Micro-and mesoscale aspects of neurodegeneration in engineered human neural networks carrying the LRRK2 G2019S mutation.

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been widely linked to Parkinson's disease, where the G2019S variant has been shown to contribute uniquely to both familial and sporadic forms of the disease. LRRK2-related mutations have been extensively studied, yet the wide variety of cellular and network events related to these mutations remain poorly understood. The advancement and availability of tools for neural engineering now enable modeling of selected pathological aspects of neurodegenerative disease in human neural networks in vitro. Our study revealed distinct pathology associated dynamics in engineered human cortical neural networks carrying the LRRK2 G2019S mutation compared to healthy isogenic control neural networks. The neurons carrying the LRRK2 G2019S mutation self-organized into networks with aberrant morphology and mitochondrial dynamics, affecting emerging structure-function relationships both at the micro-and mesoscale. Taken together, the findings of our study points toward an overall heightened metabolic demand in networks carrying the LRRK2 G2019S mutation, as well as a resilience to change in response to perturbation, compared to healthy isogenic controls.

Characterizing upper extremity fine motor function in the presence of white matter hyperintensities: A 7 T MRI cross-sectional study in older adults.

BACKGROUND: White matter hyperintensities (WMH) are a prevalent radiographic finding in the aging brain studies. Research on WMH association with motor impairment is mostly focused on the lower-extremity function and further investigation on the upper-extremity is needed. How different degrees of WMH burden impact the network of activation recruited during upper limb motor performance could provide further insight on the complex mechanisms of WMH pathophysiology and its interaction with aging and neurological disease processes. METHODS: 40 healthy elderly subjects without a neurological/psychiatric diagnosis were included in the study (16F, mean age 69.3 years). All subjects underwent ultra-high field 7 T MRI including structural and finger tapping task-fMRI. First, we quantified the WMH lesion load and its spatial distribution. Secondly, we performed a data-driven stratification of the subjects according to their periventricular and deep WMH burdens. Thirdly, we investigated the distribution of neural recruitment and the corresponding activity assessed through BOLD signal changes among different brain regions for groups of subjects. We clustered the degree of WMH based on location, numbers, and volume into three categories; ranging from mild, moderate, and severe. Finally, we explored how the spatial distribution of WMH, and activity elicited during task-fMRI relate to motor function, measured with the 9-Hole Peg Test. RESULTS: Within our population, we found three subgroups of subjects, partitioned according to their periventricular and deep WMH lesion load. We found decreased activity in several frontal and cingulate cortex areas in subjects with a severe WMH burden. No statistically significant associations were found when performing the brain-behavior statistical analysis for structural or functional data. CONCLUSION: WMH burden has an effect on brain activity during fine motor control and the activity changes are associated with varying degrees of the total burden and distributions of WMH lesions. Collectively, our results shed new light on the potential impact of WMH on motor function in the context of aging and neurodegeneration.

Structure-function dynamics of engineered, modular neuronal networks with controllable afferent-efferent connectivity.

Objective.Microfluidic devices interfaced with microelectrode arrays have in recent years emerged as powerful platforms for studying and manipulatingin vitroneuronal networks at the micro- and mesoscale. By segregating neuronal populations using microchannels only permissible to axons, neuronal networks can be designed to mimic the highly organized, modular topology of neuronal assemblies in the brain. However, little is known about how the underlying topological features of such engineered neuronal networks contribute to their functional profile. To start addressing this question, a key parameter is control of afferent or efferent connectivity within the network.Approach.In this study, we show that a microfluidic device featuring axon guiding channels with geometrical constraints inspired by a Tesla valve effectively promotes unidirectional axonal outgrowth between neuronal nodes, thereby enabling us to control afferent connectivity.Main results.Our results moreover indicate that these networks exhibit a more efficient network organization with higher modularity compared to single nodal controls. We verified this by applying designer viral tools to fluorescently label the neurons to visualize the structure of the networks, combined with extracellular electrophysiological recordings using embedded nanoporous microelectrodes to study the functional dynamics of these networks during maturation. We furthermore show that electrical stimulations of the networks induce signals selectively transmitted in a feedforward fashion between the neuronal populations.Significance.A key advantage with our microdevice is the ability to longitudinally study and manipulate both the structure and function of neuronal networks with high accuracy. This model system has the potential to provide novel insights into the development, topological organization, and neuroplasticity mechanisms of neuronal assemblies at the micro- and mesoscale in healthy and perturbed conditions.

Selective inhibition of excitatory synaptic transmission alters the emergent bursting dynamics of in vitro neural networks.

Neurons in vitro connect to each other and form neural networks that display emergent electrophysiological activity. This activity begins as spontaneous uncorrelated firing in the early phase of development, and as functional excitatory and inhibitory synapses mature, the activity typically emerges as spontaneous network bursts. Network bursts are events of coordinated global activation among many neurons interspersed with periods of silencing and are important for synaptic plasticity, neural information processing, and network computation. While bursting is the consequence of balanced excitatory-inhibitory (E/I) interactions, the functional mechanisms underlying their evolution from physiological to potentially pathophysiological states, such as decreasing or increasing in synchrony, are still poorly understood. Synaptic activity, especially that related to maturity of E/I synaptic transmission, is known to strongly influence these processes. In this study, we used selective chemogenetic inhibition to target and disrupt excitatory synaptic transmission in in vitro neural networks to study functional response and recovery of spontaneous network bursts over time. We found that over time, inhibition resulted in increases in both network burstiness and synchrony. Our results indicate that the disruption in excitatory synaptic transmission during early network development likely affected inhibitory synaptic maturity which resulted in an overall decrease in network inhibition at later stages. These findings lend support to the importance of E/I balance in maintaining physiological bursting dynamics and, conceivably, information processing capacity in neural networks.

Dissection and culturing of adult lateral entorhinal cortex layer II neurons from APP/PS1 Alzheimer model mice.

BACKGROUND: Primary neuronal cultures enable cell-biological studies of Alzheimer's disease (AD), albeit typically non-neuron-specific. The first cortical neurons affected in AD reside in layer II of the lateralmost part of the entorhinal cortex, and they undergo early accumulation of intracellular amyloid-ß, form subsequent tau pathology, and start degenerating pre-symptomatically. These vulnerable entorhinal neurons uniquely express the glycoprotein reelin and provide selective inputs to the hippocampal memory system. Gaining a more direct access to study these neurons is therefore highly relevant. NEW METHOD: We demonstrate a methodological approach for dissection and long-term culturing of adult lateral entorhinal layer II-neurons from AD-model mice. RESULTS: We maintain adult dissected lateralmost entorhinal layer II-neurons beyond two months in culture. We show that they express neuronal markers, and that they are electrophysiologically active by 15 days in vitro and continuing beyond 2 months. COMPARISON WITH EXISTING METHODS: Primary neurons are typically harvested from embryonic or early postnatal brains because such neurons are easier to culture compared to adult neurons. Methods to culture adult primary neurons have been reported, however, to our knowledge, culturing of adult entorhinal neuron-type specific primary neurons from AD-model animals have not been reported. CONCLUSIONS: Our methodological approach offers a window to study initial pathological changes in the AD disease-cascade. This includes the study of proteinopathy, single-neuron changes, and network-level dysfunction.

Neuronal avalanche dynamics and functional connectivity elucidate information propagation in vitro.

Cascading activity is commonly observed in complex dynamical systems, including networks of biological neurons, and how these cascades spread through the system is reliant on how the elements of the system are connected and organized. In this work, we studied networks of neurons as they matured over 50 days in vitro and evaluated both their dynamics and their functional connectivity structures by observing their electrophysiological activity using microelectrode array recordings. Correlations were obtained between features of their activity propagation and functional connectivity characteristics to elucidate the interplay between dynamics and structure. The results indicate that in vitro networks maintain a slightly subcritical state by striking a balance between integration and segregation. Our work demonstrates the complementarity of these two approaches-functional connectivity and avalanche dynamics-in studying information propagation in neurons in vitro, which can in turn inform the design and optimization of engineered computational substrates.

Combined targeting of pathways regulating synaptic formation and autophagy attenuates Alzheimer's disease pathology in mice.

All drug trials completed to date have fallen short of meeting the clinical endpoint of significantly slowing cognitive decline in Alzheimer's disease (AD) patients. In this study, we repurposed two FDA-approved drugs, Fasudil and Lonafarnib, targeting synaptic formation (i.e., Wnt signaling) and cellular clearance (i.e., autophagic) pathways respectively, to test their therapeutic potential for attenuating AD-related pathology. We characterized our 3xTg AD mouse colony to select timepoints for separate and combinatorial treatment of both drugs while collecting cerebrospinal fluid (CSF) using an optimized microdialysis method. We found that treatment with Fasudil reduced Aß at early and later stages of AD, whereas administration of Lonafarnib had no effect on Aß, but did reduce tau, at early stages of the disease. Induction of autophagy led to increased size of amyloid plaques when administered at late phases of the disease. We show that combinatorial treatment with both drugs was effective at reducing intraneuronal Aß and led to improved cognitive performance in mice. These findings lend support to regulating Wnt and autophagic pathways in order to attenuate AD-related pathology.

Isolation and comparison of neural stem cells from the adult rat brain and spinal cord canonical neurogenic niches.

Here, we present a unified protocol for the extraction, culture, and basic characterization of rat neural stem cells (NSCs) from all three canonical neurogenic niches in the brain and spinal cord. We describe tissue dissection and dissociation, cell culture, followed by EdU labeling and characterization of NSCs. By yielding considerable numbers of viable cells per animal, this protocol enables the establishment of substantial, long-term cell banks, thus offering cost and labor efficiency while significantly reducing the numbers of animals used.

Validation of Functional Connectivity of Engineered Neuromuscular Junction With Recombinant Monosynaptic Pseudotyped ΔG-Rabies Virus Tracing.

Current preclinical models of neurodegenerative disease, such as amyotrophic lateral sclerosis (ALS), can significantly benefit from in vitro neuroengineering approaches that enable the selective study and manipulation of neurons, networks, and functional units of interest. Custom-designed compartmentalized microfluidic culture systems enable the co-culture of different relevant cell types in interconnected but fluidically isolated microenvironments. Such systems can thus be applied for ALS disease modeling, as they enable the recapitulation and study of neuromuscular junctions (NMJ) through co-culturing of motor neurons and muscle cells in separate, but interconnected compartments. These in vitro systems are particularly relevant for investigations of mechanistic aspects of the ALS pathological cascade in engineered NMJ, as progressive loss of NMJ functionality may constitute one of the hallmarks of disease related pathology at early onset, in line with the dying back hypothesis. In such models, ability to test whether motor neuron degeneration in ALS starts at the nerve terminal or at the NMJ and retrogradely progresses to the motor neuron cell body largely relies on robust methods for verification of engineered NMJ functionality. In this study, we demonstrate the functionality of engineered NMJs within a microfluidic chip with a differentially perturbable microenvironment using a designer pseudotyped ΔG-rabies virus for retrograde monosynaptic tracing.

In Vivo Microdialysis in Mice Captures Changes in Alzheimer's Disease Cerebrospinal Fluid Biomarkers Consistent with Developing Pathology.

BACKGROUND: Preclinical models of Alzheimer's disease (AD) can provide valuable insights into the onset and progression of the disease, such as changes in concentrations of amyloid-ß (Aß) and tau in cerebrospinal fluid (CSF). However, such models are currently underutilized due to limited advancement in techniques that allow for longitudinal CSF monitoring. OBJECTIVE: An elegant way to understand the biochemical environment in the diseased brain is intracerebral microdialysis, a method that has until now been limited to short-term observations, or snapshots, of the brain microenvironment. Here we draw upon patient-based findings to characterize CSF biomarkers in a commonly used preclinical mouse model for AD. METHODS: Our modified push-pull microdialysis method was first validated ex vivo with human CSF samples, and then in vivo in an AD mouse model, permitting assessment of dynamic changes of CSF Aß and tau and allowing for better translational understanding of CSF biomarkers. RESULTS: We demonstrate that CSF biomarker changes in preclinical models capture what is observed in the brain; with a decrease in CSF Aß observed when plaques are deposited, and an increase in CSF tau once tau pathology is present in the brain parenchyma. We found that a high molecular weight cut-off membrane allowed for simultaneous sampling of Aß and tau, comparable to CSF collection by lumbar puncture in patients. CONCLUSION: Our approach can further advance AD and other neurodegenerative research by following evolving neuropathology along the disease cascade via consecutive sampling from the same animal and can additionally be used to administer pharmaceutical compounds and assess their efficacy.

Bayesian supervised machine learning classification of neural networks with pathological perturbations.

Objective.Extraction of temporal features of neuronal activity from electrophysiological data can be used for accurate classification of neural networks in healthy and pathologically perturbed conditions. In this study, we provide an extensive approach for the classification of humanin vitroneural networks with and without an underlying pathology, from electrophysiological recordings obtained using a microelectrode array (MEA) platform.Approach.We developed a Dirichlet mixture (DM) Point Process statistical model able to extract temporal features related to neurons. We then applied a machine learning algorithm to discriminate between healthy control and pathologically perturbedin vitroneural networks.Main Results.We found a high degree of separability between the classes using DM point process features (p-value <0.001 for all the features, paired t-test), which reaches 93.10 of accuracy (92.37 of ROC AUC) with the Random Forest classifier. In particular, results show a higher latency in firing for pathologically perturbed neurons (43 ± 16 ms versus 67 ± 31 ms,µIGfeature distribution).Significance.Our approach has been successful in extracting temporal features related to the neurons' behaviour, as well as distinguishing healthy from pathologically perturbed networks, including classification of responses to a transient induced perturbation.

Early functional changes associated with alpha-synuclein proteinopathy in engineered human neural networks.

A patterned spread of proteinopathy represents a common characteristic of many neurodegenerative diseases. In Parkinson's disease (PD), misfolded forms of α-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 time points in disease development, even before clear behavioral symptoms of dysfunction arise. In this study, we investigate the early pathophysiology developing after induced formation of such PD-related α-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 3 wk following proteinopathy induction to identify associated changes in network function, with a special emphasis on the measure of network criticality. Self-organized criticality represents the critical point between resilience against perturbation and adaptational flexibility, which appears to be a functional trait in self-organizing 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.

Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation.

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.

Silencing of Activity During Hypoxia Improves Functional Outcomes in Motor Neuron Networks in vitro.

The effects of hypoxia, or reduced oxygen supply, to brain tissue can be disastrous, leading to extensive loss of function. Deoxygenated tissue becomes unable to maintain healthy metabolism, which leads to increased production of reactive oxygen species (ROS) and loss of calcium homoeostasis, with damaging downstream effects. Neurons are a highly energy demanding cell type, and as such they are highly sensitive to reductions in oxygenation and some types of neurons such as motor neurons are even more susceptible to hypoxic damage. In addition to the immediate deleterious effects hypoxia can have on neurons, there can be delayed effects which lead to increased risk of developing neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), even if no immediate consequences are apparent. Furthermore, impairment of the function of various hypoxia-responsive factors has been shown to increase the risk of developing several neurodegenerative disorders. Longitudinal assessment of electrophysiological network activity is underutilised in assessing the effects of hypoxia on neurons and how their activity and communication change over time following a hypoxic challenge. This study utilised multielectrode arrays and motor neuron networks to study the response to hypoxia and the subsequent development of the neuronal activity over time, as well as the effect of silencing network activity during the hypoxic challenge. We found that motor neuron networks exposed to hypoxic challenge exhibited a delayed fluctuation in multiple network activity parameters compared to normoxic networks. Silencing of activity during the hypoxic challenge leads to maintained bursting activity, suggesting that functional outcomes are better maintained in these networks and that there are activity-dependent mechanisms involved in the network damage following hypoxia.

Bridging the Gap Between Fluid Biomarkers for Alzheimer's Disease, Model Systems, and Patients.

Alzheimer's disease (AD) is a debilitating neurodegenerative disease characterized by the accumulation of two proteins in fibrillar form: amyloid-ß (Aß) 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.

Formation of neural networks with structural and functional features consistent with small-world network topology on surface-grafted polymer particles.

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.

Connectomics of Morphogenetically Engineered Neurons as a Predictor of Functional Integration in the Ischemic Brain.

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.

A novel lab-on-chip platform enabling axotomy and neuromodulation in a multi-nodal network.

Lab-on-chip platforms, such as microfluidic chips and micro-electrode arrays (MEAs) are powerful tools that allow us to manipulate and study neurons in vitro. Microfluidic chips provide a controlled extracellular environment that structures neural networks and facilitates isolation and manipulation at a sub-cellular level. Furthermore, MEAs enable measurement of extracellular electrophysiological activity from single neurons to entire networks. Here, we demonstrate the design, fabrication and application of a 3-nodal microfluidic chip integrated with MEAs as a versatile study platform for neurobiology and pathophysiology. In this work, we evaluate the use of the microfluidic chip to structure a neural network into three separate nodes, interconnected through tunnels that isolate and guide axons into a channel, thus facilitating synaptic contacts between neurons originating from opposite nodes. Furthermore, we demonstrate the utility of the MEA for monitoring developing activity and intra-/inter nodal connectivity of the structured neural network. Finally, we demonstrate the versatility of the platform in two separate experiments. First, we demonstrate the ability to measure intra- and inter-nodal dynamic responses to a fluidically isolated chemical stimulation. Then, we demonstrate the feature of the microfluidic chip enabling the disruption of functional connectivity between nodes and examination of the immediate activity response of the neural network. The platform enables in vitro modelling of neural networks to study their functional connectomes in the context of neurodegenerative disease and CNS trauma, including spinal cord injury.

Analysis of Codman microcerebrospinal fluid shunt.

INTRODUCTION: Ventriculo-peritoneal cerebrospinal fluid (CSF) shunt is the most common method of treating pediatric hydrocephalus. The Codman microadjustable valve (CMAV) is a CSF shunt constructed for children. The objective of the study was (a) to analyze complications after insertion of a CMAV shunt in hydrocephalic children, (b) to analyze complications after replacing a CMAV by an adult-type Codman Hakim adjustable valve shunt (CHAV), and to (c) analyze the in vitro characteristics of the CMAV shunt and correlate the findings with the clinical performance of the shunt. METHODS: A retrospective study analyzed a cohort of hydrocephalic children who had received a CMAV shunt and later replaced by a CHAV shunt. We report on the complications that resulted from replacing the CMAV with the CHAV. We tested six CMAV shunts with or without an antisiphon device (ASD) in which opening pressure, resistance, sensitivity to abdominal pressure, ASD position dependency, and function were determined. The test results were correlated with the clinical performance of the shunt in the retrospective study. RESULTS: Thirty-seven children (19 boys, 18 girls) were identified. Within the first month after shunt placement, a total of 10 patients (27%) developed complications including infections, hygromas, and shunt dysfunction. Shunt survival varied from 1 week to 145 months. Over the 10-year follow-up period, 13 children had their shunts replaced, six of them with a CHAV without any further complications. A bench test of the CMAV was done to test whether the opening pressure was in agreement with the manufacturer's specifications. Our results were generally in agreement with specifications stated by the manufacturer. CONCLUSION: Replacing a CMAV with a CHAV was well tolerated by the patients. Bench test results were generally in agreement with manufacturers specifications. Replacing a CMAV with a CHAV in pediatric hydrocephalus patients can be accomplished safely.