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BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings. This resource describes a collection of neurons from a variety of light microscopy-based datasets, which can serve as a gold standard for testing automated tracing algorithms, as shown by comparison of the performance of 35 algorithms.

Songs of the Bengalese finch consist of variable sequences of syllables. The sequences follow probabilistic rules, and can be statistically described by partially observable Markov models (POMMs), which consist of states and probabilistic transitions between them. Each state is associated with a syllable, and one syllable can be associated with multiple states. This multiplicity of syllable to states association distinguishes a POMM from a simple Markov model, in which one syllable is associated with one state. The multiplicity indicates that syllable transitions are context-dependent. Here we present a novel method of inferring a POMM with minimal number of states from a finite number of observed sequences. We apply the method to infer POMMs for songs of six adult male Bengalese finches before and shortly after deafening. Before deafening, the models all require multiple states, but with varying degrees of state multiplicity for individual birds. Deafening reduces the state multiplicity for all birds. For three birds, the models become Markovian, while for the other three, the multiplicity persists for some syllables. These observations indicate that auditory feedback contributes to, but is not the only source of, the context dependencies of syllable transitions in Bengalese finch song.Author SummaryContext dependencies are widely observed in animal behaviors. We devise a novel statistical method for uncovering context dependencies in behavioral sequences. Application of the method to songs of the Bengalese finch before and shortly after deafening reveals that auditory feedback contributes significantly to context dependencies, but is not the only source. Our approach can be applied to many other behavioral sequences and aid the discovery of the underlying neural mechanisms for context dependencies.

A common challenge in neuroscience is how to decompose noisy, multi-source signals measured in experiments into biophysically interpretable components. Analysis of cortical surface electrical potentials (CSEPs) measured using electrocorticography arrays (ECoG) typifies this problem. We hypothesized that high frequency (70-1,000 Hz) CSEPs are composed of broadband (i.e., power-law) and bandlimited components with potentially differing biophysical origins. In particular, the high-gamma band (70-150 Hz) has been shown to be highly predictive for encoding and decoding behaviors and stimuli. Despite its demonstrated importance, whether high-gamma is composed of a bandlimited signal is poorly understood. To address this gap, we recorded CSEPs from rat auditory cortex and demonstrate that the evoked CSEPs are composed of multiple distinct frequency components, including high-gamma. We then show, using a novel robust regression method, that at fast timescales and on single trials during speech production, human high-gamma amplitude cannot be explained by a modulating power-law component; thus, high-gamma is band-limited. Furthermore, we show that the power-law component is less predictive of produced speech compared to the raw high-gamma amplitude. Finally, we show that the largest variance component of human ECoG signals is low-frequency and band-limited, not broadband. Together these results demonstrate that there are multiple, band-limited components of high frequency power in cortical surface electrical potentials, including the high-gamma band, which may have different biophysical origins.Significance StatementElectrocortigraphy (ECoG) records cortical surface electrical potentials (CSEPs). ECoG is utilized in both humans and animal models to understand distributed cortical processing and for brain machine interfaces. The spectral structure of evoked CSEPs is greatly debated. To address this issue, we recorded from rat auditory cortex using µECoG and human ventral sensory-motor cortex during speech production with high-density ECoG. Using novel analytic approaches, we found that evoked CSEPs are composed of multiple band-limited components, including high-gamma. These results contrast with the dominant thinking in the field that the high-frequency power in ECoG is broadband. Our results raise the possibility that distinct frequency components of ECoG are biomarkers of processing in different cortical layers.

BigNeuron is an open community bench-testing platform combining the expertise of neuroscientists and computer scientists toward the goal of setting open standards for accurate and fast automatic neuron reconstruction. The project gathered a diverse set of image volumes across several species representative of the data obtained in most neuroscience laboratories interested in neuron reconstruction. Here we report generated gold standard manual annotations for a selected subset of the available imaging datasets and quantified reconstruction quality for 35 automatic reconstruction algorithms. Together with image quality features, the data were pooled in an interactive web application that allows users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and reconstruction data, and benchmarking of automatic reconstruction algorithms in user-defined data subsets. Our results show that image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. By benchmarking automatic reconstruction algorithms, we observed that diverse algorithms can provide complementary information toward obtaining accurate results and developed a novel algorithm to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms. Finally, to aid users in predicting the most accurate automatic reconstruction results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic reconstructions.