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

Superior Neuroprotective Efficacy of LAU-0901, a Novel Platelet-Activating Factor Antagonist, in Experimental Stroke

Platelet-activating factor (PAF) accumulates during cerebral ischemia, and inhibition of this process plays a critical role in neuronal survival. Recently, we demonstrated that LAU-0901, a novel PAF receptor antagonist, is neuroprotective in experimental stroke. We used magnetic resonance imaging in conjunction with behavior and immunohistopathology to expand our understanding of this novel therapeutic approach. Sprague–Dawley rats received 2 h middle cerebral artery occlusion (MCAo) and were treated with LAU-0901 (60 mg/kg) or vehicle 2 h from MCAo onset. Behavioral function, T2-weighted imaging (T2WI), and apparent diffusion coefficients were performed on days 1, 3, and 7 after MCAo. Infarct volume and number of GFAP, ED-1, and NeuN-positive cells were conducted on day 7. Behavioral deficit was significantly improved by LAU-0901 treatment compared to vehicle on days 1, 3, and 7. Total lesion volumes computed from T2WI were significantly reduced by LAU-0901 on days 1, 3, and 7 (by 83%, 90%, and 96%, respectively), which was consistent with decreased edema formation. Histopathology revealed that LAU-0901 treatment resulted in significant reduction of cortical and subcortical infarct volumes, attenuated microglial infiltration, and promoted astrocytic and neuronal survival. These findings suggest LAU-0901 is a promising neuroprotectant and provide the basis for future therapeutics in patients suffering ischemic stroke

Top algorithms make highly correlated predictions.

<p><b>A.-B.</b> Example cells from the test set for dataset 1 (OGB-1) and dataset 3 (GCaMP6s) show highly similar predictions between most algorithms. <b>C.</b> Average correlation coefficients between predictions of different algorithms across all cells in the test set at 25 Hz (40 ms bins).</p

Overview over datasets with training and test data used in the competition.

<p>Overview over datasets with training and test data used in the competition.</p

Different spike inference metrics reach similar conclusions.

<p><b>A.</b> Area under the curve (AUC) of the inferred spike rate used as a binary predictor for the presence of spikes (evaluated at 25 Hz, 50 ms bins) on the test set. Colors indicate different datasets. Black dots are mean correlation coefficients across all <i>N</i> = 32 cells in the test set. Colored dots are jittered for better visibility. STM: Spike-triggered mixture model [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006157#pcbi.1006157.ref015" target="_blank">15</a>]; f-oopsi: fast non-negative deconvolution [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006157#pcbi.1006157.ref009" target="_blank">9</a>] <b>B.</b> Information gain of the inferred spike rate about the true spike rate on the test set (evaluated at 25 Hz, 40 ms bins).</p

Overview over different strategies used by DNN-based algorithms.

<p>Architecture briefly summarizes main components. conv: convolutional layers, typically with non-linearity; lstm: recurrent long-short-term memory unit; residual: residual blocks; max: max-pooling layers; inception: inception cells. For details, refer to the descriptions of the algorithms in the supplementary material.</p

Summary of algorithm performance.

<p>Δ correlation is computed as the mean difference in correlation coefficient compared to the STM algorithm. Δ var. exp. in % is computed as the mean relative improvement variance explained (<i>r</i>²). Note that since variance explained is a nonlinear function of correlation, algorithms can be ranked differently according to the two measures. All means are taken over <i>N</i> = 32 recordings in the test set, except for training correlation, which is computed over <i>N</i> = 60 recordings in the training set.</p