Application of Parallel Factor Analysis (PARAFAC) to Electrophysiological Data
The identification of important features in multi-electrode recordings requires the decomposition of data in order to disclose relevant features and to offer a clear graphical representation. This can be a demanding task. Parallel Factor Analysis (PARAFAC; Hitchcock, 1927; Carrol & Chang, 1970;...
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| Format: | Article |
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Frontiers Media S.A.
2015-01-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00084/full |
| _version_ | 1819059182218772480 |
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| author | Sarah Katharina eSchmitz Sarah Katharina eSchmitz Sarah Katharina eSchmitz Philipp P Hasselbach Boris eEbisch Boris eEbisch Anja eKlein Gordon ePipa Ralf A. W. Galuske Ralf A. W. Galuske |
| author_facet | Sarah Katharina eSchmitz Sarah Katharina eSchmitz Sarah Katharina eSchmitz Philipp P Hasselbach Boris eEbisch Boris eEbisch Anja eKlein Gordon ePipa Ralf A. W. Galuske Ralf A. W. Galuske |
| author_sort | Sarah Katharina eSchmitz |
| collection | DOAJ |
| description | The identification of important features in multi-electrode recordings requires the decomposition of data in order to disclose relevant features and to offer a clear graphical representation. This can be a demanding task. Parallel Factor Analysis (PARAFAC; Hitchcock, 1927; Carrol & Chang, 1970; Harshman, 1970) is a method to decompose multi-dimensional arrays in order to focus on the features of interest, and provides a distinct illustration of the results. We applied PARAFAC to analyse spatio-temporal patterns in the functional connectivity between neurons, as revealed in their spike trains recorded in cat primary visual cortex (area 18). During these recordings we reversibly deactivated feedback connections from higher visual areas in the pMS (posterior middle suprasylvian) cortex in order to study the impact of these top-down signals. Cross correlation was computed for every possible pair of the electrodes in the electrode array. PARAFAC was then used to reveal the effects of time, stimulus, and deactivation condition on the correlation patterns. Our results show that PARAFAC is able to reliably extract changes in correlation strength for different experimental conditions and display the relevant features. Thus, PARAFAC proves to be well-suited for the use in the context of electrophysiological (action potential) recordings. |
| first_indexed | 2024-12-21T14:07:02Z |
| format | Article |
| id | doaj.art-9ad9b262adc94afca9871b22f41dd2c1 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-12-21T14:07:02Z |
| publishDate | 2015-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-9ad9b262adc94afca9871b22f41dd2c12022-12-21T19:01:12ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962015-01-01810.3389/fninf.2014.0008486221Application of Parallel Factor Analysis (PARAFAC) to Electrophysiological DataSarah Katharina eSchmitz0Sarah Katharina eSchmitz1Sarah Katharina eSchmitz2Philipp P Hasselbach3Boris eEbisch4Boris eEbisch5Anja eKlein6Gordon ePipa7Ralf A. W. Galuske8Ralf A. W. Galuske9Technische Universität DarmstadtMax-Planck-Institute for Brain ResearchFrankfurt Insitute for Advanced StudiesTechnische Universität DarmstadtTechnische Universität DarmstadtMax-Planck-Institute for Brain ResearchTechnische Universität DarmstadtUniversität OsnabrückTechnische Universität DarmstadtMax-Planck-Institute for Brain ResearchThe identification of important features in multi-electrode recordings requires the decomposition of data in order to disclose relevant features and to offer a clear graphical representation. This can be a demanding task. Parallel Factor Analysis (PARAFAC; Hitchcock, 1927; Carrol & Chang, 1970; Harshman, 1970) is a method to decompose multi-dimensional arrays in order to focus on the features of interest, and provides a distinct illustration of the results. We applied PARAFAC to analyse spatio-temporal patterns in the functional connectivity between neurons, as revealed in their spike trains recorded in cat primary visual cortex (area 18). During these recordings we reversibly deactivated feedback connections from higher visual areas in the pMS (posterior middle suprasylvian) cortex in order to study the impact of these top-down signals. Cross correlation was computed for every possible pair of the electrodes in the electrode array. PARAFAC was then used to reveal the effects of time, stimulus, and deactivation condition on the correlation patterns. Our results show that PARAFAC is able to reliably extract changes in correlation strength for different experimental conditions and display the relevant features. Thus, PARAFAC proves to be well-suited for the use in the context of electrophysiological (action potential) recordings.http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00084/fullPrincipal Component Analysiscross correlationParallel factor analysiscat primary visual cortexcortical deactivation |
| spellingShingle | Sarah Katharina eSchmitz Sarah Katharina eSchmitz Sarah Katharina eSchmitz Philipp P Hasselbach Boris eEbisch Boris eEbisch Anja eKlein Gordon ePipa Ralf A. W. Galuske Ralf A. W. Galuske Application of Parallel Factor Analysis (PARAFAC) to Electrophysiological Data Frontiers in Neuroinformatics Principal Component Analysis cross correlation Parallel factor analysis cat primary visual cortex cortical deactivation |
| title | Application of Parallel Factor Analysis (PARAFAC) to Electrophysiological Data |
| title_full | Application of Parallel Factor Analysis (PARAFAC) to Electrophysiological Data |
| title_fullStr | Application of Parallel Factor Analysis (PARAFAC) to Electrophysiological Data |
| title_full_unstemmed | Application of Parallel Factor Analysis (PARAFAC) to Electrophysiological Data |
| title_short | Application of Parallel Factor Analysis (PARAFAC) to Electrophysiological Data |
| title_sort | application of parallel factor analysis parafac to electrophysiological data |
| topic | Principal Component Analysis cross correlation Parallel factor analysis cat primary visual cortex cortical deactivation |
| url | http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00084/full |
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