Abstract

Back to table of contents Previous article Next article LettersFull AccessAnalysis Spectrum of Normal and Ataxia Purkinje Cell Output and Classification Using Artificial Neural NetworkSamira Abbasi Ataollah Abbasi, Ph.D. Yashar Sarbaz, Ph.D. Afshin Ebrahimi, Ph.D.Samira AbbasiSearch for more papers by this author Ataollah AbbasiSearch for more papers by this author, Ph.D. Yashar SarbazSearch for more papers by this author, Ph.D. Afshin EbrahimiSearch for more papers by this author, Ph.D.Published Online:1 Jan 2014https://doi.org/10.1176/appi.neuropsych.13100232AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail To the Editor: Dysfunction of the Purkinje neurons leads to lack of motor coordination, or cerebellar ataxia, and impaired motor learning.1 Normal Purkinje neurons fire spontaneous action potentials under basal conditions and inhibit the deep cerebellar nuclei. Purkinje cell output displays high-frequency sodium spikes and low-frequency calcium spikes.2 These frequency bands can be investigated by signal processing tools such as power spectral density. There are explicit peaks related to each frequency band in the power spectral density of Purkinje cell output. Changes in the behavior of the ataxic Purkinje cells can be studied by evaluating possible changes in the power spectral density of their output signal. The frequencies of these peaks are altered in the ataxia condition. The use of the power spectral density is helpful in the recognition of changes in the frequency bands in ataxia conditions. This recognition can probably be helpful in understanding the role of ion channels in power spectral density and the relationship among the activity of ion channels and frequency bands. This is a good method for relating frequency with the activity of ion channels to facilitate an understanding of cerebellar ataxia alternations. The ability of Purkinje cells to generate sodium and calcium spikes in different patterns of activity (tonic or burst) can be investigated by using the power spectral density of outputs. Thus, we propose that changes in the firing activity of the Purkinje cell can be detected by analyzing the power spectral density of the membrane potential. Peak frequencies can be measured by power spectrum analysis. These frequencies are altered in ataxia conditions. It is important to understand which mechanism is responsible for this alternation. The spectral changes in the ataxia condition can be caused by the change in ionic currents. Thus, analysis of the power spectral density of the Purkinje cell output may represent changes in the ionic currents, and we think that an early diagnosis is possible by using power spectrum analysis.In addition, the frequencies of the peaks are appropriate features for differentiating normal cells from ataxic cells and can be used in the analysis and classification of the Purkinje cell output.Peak frequencies of the Purkinje cell output can be used by an artificial neural network classifier to classify the signal into normal or ataxia groups. The proposed classifier may be used as a tool for helping clinicians control cerebellar ataxia.An artificial neural network is a set of mathematical neurons that is an effective classification tool, and it is used by different researchers in various biological systems for classifying disease states from normal ones.3,4We think that a classification based on power spectral density analysis would better define different types of Purkinje cell outputs, which may be similar during the onset of disease. Use of this classification system helps in the segregation of different Purkinje cell outputs. Therefore, cerebellar ataxia will be better diagnosed, and early diagnosis is possible.Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran (SA, AA); Department of Emerging Technology, School of Engineering, University of Tabriz, Tabriz, Iran (YS); and Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran (AE)Correspondence: Ataollah Abbasi; e-mail: [email protected]References1 Brown S-A, Loew LM: Computational analysis of calcium signaling and membrane electrophysiology in cerebellar Purkinje neurons associated with ataxia. BMC Syst Biol 2012; 6:70Crossref, Medline, Google Scholar2 Ze'ev RA, Warrier A, Trauner D, Zhang X: A signal processing analysis of Purkinje cells in vitro. Front Neural Circuits 2010; 4:13Medline, Google Scholar3 Veezhinathan M, Ramkrishnan S: Neural network–based classification of normal and abnormal pulmonary function using spirometric measurements. J Mech Med Biol 2007; 7:151–161Crossref, Google Scholar4 Haseena H, Joseph PK, Abraham TM: Artificial neural network based ECG arrhythmia classification. J Mech Med Biol 2009; 9:507–525Crossref, Google Scholar FiguresReferencesCited byDetailsCited ByNone Volume 26Issue 4 Fall 2014Pages E14-E14 Metrics PDF download History Published online 1 January 2014 Published in print 1 October 2014

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.