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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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Detection performance evaluation on simulated data by using Receiver Operating Characteristic (ROC) curve analysis.The horizontal axis shows the probability of false alarm (Pfa), and the vertical axis shows the probability of correct detection (Pcd). Each ROC curve represents the software PSC detection performance at a fixed SNR (3, 6 or 9) with different detection thresholds. The detection thresholds ranges from −2σ to 3σ from the mean of the maximal template convolution values. The ROC curve for each combination of the detection threshold and SNR was calculated by averaging the performance over 200 trials.
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pone-0015517-g004: Detection performance evaluation on simulated data by using Receiver Operating Characteristic (ROC) curve analysis.The horizontal axis shows the probability of false alarm (Pfa), and the vertical axis shows the probability of correct detection (Pcd). Each ROC curve represents the software PSC detection performance at a fixed SNR (3, 6 or 9) with different detection thresholds. The detection thresholds ranges from −2σ to 3σ from the mean of the maximal template convolution values. The ROC curve for each combination of the detection threshold and SNR was calculated by averaging the performance over 200 trials.

Mentions: Our technique was tested under different SNR and detection threshold scenarios. To ensure statistically meaningful results, for each SNR value, 200 independent Monte Carlo trials were performed, and the technique was applied by varying the detection threshold values between and (in increments of 1σ), where μ and σ are the mean and standard deviation of the maximum convolution value cmax obtained in the training stage. Based on the detection results, Pfa and Pcd were calculated by averaging over trials, and plotted as receiver operating characteristic (ROC) curves in Figure 4. In all ROC curves, false alarms and correct detection are traded off at varying threshold values. Depending on the cost associated with omission and false alarm errors, the optimal detection threshold can be set. At low SNRs, the ROC curves are more spread for the detection thresholds chosen around the mean, μ, indicating higher sensitivity to the choice of threshold. Conversely, at SNR≥9, a situation likely to be found in actual recordings, the choice of threshold is less critical, as performances tend to cluster around the optimal point (Pfa = 0, Pcd = 1).


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

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

Detection performance evaluation on simulated data by using Receiver Operating Characteristic (ROC) curve analysis.The horizontal axis shows the probability of false alarm (Pfa), and the vertical axis shows the probability of correct detection (Pcd). Each ROC curve represents the software PSC detection performance at a fixed SNR (3, 6 or 9) with different detection thresholds. The detection thresholds ranges from −2σ to 3σ from the mean of the maximal template convolution values. The ROC curve for each combination of the detection threshold and SNR was calculated by averaging the performance over 200 trials.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2991367&req=5

pone-0015517-g004: Detection performance evaluation on simulated data by using Receiver Operating Characteristic (ROC) curve analysis.The horizontal axis shows the probability of false alarm (Pfa), and the vertical axis shows the probability of correct detection (Pcd). Each ROC curve represents the software PSC detection performance at a fixed SNR (3, 6 or 9) with different detection thresholds. The detection thresholds ranges from −2σ to 3σ from the mean of the maximal template convolution values. The ROC curve for each combination of the detection threshold and SNR was calculated by averaging the performance over 200 trials.
Mentions: Our technique was tested under different SNR and detection threshold scenarios. To ensure statistically meaningful results, for each SNR value, 200 independent Monte Carlo trials were performed, and the technique was applied by varying the detection threshold values between and (in increments of 1σ), where μ and σ are the mean and standard deviation of the maximum convolution value cmax obtained in the training stage. Based on the detection results, Pfa and Pcd were calculated by averaging over trials, and plotted as receiver operating characteristic (ROC) curves in Figure 4. In all ROC curves, false alarms and correct detection are traded off at varying threshold values. Depending on the cost associated with omission and false alarm errors, the optimal detection threshold can be set. At low SNRs, the ROC curves are more spread for the detection thresholds chosen around the mean, μ, indicating higher sensitivity to the choice of threshold. Conversely, at SNR≥9, a situation likely to be found in actual recordings, the choice of threshold is less critical, as performances tend to cluster around the optimal point (Pfa = 0, Pcd = 1).

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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