A fractal based model of diffusion MRI in cortical grey matter

Diffusion Weighted Magnetic Resonance (DWMR) Imaging is an important tool in diagnostic neuroimaging, but the biophysical basis of the DWMR signal from biological tissue is not entirely understood. Testable, theoretical models relating the DWMR signal to the tissue, therefore, are crucial. This work presents a toy version of such a model of water DWMR signals in brain grey matter. The model is based on biophysical characteristics and all model parameters are directly interpretable as biophysical properties such as diffusion coefficients and membrane permeability allowing comparison to known values. In the model, a computer generated Diffusion Limited Aggregation (DLA) cluster is used to describe the collected membrane morphology of the cells in cortical grey matter. Using credible values for all model parameters model output is compared to experimental DWMR data from normal human grey matter and it is found that this model does reproduce the observed signal. The model is then used for simulating the effect on the DWMR signal of cellular events known to occur in ischemia. These simulations show that a combination of effects is necessary to reproduce the signal changes observed in ischemic tissue and demonstrate that the model has potential for interpreting DWMR signal origins and tissue changes in ischemia. Further studies are required to validate these results and compare them with other modeling approaches. With such models, it is anticipated that sensitivity and specificity of DWMR in tissues can be improved, leading to better understanding of the origins of MR signals in biological tissues, and improved diagnostic capability

Making sense: dopamine activates conscious self-monitoring through medial prefrontal cortex

When experiences become meaningful to the self, they are linked to synchronous activity in a paralimbic network of self-awareness and dopaminergic activity. This network includes medial prefrontal and medial parietal/posterior cingulate cortices, where transcranial magnetic stimulation may transiently impair self-awareness. Conversely, we hypothesize that dopaminergic stimulation may improve self-awareness and metacognition (i.e., the ability of the brain to consciously monitor its own cognitive processes). Here, we demonstrate improved noetic (conscious) metacognition by oral administration of 100 mg dopamine in minimal self-awareness. In a separate experiment with extended self-awareness dopamine improved the retrieval accuracy of memories of self-judgment (autonoetic, i.e., explicitly self-conscious) metacognition. Concomitantly, magnetoencephalography (MEG) showed increased amplitudes of oscillations (power) preferentially in the medial prefrontal cortex. Given that electromagnetic activity in this region is instrumental in self-awareness, this explains the specific effect of dopamine on explicit self-awareness and autonoetic metacognition

Automated Decision-Support System for Prediction of Treatment Responders in Acute Ischemic Stroke

MRI is widely used in the assessment of acute ischemic stroke. In particular, it identifies the mismatch between hypoperfused and the permanently damaged tissue, the PWI-DWI mismatch volume. It is used to help triage patients into active or supportive treatment pathways. COMBAT Stroke is an automated software tool for estimating the mismatch volume and ratio based on MRI. Herein, we validate the decision made by the software with actual clinical decision rendered. Furthermore, we evaluate the association between treatment decisions (both automated and actual) and outcomes. COMBAT Stroke was used to determine PWI-DWI mismatch volume and ratio in 228 patients from two European multi-center stroke databases. We performed confusion matrix analysis to summarize the agreement between the automated selection and the clinical decision. Finally, we evaluated the clinical and imaging outcomes of the patients in the four entries of the confusion matrix (true positive, true negative, false negative, and false positive). About 186 of 228 patients with acute stroke underwent thrombolytic treatment, with the remaining 42 receiving supportive treatment only. Selection based on radiographic criteria using COMBAT Stroke classified 142 patients as potential candidates for thrombolytic treatment and 86 for supportive treatment; 60% sensitivity and 29% specificity. The patients deemed eligible for thrombolytic treatment by COMBAT Stroke demonstrated significantly higher rates of compromised tissue salvage, less neurological deficit, and were more likely to experience thrombus dissolving and reestablishment of normal blood flow at 24 h follow-up compared to those who were treated without substantial PWI-DWI mismatch. These results provide evidence that COMBAT Stroke, in addition to clinical assessment, may offer an optimal framework for a fast, efficient, and standardized clinical support tool to select patients for thrombolysis in acute ischemic stroke

The Larmor frequency shift of a white matter magnetic microstructure model with multiple sources

Magnetic susceptibility imaging may provide valuable information about chemical composition and microstructural organization of tissue. However, its estimation from the MRI signal phase is particularly difficult as it is sensitive to magnetic tissue properties ranging from the molecular to macroscopic scale. The MRI Larmor frequency shift measured in white matter (WM) tissue depends on the myelinated axons and other magnetizable sources such as iron-filled ferritin. We have previously derived the Larmor frequency shift arising from a dense media of cylinders with scalar susceptibility and arbitrary orientation dispersion. Here we extend our model to include microscopic WM susceptibility anisotropy as well as spherical inclusions with scalar susceptibility to represent subcellular structures, biologically stored iron etc. We validate our analytical results with computer simulations and investigate the feasibility of estimating susceptibility using simple iterative linear least squares without regularization or preconditioning. This is done in a digital brain phantom synthesized from diffusion MRI (dMRI) measurements of an ex vivo mouse brain at ultra-high field.Comment: 70 pages, 14 figure

Dose Optimization for Using the Contrast Agent Gadofosveset in Magnetic Resonance Imaging (MRI) of Domestic Pig Brain

Pigs are useful models in stroke research, and Magnetic Resonance Imaging (MRI) is a useful tool for measurements of brain pathophysiology. Perfusion Weighed Imaging (PWI) with standard Gd-based chelates (i.e. gadobutrol) provides crucial information about breakdown of the Blood-Brain-Barrier (BBB) in patients. Gadofosveset is also a Gd-based contrast agent, but with a higher binding to serum albumin. The prolonged plasma-half life of gadofosveset allows the acquisition of steady state angiographies, which may increase the sensitivity for detection of BBB leakage. We hypothesize that the contrast dosage with gadofosveset can be optimized for PWI and subsequent steady-state Magnetic Resonance Angiography (MRA) in pigs. Anesthetized domestic pigs (females; N=6) were MRI scanned four times in one day: they were initially imaged during a standard gadobutrol bolus injection (0.1 mmol/kg). Then they received three successive gadofosveset bolus injections of varying dosages (0.015-0.09 mmol/kg). Based on projection from our data, we suggest that a bolus injection of 0.0916 mmol/kg gadofosveset would yield contrast similar to that of a standard dose of 0.1 mmol/kg gadobutrol in dynamic susceptibility contrast MRI at 3 T. In conclusion, our results demonstrate the feasibility of gadofosveset based PWI in pig brain research. The relaxation and plasma half-life properties allow detailed steady-state MRA angiographies and may prove useful in detecting subtle BBB disruption of significance in stroke models and human patients

Model-based inference from microvascular measurements: Combining experimental measurements and model predictions using a Bayesian probabilistic approach

Objective: In vivo imaging of the microcirculation and network-oriented modeling have emerged as powerful means of studying microvascular function and understanding its physiological significance. Network-oriented modeling may provide the means of summarizing vast amounts of data produced by high-throughput imaging techniques in terms of key, physiological indices. To estimate such indices with sufficient certainty, however, network-oriented analysis must be robust to the inevitable presence of uncertainty due to measurement errors as well as model errors. Methods: We propose the Bayesian probabilistic data analysis framework as a means of integrating experimental measurements and network model simulations into a combined and statistically coherent analysis. The framework naturally handles noisy measurements and provides posterior distributions of model parameters as well as physiological indices associated with uncertainty. Results: We applied the analysis framework to experimental data from three rat mesentery networks and one mouse brain cortex network. We inferred distributions for more than 500 unknown pressure and hematocrit boundary conditions. Model predictions were consistent with previous analyses, and remained robust when measurements were omitted from model calibration. Conclusion: Our Bayesian probabilistic approach may be suitable for optimizing data acquisition and for analyzing and reporting large data sets acquired as part of microvascular imaging studies