studying their self-organizing behavior into neural networks as well as responses to stroke-related pathology in vitro; derive structural and functional connectomes from these networks in healthy and perturbed conditions; and identify and extract key elements regulating neural network dynamics, which might predict the behavior of grafted engineered neurons post-transplantation in the stroke-injured brain. Copyright © 2019 Sandvig and Sandvig. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Neuroplasticity after ischaemic injury involves both spontaneous rewiring of neural networks and circuits as well as functional responses in neurogenic niches. These events involve complex interactions with activated microglia, which evolve in a dynamic manner over time. Although the exact mechanisms underlying these interactions remain poorly understood, increasing experimental evidence suggests a determining role of pro‐ and anti‐inflammatory microglial activation profiles in shaping both synaptogenesis and neurogenesis. While the inflammatory response of microglia was thought to be detrimental, a more complex profile of the role of microglia in tissue remodelling is emerging. Experimental evidence suggests that microglia in response to injury can rapidly modify neuronal activity and modulate synaptic function, as well as be beneficial for the proliferation and integration of neural progenitor cells (NPCs) from endogenous neurogenic niches into functional networks thereby supporting stroke recovery. The manner in which microglia contribute towards sculpting neural synapses and networks, both in terms of activity‐dependent and homeostatic plasticity, suggests that microglia‐mediated pro‐ and/or anti‐inflammatory activity may significantly contribute towards spontaneous neuronal plasticity after ischaemic lesions. In this review, we first introduce some of the key cellular and molecular mechanisms underlying neuroplasticity in stroke and then proceed to discuss the crosstalk between microglia and endogenous neuroplasticity in response to brain ischaemia with special focus on the engagement of synapses and neural networks and their implications for grey matter integrity and function in stroke repair. Locked until 22.5.2019 due to copyright restrictions. This is the peer reviewed version of an article, which has been published in final form at [https://doi.org/10.1111/ejn.13959]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

Effective transplant-mediated repair of ischemic brain lesions entails extensive tissue remodeling, especially in the ischemic core. Neural stem cells (NSCs) are promising reparative candidates for stroke induced lesions, however, their survival and integration with the host-tissue post-transplantation is poor. In this study, we address this challenge by testing whether co-grafting of NSCs with olfactory ensheathing cells (OECs), a special type of glia with proven neuroprotective, immunomodulatory, and angiogenic effects, can promote graft survival and host tissue remodelling. Transient focal cerebral ischemia was induced in adult rats by a 60-min middle cerebral artery occlusion (MCAo) followed by reperfusion. Ischemic lesions were verified by neurological testing and magnetic resonance imaging. Transplantation into the globus pallidus of NSCs alone or in combination with OECs was performed at two weeks post-MCAo, followed by histological analyses at three weeks post-transplantation. We found evidence of extensive vascular remodelling in the ischemic core as well as evidence of NSC motility away from the graft and into the infarct border in severely lesioned animals co-grafted with OECs. These findings support a possible role of OECs as part of an in situ tissue engineering paradigm for transplant mediated repair of ischemic brain lesions. This is the authors' manuscript to the article (preprint). The final publication is available at Springer via https://link.springer.com/article/10.1007%2Fs11064-016-2098-3