Advanced Brain-on-a-Chip Models and Custom Analysis Tools
Brain activity is the orchestrated functioning of interconnected brain regions. Typical in vitro models aim to mimic this complexity using single human pluripotent stem cell-derived neuronal networks. However, the field is constantly evolving to model brain functions more accurately through the use of new paradigms, such as brain-on-a-chip models with compartmentalized structures and integrated sensors. These advanced methods create novel data that require more complex analysis approaches.
The previously introduced circular tripartite network concept models the connectivity between spatially diverse neuronal structures. This platform consists of a microfluidic device allowing axonal connectivity between separated neuronal networks, with an embedded microelectrode array (MEA) to record both local and global electrophysiological activity patterns in the closed circuitry. The existing tools are suboptimal for the analysis of the data produced with this model. Here, we introduce advanced tools for synchronization and functional connectivity assessment, which enable a comprehensive evaluation of the interrelations within the circular tripartite network.
Assessing the Impact of Kainic Acid on Multicompartmental Networks
We used our custom-designed analysis to assess the interrelations between the kainic acid (KA)-exposed proximal compartment and its nonexposed distal neighbors before and after KA exposure. Novel multilevel circuitry bursting patterns were detected and analyzed in parallel with the inter- and intracompartmental functional connectivity. The effect of KA on the proximal compartment was captured, and the spread of this effect to the nonexposed distal compartments was revealed.
KA induced divergent changes in bursting behaviors, which may be explained by distinct baseline activity and varied intra- and intercompartmental connectivity strengths. The circular tripartite network concept combined with our developed analysis advances both face and construct validity in modeling human epilepsy in vitro.
Developing Advanced Analysis Tools for Multicompartmental MEA Data
The circular tripartite network platform, known as MEMO, contains a custom MEA combined with a microfluidic device made of polydimethylsiloxane (PDMS). The device integrates three pentagonal compartments with 50 microtunnels between each pair of compartments, allowing axonal connectivity between the separated neuronal networks. The custom MEAs feature 72 electrodes organized in three areas corresponding to the compartments, with a single large reference electrode covering the area.
Human embryonic stem cell-derived cortical neurons were cultured on the MEMO platform, and their development and functional maturation were monitored through long-term electrophysiological recordings. To assess the impact of KA exposure, the experimental procedure involved inducing seizure-like activity by acutely exposing the proximal C compartment to KA.
The raw data were processed using a custom MATLAB pipeline, including spike detection with the stationary wavelet transform-based Teager energy operator (SWTTEO) algorithm. To accurately characterize the network activity, we developed a novel tool capable of assessing local network- and global circuitry-level synchronous behavior specifically designed for multicompartmental MEA analysis.
Iterative Development of the Burst Detection Algorithm
The development of the burst detection algorithm involved an initial evaluation of existing methods to determine the template algorithm, followed by an iterative feature enhancement process. The selected algorithm was the ISIN threshold-based burst detector published by Bakkum et al. (2014), which showed good performance and sufficient adaptivity with our data.
The algorithm was expanded with several steps to achieve an enhanced and efficient tool for assessing multicompartmental neuronal activity. This included adaptive NB merging, removal of extremely short NBs, minimum participating channels criteria, individual channel contribution assessment, and the final step of circuitry burst detection.
The circuitry burst detection algorithm searched for temporally intersecting segments of local network bursts (NBs) in the compartments and classified these as circuitry-level bursts (CBs). The nonrandomness of the observed baseline temporal alignment of NBs was validated, confirming the genuine synchronous CB firing of the three-compartment networks.
Revealing Multilevel Synchronous Activity Patterns
The developed analysis tool was able to detect three levels of synchronous bursting activity: local NBs within individual compartments, intermediate circuitry bursts (ICBs) involving two compartments, and fully matured CBs with synchronous orchestration of all three local networks.
Local Network Bursts (NBs):
NBs represent the synchronous activity of a distinct brain node, with the proximal C compartment exposed to KA modeling the seizure-onset zone.
Intermediate Circuitry Bursts (ICBs):
ICBs depict the orchestration of two compartments, reflecting the interplay of two interconnected cortical regions.
Circuitry Bursts (CBs):
CBs are the fully matured synchronous activity patterns across the entire circular tripartite network, modeling the synchronous activity of three interconnected cortical regions.
Evaluating the Impact of Kainic Acid Exposure
The application of the developed analysis tool revealed prominent changes in the synchronous activity patterns, especially in the KA-exposed proximal C compartment. These included:
- Increase in NB number and duration: Potentially due to the elevation of tonic spiking after KA exposure.
- Decrease in mean interburst interval (IBI) and spike frequency in NBs: Indicating a shift toward less prominent but more temporally spread patterns.
- Decrease in mean spikes in NBs: Suggesting the initially condensed synchronous activity breaking down.
The KA-induced alterations in the proximal compartment also spread to the nonexposed distal compartments, though with a weaker effect. This could be due to the distal networks’ resistance to excitation through proportional inhibition.
The global impact of KA exposure was further confirmed by the significant changes in CB parameters, such as decreases in mean channels in the CB and mean dominating channels in the CB. These results indicate a shift in the overall network orchestration patterns.
The functional connectivity analysis using the CorSE method provided additional support, revealing clear trends in average intra- and intercompartmental connectivity alterations within most MEMOs after KA exposure.
Implications and Future Applications
The circular tripartite network model combined with the established analysis pipeline creates a promising platform for in vitro research on neurological disorders, particularly epilepsy. The ability to model the spatial propagation of hyperactivity is a key advantage over single-network models.
Furthermore, the platform has great potential for modeling disease progression in patients with neurodegenerative disorders, as it enables the evaluation of complex network dynamics and the impact of pharmacological interventions on global brain function.
The developed analysis tools and the insights gained from this study contribute to the advancement of both face and construct validity in modeling human neurological disorders using in vitro platforms. As the field continues to evolve, these innovative approaches will be instrumental in unlocking new possibilities for understanding and treating complex brain-related conditions.
