SFDI biomarkers provide a quantitative ulcer risk metric and can be used to predict diabetic foot ulcer onset.

AIMS: Annually, up to 4% of people with diabetes present with a chronic foot ulcer. Quantitative real-time testing to identify patients at risk for ulceration can guide preventative care. Here, we assess whether a non-invasive optical imaging technique, Spatial Frequency Domain Imaging (SFDI), can identify patients at the highest risk for ulceration and predict ulcer onset. METHODS: We imaged 252 subjects with diabetes at Kaiser Permanente, Southern California. SFDI derived tissue biomarkers of microcirculation were compared between subjects with and without a history of ulceration, and subjects who did or did not develop ulcers after 1 year. RESULTS: Feet of subjects at the highest risk (i.e. history of ulceration) had significantly lower hemoglobin in the papillary dermis (HbT1), along with higher oxygenation (StO2) due to poor extraction. These subjects also had more homogeneous hemoglobin spread in the reticular dermis (HbT2) and tissue scattering (related to skin structure). Prediction based on HbT1 and tissue scattering identified new ulcerations and performed with sensitivity/specificity of 68.8%/64.8% and 75.0%/69.1%, respectively. CONCLUSION: These results show that SFDI hemoglobin distribution and oxygenation biomarkers provide a quantitative basis for ulcer risk stratification and ulcer onset prediction.

GABAergic inhibition reduces the impact of synaptic excitation on somatic excitation.

The effect of excitatory synaptic input on the excitation of the cell body is believed to vary depending on where and when the synaptic activation occurs in dendritic trees and the spatiotemporal modulation by inhibitory synaptic input. However, few studies have examined how individual synaptic inputs influence the excitability of the cell body in spontaneously active neuronal networks mainly because of the lack of an appropriate method. We developed a calcium imaging technique that monitors synaptic inputs to hundreds of spines from a single neuron with millisecond resolution in combination with whole-cell patch-clamp recordings of somatic excitation. In rat hippocampal CA3 pyramidal neurons ex vivo, a fraction of the excitatory synaptic inputs were not detectable in the cell body against background noise. These synaptic inputs partially restored their somatic impact when a GABAA receptor blocker was intracellularly perfused. Thus, GABAergic inhibition reduces the influence of some excitatory synaptic inputs on the somatic excitability. Numerical simulation using a single neuron model demonstrates that the timing and locus of a dendritic GABAergic input are critical to exert this effect. Moreover, logistic regression analyses suggest that the GABAergic inputs sectionalize spine activity; that is, only some subsets of synchronous synaptic activity seemed to be preferably passed to the cell body. Thus, dendrites actively sift inputs from specific presynaptic cell assemblies.

Automated detection and analysis of depolarization events in human cardiomyocytes using MaDEC.

Optical imaging-based methods for assessing the membrane electrophysiology of in vitro human cardiac cells allow for non-invasive temporal assessment of the effect of drugs and other stimuli. Automated methods for detecting and analyzing the depolarization events (DEs) in image-based data allow quantitative assessment of these different treatments. In this study, we use 2-photon microscopy of fluorescent voltage-sensitive dyes (VSDs) to capture the membrane voltage of actively beating human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). We built a custom and freely available Matlab software, called MaDEC, to detect, quantify, and compare DEs of hiPS-CMs treated with the ß-adrenergic drugs, propranolol and isoproterenol. The efficacy of our software is quantified by comparing detection results against manual DE detection by expert analysts, and comparing DE analysis results to known drug-induced electrophysiological effects. The software accurately detected DEs with true positive rates of 98-100% and false positive rates of 1-2%, at signal-to-noise ratios (SNRs) of 5 and above. The MaDEC software was also able to distinguish control DEs from drug-treated DEs both immediately as well as 10min after drug administration.

Accurate detection of low signal-to-noise ratio neuronal calcium transient waves using a matched filter.

BACKGROUND: Calcium imaging has become a fundamental modality for studying neuronal circuit dynamics both in vitro and in vivo. However, identifying calcium events (CEs) from spectral data remains laborious and difficult, especially since the signal-to-noise ratio (SNR) often falls below 2. Existing automated signal detection methods are generally applied at high SNRs, leaving a large need for an automated algorithm that can accurately extract CEs from fluorescence intensity data of SNR 2 and below. NEW METHOD: In this work we develop a Matched filter for Multi-unit Calcium Event (MMiCE) detection to extract CEs from fluorescence intensity traces of simulated and experimentally recorded neuronal calcium imaging data. RESULTS: MMiCE reached perfect performance on simulated data with SNR ≥ 2 and a true positive (TP) rate of 98.27% (± 1.38% with a 95% confidence interval), and a false positive(FP) rate of 6.59% (± 2.56%) on simulated data with SNR 0.2. On real data, verified by patch-clamp recording, MMiCE performed with a TP rate of 100.00% (± 0.00) and a FP rate of 2.04% (± 4.10). COMPARISON WITH EXISTING METHOD(S): This high level of performance exceeds existing methods at SNRs as low as 0.2, which are well below those used in previous studies (SNR ≃ 5-10). CONCLUSION: Overall, the MMiCE detector performed exceptionally well on both simulated data, and experimentally recorded neuronal calcium imaging data. The MMiCE detector is accurate, reliable, well suited for wide-spread use, and freely available at sites.uci.edu/aggies or from the corresponding author.

Intrinsic dimensionality of extracellular action potentials.

Linear approaches to low-dimensional feature extraction may not be appropriate when statistical data are generated by a nonlinear interaction of parameters. Equally inadequate are linear methods for determining the dimension of the feature space. This article estimates the intrinsic dimension of extracellular action potentials (EAPs), which can be viewed as the minimum number of nonlinearly interacting parameters sufficient to describe the data. When combined with nonlinear feature extraction methods, this information may lead to a more faithful, low-dimensional EAP representation. These points are demonstrated using EAPs recorded experimentally by a multisensor electrode.

A supervised multi-sensor matched filter for the detection of extracellular action potentials.

Multi-sensor extracellular recording takes advantage of several electrode channels to record from multiple neurons at the same time. However, the resulting low signal-to-noise ratio (SNR) combined with biological noise makes signal detection, the first step of any neurophysiological data analysis, difficult. A matched filter was therefore designed to better detect extracellular action potentials (EAPs) from multi-sensor extracellular recordings. The detector was tested on tetrode data from a locust antennal lobe and assessed against three trained analysts. 25 EAPs and noise samples were selected manually from the data and used for training. To reduce complexity, the filter assumed that the underlying noise in the data was spatially white. The detector performed with an average TP and FP rate of 84.62% and 16.63% respectively. This high level of performance indicates the algorithm is suitable for widespread use.