PMID- 16407432
OWN - NLM
STAT- MEDLINE
DCOM- 20060628
LR  - 20131121
IS  - 0022-3077 (Print)
IS  - 0022-3077 (Linking)
VI  - 95
IP  - 5
DP  - 2006 May
TI  - Local shuffling of spike trains boosts the accuracy of spike train spectral 
      analysis.
PG  - 3245-56
AB  - Spectral analysis of neuronal spike trains is an important tool in understanding 
      the characteristics of neuronal activity by providing insights into normal and 
      pathological periodic oscillatory phenomena. However, the refractory period 
      creates high-frequency modulations in spike-train firing rate because any rise in 
      the discharge rate causes a descent in subsequent time bins, leading to 
      multifaceted modifications in the structure of the spectrum. Thus the power 
      spectrum of the spiking activity (autospectrum) displays elevated energy in high 
      frequencies relative to the lower frequencies. The spectral distortion is more 
      dominant in neurons with high firing rates and long refractory periods and can 
      lead to reduced identification of low-frequency oscillations (such as the 5- to 
      10-Hz burst oscillations typical of Parkinsonian basal ganglia and thalamus). We 
      propose a compensation process that uses shuffling of interspike intervals (ISIs) 
      for reliable identification of oscillations in the entire frequency range. This 
      compensation is further improved by local shuffling, which preserves the slow 
      changes in the discharge rate that may be lost in global shuffling. Cross-spectra 
      of pairs of neurons are similarly distorted regardless of their correlation 
      level. Consequently, identification of low-frequency synchronous oscillations, 
      even for two neurons recorded by a single electrode, is improved by ISI 
      shuffling. The ISI local shuffling is computed with confidence limits that are 
      based on the first-order statistics of the spike trains, thus providing a 
      reliable estimation of auto- and cross-spectra of spike trains and making it an 
      optimal tool for physiological studies of oscillatory neuronal phenomena.
FAU - Rivlin-Etzion, Michal
AU  - Rivlin-Etzion M
AD  - Center for Neural Computation, The Hebrew University, Jerusalem, Israel. 
      michriv2@alice.nc.huji.ac.il
FAU - Ritov, Ya'acov
AU  - Ritov Y
FAU - Heimer, Gali
AU  - Heimer G
FAU - Bergman, Hagai
AU  - Bergman H
FAU - Bar-Gad, Izhar
AU  - Bar-Gad I
LA  - eng
PT  - Comparative Study
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20060111
PL  - United States
TA  - J Neurophysiol
JT  - Journal of neurophysiology
JID - 0375404
RN  - 0 (Neurotoxins)
RN  - 9P21XSP91P (1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine)
SB  - IM
MH  - 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine/pharmacology
MH  - Action Potentials/drug effects/*physiology
MH  - Animals
MH  - Globus Pallidus/cytology
MH  - Haplorhini
MH  - *Models, Neurological
MH  - Models, Statistical
MH  - Neurons/drug effects/*physiology
MH  - Neurotoxins/pharmacology
MH  - Oscillometry
MH  - *Spectrum Analysis
MH  - Time Factors
EDAT- 2006/01/13 09:00
MHDA- 2006/06/29 09:00
CRDT- 2006/01/13 09:00
PHST- 2006/01/13 09:00 [pubmed]
PHST- 2006/06/29 09:00 [medline]
PHST- 2006/01/13 09:00 [entrez]
AID - 00055.2005 [pii]
AID - 10.1152/jn.00055.2005 [doi]
PST - ppublish
SO  - J Neurophysiol. 2006 May;95(5):3245-56. doi: 10.1152/jn.00055.2005. Epub 2006 Jan 
      11.