Neural cytoarchitecture is heterogeneous, varying both across and within brain regions. The consistent identification of regions of interest is one of the most critical aspects in examining neurocircuitry, as these structures serve as the vital landmarks with which to map brain pathways. Access to continuous, three-dimensional volumes that span multiple brain areas not only provides richer context for identifying such landmarks, but also enables a deeper probing of the microstructures within. Here, we describe a three-dimensional X-ray microtomography imaging dataset of a well-known and validated thalamocortical sample, encompassing a range of cortical and subcortical structures. In doing so, we provide the field with access to a micron-scale anatomical imaging dataset ideal for studying heterogeneity of neural structure. Background and SummaryWhether focusing on a large swath of cortex or a single subcortical nucleus, consistent and reliable visualization of cytoarchitecture is critical for the creation of reference points which demarcate the brain's landscape [1]. This is true not only for the identification of landmarks (or regions of interest), but also the study of local circuits therein. Indeed, it is the distinguishing features in brain cytoarchitecture which arise at small, local scales (i.e., through clusters of cells which are packed in discrete barrels or layers; see [2,3]) that continue to emerge across larger spatial scales to reveal the presence of functionally distinct regions. Thus, detailed views into the brain's architecture can be used to experimentally manipulate circuits, and to advance the field's understanding and integration of each of these overarching systems.With advances in the reconstruction and analysis of significantly larger brain volumes, neuroscientists are now able to visualize patterns of microarchitecture that arise at a scale previously inaccessible using traditional methods [4,5,6]. Examples such as CLARITY [7], expansion microscopy [8], serial two photon tomography [9, 10, 11], multi-beam scanning electron microscopy [12], and X-ray microtomography [13,14,15,16,17], now provide access to several regions of interest within a volume of tissue simultaneously, providing rich context to study both local circuitry and long-range projections. With many of these new techniques, it is possible to image and analyze large intact anatomical samples that preserve the connectivity between multiple regions of interest [18,19], thus providing a lens into the heterogeneity of neural structure within and across different brain areas.Here, we introduce a three-dimensional neuroanatomical dataset extracted from a validated, in-vitro mouse thalamocortical sample spanning six anatomically distinct regions of interest (somatosensory cortex, two thalamic nuclei, zona incerta, striatum and hypothalamus) [18]. This dataset was reconstructed using X-ray microtomography to reveal a diverse composition of microstructures (e.g., myelinated axons, cell bodies, and vasculature) within each region at isotr...
Neural microarchitecture is heterogeneous, varying both across and within brain regions. The consistent identification of regions of interest is one of the most critical aspects in examining neurocircuitry, as these structures serve as the vital landmarks with which to map brain pathways. Access to continuous, three-dimensional volumes that span multiple brain areas not only provides richer context for identifying such landmarks, but also enables a deeper probing of the microstructures within. Here, we describe a three-dimensional X-ray microtomography imaging dataset of a well-known and validated thalamocortical sample, encompassing a range of cortical and subcortical structures from the mouse brain . In doing so, we provide the field with access to a micron-scale anatomical imaging dataset ideal for studying heterogeneity of neural structure.
Methods for resolving the brain's microstructure are rapidly improving, allowing us to image large brain volumes at high resolutions. As a result, the interrogation of samples spanning multiple diversified brain regions is becoming increasingly common. Understanding these samples often requires multiscale processing: segmentation of the detailed microstructure and large-scale modelling of the macrostructure. Current brain mapping algorithms often analyze data only at a single scale, and optimization for each scale occurs independently, potentially limiting the consistency, performance, and interpretability. In this work we introduce a deep learning framework for segmentation of brain structure at multiple scales. We leverage a modified U-Net architecture with a multi-task learning objective and unsupervised pre-training to simultaneously model both the micro and macro architecture of the brain. We successfully apply our methods to a heterogeneous, three-dimensional, X-ray micro-CT dataset spanning multiple regions in the mouse brain, and show that our approach consistently outperforms another multi-task architecture, and is competitive with strong single-task baselines at both scales.
Most existing black-box optimization methods assume that all variables in the system being optimized have equal cost and can change freely at each iteration. However, in many real world systems, inputs are passed through a sequence of different operations or modules, making variables in earlier stages of processing more costly to update. Such structure imposes a cost on switching variables in early parts of a data processing pipeline. In this work, we propose a new algorithm for switch cost-aware optimization called Lazy Modular Bayesian Optimization (LaMBO). This method efficiently identifies the global optimum while minimizing cost through a passive change of variables in early modules. The method is theoretical grounded and achieves vanishing regret when augmented with switching cost. We apply LaMBO to multiple synthetic functions and a three-stage image segmentation pipeline used in a neuroscience application, where we obtain promising improvements over prevailing cost-aware Bayesian optimization algorithms. Our results demonstrate that LaMBO is an effective strategy for black-box optimization that is capable of minimizing switching costs in modular systems.
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