A deep learning algorithm for 3D cell detection in whole mouse brain image datasets.

Understanding the function of the nervous system necessitates mapping the spatial distributions of its constituent cells defined by function, anatomy or gene expression. Recently, developments in tissue preparation and microscopy allow cellular populations to be imaged throughout the entire rodent brain. However, mapping these neurons manually is prone to bias and is often impractically time consuming. Here we present an open-source algorithm for fully automated 3D detection of neuronal somata in mouse whole-brain microscopy images using standard desktop computer hardware. We demonstrate the applicability and power of our approach by mapping the brain-wide locations of large populations of cells labeled with cytoplasmic fluorescent proteins expressed via retrograde trans-synaptic viral infection.

aMAP is a validated pipeline for registration and segmentation of high-resolution mouse brain data.

The validation of automated image registration and segmentation is crucial for accurate and reliable mapping of brain connectivity and function in three-dimensional (3D) data sets. While validation standards are necessarily high and routinely met in the clinical arena, they have to date been lacking for high-resolution microscopy data sets obtained from the rodent brain. Here we present a tool for optimized automated mouse atlas propagation (aMAP) based on clinical registration software (NiftyReg) for anatomical segmentation of high-resolution 3D fluorescence images of the adult mouse brain. We empirically evaluate aMAP as a method for registration and subsequent segmentation by validating it against the performance of expert human raters. This study therefore establishes a benchmark standard for mapping the molecular function and cellular connectivity of the rodent brain.

The stimulus selectivity and connectivity of layer six principal cells reveals cortical microcircuits underlying visual processing.

Sensory computations performed in the neocortex involve layer six (L6) cortico-cortical (CC) and cortico-thalamic (CT) signaling pathways. Developing an understanding of the physiological role of these circuits requires dissection of the functional specificity and connectivity of the underlying individual projection neurons. By combining whole-cell recording from identified L6 principal cells in the mouse primary visual cortex (V1) with modified rabies virus-based input mapping, we have determined the sensory response properties and upstream monosynaptic connectivity of cells mediating the CC or CT pathway. We show that CC-projecting cells encompass a broad spectrum of selectivity to stimulus orientation and are predominantly innervated by deep layer V1 neurons. In contrast, CT-projecting cells are ultrasparse firing, exquisitely tuned to orientation and direction information, and receive long-range input from higher cortical areas. This segregation in function and connectivity indicates that L6 microcircuits route specific contextual and stimulus-related information within and outside the cortical network.

Charting monosynaptic connectivity maps by two-color light-sheet fluorescence microscopy.

Cellular resolution three-dimensional (3D) visualization of defined, fluorescently labeled long-range neuronal networks in the uncut adult mouse brain has been elusive. Here, a virus-based strategy is described that allowed fluorescent labeling of centrifugally projecting neuronal populations in the ventral forebrain and their directly, monosynaptically connected bulbar interneurons upon a single stereotaxic injection into select neuronal populations. Implementation of improved tissue clearing combined with light-sheet fluorescence microscopy permitted imaging of the resulting connectivity maps in a single whole-brain scan. Subsequent 3D reconstructions revealed the exact distribution of the diverse neuronal ensembles monosynaptically connected with distinct bulbar interneuron populations. Moreover, rehydratation of brains after light-sheet fluorescence imaging enabled the immunohistochemical identification of synaptically connected neurons. Thus, this study describes a method for identifying monosynaptic connectivity maps from distinct, virally labeled neuronal populations that helps in better understanding of information flow in neural systems.