Criticality as a measure of developing proteinopathy in engineered human neural networks

Abstract

A patterned spread of proteinopathy represents a common characteristic of many neurodegenerative diseases. In Parkinson's disease (PD), misfolded forms of alpha-synuclein proteins aggregate and accumulate in hallmark pathological inclusions termed Lewy bodies and Lewy neurites, which seems to affect selectively vulnerable neuronal populations and propagate within interconnected neuronal networks. Research findings suggest that these proteinopathic inclusions are present at very early timepoints in disease development, even before strong behavioural symptoms of dysfunction arise, but that these underlying pathologies might be masked by homeostatic processes working to maintain the function of the degenerating neural circuits. This study investigates whether inducing the PD-related alpha-synuclein pathology in engineered human neural networks can be associated with changes in network function, and particularly with network criticality states. 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. By monitoring the developing neural network activity through the use of multielectrode arrays (MEAs) for a period of three weeks following proteinopathy induction, we show that although this developing pathology is not clearly manifest in standard measurements of network function, it may be discerned by differences in network criticality states.

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

“…Furthermore, impaired axonal growth and guidance would explain what is observed as aberrant (and consequently inefficient) network wiring in the LRRK2 G2019S networks, with large numbers of neurites crossing perpendicular to the axonal tunnels in the synaptic compartment. Based on the general structure-function relationships of neural networks (Valderhaug, 2020), resulting aberrant wiring might again affect neurotransmission efficacy, causing less efficient signal propagation within the network, again affecting the overall energy status of the neuron and consequently the motility and distribution of mitochondria (Saxton and Hollenbeck, 2012).…”

“…It is thus highly feasible and relevant to combine such advanced multidisciplinary approaches to investigate the micro-and mesoscale aspects of LRRK2 human neural networks with a focus on the effect and development of alpha-synucleinopathy in the future. Furthermore, analogs of the validated PD network biomarker -PDRP (Parkinsons disease related pattern) (Niethammer and Eidelberg, 2012) and PDCP (Parkinsons disease cognitive pattern) (Schindlbeck and Eidelberg, 2018)-could be applied to the electrophysiological data using graph theory, and utilized to investigate disease specific network patterns, such as small-worldness (Bassett and Bullmore, 2017;Ko et al, 2018;Schindlbeck et al, 2019;Valderhaug et al, 2019;Valderhaug, 2020;Fiskum et al, 2024), in advanced cellular models based on engineered neural networks. Furthermore, fluorescence imaging can be applied to enable to visualize neuronal activity for functional characterization in such disease models.…”

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

“…Recordings were obtained through the MEA2100 in vitro head stage, interface system and suite software (Multi Channel Systems; Reutlingen, Germany), and are available within the Mendeley data repository ( [22]). Samples of the electrophysiological activity of the 3D neural networks on the PPs were recorded for durations of approximately 90 consecutive seconds throughout the experimental period, with a sampling rate of 10 kHz, where a waveform amplitude exceeding a threshold of ±5 s.d.…”

“…The electrophysiological raw-data files are available within Mendeley as Devold Valderhaug, Vibeke; Sandvig, Axel; Sandvig, Ioanna (2019), ‘oeRecordings from 3D NSC-derived neural networks', [22] under a CC-BY licence. The MEA Analysis Toolbox is available for download at .…”