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Novel use of matched filtering for synaptic event detection and extraction.

Shi Y, Nenadic Z, Xu X - PLoS ONE (2010)

Bottom Line: This new technique was applied to quantify and compare the EPSCs obtained from excitatory pyramidal cells and fast-spiking interneurons.In addition, our technique has been further applied to the detection and analysis of inhibitory postsynaptic current (IPSC) responses.Given the general purpose of our matched filtering and signal recognition algorithms, we expect that our technique can be appropriately modified and applied to detect and extract other types of electrophysiological and optical imaging signals.

View Article: PubMed Central - PubMed

Affiliation: Department of Anatomy and Neurobiology, School of Medicine, University of California Irvine, Irvine, California, USA.

ABSTRACT
Efficient and dependable methods for detection and measurement of synaptic events are important for studies of synaptic physiology and neuronal circuit connectivity. As the published methods with detection algorithms based upon amplitude thresholding and fixed or scaled template comparisons are of limited utility for detection of signals with variable amplitudes and superimposed events that have complex waveforms, previous techniques are not applicable for detection of evoked synaptic events in photostimulation and other similar experimental situations. Here we report on a novel technique that combines the design of a bank of approximate matched filters with the detection and estimation theory to automatically detect and extract photostimluation-evoked excitatory postsynaptic currents (EPSCs) from individually recorded neurons in cortical circuit mapping experiments. The sensitivity and specificity of the method were evaluated on both simulated and experimental data, with its performance comparable to that of visual event detection performed by human operators. This new technique was applied to quantify and compare the EPSCs obtained from excitatory pyramidal cells and fast-spiking interneurons. In addition, our technique has been further applied to the detection and analysis of inhibitory postsynaptic current (IPSC) responses. Given the general purpose of our matched filtering and signal recognition algorithms, we expect that our technique can be appropriately modified and applied to detect and extract other types of electrophysiological and optical imaging signals.

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Extension of the method to the detection of IPSCs with matched filters.A and B are the original and sign-inverted IPSC response traces, respectively, which were from a layer 5 pyramidal neuron in the prelimbic area of mouse prefrontal cortex. The small red circles in A and B indicate the location of the recorded cell body. C and D are the illustration of matched-filtering detection of inverted IPSCs, reminiscent of EPSC detection (See Figure 2). The data traces for C and D are from the map sites indicated by the red and blue stars in B, respectively. The black traces are raw signals and the blue one shown in C is a high-pass filtered signal trace. In C and D, five exemplary convolution traces (green) produced with five matched filters (purple) are shown. The original EPSC templates (blue) used to synthesize the matched filters are also shown next to the convolution traces. The data trace in C has one large direct response, superimposed with two IPSCs that are color coded and individually extracted (shown below the original trace with the crosses indicating the event peaks), while the data trace in D contains three IPSC events (color coded and individually extracted, shown below the original trace). See Figure 2 for other conventions. E, F and G are the color-coded maps of average input amplitude, the IPSC numbers, and the first detected IPSC latency per site, respectively, for the raw data map shown in A. The small white circles indicate the location of the recorded cell body. L: lateral; V: ventral. The average input amplitude in each stimulation site is the mean amplitude of IPSCs in the response analysis window, with the baseline spontaneous response subtracted from the photostimulation response of the same site. The number of IPSCs and the arrival time or latency of the first detected IPSC per site are also measured and plotted.
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pone-0015517-g008: Extension of the method to the detection of IPSCs with matched filters.A and B are the original and sign-inverted IPSC response traces, respectively, which were from a layer 5 pyramidal neuron in the prelimbic area of mouse prefrontal cortex. The small red circles in A and B indicate the location of the recorded cell body. C and D are the illustration of matched-filtering detection of inverted IPSCs, reminiscent of EPSC detection (See Figure 2). The data traces for C and D are from the map sites indicated by the red and blue stars in B, respectively. The black traces are raw signals and the blue one shown in C is a high-pass filtered signal trace. In C and D, five exemplary convolution traces (green) produced with five matched filters (purple) are shown. The original EPSC templates (blue) used to synthesize the matched filters are also shown next to the convolution traces. The data trace in C has one large direct response, superimposed with two IPSCs that are color coded and individually extracted (shown below the original trace with the crosses indicating the event peaks), while the data trace in D contains three IPSC events (color coded and individually extracted, shown below the original trace). See Figure 2 for other conventions. E, F and G are the color-coded maps of average input amplitude, the IPSC numbers, and the first detected IPSC latency per site, respectively, for the raw data map shown in A. The small white circles indicate the location of the recorded cell body. L: lateral; V: ventral. The average input amplitude in each stimulation site is the mean amplitude of IPSCs in the response analysis window, with the baseline spontaneous response subtracted from the photostimulation response of the same site. The number of IPSCs and the arrival time or latency of the first detected IPSC per site are also measured and plotted.

Mentions: Given the general applicability of our matched filter detection and extraction algorithms, our method can be appropriately modified and further applied to detection and extraction of other types of electrophysiological signals. For example, the technique has been easily modified to accommodate detection and analysis of inhibitory postsynaptic current (IPSC) responses. As illustrated in Figure 8A and B, for the IPSC detection, we first inverted the sign of IPSC responses, so the outward IPSC responses turned into EPSC-like inward responses. Note that compared to EPSCs, inverted IPSCs tend to have different waveforms with longer response durations (see Figures 1 and 8). As done in EPSC detection, the bank of matched filters was then generated based upon the inverted IPSCs and automated detection was applied for IPSC map data analysis and plotting (Figure 8C–G). Similar to EPSC detection, our method achieved excellent performance in IPSC detection across datasets.


Novel use of matched filtering for synaptic event detection and extraction.

Shi Y, Nenadic Z, Xu X - PLoS ONE (2010)

Extension of the method to the detection of IPSCs with matched filters.A and B are the original and sign-inverted IPSC response traces, respectively, which were from a layer 5 pyramidal neuron in the prelimbic area of mouse prefrontal cortex. The small red circles in A and B indicate the location of the recorded cell body. C and D are the illustration of matched-filtering detection of inverted IPSCs, reminiscent of EPSC detection (See Figure 2). The data traces for C and D are from the map sites indicated by the red and blue stars in B, respectively. The black traces are raw signals and the blue one shown in C is a high-pass filtered signal trace. In C and D, five exemplary convolution traces (green) produced with five matched filters (purple) are shown. The original EPSC templates (blue) used to synthesize the matched filters are also shown next to the convolution traces. The data trace in C has one large direct response, superimposed with two IPSCs that are color coded and individually extracted (shown below the original trace with the crosses indicating the event peaks), while the data trace in D contains three IPSC events (color coded and individually extracted, shown below the original trace). See Figure 2 for other conventions. E, F and G are the color-coded maps of average input amplitude, the IPSC numbers, and the first detected IPSC latency per site, respectively, for the raw data map shown in A. The small white circles indicate the location of the recorded cell body. L: lateral; V: ventral. The average input amplitude in each stimulation site is the mean amplitude of IPSCs in the response analysis window, with the baseline spontaneous response subtracted from the photostimulation response of the same site. The number of IPSCs and the arrival time or latency of the first detected IPSC per site are also measured and plotted.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2991367&req=5

pone-0015517-g008: Extension of the method to the detection of IPSCs with matched filters.A and B are the original and sign-inverted IPSC response traces, respectively, which were from a layer 5 pyramidal neuron in the prelimbic area of mouse prefrontal cortex. The small red circles in A and B indicate the location of the recorded cell body. C and D are the illustration of matched-filtering detection of inverted IPSCs, reminiscent of EPSC detection (See Figure 2). The data traces for C and D are from the map sites indicated by the red and blue stars in B, respectively. The black traces are raw signals and the blue one shown in C is a high-pass filtered signal trace. In C and D, five exemplary convolution traces (green) produced with five matched filters (purple) are shown. The original EPSC templates (blue) used to synthesize the matched filters are also shown next to the convolution traces. The data trace in C has one large direct response, superimposed with two IPSCs that are color coded and individually extracted (shown below the original trace with the crosses indicating the event peaks), while the data trace in D contains three IPSC events (color coded and individually extracted, shown below the original trace). See Figure 2 for other conventions. E, F and G are the color-coded maps of average input amplitude, the IPSC numbers, and the first detected IPSC latency per site, respectively, for the raw data map shown in A. The small white circles indicate the location of the recorded cell body. L: lateral; V: ventral. The average input amplitude in each stimulation site is the mean amplitude of IPSCs in the response analysis window, with the baseline spontaneous response subtracted from the photostimulation response of the same site. The number of IPSCs and the arrival time or latency of the first detected IPSC per site are also measured and plotted.
Mentions: Given the general applicability of our matched filter detection and extraction algorithms, our method can be appropriately modified and further applied to detection and extraction of other types of electrophysiological signals. For example, the technique has been easily modified to accommodate detection and analysis of inhibitory postsynaptic current (IPSC) responses. As illustrated in Figure 8A and B, for the IPSC detection, we first inverted the sign of IPSC responses, so the outward IPSC responses turned into EPSC-like inward responses. Note that compared to EPSCs, inverted IPSCs tend to have different waveforms with longer response durations (see Figures 1 and 8). As done in EPSC detection, the bank of matched filters was then generated based upon the inverted IPSCs and automated detection was applied for IPSC map data analysis and plotting (Figure 8C–G). Similar to EPSC detection, our method achieved excellent performance in IPSC detection across datasets.

Bottom Line: This new technique was applied to quantify and compare the EPSCs obtained from excitatory pyramidal cells and fast-spiking interneurons.In addition, our technique has been further applied to the detection and analysis of inhibitory postsynaptic current (IPSC) responses.Given the general purpose of our matched filtering and signal recognition algorithms, we expect that our technique can be appropriately modified and applied to detect and extract other types of electrophysiological and optical imaging signals.

View Article: PubMed Central - PubMed

Affiliation: Department of Anatomy and Neurobiology, School of Medicine, University of California Irvine, Irvine, California, USA.

ABSTRACT
Efficient and dependable methods for detection and measurement of synaptic events are important for studies of synaptic physiology and neuronal circuit connectivity. As the published methods with detection algorithms based upon amplitude thresholding and fixed or scaled template comparisons are of limited utility for detection of signals with variable amplitudes and superimposed events that have complex waveforms, previous techniques are not applicable for detection of evoked synaptic events in photostimulation and other similar experimental situations. Here we report on a novel technique that combines the design of a bank of approximate matched filters with the detection and estimation theory to automatically detect and extract photostimluation-evoked excitatory postsynaptic currents (EPSCs) from individually recorded neurons in cortical circuit mapping experiments. The sensitivity and specificity of the method were evaluated on both simulated and experimental data, with its performance comparable to that of visual event detection performed by human operators. This new technique was applied to quantify and compare the EPSCs obtained from excitatory pyramidal cells and fast-spiking interneurons. In addition, our technique has been further applied to the detection and analysis of inhibitory postsynaptic current (IPSC) responses. Given the general purpose of our matched filtering and signal recognition algorithms, we expect that our technique can be appropriately modified and applied to detect and extract other types of electrophysiological and optical imaging signals.

Show MeSH