Isolation of neural activities from respiratory and heartbeat noises for in vivo optical recording in guinea pigs using independent component analysis

2003 ◽  
Vol 352 (1) ◽  
pp. 9-12 ◽  
Author(s):  
Shinji Inagaki ◽  
Takusige Katura ◽  
Hideo Kawaguchi ◽  
Wen-Jie Song
Keyword(s):  
Independent Component Analysis ◽  
Guinea Pigs ◽  
Component Analysis ◽  
Optical Recording ◽  
Independent Component ◽  
Neural Activities

Independent component analysis for the detection of in-vivo intrinsic signals from an optical imager of retinal function

2007 ◽  
Author(s):  
Eduardo S. Barriga ◽  
Marios Pattichis ◽  
Michael Abramoff ◽  
Dan T'so ◽  
Young Kwon ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Retinal Function ◽  
Independent Component ◽  
Intrinsic Signals

scholarly journals Extraction of Neural Activity from In Vivo Optical Recordings Using Multiple Independent Component Analysis

2007 ◽  
Vol 127 (10) ◽  
pp. 1642-1650 ◽  
Author(s):  
Takamasa Yoshida ◽  
Masanori Sakagami ◽  
Kyoko Yamazaki ◽  
Takusige Katura ◽  
Mitsumasa Iwamoto ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Neural Activity ◽  
Component Analysis ◽  
Independent Component

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.

scholarly journals Independent component analysis for automated decomposition of in vivo magnetic resonance spectra

2003 ◽  
Vol 50 (4) ◽  
pp. 697-703 ◽  
Author(s):  
Christophe Ladroue ◽  
Franklyn A. Howe ◽  
John R. Griffiths ◽  
A. Rosemary Tate
Keyword(s):  
Magnetic Resonance ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Magnetic Resonance Spectra
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