Validation of Independent Component Analysis for Rapid Spike Sorting of Optical Recording Data

Abstract

Independent component analysis (ICA) is a technique that can be used to extract the source signals from sets of signal mixtures where the sources themselves are unknown. The analysis of optical recordings of invertebrate neuronal networks with fast voltage-sensitive dyes could benefit greatly from ICA. These experiments can generate hundreds of voltage traces containing both redundant and mixed recordings of action potentials originating from unknown numbers of neurons. ICA can be used as a method for converting such complex data sets into single-neuron traces, but its accuracy for doing so has never been empirically evaluated. Here, we tested the accuracy of ICA for such blind source separation by simultaneously performing sharp electrode intracellular recording and fast voltage-sensitive dye imaging of neurons located in the central ganglia of Tritonia diomedea and Aplysia californica, using a 464-element photodiode array. After running ICA on the optical data sets, we found that in 34 of 34 cases the intracellularly recorded action potentials corresponded 100% to the spiking activity of one of the independent components returned by ICA. We also show that ICA can accurately sort action potentials into single neuron traces from a series of optical data files obtained at different times from the same preparation, allowing one to monitor the network participation of large numbers of individually identifiable neurons over several recording episodes. Our validation of the accuracy of ICA for extracting the neural activity of many individual neurons from noisy, mixed, and redundant optical recording data sets should enable the use of this powerful large-scale imaging approach for studies of invertebrate and suitable vertebrate neuronal networks.

Motor neuronal activity varies least among individuals when it matters most for behavior

“…In particular, it might be useful to include the activity of the motor neurons for the intrinsic muscles of the odontophore such as B15/B16, the motor neurons for the accessory radular closer (ARC) muscle [Cohen et al 1978; the ARC muscle is also known as I5 (Howells 1942)], or B48, the motor neuron for the I7-I10 muscles, which contribute to opening of the radula (Evans et al 1996). Although we were unable to distinguish these motor neurons on BN3, in future studies it might be possible to combine the suspended buccal mass preparation (McManus et al 2012) with voltage-sensitive dyes (Hill et al 2010;Morton et al 1991) to obtain a more complete picture of the full activity of all the motor neurons during feeding. Including features that did not always occur in every motor program, but were more likely to occur during swallowing than during biting, might also improve discrimination, such as activity in the B38 motor neurons, which are more active in swallowing than in biting , and the activity of the B3 motor neurons, which are more active in swallowing than in biting (Lu et al 2013).…”

Variable Neuronal Participation in Stereotypic Motor Programs

“…The accuracy of ICA was recently validated by us using simultaneous intracellular and optical recordings from neurons in various central ganglia of T. diomedea and A. californica [29].…”

Multifocal Fluorescence Microscope for Fast Optical Recordings of Neuronal Action Potentials

“…Previous studies have shown that multiphoton microscopy signals can be resolved reliably in highly scattering tissue such as brain slice at depths of 200 mm or more (13). Furthermore, FTM at greater depths may benefit from image processing techniques that assign photons to targets in noisy environments (29)(30)(31). Finally, there has been significant effort to develop both red-shifted genetically encoded indicators (7,32) as well as opsins (33,34) that will eventually allow for simultaneous and nonoverlapping activation and detection of neural activity, an endeavor for which the FTM would also provide advantages.…”