Kinnison, J. | bioRxiv Science

bioRxiv2018

Flexible Learning-Free Segmentation and Reconstruction for Sparse Neuronal Circuit Tracing

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically constructing more complete and realistic circuits. In practice, such algorithms are often too error-prone and computationally expensive to be immediately useful. We address these shortcomings with a new fast, flexible, learning-free method for sparse segmentation and reconstruction of neural volumes. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (CT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipelines reconstructions, which are of sufficient quality for preliminary biological study. Compared to existing supervised learning methods, it is both significantly faster (up to several orders of magnitude) and produces high-quality reconstructions that are robust to noise and artifacts.

neuroscience↗

bioRxiv2017

Intersubject brain network organization during dynamic anxious anticipation

How do large-scale brain networks reorganize during the waxing and waning of anxious anticipation? Here, threat was dynamically modulated during functional MRI as two circles slowly meandered on the screen; if they touched, an unpleasant shock was delivered. We employed intersubject network analysis, which allows the investigation of network-level properties \"across brains,\" and sought to determine how network properties changed during periods of approach (circles moving closer) and periods of retreat (circles moving apart). Dynamic threat altered network cohesion across the salience, executive, and task-negative networks, as well as subcortical regions. Functional connections between subcortical regions and the salience network also increased during approach vs. retreat, including the putative periaqueductal gray, habenula, and amygdala, showing that the latter is involved under conditions of relatively prolonged and uncertain threat (the bed nucleus of the stria terminalis was observed during both approach and retreat). Together, our findings unraveled dynamic properties of large-scale networks across participants while threat levels varied continuously, and demonstrate the potential of characterizing emotional processing at the level of distributed networks.\n\nSignificance StatementUnderstanding the brain basis of anxious anticipation is important not only from a basic research perspective, but because aberrant responding to uncertain future negative events is believed to be central to anxiety disorders. Although previous studies have investigated how brain responses are sensitive to threat proximity, little is known about how patterns of response co-activation change during dynamic manipulations of threat. To address these important gaps in the literature, we studied the dynamics of emotional processing at the level of large-scale brain networks by devising a manipulation in which threat was dynamically modulated during functional MRI scanning.

neuroscience↗