A Queryable Graph Representation of Vascular Connectivity in the Whole Mouse Brain.

We construct a graph representation for the topology and geometry of the vasculature presenting across the whole mouse brain (dataset: Knife-Edge Scanning Microscope Brain Atlas India Ink). We use our graph representation to calculate preliminary estimates of the average radius as 4:8 µm, total vascular volume as 1:1000 mm3, total vascular surface area as 6:5511 cm2, and total vascular length of 2866:6567 cm. We then isolate a posterior cerebral region, derive its graph representation, and then import that representation to a Neo4j graph database. We then detail how researchers can query this database online to isolate specific vascular networks for further analysis and reconstruction.

Towards An Open-Source Framework For The Analysis Of Cerebrovasculature Structure.

The use of graphs to analyze cerebrovascular networks is quite common in studies of the microcirculation. While we have learned a lot from studies utilizing graphs as a tool for the analysis of microvessels, most methodologies for these procedures have only been described in brief and most are not publicly accessible. In this work, we introduce the foundation for an anticipated open-source framework that we hope will streamline the analysis of cerebrovascular structure. We believe that a standardized and accessible framework for the analysis vascular filaments is not only needed, but is necessary, for studies charting the microcirculation on image volumes spanning several grains of tissue. We set forth the foundations for a comprehensive and complete framework in our current work.

Learning to distinguish cerebral vasculature data from mechanical chatter in India-ink images acquired using knife-edge scanning microscopy.

We introduce a simple, yet effective, procedure for accurate classification of connected components embedded in biological images. In our method, a training set is generated from user-delineated features of manually-labeled examples; we subsequently train a classifier using the resultant training set. The overall process is described using imaging data acquired from an India-ink perfused C57BL/6J mouse brain using Knife Edge Scanning Microscopy. We illustrate the procedure through segmentation of cerebral vasculature structures from mechanical noise using trained classifiers. The features extracted by our procedure show high discriminatory power between classes; the classifiers (linear SVM, Gaussian SVM, and GentleBoost decision tree ensemble) trained using these features achieved high performance: F1-scores reported for linear SVM, Gaussian SVM, and GentleBoost decision tree ensemble were 0.963, 0.956, and 0.963 respectively.

Knife-edge scanning microscopy for in silico study of cerebral blood flow: From biological imaging data to flow simulations.

Knife-edge scanning microscopy provides the capability to image whole-brain cerebral microvasculature of small organisms, such as mice, at sub-micron resolution, providing a feasible foundation for the reconstruction of circulatory pathways from the systemic to cellular scale. In this paper, we illustrate the feasibility of using this data to model cerebral blood flow using numerical simulations. Starting with a small vascular element in microcirculation of interest, we present its segmentation from the imaging-data volume, construction of its triangular surface mesh, assembly of its tetrahedral volumetric mesh from the surface, and then conclude with Stokes flow simulation of plasma through the microvascular vessel.

Automated neurovascular tracing and analysis of the knife-edge scanning microscope Rat Nissl data set using a computing cluster.

We present a novel, parallelizable algorithm capable of automatically reconstructing and calculating anatomical statistics of cerebral vascular networks embedded in large volumes of Rat Nissl-stained data. In this paper, we report the results of our method using Rattus somatosensory cortical data acquired using Knife-Edge Scanning Microscopy. Our algorithm performs the reconstruction task with averaged precision, recall, and F2-score of 0.978, 0.892, and 0.902 respectively. Calculated anatomical statistics show some conformance to values previously reported. The results that can be obtained from our method are expected to help explicate the relationship between the structural organization of the microcirculation and normal (and abnormal) cerebral functioning.

The inability of emergency physicians to adequately clinically estimate the underlying hemodynamic profiles of acutely ill patients.

OBJECTIVE: Emergency physicians (EPs) estimate the underlying hemodynamics of acutely ill patients and use them to help both diagnose and formulate a treatment plan. This trial compared the EP clinically derived estimates of cardiac output (CO) and systemic vascular resistance (SVR) to those measured noninvasively. METHODS: Forty acutely ill emergency department patients with a broad range of diagnosis and blood pressure (BP) and pulse were monitored for 2 hours using novel noninvasive finger cuff technology (Nexfin; BMEYE, Amsterdam, The Netherlands). The Nexfin device provides continuous BP monitoring and, from the resulting pulse pressure waveform, calculates beat-to-beat CO and SVR. At baseline assessment and after 2 hours of testing and therapy, treating EPs were asked to estimate the CO and SVR (low, normal, or high), and these were compared with Nexfin measurements. RESULTS: Twenty-five men and 15 women were enrolled with a mean age of 62.2 years (SD, 12.6 years). Eighteen had acute shortness of breath; 11, with probable stroke syndrome; 3, with suspected sepsis; and 8, with a systolic BP greater than 180 or less than 100 mm Hg. Concordance tables showed that there was very little agreement (κ values) between either the compared initial CO (-0.0873) and SVR (-0.0645) or the 2-hour values (-0.0645 and -0.1949, respectively). CONCLUSIONS: Emergency physicians cannot accurately estimate the underlying hemodynamic profiles of acutely ill patients when compared with more objective measurements. This inaccuracy may have important clinical ramifications. Further study is needed to determine how to use these measured continuous CO and SVR monitoring values.

Noninvasive continuous or intermittent blood pressure and heart rate patient monitoring in the ED.

OBJECTIVE: Continuous invasive blood pressure (BP) and heart rate (HR) monitoring in the emergency department (ED) is valuable in managing critically ill patients. Novel noninvasive finger cuff technology allows this same uninterrupted monitoring for almost any individual. This exploratory study compares ED noninvasive continuous to intermittent measurements of these variables. METHODS: A variety of acutely ill ED patients (n = 40) with broad ranges of BP and HR underwent simultaneous monitoring using interval standard automated ED devices and continuous finger cuff technology (Nexfin; Bmeye, Amsterdam, The Netherlands) over a period of 2 hours. At baseline and at 15-minute intervals, simultaneous measurements for BP and HR were recorded and compared. RESULTS: There were 25 men and 15 women enrolled with a mean age of 62.2 years (SD, 12.6). Eighteen had acute dyspnea, 11 with probable stroke syndrome, 3 with suspected sepsis, and 8 with a systolic BP greater than 180 or less than 100 mm Hg. Blood pressure and HR values (n = 344) simultaneously measured by automated ED equipment and the Nexfin finger cuff device were compared. The Pearson correlation coefficients were high, and the Bland-Altman plots showed good agreement between the 2 determinations. CONCLUSION: Continuous BP and HR monitoring measured by the Nexfin finger cuff device in this trial showed reasonable agreement when compared with the intermittent values obtained by automated ED equipment. However, theoretically, noninvasive and continuous monitoring of the BP and HR might better reflect underlying hemodynamics than these same measurements obtained intermittently and, thus, could be important in patient management. More study is needed to determine the optimal method of monitoring these parameters.