A Comprehensive, FAIR File Format for Neuroanatomical Structure Modeling.

With advances in microscopy and computer science, the technique of digitally reconstructing, modeling, and quantifying microscopic anatomies has become central to many fields of biological research. MBF Bioscience has chosen to openly document their digital reconstruction file format, the Neuromorphological File Specification, available at www.mbfbioscience.com/filespecification (Angstman et al., 2020). The format, created and maintained by MBF Bioscience, is broadly utilized by the neuroscience community. The data format's structure and capabilities have evolved since its inception, with modifications made to keep pace with advancements in microscopy and the scientific questions raised by worldwide experts in the field. More recent modifications to the neuromorphological file format ensure it abides by the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles promoted by the International Neuroinformatics Coordinating Facility (INCF; Wilkinson et al., Scientific Data, 3, 160018,, 2016). The incorporated metadata make it easy to identify and repurpose these data types for downstream applications and investigation. This publication describes key elements of the file format and details their relevant structural advantages in an effort to encourage the reuse of these rich data files for alternative analysis or reproduction of derived conclusions.

Automatic navigation system for the mouse brain.

Identification and delineation of brain regions in histologic mouse brain sections is especially pivotal for many neurogenomics, transcriptomics, proteomics, and connectomics studies, yet this process is prone to observer error and bias. Here we present a novel brain navigation system, named NeuroInfo, whose general principle is similar to that of a global positioning system (GPS) in a car. NeuroInfo automatically navigates an investigator through the complex microscopic anatomy of histologic sections of mouse brains (thereafter: "experimental mouse brain sections"). This is achieved by automatically registering a digital image of an experimental mouse brain section with a three-dimensional (3D) digital mouse brain atlas that is essentially based on the third version of the Allen Mouse Brain Common Coordinate Framework (CCF v3), retrieving graphical region delineations and annotations from the 3D digital mouse brain atlas, and superimposing this information onto the digital image of the experimental mouse brain section on a computer screen. By doing so, NeuroInfo helps in solving the long-standing problem faced by researchers investigating experimental mouse brain sections under a light microscope-that of correctly identifying the distinct brain regions contained within the experimental mouse brain sections. Specifically, NeuroInfo provides an intuitive, readily-available computer microscopy tool to enhance researchers' ability to correctly identify specific brain regions in experimental mouse brain sections. Extensive validation studies of NeuroInfo demonstrated that this novel technology performs remarkably well in accurately delineating regions that are large and/or located in the dorsal parts of mouse brains, independent on whether the sections were imaged with fluorescence or bright-field microscopy. This novel navigation system provides a highly efficient way for registering a digital image of an experimental mouse brain section with the 3D digital mouse brain atlas in a minute and accurate delineation of the image in real-time.

Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting.

Stereologic cell counting has had a major impact on the field of neuroscience. A major bottleneck in stereologic cell counting is that the user must manually decide whether or not each cell is counted according to three-dimensional (3D) stereologic counting rules by visual inspection within hundreds of microscopic fields-of-view per investigated brain or brain region. Reliance on visual inspection forces stereologic cell counting to be very labor-intensive and time-consuming, and is the main reason why biased, non-stereologic two-dimensional (2D) "cell counting" approaches have remained in widespread use. We present an evaluation of the performance of modern automated cell detection and segmentation algorithms as a potential alternative to the manual approach in stereologic cell counting. The image data used in this study were 3D microscopic images of thick brain tissue sections prepared with a variety of commonly used nuclear and cytoplasmic stains. The evaluation compared the numbers and locations of cells identified unambiguously and counted exhaustively by an expert observer with those found by three automated 3D cell detection algorithms: nuclei segmentation from the FARSIGHT toolkit, nuclei segmentation by 3D multiple level set methods, and the 3D object counter plug-in for ImageJ. Of these methods, FARSIGHT performed best, with true-positive detection rates between 38 and 99% and false-positive rates from 3.6 to 82%. The results demonstrate that the current automated methods suffer from lower detection rates and higher false-positive rates than are acceptable for obtaining valid estimates of cell numbers. Thus, at present, stereologic cell counting with manual decision for object inclusion according to unbiased stereologic counting rules remains the only adequate method for unbiased cell quantification in histologic tissue sections.

Robust tracking and quantification of C. elegans body shape and locomotion through coiling, entanglement, and omega bends.

The behavior of the well-characterized nematode, Caenorhabditis elegans (C. elegans), is often used to study the neurologic control of sensory and motor systems in models of health and neurodegenerative disease. To advance the quantification of behaviors to match the progress made in the breakthroughs of genetics, RNA, proteins, and neuronal circuitry, analysis must be able to extract subtle changes in worm locomotion across a population. The analysis of worm crawling motion is complex due to self-overlap, coiling, and entanglement. Using current techniques, the scope of the analysis is typically restricted to worms to their non-occluded, uncoiled state which is incomplete and fundamentally biased. Using a model describing the worm shape and crawling motion, we designed a deformable shape estimation algorithm that is robust to coiling and entanglement. This model-based shape estimation algorithm has been incorporated into a framework where multiple worms can be automatically detected and tracked simultaneously throughout the entire video sequence, thereby increasing throughput as well as data validity. The newly developed algorithms were validated against 10 manually labeled datasets obtained from video sequences comprised of various image resolutions and video frame rates. The data presented demonstrate that tracking methods incorporated in WormLab enable stable and accurate detection of these worms through coiling and entanglement. Such challenging tracking scenarios are common occurrences during normal worm locomotion. The ability for the described approach to provide stable and accurate detection of C. elegans is critical to achieve unbiased locomotory analysis of worm motion.

Characterization of neurophysiological and behavioral changes, MRI brain volumetry and 1H MRS in zQ175 knock-in mouse model of Huntington's disease.

Huntington's disease (HD) is an autosomal neurodegenerative disorder, characterized by severe behavioral, cognitive, and motor deficits. Since the discovery of the huntingtin gene (HTT) mutation that causes the disease, several mouse lines have been developed using different gene constructs of Htt. Recently, a new model, the zQ175 knock-in (KI) mouse, was developed (see description by Menalled et al, [1]) in an attempt to have the Htt gene in a context and causing a phenotype that more closely mimics HD in humans. Here we confirm the behavioral phenotypes reported by Menalled et al [1], and extend the characterization to include brain volumetry, striatal metabolite concentration, and early neurophysiological changes. The overall reproducibility of the behavioral phenotype across the two independent laboratories demonstrates the utility of this new model. Further, important features reminiscent of human HD pathology are observed in zQ175 mice: compared to wild-type neurons, electrophysiological recordings from acute brain slices reveal that medium spiny neurons from zQ175 mice display a progressive hyperexcitability; glutamatergic transmission in the striatum is severely attenuated; decreased striatal and cortical volumes from 3 and 4 months of age in homo- and heterozygous mice, respectively, with whole brain volumes only decreased in homozygotes. MR spectroscopy reveals decreased concentrations of N-acetylaspartate and increased concentrations of glutamine, taurine and creatine + phosphocreatine in the striatum of 12-month old homozygotes, the latter also measured in 12-month-old heterozygotes. Motor, behavioral, and cognitive deficits in homozygotes occur concurrently with the structural and metabolic changes observed. In sum, the zQ175 KI model has robust behavioral, electrophysiological, and histopathological features that may be valuable in both furthering our understanding of HD-like pathophyisology and the evaluation of potential therapeutic strategies to slow the progression of disease.