Wavelet transform-based de-noising for two-photon imaging of synaptic Ca2+ transients.
Wavelet transform-based de-noising for two-photon imaging of synaptic Ca2+ transients.
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基于小波变换的突触 Ca2 瞬变双光子成像去噪。
DOI:
10.1016/j.bpj.2013.01.015
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发表时间:
2013
影响因子:
3.4
通讯作者:
Tigaret CM
中科院分区:
文献类型:
--
作者:
Tigaret CM
Postsynaptic Ca2+transients triggered by neurotransmission at excitatory synapses are a key signaling step for the induction of synaptic plasticity and are typically recorded in tissue slices using two-photon fluorescence imaging with Ca2+-sensitive dyes. The signals generated are small with very low peak signal/noise ratios (pSNRs) that make detailed analysis problematic. Here, we implement a wavelet-based de-noising algorithm (PURE-LET) to enhance signal/noise ratio for Ca2+fluorescence transients evoked by single synaptic events under physiological conditions. Using simulated Ca2+transients with defined noise levels, we analyzed the ability of the PURE-LET algorithm to retrieve the underlying signal. Fitting single Ca2+transients with an exponential rise and decay model revealed a distortion ofτrisebut improved accuracy and reliability ofτdecayand peak amplitude after PURE-LET de-noising compared to raw signals. The PURE-LET de-noising algorithm also provided a ∼30-dB gain in pSNR compared to ∼16-dB pSNR gain after an optimized binomial filter. The higher pSNR provided by PURE-LET de-noising increased discrimination accuracy between successes and failures of synaptic transmission as measured by the occurrence of synaptic Ca2+transients by ∼20% relative to an optimized binomial filter. Furthermore, in comparison to binomial filter, no optimization of PURE-LET de-noising was required for reducing arbitrary bias. In conclusion, the de-noising of fluorescent Ca2+transients using PURE-LET enhances detection and characterization of Ca2+responses at central excitatory synapses.
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影响因子:
64.8
作者:
Mainen, ZF;Malinow, R;Svoboda, K
通讯作者:
Svoboda, K
影响因子:
1.9
作者:
A. T. Young
通讯作者:
A. T. Young
影响因子:
2
作者:
P. Besbeas;I. Feis;T. Sapatinas
通讯作者:
P. Besbeas;I. Feis;T. Sapatinas
影响因子:
16.2
作者:
Enoki, Ryosuke;Hu, Yi-ling;Fine, Alan
通讯作者:
Fine, Alan
影响因子:
--
作者:
Goldberg,JesseH;Yuste,Rafael
通讯作者:
Yuste,Rafael