Benchmarking spike rate inference in population calcium imaging

A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy fluorescence traces. We systematically evaluate different spike inference algorithms on a large benchmark dataset (>100,000 spikes) recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCaMP6). In addition, we introduce a new algorithm based on supervised learning in flexible probabilistic models and find that it performs better than other published techniques. Importantly, it outperforms other algorithms even when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can be used to further improve the spike prediction accuracy and generalization performance of the model. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmarking different methods with real-world datasets may greatly facilitate future algorithmic developments in neuroscience

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience

Review of the Physiology and Anesthetic Considerations for Pleuroscopy/Medical Thoracoscopy

Pleuroscopy or medical thoracoscopy is the second most common utilized procedure after bronchoscopy in the promising field of interventional pulmonology. Its main application is for the diagnosis and management of benign or malignant pleural effusions. Entry into the hemithorax is associated with pain and patient discomfort, whereas concurrently, notable pathophysiologic alterations occur. Therefore, frequently procedural sedation and analgesia is needed, not only to alleviate the patient's emotional stress and discomfort by mitigating the anxiety and minimizing the pain but also for yielding better procedural conditions for the operator. The scope of this review is to present the physiologic derangements occurring in pleuroscopy and compare the various anesthetic techniques and sedative agents that are currently being used in this context

Pupil Fluctuations Track Fast Switching of Cortical States during Quiet Wakefulness

Neural responses are modulated by brain state, which varies with arousal, attention, and behavior. In mice, running and whisking desynchronize the cortex and enhance sensory responses, but the quiescent periods between bouts of exploratory behaviors have not been well studied. We found that these periods of "quiet wakefulness" were characterized by state fluctuations on a timescale of 1-2 s. Small fluctuations in pupil diameter tracked these state transitions in multiple cortical areas. During dilation, the intracellular membrane potential was desynchronized, sensory responses were enhanced, and population activity was less correlated. In contrast, constriction was characterized by increased low-frequency oscillations and higher ensemble correlations. Specific subtypes of cortical interneurons were differentially activated during dilation and constriction, consistent with their participation in the observed state changes. Pupillometry has been used to index attention and mental effort in humans, but the intracellular dynamics and differences in population activity underlying this phenomenon were previously unknown

Performance of estimator <i>C</i>_{sparse+latent} expressed as validation loss (eq. 10) relative to the other estimators: <i>C</i>_sample, <i>C</i>_diag, <i>C</i>_factor, and <i>C</i>_sparse.

<p>Covariance estimators <i>C</i>_sample, <i>C</i>_diag, <i>C</i>_factor, and <i>C</i>_sparse produced consistently greater validation losses than <i>C</i>_{sparse+latent} (<i>p</i> < 0.01 in each comparison, Wilcoxon signed rank test, <i>n</i> = 27 sites in 14 mice). The box plots indicate the 25^<i>th</i>, 50^<i>th</i>, and 75^<i>th</i> percentiles with the whiskers extending to the minimum and maximum values after excluding the outliers marked with ‘+’.</p

Acquisition of neural signals for the estimation of noise correlations.

<p>Visual stimuli comprising full-field drifting gratings interleaved with blank screens (<b>A</b>) presented during two-photon recordings of somatic calcium signals using fast 3D random-access microscopy (<b>B</b>). <b>C–F</b>. Calcium activity data from an example site. <b>C</b>. Representative calcium signals of seven cells, downsampled to 20 Hz, out of the 292 total recorded cells. Spiking activity inferred by nonnegative deconvolution is shown by red ticks below the trace. <b>D</b>. The spatial arrangement and orientation tuning of the 292 cells from the imaged site. The cells’ colors indicate their orientation preferences. The gray cells were not significantly tuned. <b>E</b>. The sample noise correlation matrix of the activity of the neural population. <b>F</b>. Histogram of noise correlation coefficients in one site. The red line indicates the mean correlation coefficient of 0.020.</p

Performance of covariance estimators on samples drawn from Ising models.

<p><b>A–D</b> Validation losses of covariance matrix estimators relative to the estimator whose structure matches the ground truth. The calculation is performed identically to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004083#pcbi.1004083.g001" target="_blank">Fig. 1</a> Row 6 except Ising models are used as ground truth.</p

Properties of <i>C</i>_{sparse+latent} estimates from all imaged sites.

<div><p>Each point represents an imaged site with its color indicating the population size as shown in panels A and B. The example site from Figs. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004083#pcbi.1004083.g003" target="_blank">3</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004083#pcbi.1004083.g005" target="_blank">5</a> is circled in blue.</p> <p><b>A.</b> The number of inferred latent units <i>vs</i>. population size. <b>B.</b> The connectivity of the sparse component of partial correlations as a function of population size. <b>C.</b> The average sample correlations <i>vs</i>. the average partial correlations (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004083#pcbi.1004083.e007" target="_blank">Eq. 4</a>) of the <i>C</i>_{sparse+latent} estimate. <b>D.</b> The percentage of negative interactions vs. connectivity in the <i>C</i>_{sparse+latent} estimates.</p></div