Neuronal avalanche dynamics and functional connectivity elucidate information propagation in vitro

Heiney, Kristine; Ramstad, Ola Huse; Fiskum, Vegard; Sandvig, Axel; Sandvig, Ioanna; Nichele, Stefano

doi:10.3389/fncir.2022.980631

Cited by 8 publications

(11 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neurons within the six four-nodal devices self-organized into highly complex networks by 14 DIV ( Figure 1A ). At this stage, electrophysiological recordings furthermore indicated that spontaneous neural activity had started transitioning from immature tonic firing to synchronous bursting within the nodes, consistent with previous findings ( Figure 1B ) (1, 3, 20, 40, 41).…”

Section: Resultssupporting

confidence: 90%

“…This combined approach can be challenging to implement in vivo . An increasing body of literature, including recent studies from our group, has characterized how in vitro neural networks develop and mature in healthy, i.e., unperturbed, conditions (1, 3, 20, 40, 41). Such studies provide valuable insights into the intrinsic self-organizing behaviour of engineered neural networks.…”

Section: Discussionmentioning

confidence: 99%

“…Engineered biological neural networks facilitate the study of dynamic properties of network structure and function at the microscale (i.e., subcellular and cellular level) and mesoscale (i.e., network level), in highly controllable experimental conditions. There is compelling evidence that neurons in vitro maintain their inherent self-organizing properties and over time form neural networks with complex structure and function, thus recapitulating fundamental behaviour of brain networks (1)(2)(3)(4). As such, engineered neural networks offer a complementary approach to in vivo studies for investigations of neural structure and function from the subcellular to the network level.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reverse Engineering of Feedforward Cortical-Hippocampal Neural Networks Relevant for Preclinical Disease Modelling

Hanssen,

Winter-Hjelm,

Niethammer

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Engineered biological neural networks are indispensable tools for investigating neural function in both healthy and diseased states from the subcellular to the network level. Neurons in vitro self-organize over time into networks of increasing structural and functional complexity, thus maintaining emergent dynamics of neurons in the brain. While in vitro neural network model systems have advanced significantly over the past decade, there is still a need for models able to recapitulate topological and functional organization of brain networks. Especially relevant in this context are interfaces which can support the establishment of multinodal interconnected networks of different neural populations, which at the same time enable control of the direction of connectivity between the nodes. An added required feature of such interfaces is compatibility with electrophysiological techniques and optical imaging tools to facilitate studies of neural network structure-function dynamics. In this study, we applied a custom-designed microfluidic device with Tesla-valve inspired microchannels for structuring multinodal neural networks with controllable feedforward connectivity between rat primary cortical and hippocampal neurons. By interfacing these devices with nanoporous microelectrode arrays, we demonstrate how both spontaneously evoked and stimulation induced activity propagates, as intended, in a feedforward pattern between the different interconnected neural nodes. Moreover, we show that these multinodal networks exhibit functional dynamics suggestive of capacity for both segregated and integrated activity. To advocate the broader applicability of this model system, we also provide proof of concept of long-term culturing of subregion- and layer specific neurons extracted from the entorhinal cortex and hippocampus of adult Alzheimers-model mice and rats. We show that these neurons re-form structural connections and develop spontaneous spiking activity after 15 days in vitro. Our results thus highlight the suitability and potential of our approach for reverse engineering of biologically and anatomically relevant multinodal neural networks supporting the study of dynamic structure-function relationships in both healthy and pathological conditions.

show abstract

Section: Resultssupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reverse Engineering of Feedforward Cortical-Hippocampal Neural Networks Relevant for Preclinical Disease Modelling

Hanssen,

Winter-Hjelm,

Niethammer

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Rat cortical neurons from Sprague Dawley rats (Gibco, A36511, neuronal purity: 96%) were plated at a density of 1000 cells mm −2 , equalling 20 000 cells per chamber in the two-nodal platforms and 40 000 cells in the controls. This plating density was chosen based on previous studies indicating that at least 250 cells mm −2 are required for networks to establish synchronized activity [46], and that higher densities can lead to more complex activity dynamics [47]. Half the cell media was replaced with fresh cell media 4 h after plating, and again after 24 h. From here on, half the cell media was replaced every second day until the cultures were ended at 28 DIV.…”

Section: Cell Plating and Maintenancementioning

confidence: 99%

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

et al. 2023

Self Cite

View full text Add to dashboard Cite

Microfluidic devices interfaced with microelectrode arrays have in recent years emerged as powerful platforms for studying and manipulating in vitro neuronal 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. 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. 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. 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.

show abstract

“…However, we and others have shown that neural network dynamics at the microscale and mesoscale can be studied with advanced cellular models utilizing the inherent self-organizing properties of neurons into complex, computationally competent networks, akin to networks in the brain (Pasquale, Massobrio et al 2008, Downes, Hammond et al 2012, Poli, Pastore et al 2015, Schroeter, Charlesworth et al 2015, Fiskum, Sandvig et al 2021, Antonello, Varley et al 2022, Heiney, Huse Ramstad et al 2022). Relevant applications of these methods to model ALS have been able to capture signs of hyperexcitability and time-dependent changes in networks of patient derived MNs (Wainger, Kiskinis et al 2014, Ronchi, Buccino et al 2021, Sommer, Rajkumar et al 2022).…”

Section: Introductionmentioning

confidence: 99%

ALS patient-derived motor neuron networks exhibit microscale dysfunction and mesoscale compensation rendering them highly vulnerable to perturbation

Fiskum,

Winter-Hjelm,

Christiansen

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Amyotrophic lateral sclerosis affects upper and lower motor neurons, causing progressive neuropathology leading to structural and functional alterations of affected neural networks long prior to development of symptoms. Certain genetic mutations, such as expansions in C9orf72, predispose motor neuron populations to pathological dysfunction. However, it is not known how underlying pathological predisposition affects structural and functional dynamics within vulnerable networks. Here, we studied micro-and mesoscale dynamics of ALS patient derived motor neuron networks over time. We show, for the first time, that ALS patient derived motor neurons with endogenous genetic predisposition develop classical ALS cytopathology in the form of cytoplasmic TDP-43 inclusions and self-organise into computationally efficient networks, albeit with functional hallmarks of higher metabolic cost compared to healthy controls. These hallmarks included microscale impairments and mesoscale compensation including increased centralisation of function. Moreover, we show that these networks are highly susceptible to transient perturbation by exhibiting induced hyperactivity.

show abstract

Neuronal avalanche dynamics and functional connectivity elucidate information propagation in vitro

Cited by 8 publications

References 44 publications

Reverse Engineering of Feedforward Cortical-Hippocampal Neural Networks Relevant for Preclinical Disease Modelling

Reverse Engineering of Feedforward Cortical-Hippocampal Neural Networks Relevant for Preclinical Disease Modelling

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

ALS patient-derived motor neuron networks exhibit microscale dysfunction and mesoscale compensation rendering them highly vulnerable to perturbation

Contact Info

Product

Resources

About