scholarly journals Blind identification of evoked human brain activity with independent component analysis of optical data

2009 ◽  
Vol 30 (8) ◽  
pp. 2382-2392 ◽  
Author(s):  
Joanne Markham ◽  
Brian R. White ◽  
Benjamin W. Zeff ◽  
Joseph P. Culver
Keyword(s):  
Independent Component Analysis ◽  
Human Brain ◽  
Brain Activity ◽  
Component Analysis ◽  
Independent Component ◽  
Optical Data ◽  
Blind Identification

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

2010 ◽  
Vol 104 (6) ◽  
pp. 3721-3731 ◽  
Author(s):  
Evan S. Hill ◽  
Caroline Moore-Kochlacs ◽  
Sunil K. Vasireddi ◽  
Terrence J. Sejnowski ◽  
William N. Frost
Keyword(s):  
Independent Component Analysis ◽  
Action Potentials ◽  
Neuronal Networks ◽  
Single Neuron ◽  
Component Analysis ◽  
Optical Recording ◽  
Independent Component ◽  
Optical Data ◽  
Data Sets ◽  
Complex Data

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.

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