Rat Brain Global Ischemia-Induced Diffusion Changes Revisited: Biophysical Modeling of the Water and NAA MR "Diffusion Signal".

PURPOSE: To assess changes in intracellular diffusion as a mechanism for the reduction in water ADC that accompanies brain injury. Using NAA as a marker of neuronal cytoplasmic diffusion, NAA diffusion was measured before and after global ischemia (immediately postmortem) in the female Sprague-Dawley rat. METHODS: Diffusion-weighted PRESS spectra, with diffusion encoding in a single direction, were acquired from large voxels of rat brain gray matter in vivo and postischemia employing either pairs of pulsed half-sine-shaped gradients (in vivo and postischemia, bmax  = 19 ms/µm2 ) or sinusoidal oscillating gradients (in vivo only) with frequencies of 99.2-250 Hz. A 2D randomly oriented cylinder (neurite) model gave estimates of longitudinal and transverse diffusivities (DL and DT , respectively). In this model, DL represents the "free" diffusivity of NAA, whereas DT reflects highly restricted diffusion. Using oscillating gradients, the frequency dependence of DT [DT (ω)] gave estimates of the cylinder (axon/dendrite) radius. RESULTS: A 10% decrease in DL,NAA followed global ischemia, dropping from 0.391 ± 0.012 µm2 /ms to 0.350 ± 0.009 µm2 /ms. Modeling DT,NAA (ω) provided an estimate of the neurite radius of 1.0 ± 0.6 µm. CONCLUSION: Whereas the increase in apparent intraneuronal viscosity suggested by changes in DL,NAA may contribute to the overall reduction in water ADC associated with brain injury, it is not sufficient to be the sole explanation. Estimates of neurite radius based on DT (ω) were consistent with literature values.

Accuracy and reliability of diffusion imaging models.

Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain's white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927-1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL's BedpostX [BPX], DSI Studio's Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3's Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI.

Dynamic Contrast Enhancement (DCE) MRI-Derived Renal Perfusion and Filtration: Basic Concepts.

Dynamic contrast-enhanced (DCE) MRI monitors the transit of contrast agents, typically gadolinium chelates, through the intrarenal regions, the renal cortex, the medulla, and the collecting system. In this way, DCE-MRI reveals the renal uptake and excretion of the contrast agent. An optimal DCE-MRI acquisition protocol involves finding a good compromise between whole-kidney coverage (i.e., 3D imaging), spatial and temporal resolution, and contrast resolution. By analyzing the enhancement of the renal tissues as a function of time, one can determine indirect measures of clinically important single-kidney parameters as the renal blood flow, glomerular filtration rate, and intrarenal blood volumes. Gadolinium-containing contrast agents may be nephrotoxic in patients suffering from severe renal dysfunction, but otherwise DCE-MRI is clearly useful for diagnosis of renal functions and for assessing treatment response and posttransplant rejection.Here we introduce the concept of renal DCE-MRI, describe the existing methods, and provide an overview of preclinical DCE-MRI applications to illustrate the utility of this technique to measure renal perfusion and glomerular filtration rate in animal models.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This introduction is complemented by two separate publications describing the experimental procedure and data analysis.

Analysis Protocol for Dynamic Contrast Enhanced (DCE) MRI of Renal Perfusion and Filtration.

Here we present an analysis protocol for dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data of the kidneys. It covers comprehensive steps to facilitate signal to contrast agent concentration mapping via T1 mapping and the calculation of renal perfusion and filtration parametric maps using model-free approaches, model free analysis using deconvolution, the Toft's model and a Bayesian approach.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concept and experimental procedure.

Test-Retest Performance of a 1-Hour Multiparametric MR Image Acquisition Pipeline With Orthotopic Triple-Negative Breast Cancer Patient-Derived Tumor Xenografts.

Preclinical imaging is critical in the development of translational strategies to detect diseases and monitor response to therapy. The National Cancer Institute Co-Clinical Imaging Resource Program was launched, in part, to develop best practices in preclinical imaging. In this context, the objective of this work was to develop a 1-hour, multiparametric magnetic resonance image-acquisition pipeline with triple-negative breast cancer patient-derived xenografts (PDXs). The 1-hour, image-acquisition pipeline includes T1- and T2-weighted scans, quantitative T1, T2, and apparent diffusion coefficient (ADC) parameter maps, and dynamic contrast-enhanced (DCE) time-course images. Quality-control measures used phantoms. The triple-negative breast cancer PDXs used for this study averaged 174 ± 73 µL in volume, with region of interest-averaged T1, T2, and ADC values of 1.9 ± 0.2 seconds, 62 ± 3 milliseconds, and 0.71 ± 0.06 µm2/ms (mean ± SD), respectively. Specific focus was on assessing the within-subject test-retest coefficient-of-variation (CVWS) for each of the magnetic resonance imaging metrics. Determination of PDX volume via manually drawn regions of interest is highly robust, with ∼1% CVWS. Determination of T2 is also robust with a ∼3% CVWS. Measurements of T1 and ADC are less robust with CVWS values in the 6%-11% range. Preliminary DCE test-retest time-course determinations, as quantified by area under the curve and Ktrans from 2-compartment exchange (extended Tofts) modeling, suggest that DCE is the least robust protocol, with ∼30%-40% CVWS.

Bayesian Modeling of NMR Data: Quantifying Longitudinal Relaxation in Vivo, and in Vitro with a Tissue-Water-Relaxation Mimic (Crosslinked Bovine Serum Albumin).

Recently, a number of MRI protocols have been reported that seek to exploit the effect of dissolved oxygen (O2, paramagnetic) on the longitudinal 1H relaxation of tissue water, thus providing image contrast related to tissue oxygen content. However, tissue water relaxation is dependent on a number of mechanisms, and this raises the issue of how best to model the relaxation data. This problem, the model selection problem, occurs in many branches of science and is optimally addressed by Bayesian probability theory. High signal-to-noise, densely sampled, longitudinal 1H relaxation data were acquired from rat brain in vivo and from a cross-linked bovine serum albumin (xBSA) phantom, a sample that recapitulates the relaxation characteristics of tissue water in vivo. Bayesian-based model selection was applied to a cohort of five competing relaxation models: (i) monoexponential, (ii) stretched-exponential, (iii) biexponential, (iv) Gaussian (normal) R1-distribution, and (v) gamma R1-distribution. Bayesian joint analysis of multiple replicate datasets revealed that water relaxation of both the xBSA phantom and in vivo rat brain was best described by a biexponential model, while xBSA relaxation datasets truncated to remove evidence of the fast relaxation component were best modeled as a stretched exponential. In all cases, estimated model parameters were compared to the commonly used monoexponential model. Reducing the sampling density of the relaxation data and adding Gaussian-distributed noise served to simulate cases in which the data are acquisition-time or signal-to-noise restricted, respectively. As expected, reducing either the number of data points or the signal-to-noise increases the uncertainty in estimated parameters and, ultimately, reduces support for more complex relaxation models.

Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF.

PURPOSE: This study aims to develop a constrained local arterial input function (cL-AIF) to improve quantitative analysis of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) data by accounting for the contrast-agent bolus amplitude error in the voxel-specific AIF. PROCEDURES: Bayesian probability theory-based parameter estimation and model selection were used to compare tracer kinetic modeling employing either the measured remote-AIF (R-AIF, i.e., the traditional approach) or an inferred cL-AIF against both in silico DCE-MRI data and clinical, cervical cancer DCE-MRI data. RESULTS: When the data model included the cL-AIF, tracer kinetic parameters were correctly estimated from in silico data under contrast-to-noise conditions typical of clinical DCE-MRI experiments. Considering the clinical cervical cancer data, Bayesian model selection was performed for all tumor voxels of the 16 patients (35,602 voxels in total). Among those voxels, a tracer kinetic model that employed the voxel-specific cL-AIF was preferred (i.e., had a higher posterior probability) in 80 % of the voxels compared to the direct use of a single R-AIF. Maps of spatial variation in voxel-specific AIF bolus amplitude and arrival time for heterogeneous tissues, such as cervical cancer, are accessible with the cL-AIF approach. CONCLUSIONS: The cL-AIF method, which estimates unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better modeling of DCE-MRI data than the use of a single, measured R-AIF. The Bayesian-based data analysis described herein affords estimates of uncertainties for each model parameter, via posterior probability density functions, and voxel-wise comparison across methods/models, via model selection in data modeling.

Intracellular water preexchange lifetime in neurons and astrocytes.

PURPOSE: To determine the intracellular water preexchange lifetime, τi , the "average residence time" of water, in the intracellular milieu of neurons and astrocytes. The preexchange lifetime is important for modeling a variety of MR data sets, including relaxation, diffusion-sensitive, and dynamic contrast-enhanced data sets. METHODS: Herein, τi in neurons and astrocytes is determined in a microbead-adherent, cultured cell system. In concert with thin-slice selection, rapid flow of extracellular media suppresses extracellular signal, allowing determination of the transcytolemmal-exchange-dominated, intracellular T1 . With this knowledge, and that of the intracellular T1 in the absence of exchange, τi can be derived. RESULTS: Under normal culture conditions, τi for neurons is 0.75 ± 0.05 s versus 0.57 ± 0.03 s for astrocytes. Both neuronal and astrocytic τi s decrease within 30 min after the onset of oxygen-glucose deprivation, with the astrocytic τi showing a substantially greater decrease than the neuronal τi . CONCLUSIONS: Given an approximate intra- to extracellular volume ratio of 4:1 in the brain, these data imply that, under normal physiological conditions, an MR experimental characteristic time of less than 0.012 s is required for a nonexchanging, two-compartment (intra- and extracellular) model to be valid for MR studies. This characteristic time shortens significantly (i.e., 0.004 s) under injury conditions. Magn Reson Med 79:1616-1627, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Are complex DCE-MRI models supported by clinical data?

PURPOSE: To ascertain whether complex dynamic contrast enhanced (DCE) MRI tracer kinetic models are supported by data acquired in the clinic and to determine the consequences of limited contrast-to-noise. METHODS: Generically representative in silico and clinical (cervical cancer) DCE-MRI data were examined. Bayesian model selection evaluated support for four compartmental DCE-MRI models: the Tofts model (TM), Extended Tofts model, Compartmental Tissue Uptake model (CTUM), and Two-Compartment Exchange model. RESULTS: Complex DCE-MRI models were more sensitive to noise than simpler models with respect to both model selection and parameter estimation. Indeed, as contrast-to-noise decreased, complex DCE models became less probable and simpler models more probable. The less complex TM and CTUM were the optimal models for the DCE-MRI data acquired in the clinic. [In cervical tumors, Ktrans, Fp, and PS increased after radiotherapy (P = 0.004, 0.002, and 0.014, respectively)]. CONCLUSION: Caution is advised when considering application of complex DCE-MRI kinetic models to data acquired in the clinic. It follows that data-driven model selection is an important prerequisite to DCE-MRI analysis. Model selection is particularly important when high-order, multiparametric models are under consideration. (Parameters obtained from kinetic modeling of cervical cancer clinical DCE-MRI data showed significant changes at an early stage of radiotherapy.) Magn Reson Med 77:1329-1339, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Rapid Quantitative Pharmacodynamic Imaging with Bayesian Estimation.

We recently described rapid quantitative pharmacodynamic imaging, a novel method for estimating sensitivity of a biological system to a drug. We tested its accuracy in simulated biological signals with varying receptor sensitivity and varying levels of random noise, and presented initial proof-of-concept data from functional MRI (fMRI) studies in primate brain. However, the initial simulation testing used a simple iterative approach to estimate pharmacokinetic-pharmacodynamic (PKPD) parameters, an approach that was computationally efficient but returned parameters only from a small, discrete set of values chosen a priori. Here we revisit the simulation testing using a Bayesian method to estimate the PKPD parameters. This improved accuracy compared to our previous method, and noise without intentional signal was never interpreted as signal. We also reanalyze the fMRI proof-of-concept data. The success with the simulated data, and with the limited fMRI data, is a necessary first step toward further testing of rapid quantitative pharmacodynamic imaging.

Renal DCE-MRI Model Selection Using Bayesian Probability Theory.

The goal of this work was to demonstrate the utility of Bayesian probability theory-based model selection for choosing the optimal mathematical model from among 4 competing models of renal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. DCE-MRI data were collected on 21 mice with high (n = 7), low (n = 7), or normal (n = 7) renal blood flow (RBF). Model parameters and posterior probabilities of 4 renal DCE-MRI models were estimated using Bayesian-based methods. Models investigated included (1) an empirical model that contained a monoexponential decay (washout) term and a constant offset, (2) an empirical model with a biexponential decay term (empirical/biexponential model), (3) the Patlak-Rutland model, and (4) the 2-compartment kidney model. Joint Bayesian model selection/parameter estimation demonstrated that the empirical/biexponential model was strongly favored for all 3 cohorts, the modeled DCE signals that characterized each of the 3 cohorts were distinctly different, and individual empirical/biexponential model parameter values clearly distinguished cohorts of low and high RBF from one another. The Bayesian methods can be readily extended to a variety of model analyses, making it a versatile and valuable tool for model selection and parameter estimation.

Prolactin and fMRI response to SKF38393 in the baboon.

Background. This study's goal was to provide dose-response data for a dopamine agonist in the baboon using standard methods (replicate measurements at each dose, across a range of doses), as a standard against which to subsequently validate a novel pharmacological MRI (phMRI) method. Dependent variables were functional MRI (fMRI) data from brain regions selected a priori, and systemic prolactin release. Necessary first steps included estimating the magnitude and time course of prolactin response to anesthesia alone and to various doses of agonist. These first steps ("time course studies") were performed with three agonists, and the results were used to select promising agonists and to guide design details for the single-dose studies needed to generate dose-response curves. Methods. We studied 6 male baboons (Papio anubis) under low-dose isoflurane anesthesia after i.m. ketamine. Time course studies charted the changes in plasma prolactin levels over time after anesthesia alone or after an intravenous (i.v.) dose of the dopamine D 1-like agonists SKF82958 and SKF38393 or the D 2-like agonist pramipexole. In the single-dose dopamine agonist studies, one dose of SKF38393 (ranging from 0.0928-9.28 mg/kg, N = 5 animals) or pramipexole (0.00928-0.2 mg/kg, N = 1) was given i.v. during a 40-min blood oxygen level dependent (BOLD) fMRI session, to determine BOLD and plasma prolactin responses to different drug concentrations. BOLD response was quantified as the area under the time-signal curve for the first 15 min after the start of the drug infusion, compared to the linearly predicted signal from the baseline data before drug. The ED50 (estimated dose that produces 50% of the maximal possible response to drug) for SKF38393 was calculated for the serum prolactin response and for phMRI responses in hypothalamus, pituitary, striatum and midbrain. Results. Prolactin rose 2.4- to 12-fold with anesthesia alone, peaking around 50-90 min after ketamine administration and gradually tapering off but still remaining higher than baseline on isoflurane 3-5 h after ketamine. Baseline prolactin level increased with age. SKF82958 0.1 mg/kg i.v. produced no noticeable change in plasma prolactin concentration. SKF38393 produced a substantial increase in prolactin release that peaked at around 20-30 min and declined to pre-drug levels in about an hour. Pramipexole quickly reduced prolactin levels below baseline, reaching a nadir 2-3 h after infusion. SKF38393 produced clear, dose-responsive BOLD signal changes, and across the four regions, ED50 was estimated at 2.6-8.1 mg/kg. Conclusions. In the baboon, the dopamine D 1 receptor agonist SKF38393 produces clear plasma prolactin and phMRI dose-response curves. Variability in age and a modest sample size limit the precision of the conclusions.

Automating the analysis of EEG recordings from prematurely-born infants: a Bayesian approach.

OBJECTIVE: To implement an automated analysis of EEG recordings from prematurely-born infants and thus provide objective, reproducible results. METHODS: Bayesian probability theory is employed to compute the posterior probability for developmental features of interest in EEG recordings. Currently, these features include smooth delta waves (0.5-1.5Hz, >100µV), delta brushes (delta portion: 0.5-1.5Hz, >100µV; "brush" portion: 8-22Hz, <75µV), and interburst intervals (<10µV), though the approach taken can be generalized to identify other EEG features of interest. RESULTS: When compared with experienced electroencephalographers, the algorithm had a true positive rate between 72% and 79% for the identification of delta waves (smooth or "brush") and interburst intervals, which is comparable to the inter-rater reliability. When distinguishing between smooth delta waves and delta brushes, the algorithm's true positive rate was between 53% and 88%, which is slightly less than the inter-rater reliability. CONCLUSION: Bayesian probability theory can be employed to consistently identify features of EEG recordings from premature infants. SIGNIFICANCE: The identification of features in EEG recordings provides a first step towards the automated analysis of EEG recordings from premature infants.

Inverse problems in cancellous bone: estimation of the ultrasonic properties of fast and slow waves using Bayesian probability theory.

Quantitative ultrasonic characterization of cancellous bone can be complicated by artifacts introduced by analyzing acquired data consisting of two propagating waves (a fast wave and a slow wave) as if only one wave were present. Recovering the ultrasonic properties of overlapping fast and slow waves could therefore lead to enhancement of bone quality assessment. The current study uses Bayesian probability theory to estimate phase velocity and normalized broadband ultrasonic attenuation (nBUA) parameters in a model of fast and slow wave propagation. Calculations are carried out using Markov chain Monte Carlo with simulated annealing to approximate the marginal posterior probability densities for parameters in the model. The technique is applied to simulated data, to data acquired on two phantoms capable of generating two waves in acquired signals, and to data acquired on a human femur condyle specimen. The models are in good agreement with both the simulated and experimental data, and the values of the estimated ultrasonic parameters fall within expected ranges.

Dynamic susceptibility contrast MRI with localized arterial input functions.

Compared to gold-standard measurements of cerebral perfusion with positron emission tomography using H(2)[(15)O] tracers, measurements with dynamic susceptibility contrast MR are more accessible, less expensive, and less invasive. However, existing methods for analyzing and interpreting data from dynamic susceptibility contrast MR have characteristic disadvantages that include sensitivity to incorrectly modeled delay and dispersion in a single, global arterial input function. We describe a model of tissue microcirculation derived from tracer kinetics that estimates for each voxel a unique, localized arterial input function. Parameters of the model were estimated using Bayesian probability theory and Markov-chain Monte Carlo, circumventing difficulties arising from numerical deconvolution. Applying the new method to imaging studies from a cohort of 14 patients with chronic, atherosclerotic, occlusive disease showed strong correlations between perfusion measured by dynamic susceptibility contrast MR with localized arterial input function and perfusion measured by quantitative positron emission tomography with H(2)[(15)O]. Regression to positron emission tomography measurements enabled conversion of dynamic susceptibility contrast MR to a physiologic scale. Regression analysis for localized arterial input function gave estimates of a scaling factor for quantitation that described perfusion accurately in patients with substantial variability in hemodynamic impairment.

High dynamic-range magnetic resonance spectroscopy (MRS) time-domain signal analysis.

In the absence of water signal suppression, the proton magnetic resonance spectroscopy ((1)H MRS) in vivo water resonance signal-to-noise ratio (SNR) is orders of magnitude larger than the SNR of all the other resonances. In this case, because the high-SNR water resonance dominates the data, it is difficult to obtain reliable parameter estimates for the low SNR resonances. Herein, a new model is described that offers a solution to this problem. In this model, the time-domain signal for the low SNR resonances is represented as the conventional sum of exponentially decaying complex sinusoids. However, the time-domain signal for the high SNR water resonance is assumed to be a complex sinusoid whose amplitude is slowly varying from pure exponential decay and whose phase is slowly varying from a constant frequency. Thus, the water resonance has only an instantaneous amplitude and frequency. The water signal is neither filtered nor subtracted from the data. Instead, Bayesian probability theory is used to simultaneously estimate the frequencies, decay-rate constants, and amplitudes for all the low SNR resonances, along with the water resonance's time-dependent amplitude and phase. While computationally intensive, this approach models all of the resonances, including the water and the metabolites of interest, to within the noise level.

Optimal decay rate constant estimates from phased array data utilizing joint Bayesian analysis.

Since their initial description, phased array coils have become increasingly popular due to their ease of customization for various applications. Numerous methods for combining data from individual channels have been proposed that attempt to optimize the SNR of the resultant images. One issue that has received comparatively little attention is how to apply these combination techniques to a series of images obtained from phased array coils that are then analyzed to produce quantitative estimates of tissue parameters. Herein, instead of the typical goal of maximizing the SNR in a single image, we are interested in maximizing the accuracy and precision of parameter estimates that are obtained from a series of such images. Our results demonstrate that a joint Bayesian analysis offers a "worry free" method for obtaining optimal parameter estimates from data generated by multiple coils (channels) from a single object (source). We also compare the properties of common channel combination techniques under different conditions to the results obtained from the joint Bayesian analysis. If the noise variance is constant for all channels, a sensitivity weighted average provides parameter estimates equivalent to the joint analysis. If both the noise variance and signal intensity are similar in all channels, a simple channel average gives an adequate result. However, if the noise variance differs between channels, an "ideal weighted" approach should be applied, where data are combined after weighting by the channel amplitude divided by the noise variance. Only this "ideal weighting" provides results similar to the automatic-weighting inherent in the joint Bayesian approach.

Magnetization transfer induced biexponential longitudinal relaxation.

Longitudinal relaxation of brain water (1)H magnetization in mammalian brain in vivo is typically analyzed on a per-voxel basis using a monoexponential model, thereby assigning a single relaxation time constant to all (1)H magnetization within a given voxel. This approach was tested by obtaining inversion recovery (IR) data from gray matter of rats at 64 exponentially spaced recovery times. Using Bayesian probability for model selection, brain water data were best represented by a biexponential function characterized by fast and slow relaxation components. At 4.7T, the amplitude fraction of the rapidly relaxing component is 3.4% +/- 0.7% with a rate constant of 44 +/- 12 s(-1) (mean +/- SD; 174 voxels from four rats). The rate constant of the slow relaxing component is 0.66 +/- 0.04 s(-1). At 11.7T, the corresponding values are 6.9% +/- 0.9%, 19 +/- 5 s(-1), and 0.48 +/- 0.02 s(-1) (151 voxels from four rats). Several putative mechanisms for biexponential relaxation behavior were evaluated, and magnetization transfer (MT) between bulk water protons and nonaqueous protons was determined to be the source of biexponential longitudinal relaxation. MR methods requiring accurate quantification of longitudinal relaxation may need to take this effect explicitly into account.

Microstructural changes of the baboon cerebral cortex during gestational development reflected in magnetic resonance imaging diffusion anisotropy.

Cerebral cortical development involves complex changes in cellular architecture and connectivity that occur at regionally varying rates. Using diffusion tensor magnetic resonance imaging (DTI) to analyze cortical microstructure, previous studies have shown that cortical maturation is associated with a progressive decline in water diffusion anisotropy. We applied high-resolution DTI to fixed postmortem fetal baboon brains and characterized regional changes in diffusion anisotropy using surface-based visualization methods. Anisotropy values vary within the thickness of the cortical sheet, being higher in superficial layers. At a regional level, anisotropy at embryonic day 90 (E90; 0.5 term; gestation lasts 185 d in this species) is low in allocortical and periallocortical regions near the frontotemporal junction and is uniformly high throughout isocortex. At E125 (0.66 term), regions having relatively low anisotropy (greater maturity) include cortex in and near the Sylvian fissure and the precentral gyrus. By E146 (0.8 term), cortical anisotropy values are uniformly low and show less regional variation. Expansion of cortical surface area does not occur uniformly in all regions. Measured using surface-based methods, cortical expansion over E125-E146 was larger in parietal, medial occipital, and lateral frontal regions than in inferior temporal, lateral occipital, and orbitofrontal regions. However, the overall correlation between the degree of cortical expansion and cortical anisotropy is modest. These results extend our understanding of cortical development revealed by histologic methods. The approach presented here can be applied in vivo to the study of normal brain development and its disruption in human infants and experimental animal models.

Bayesian estimation of the underlying bone properties from mixed fast and slow mode ultrasonic signals.

We recently proposed that the observed apparent negative dispersion in bone can arise from the interference between fast wave and slow wave modes, each exhibiting positive dispersion [Marutyan et al., J. Acoust. Soc. Am. 120, EL55-EL61 (2006)]. In the current study, we applied Bayesian probability theory to solve the inverse problem: extracting the underlying properties of bone. Simulated mixed mode signals were analyzed using Bayesian probability. The calculations were implemented using the Markov chain Monte Carlo with simulated annealing to draw samples from the marginal posterior probability for each parameter.