Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signaling in a Model of Niemann-Pick Disease Type C.

ABSTRACT: Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle sub-diffraction resolution changes of synaptic morphology pose significant challenges to this endeavor. Here, specially designed graphene microelectrode arrays (G-MEAs) are used, which are compatible with high spatial resolution imaging across various scales as well as permit high temporal resolution electrophysiological recordings to address these challenges. Furthermore, alongside G-MEAs, an easy-to-implement machine learning algorithm is developed to efficiently process the large datasets collected from MEA recordings. It is demonstrated that

SUBMITTER: Lu M 

PROVIDER: S-EPMC11600250 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature