Accurate assessment of connectional anatomy of primate brains can be an important avenue to better understand the structural and functional organization of brains. To this end, numerous connectome projects have been initiated to create a comprehensive map of the connectional anatomy over a large spatial expanse. Tractography based on diffusion MRI (dMRI) data has been used as a tool by many connectome projects in that it is widely used to visualize axonal pathways and reveal microstructural features on living brains. However, the measures obtained from dMRI are indirect inference of microstructures. This intrinsic limitation reduces the reliability of dMRI in constructing connectomes for brains. In this work, we proposed a framework to increase the accuracy of constructing a dMRI-based connectome on macaque brains by integrating meso-scale connective information from tract-tracing data and micro-scale axonal orientation information from myelin stain data. Our results suggest that this integrative framework could advance the mapping accuracy of dMRI based connections and axonal pathways, and demonstrate the prospect of the proposed framework in constructing a large-scale connectome on living primate brains.
Objective: Controlling neural activity enables the possibility of manipulating sensory perception, cognitive processes, and body movement, in addition to providing a powerful framework for functionally disentangling the neural circuits that underlie these complex phenomena. Over the last decade, optogenetic stimulation has become an increasingly important and powerful tool for understanding neural circuit function, owing to the ability to target specific cell types and bidirectionally modulate neural activity. To date, most stimulation has been provided in open-loop or in an on/off closed-loop fashion, where previously-determined stimulation is triggered by an event. Here, we describe and demonstrate a design approach for precise optogenetic control of neuronal firing rate modulation using feedback to guide stimulation continuously.
Approach: Using the rodent somatosensory thalamus as an experimental testbed for realizing desired time-varying patterns of firing rate modulation, we utilized a moving average exponential filter to estimate firing rate online from single-unit spiking measured extracellularly. This estimate of instantaneous rate served as feedback for a proportional integral (PI) controller, which was designed during the experiment based on a linear-nonlinear Poisson (LNP) model of the neuronal response to light.
Main results: The LNP model fit during the experiment enabled robust closed-loop control, resulting in good tracking of sinusoidal and non-sinusoidal targets, and rejection of unmeasured disturbances. Closed-loop control also enabled manipulation of trial-to-trial variability.
Significance: Because neuroscientists are faced with the challenge of dissecting the functions of circuit components, the ability to maintain control of a region of interest in spite of changes in ongoing neural activity will be important for disambiguating function within networks. Closed-loop stimulation strategies are ideal for control that is robust to such changes, and the employment of continuous feedback to adjust stimulation in real-time can improve the quality of data collected using optogenetic manipulation.
This study presents a new algorithm to adaptively detect change points of functional connectivity networks in the brain. It uses scans from resting-state functional magnetic resonance imaging (rsfMRI) which is one of the major tools to investigate intrinsic brain functionality. Different regions of the resting brain form networks that change states within a few seconds to minutes. The change points of these networks are different in normal and disordered brain functions and their understanding can help in identification of brain disorders. These changes arise from many unknown factors and extraction of these change points is one of the the major challenges in the absence of any ground truth. Our algorithm detects these change points adaptively by computing sum of absolute sign differences of adjacent images in rsfMRI scans using measures from image and video processing. We demonstrate the effectiveness of the proposed algorithm and show that these change points can be detected reliably in both task-based and resting-state networks. The outcomes also point to new directions for future work.
Cardiovascular simulations have great potential as a clinical tool for planning and evaluating patient-specific treatment strategies for those suffering from congenital heart diseases, specifically Fontan patients. However, several bottlenecks have delayed wider deployment of the simulations for clinical use; the main obstacle is simulation cost. Currently, time-averaged clinical flow measurements are utilized as numerical boundary conditions (BCs) in order to reduce the computational power and time needed to offer surgical planning within a clinical time frame. Nevertheless, pulsatile blood flow is observed in vivo, and its significant impact on numerical simulations has been demonstrated. Therefore, it is imperative to carry out a comprehensive study analyzing the sensitivity of using time-averaged BCs. In this study, sensitivity is evaluated based on the discrepancies between hemodynamic metrics calculated using time-averaged and pulsatile BCs; smaller discrepancies indicate less sensitivity. The current study incorporates a comparison between 3D patient-specific CFD simulations using both the time-averaged and pulsatile BCs for 101 Fontan patients. The sensitivity analysis involves two clinically important hemodynamic metrics: hepatic flow distribution (HFD) and indexed power loss (iPL). Paired demographic group comparisons revealed that HFD sensitivity is significantly different between single and bilateral superior vena cava cohorts but no other demographic discrepancies were observed for HFD or iPL. Multivariate regression analyses show that the best predictors for sensitivity involve flow pulsatilities, time-averaged flow rates, and geometric characteristics of the Fontan connection. These predictors provide patient-specific guidelines to determine the effectiveness of analyzing patient-specific surgical options with time-averaged BCs within a clinical time frame.
by
Baptiste Coudrillier;
Diogo M. Geraldes;
Nghia T. Vo;
Robert Atwood;
Christina Reinhard;
Ian C. Campbell;
Yazdan Raji;
Julie Albon;
Richard L. Abel;
Christopher Ethier
The lamina cribrosa (LC) is a complex mesh-like tissue in the posterior eye. Its biomechanical environment is thought to play a major role in glaucoma, the second most common cause of blindness. Due to its small size and relative inaccessibility, high-resolution measurements of LC deformation, important in characterizing LC biomechanics, are challenging. Here we present a novel noninvasive imaging method, which enables measurement of the three-dimensional deformation of the LC caused by acute elevation of intraocular pressure (IOP). Posterior segments of porcine eyes were imaged using synchrotron radiation phase contrast micro-computed tomography (PC μCT) at IOPs between 6 and 37 mmHg. The complex trabecular architecture of the LC was reconstructed with an isotropic spatial resolution of 3.2 μm. Scans acquired at different IOPs were analyzed with digital volume correlation (DVC) to compute full-field deformation within the LC. IOP elevation caused substantial tensile, shearing and compressive devformation within the LC, with maximum tensile strains at 30 mmHg averaging 5.5%, and compressive strains reaching 20%. We conclude that PC μCT provides a novel high-resolution method for imaging the LC, and when combined with DVC, allows for full-field 3D measurement of ex vivo LC biomechanics at high spatial resolution.
3D organ contouring is an essential step in radiation therapy treatment planning for organ dose estimation as well as for optimizing plans to reduce organs-at-risk doses. Manual contouring is time-consuming and its inter-clinician variability adversely affects the outcomes study. Such organs also vary dramatically on sizes - up to two orders of magnitude difference in volumes. In this paper, we present BrainSegNet, a novel 3D fully convolutional neural network (FCNN) based approach for automatic segmentation of brain organs. Brain-SegN et takes a multiple resolution paths approach and uses a weighted loss function to solve the major challenge of the large variability in organ sizes. We evaluated our approach with a dataset of 46 Brain CT image volumes with corresponding expert organ contours as reference. Compared with those of LiviaNet and V-Net, BrainSegNet has a superior performance in segmenting tiny or thin organs, such as chiasm, optic nerves, and cochlea, and outperforms these methods in segmenting large organs as well. BrainSegNet can reduce the manual contouring time of a volume from an hour to less than two minutes, and holds high potential to improve the efficiency of radiation therapy workflow.
Background: We present a fundamental theoretical framework for analysis of energy dissipation in any component of the circulatory system and formulate the full energy budget for both venous and arterial circulations. New indices allowing disease-specific subject-to-subject comparisons and disease-to-disease hemodynamic evaluation (quantifying the hemodynamic severity of one vascular disease type to the other) are presented based on this formalism.
Methods and Results: Dimensional analysis of energy dissipation rate with respect to the human circulation shows that the rate of energy dissipation is inversely proportional to the square of the patient body surface area and directly proportional to the cube of cardiac output. This result verified the established formulae for energy loss in aortic stenosis that was solely derived through empirical clinical experience. Three new indices are introduced to evaluate more complex disease states: (1) circulation energy dissipation index (CEDI), (2) aortic valve energy dissipation index (AV-EDI), and (3) total cavopulmonary connection energy dissipation index (TCPC-EDI). CEDI is based on the full energy budget of the circulation and is the proper measure of the work performed by the ventricle relative to the net energy spent in overcoming frictional forces. It is shown to be 4.01 ± 0.16 for healthy individuals and above 7.0 for patients with severe aortic stenosis. Application of CEDI index on single-ventricle venous physiology reveals that the surgically created Fontan circulation, which is indeed palliative, progressively degrades in hemodynamic efficiency with growth (p < 0.001), with the net dissipation in a typical Fontan patient (Body surface area = 1.0 m2) being equivalent to that of an average case of severe aortic stenosis. AV-EDI is shown to be the proper index to gauge the hemodynamic severity of stenosed aortic valves as it accurately reflects energy loss. It is about 0.28 ± 0.12 for healthy human valves. Moderate aortic stenosis has an AV-EDI one order of magnitude higher while clinically severe aortic stenosis cases always had magnitudes above 3.0. TCPC-EDI represents the efficiency of the TCPC connection and is shown to be negatively correlated to the size of a typical "bottle-neck" region (pulmonary artery) in the surgical TCPC pathway (p < 0.05).
Conclusions: Energy dissipation in the human circulation has been analyzed theoretically to derive the proper scaling (indexing) factor. CEDI, AV-EDI, and TCPC-EDI are proper measures of the dissipative characteristics of the circulatory system, aortic valve, and the Fontan connection, respectively.
Flow structures, hemodynamics and the hydrodynamic surgical pathway resistances of the final stage functional single ventricle reconstruction, namely the total cavopulmonary connection (TCPC) anatomy, have been investigated extensively. However, the second stage surgical anatomy (i.e., bi-directional Glenn or hemi-Fontan template) has received little attention. We thus initiated a multi-faceted study, involving magnetic resonance imaging (MRI), phase contrast MRI, computational and experimental fluid dynamics methodologies, focused on the second stage of the procedure. Twenty three-dimensional computer and rapid prototype models of 2nd stage TCPC anatomies were created, including idealized parametric geometries (n = 6), patient-specific anatomies (n = 7), and their virtual surgery variant (n = 7). Results in patient-specific and idealized models showed that the Glenn connection template is hemodynamically more efficient with (83% p = 0.08 in patient-specific models and 66% in idealized models) lower power losses compared to hemi-Fontan template, respectively, due to its direct end-to-side anastomosis. Among the several secondary surgical geometrical features, stenosis at the SVC anastomosis or in pulmonary branches was found to be the most critical parameter in increasing the power loss. The pouch size and flare shape were found to be less significant. Compared to the third stage surgery the hydrodynamic resistance of the 2nd stage is considerably lower (both in idealized models and in anatomical models at MRI resting conditions) for both hemi- and Glenn templates. These results can impact the surgical design and planning of the staged TCPC reconstruction.
Diffusion tensor imaging (DTI), high angular resolution diffusion imaging (HARDI), and diffusion spectrum imaging (DSI) have been widely used in the neuroimaging field to examine the macro-scale fiber connection patterns in the cerebral cortex. However, the topographic and geometric relationships between diffusion imaging derived streamline fiber connection patterns and cortical folding patterns remain largely unknown. This paper specifically identifies and characterizes the U-shapes of diffusion imaging derived streamline fibers via a novel fiber clustering framework and examines their co-localization patterns with cortical sulci based on DTI, HARDI, and DSI datasets of human, chimpanzee and macaque brains. We verified the presence of these U-shaped streamline fibers that connect neighboring gyri by coursing around cortical sulci such as the central sulcus, pre-central sulcus, post-central sulcus, superior temporal sulcus, inferior frontal sulcus, and intra-parietal sulcus. This study also verified the existence of U-shape fibers across data modalities (DTI/HARDI/DSI) and primate species (macaque, chimpanzee and human), and suggests that the common pattern of U-shape fibers coursing around sulci is evolutionarily-preserved in cortical architectures.
Total cavopulmonary connection is the result of a series of palliative surgical repairs performed on patients with single ventricle heart defects. The resulting anatomy has complex and unsteady hemodynamics characterized by flow mixing and flow separation. Although varying degrees of flow pulsatility have been observed in vivo, non-pulsatile (time-averaged) boundary conditions have traditionally been assumed in hemodynamic modeling, and only recently have pulsatile conditions been incorporated without completely characterizing their effect or importance. In this study, 3D numerical simulations with both pulsatile and non-pulsatile boundary conditions were performed for 24 patients with different anatomies and flow boundary conditions from Georgia Tech database. Flow structures, energy dissipation rates and pressure drops were compared under rest and simulated exercise conditions. It was found that flow pulsatility is the primary factor in determining the appropriate choice of boundary conditions, whereas the anatomic configuration and cardiac output had secondary effects. Results show that the hemodynamics can be strongly influenced by the presence of pulsatile flow. However, there was a minimum pulsatility threshold, identified by defining a weighted pulsatility index (wPI), above which the influence was significant. It was shown that when wPI < 30%, the relative error in hemodynamic predictions using time-averaged boundary conditions was less than 10% compared to pulsatile simulations. In addition, when wPI < 50, the relative error was less than 20%. A correlation was introduced to relate wPI to the relative error in predicting the flow metrics with non-pulsatile flow conditions.