Purpose: To demonstrate the feasibility of whole-brain multidimensional T 1 -T 2 relaxation imaging on a portable 0.064 T MRI scanner. Methods: A two-dimensional inversion-recovery, fast spin-echo acquisition was used to jointly encode T 1 and T 2 relaxation. Joint relaxation distributions were reconstructed voxel-wise using marginal-distribution constrained optimization (MADCO). The approach was evaluated in a quantitative relaxation MRI phantom and subsequently applied in vivo in a healthy volunteer. Joint distributions, marginal distributions, distribution-derived measures, and statistical dependencies between the two relaxation dimensions were examined. Results: Phantom joint relaxation estimates showed good overall agreement with mono-exponential relaxation measurements and previously reported values at 0.064 T. The in vivo acquisition provided whole-brain coverage over a broad range of relaxation weightings and tissue contrasts. Reconstructed joint T 1 -T 2 distributions showed spatially organized features across the relaxation space, including broad and overlapping relaxation components that were not fully represented by single-value relaxation maps or by either 1D marginal distribution alone. Selected regions of the joint relaxation space produced component maps with distinct spatial patterns. Statistical analysis further showed a dependence between the two relaxation dimensions, supporting the presence of added information in the joint distribution that is lost when treating T 1 and T 2 separately. Conclusion: Whole-brain multidimensional T 1 -T 2 relaxation imaging is clinically feasible on a portable 0.064 T MRI scanner. Joint relaxation distributions provide information beyond single-value relaxation mapping and may support further developments of quantitative multidimensional imaging at ultra-low field.

The majority of MR-based brain imaging methods provides macroscopic information averaged over the entire imaging voxel. Yet tissue composition and microstructure are heterogeneous within the cubic millimeter-sized MRI voxel that contains numerous distinct water pools at mesoscopic, microscopic, and nanoscopic length scales. Accurately measuring their individual characteristics in live human brain has the potential to reveal hidden salient meso/micro-structural features and uncover subtle changes that may occur in development, neurological disorders, trauma, etc. Nevertheless, because of many technical and scientific challenges, there is a dearth of robust, quantitative methods to probe tissue water dynamics at these subvoxel length scales. Here we present a novel empirical spectroscopic diffusion MRI method that estimates the probability density function (pdf) of diffusion tensors, i.e., the diffusion tensor distribution (DTD) in the human brain in-vivo . Our method entails performing a multi-dimensional Inverse Laplace Transform (ILT) which is generally an ill-posed and ill-conditioned problem. However, we overcome these obstacles using a hierarchy of lower-dimensional marginal distributions of the DTD estimated from diffusion weighted (DW) signals obtained from single, double, and triple pulsed-field gradient (PFG) experiments. Iteratively applying this hierarchy of marginal distribution progressively shrinks the space of admissible solutions. We extensively vet this framework with simulated DWI data obtained from realistic DTD motifs that mimic different cell and tissue properties seen in the brain. We then experimentally test our approach in vivo in brains of healthy normal human subjects. We segment the reconstructed DTD within a voxel to identify signatures of different tissue and cell types, and cluster these DTDs to identify various water pools. We use the high dimensional spectrum to robustly remove the free water compartment that often confounds tissue microstructure. We take ensemble averages of invariants of the micro-diffusion tensors, and measure and map their distributions to visualize salient intrinsic mesoscopic features. Since DTD MRI subsumes DTI, we also compute the family of DTI-derived quantitative imaging biomarkers from the moments of the distributions of the mean diffusivity and FA derived from the DTD. Our approach has great translational potential, revealing new microstructural features not observed previously observed in in vivo MRI.

The mechanical stiffness of brain parenchyma varies across physiological states and pathophysiological conditions, such as during normal and abnormal development, in degenerative diseases and disorders, like Alzheimer's disease and traumatic brain injury (TBI), neuronal activation, and sleep via the glymphatic brain waste clearance mechanism. Despite its biological and clinical importance, relatively few techniques exist to measure and map mechanical properties of brain tissue non-invasively and in vivo . MR elastography (MRE) is an established method that has been widely used to estimate tissue stiffness in the liver by applying mechanical waves using an external tamper and measuring their resulting deformations. However, applying MRE in the brain is more challenging due to the skull and cerebrospinal fluid (CSF), which impede mechanical wave propagation, and tissue mechanical anisotropy, which requires a 4 th -order tensor description. In this study, we propose using the intrinsic deformation of brain tissue caused by periodic cardiac pulsation to measure the 4 th -order stiffness tensor throughout the brain while simultaneously estimating the 2 nd -order diffusion tensor in each voxel throughout the cardiac cycle, which we use as a priori information in the reconstruction of the stiffness tensor. While the DTI-derived mean diffusivity (MD) appears uniform throughout brain parenchyma, stiffness maps obtained at about 1 Hz (i.e., at the fundamental cardiac frequency) show contrast within gray matter, and within white matter pathways such as along the corpus callosum, internal capsule, corona radiata, etc. Generally, stiffness differences at internal tissue boundaries are expected to lead to local stress concentration, which may predispose tissues to damage in TBI. Therefore, our novel tamperless MRE method has the potential to not only identify such interfaces, but assess changes in tissue stiffness there that might occur following injury.

Imaging the live human brain at the mesoscopic scale is a desideratum in basic and clinical neurosciences. Despite the promise of diffusion MRI, the lack of an accurate model relating the measured signal and the associated microstructure has hampered its success. The widely used diffusion tensor MRI (DTI) model assumes an anisotropic Gaussian diffusion process in each voxel, but lacks the ability to capture intravoxel heterogeneity. This study explores the extension of the DTI model to mesoscopic length scales by use of the diffusion tensor distribution (DTD) model, which assumes a Gaussian diffusion process in each subvoxel. DTD MRI has shown promise in addressing some limitations of DTI, particularly in distinguishing among different types of brain cancers and elucidating multiple fiber populations within a voxel. However, its validity in live brain tissue has never been established. Here, multiple diffusion-encoded (MDE) data were acquired in the living human brain using a 3 Tesla MRI scanner with large diffusion weighting factors. Two different diffusion times (Δ = 37, 74 ms) were employed, with other scanning parameters fixed to assess signal decay differences. In vivo diffusion-weighted signals in gray and white matter were nearly identical at the two diffusion times. Fitting the signals to the DTD model yielded indistinguishable results, except in the cerebrospinal fluid (CSF)-filled voxels likely due to pulsatile flow. Overall, the study supports the time invariance of water diffusion at the mesoscopic scale in live brain parenchyma, extending the validity of the anisotropic Gaussian diffusion model in clinical brain imaging.

Subcortical nuclei and other deep brain structures play essential roles in regulating the central and peripheral nervous systems. However, many of these nuclei and their subregions are challenging to identify and delineate in conventional MRI due to their small size, hidden location, and often subtle contrasts compared to neighboring regions. To address these limitations, we scanned the whole brain of the marmoset monkeys in ex vivo using a clinically feasible diffusion MRI method, called the mean apparent propagator (MAP)-MRI, along with T2W and MTR (T1-like contrast) images acquired at 7 Tesla. Additionally, we registered these multimodal MRI volumes to the high-resolution images of matched whole-brain histology sections with seven different stains obtained from the same brain specimens. At high spatial resolution, the microstructural parameters and fiber orientation distribution functions derived with MAP-MRI can distinguish the subregions of many subcortical and deep brain structures, including fiber tracts of different sizes and orientations. The good correlation with multiple but distinct histological stains from the same brain serves as a thorough validation of the structures identified with MAP-MRI and other MRI parameters. Moreover, the anatomical details of deep brain structures found in the volumes of MAP-MRI parameters are not visible in conventional T1W or T2W images. The high-resolution mapping using novel MRI contrasts, combined and correlated with histology, can elucidate structures that were previously invisible radiologically. Thus, this multimodal approach offers a roadmap toward identifying salient brain areas in vivo in future neuroradiological studies. It also provides a useful anatomical standard reference for the region definition of subcortical targets and the generation of a 3D digital template atlas for the marmoset brain research (Saleem et al., 2023). Additionally, we conducted a cross-species comparison between marmoset and macaque monkeys using results from our previous studies (Saleem et al., 2021). We found that the two species had distinct patterns of iron distribution in subregions of the basal ganglia, red nucleus, and deep cerebellar nuclei, confirmed with T2W MRI and histology.

A custom segmentation workflow was applied to ex vivo high-field MR images of rat brains acquired following in vivo intraventricular contrast agent infusion to generate maps of the perivascular spaces (PVS). The resulting perivascular network segmentations enabled analysis of perivascular connections to the ventricles, parenchymal solute clearance, and dispersive solute transport within PVS. Numerous perivascular connections between the brain surface and the ventricles suggest the ventricles integrate into a PVS-mediated clearance system and raise the possibility of cerebrospinal fluid (CSF) return from the subarachnoid space to the ventricles via PVS. Assuming rapid solute exchange between the PVS and CSF spaces primarily by advection, the extensive perivascular network decreased the mean clearance distance from parenchyma to the nearest CSF compartment resulting in an over 21-fold reduction in the estimated diffusive clearance time scale, irrespective of solute diffusivity. This corresponds to an estimated diffusive clearance time scale under 10 min for amyloid-beta which suggests that the widespread distribution of PVS may render diffusion an effective parenchymal clearance mechanism. Additional analysis of oscillatory solute dispersion within PVS indicates that advection rather than dispersion is likely the primary transport mechanism for dissolved compounds greater than 66 kDa in the long (> 2 mm) perivascular segments identified here, although dispersion may be significant for smaller compounds in shorter perivascular segments.

Neural tissue microstructure plays an important role in developmental, physiological and pathophysiological processes. Diffusion tensor distribution (DTD) MRI helps probe subvoxel heterogeneity by describing water diffusion within a voxel using an ensemble of non-exchanging compartments characterized by a probability density function of diffusion tensors. In this study, we provide a new framework for acquiring multiple diffusion encoding (MDE) images and estimating DTD from them in the human brain in vivo. We interfused pulsed field gradients (iPFG) in a single spin echo to generate arbitrary b-tensors of rank one, two, or three without introducing concomitant gradient artifacts. Employing well-defined diffusion encoding parameters we show that iPFG retains salient features of a traditional multiple-PFG (mPFG/MDE) sequence while reducing the echo time and coherence pathway artifacts thereby extending its applications beyond DTD MRI. Our DTD is a maximum entropy tensor-variate normal distribution whose tensor random variables are constrained to be positive definite to ensure their physicality. In each voxel, the second-order mean and fourth-order covariance tensors of the DTD are estimated using a Monte Carlo method that synthesizes micro-diffusion tensors with corresponding size, shape, and orientation distributions to best fit the measured MDE images. From these tensors we obtain the spectrum of diffusion tensor ellipsoid sizes and shapes, and the microscopic orientation distribution function (μODF) and microscopic fractional anisotropy (μFA) that disentangle the underlying heterogeneity within a voxel. Using the DTD-derived μODF, we introduce a new method to perform fiber tractography capable of resolving complex fiber configurations. The results revealed microscopic anisotropy in various gray and white matter regions and skewed MD distributions in cerebellar gray matter not observed previously. DTD MRI tractography captured complex white matter fiber organization consistent with known anatomy. DTD MRI also resolved some degeneracies associated with diffusion tensor imaging (DTI) and elucidated the source of diffusion heterogeneity which may help improve the diagnosis of various neurological diseases and disorders.

Neural tissue microstructure plays a key role in developmental, physiological and pathophysiological processes. In the continuing quest to characterize it at ever finer length scales, we use a novel diffusion tensor distribution (DTD) paradigm to probe microstructural features much smaller than the nominal MRI voxel size. We first assume the DTD is a normal tensor variate distribution constrained to lie on the manifold of positive definite matrices, characterized by a mean and covariance tensor. We then estimate the DTD using Monte Carlo signal inversion combined with parsimonious model selection framework that exploits a hierarchy of symmetries of mean and covariance tensors. High resolution multiple pulsed field gradient (mPFG) MRI measurements were performed on a homogeneous isotropic diffusion phantom (PDMS) for control, and excised visual cortex and spinal cord of macaque monkey to investigate the capabilities of DTD MRI in revealing neural tissue microstructural features using strong gradients not typically available in clinical MRI scanners. DTD-derived stains and glyphs, which disentangle size, shape, and orientation heterogeneities of microscopic diffusion tensors, are presented for all samples along with the distribution of the mean diffusivity (MD) within each voxel. We also present a new glyph to visualize the symmetric (kurtosis) and asymmetric parts of the fourth-order covariance tensor. An isotropic mean diffusion tensor and zero covariance tensor was found for the isotropic PDMS phantom, as expected, while the covariance tensor (both symmetric and asymmetric parts) for neural tissue was non-zero indicating that the kurtosis tensor may not be sufficient to fully describe the microstructure. Cortical layers were clearly delineated in the higher moments of the MD spectrum consistent with histology, and microscopic anisotropy was detected in both gray and white matter of neural tissue. DTD MRI captures crossing and splaying white matter fibers penetrating into the cortex, and skewed fiber diameter distributions in the white matter tracts within the cortex and spinal cord. DTD MRI was also shown to subsume diffusion tensor imaging (DTI) while providing additional microstructural information about tissue heterogeneity and microscopic anisotropy within each voxel.

The ability to characterize heterogeneous and anisotropic water diffusion processes within macroscopic MRI voxels non-invasively and in vivo is a desideratum in biology, neuroscience, and medicine. While an MRI voxel may contain approximately a microliter of tissue, our goal is to examine intravoxel diffusion processes on the order of picoliters. Here we propose a new theoretical framework and efficient experimental design to describe and measure such intravoxel structural heterogeneity and anisotropy. We assume that a constrained normal tensor-variate distribution (CNTVD) describes the variability of positive definite diffusion tensors within a voxel which extends its applicability to a wide range of b-values while preserving the richness of diffusion tensor distribution (DTD) paradigm unlike existing models. We introduce a new Monte Carlo (MC) scheme to synthesize realistic 6D DTD numerical phantoms and invert the MR signal. We show that the signal inversion is well-posed and estimate the CNTVD parameters parsimoniously by exploiting the different symmetries of the mean and covariance tensors of CNTVD. The robustness of the estimation pipeline is assessed by adding noise to calculated MR signals and compared with the ground truth. A family of invariant parameters and glyphs which characterize microscopic shape, size and orientation heterogeneity within a voxel are also presented.

The first phase of the Human Connectome Project pioneered advances in MRI technology for mapping the macroscopic structural connections of the living human brain through the engineering of a whole-body human MRI scanner equipped with maximum gradient strength of 300 mT/m, the highest ever achieved for human imaging. While this instrument has made important contributions to the understanding of macroscale connectional topology, it has also demonstrated the potential of dedicated high-gradient performance scanners to provide unparalleled in vivo assessment of neural tissue microstructure. Building on the initial groundwork laid by the original Connectome scanner, we have now embarked on an international, multi-site effort to build the next-generation human 3T Connectome scanner (Connectome 2.0) optimized for the study of neural tissue microstructure and connectional anatomy across multiple length scales. In order to maximize the resolution of this in vivo microscope for studies of the living human brain, we will push the diffusion resolution limit to unprecedented levels by (1) nearly doubling the current maximum gradient strength from 300 mT/m to 500 mT/m and tripling the maximum slew rate from 200 T/m/s to 600 T/m/s through the design of a one-of-a-kind head gradient coil optimized to minimize peripheral nerve stimulation; (2) developing high-sensitivity multi-channel radiofrequency receive coils for in vivo and ex vivo human brain imaging; (3) incorporating dynamic field monitoring to minimize image distortions and artifacts; (4) developing new pulse sequences to integrate the strongest diffusion encoding and highest spatial resolution ever achieved in the living human brain; and (5) calibrating the measurements obtained from this next-generation instrument through systematic validation of diffusion microstructural metrics in high-fidelity phantoms and ex vivo brain tissue at progressively finer scales with accompanying diffusion simulations in histology-based micro-geometries. We envision creating the ultimate diffusion MRI instrument capable of capturing the complex multi-scale organization of the living human brain - from the microscopic scale needed to probe cellular geometry, heterogeneity and plasticity, to the mesoscopic scale for quantifying the distinctions in cortical structure and connectivity that define cyto- and myeloarchitectonic boundaries, to improvements in estimates of macroscopic connectivity.

Switching gradients generate eddy currents and mechanical vibrations of the gradient assembly causing errors in the gradient time integrals. This results in image distortions in k-space and inaccuracies in q-space imaging. The purpose of this work is to develop an MRI based unbiased measurement of the switched gradient impulse response function (sGIRF). A new gradient pattern, called the Tukey windowed Shifted Sine-Integral (Tw-SSI) pulse, is introduced to excite the gradient eigenmodes uniformly over a user-defined bandwidth. A 3D MRI-based method with Hadamard encoding was developed to map the spatiotemporal magnetic field generated after the excitation pulse to obtain the sGIRF for all the three gradient axes simultaneously. Compared to an energy-equivalent traditional trapezoidal pulse, the Tw-SSI pulse is able to excite the weak bandlimited cross-terms of the sGIRF by uniformly distributing the energy across eigenmodes. The developed field mapping method is sensitive enough to capture both the direct and cross-terms in the sGIRF. The various mechanical resonant modes of the gradient coils are also revealed, which were found to last longer than eddy currents in the shielded gradient coil studied. Tunable Tw-SSI pulse offers the flexibility to perform unbiased sGIRF measurements over a bandwidth of interest. Rapid MRI field mapping can be easily implemented in any MRI system. The method may be used to perform gradient pre-emphasis, to evaluate new gradient coil designs, and to characterize higher order shims.

Measuring and mapping the diffusion tensor distribution (DTD) via MRI holds the promise of revealing the tissue microstructure at sub-voxel resolution. Recent approaches have used the covariance of diffusion tensor as a means to characterize DTD within a voxel [1], [2]. It can be shown that several terms in the covariance matrix are not observable when using standard rank-1 b-matrix acquisition schemes using single pulsed field gradient (PFG) MR sequences, emphasizing the need for higher-rank b-matrix acquisitions for DTD imaging. Several pulse sequences have been introduced for higher-rank b-matrix encoding [2]–[4] but their applicability is limited by their long echo times and/or the lack of well-defined diffusion times. A new efficient, easy to implement b-matrix encoding strategy is presented which is capable of generating ranks 1, 2 and 3 b-matrices with well-defined diffusion times. The presented method is also immune to concomitant field errors which are thought to confound DTD estimation [5]. The developed method is tested on a macroscopic and microscopically isotropic polydimethylsiloxane (PDMS) phantom, and on excised rat brain tissue.

Investigating the mechanisms by which metabolic wastes are cleared from nervous tissue is important for understanding natural function and the pathophysiology of several neurological disorders including Alzheimer’s disease. Recent evidence suggests clearance may be the function of annular spaces around cerebral blood vessels, called perivascular spaces (PVS), through which cerebrospinal fluid (CSF) is transported from the subarachnoid space into brain parenchyma to exchange with interstitial fluid (also known as the glymphatic system). In this work, an MRI-based methodology was developed to reconstruct the PVS network in whole rat brain to better elucidate both PVS uptake and clearance pathways. MR visible tracer (Gd-albumin) was infused in vivo into the CSF-filled lateral ventricle followed by ex vivo high-resolution MR imaging at 17.6 T with an image voxel volume two orders of magnitude smaller than previously reported. Imaged tracer distribution patterns were reconstructed to obtain a more complete brain PVS network. Several PVS connections were repeatedly highlighted across different animals, and new PVS connections between ventricles and different parts of the brain parenchyma were revealed suggesting a possible role for the ventricles as a source or sink for solutes in the brain. In the future, this methodology may be applied to understand changes in the PVS network with disease.

Creeping flows govern many important physiological phenomena such as elevated interstitial fluid flows in tumors, glymphatic flows in the brain, among other applications. However, few methods exist to measure such slow flows non-invasively in optically opaque biological tissues in vivo. Phase-contrast MRI is a velocimetry technique routinely used in the clinic to measure fast flows in biological tissues, such as blood and cerebrospinal fluid (CSF), in the order of cm/s. Use of this technique to encode slower flows is hampered by diffusion weighting and phase error introduced by gradient hardware imperfections. In this study, a new PC-MRI technique is developed using stimulated echo preparation to overcome these challenges. Flows as slow as 1 mu m/s are measured and validated using controlled water flow through a pipe at 4.7 T. The error in measured flow rate obtained by integrating the measured velocity over the cross-sectional area of the pipe is less than 10%. The developed method was also able to capture slow natural convection flows appearing in liquids placed inside a horizontal bore magnet. Monitoring the 4D velocity vector field revealed that the natural convection flows decay exponentially with time. This method could be applied in future to study creeping flows, e.g. in tissue. (C) 2018 Elsevier Inc. All rights reserved.

BackgroundBrainstem gliomas are aggressive and difficult to treat. Growth of these tumors may be characterized with MRI methods.PurposeTo visualize longitudinal changes in tumor volume, vascular leakiness, and tissue microstructure in an animal model of brainstem glioma.Study TypeProspective animal model.Animal ModelMale Sprague–Dawley rats (n = 9) were imaged with 9L gliosarcoma cells infused into the pontine reticular formation of the brainstem. The MRI tumor microenvironment was studied at 3 and 10 days postimplantation of tumor cells.Field Strength/SequenceDiffusion tensor imaging (DTI) and dynamic contrast‐enhanced (DCE)‐MRI were performed at 4.7T using spin‐echo multislice echo planar imaging and gradient echo multislice imaging, respectively.AssessmentTumor leakiness was assessed by the forward volumetric transfer constant, Ktrans, estimated from DCE‐MRI data. Tumor structure was evaluated with fractional anisotropy (FA) obtained from DTI. Tumor volumes, delineated by a T1 map, T2‐weighted image, FA, and DCE signal enhancement were compared.Statistical TestsChanges in the assessed parameters within and across the groups (ie, rats 3 and 10 days post tumor cell implantation) were evaluated with Wilcoxon rank‐sum tests.ResultsDay 3 tumors were visible mainly on contrast‐enhanced images, while day 10 tumors were visible in both contrast‐enhanced and diffusion‐weighted images. Mean Ktrans at day 10 was 41% lower than at day 3 (P = 0.23). In day 10 tumors, FA was regionally lower in the tumor compared to normal tissue (P = 0.0004), and tumor volume, segmented based on FA map, was significantly smaller (P ≤ 0.05) than that obtained from other contrasts.Data ConclusionContrast‐enhanced MRI was found to be more sensitive in detecting early‐stage tumor boundaries than other contrasts. Areas of the tumor outlined by DCE‐MRI and DTI were significantly different. Over the observed period of tumor growth, average vessel leakiness decreased with tumor progression.Level of Evidence: 2Technical Efficacy: Stage 3J. Magn. Reson. Imaging 2019;49:1322–1332.

Systemic drug delivery to solid tumors involving macromolecular therapeutic agents is challenging for many reasons. Amongst them is their chaotic microvasculature which often leads to inadequate and uneven uptake of the drug. Localized drug delivery can circumvent such obstacles and convection-enhanced delivery (CED) - controlled infusion of the drug directly into the tissue - has emerged as a promising delivery method for distributing macromolecules over larger tissue volumes. In this study, a three-dimensional MR image-based computational porous media transport model accounting for realistic anatomical geometry and tumor leakiness was developed for predicting the interstitial flow field and distribution of albumin tracer following CED into the hind-limb tumor (KHT sarcoma) in a mouse. Sensitivity of the model to changes in infusion flow rate, catheter placement and tissue hydraulic conductivity were investigated. The model predictions suggest that 1) tracer distribution is asymmetric due to heterogeneous porosity; 2) tracer distribution volume varies linearly with infusion volume within the whole leg, and exponentially within the tumor reaching a maximum steady-state value; 3) infusion at the center of the tumor with high flow rates leads to maximum tracer coverage in the tumor with minimal leakage outside; and 4) increasing the tissue hydraulic conductivity lowers the tumor interstitial fluid pressure and decreases the tracer distribution volume within the whole leg and tumor. The model thus predicts that the interstitial fluid flow and drug transport is sensitive to porosity and changes in extracellular space. This image-based model thus serves as a potential tool for exploring the effects of transport heterogeneity in tumors.

Recently MR image based computational models are being developed to assist targeted drug delivery in the brain by helping determine appropriate catheter position, drug dose among others to achieve the desired drug distribution [1–3]. Such a planning might be important to prevent damaging healthier tissues because many of the drugs (e.g. chemotherapeutic agents) are usually toxic and needs to be concentrated in specific regions of interest (e.g. tumor). However, for the image based model to make accurate predictions, it is important to segment the image and assign appropriate tissue properties such as hydraulic conductivity which are known to vary significantly within the brain. For example, it has been experimentally found that drugs injected into brain parenchyma get preferentially transported along the white matter tracts compared to the gray matter regions [4]. Segmenting MR images is a challenging task since the pixel intensities between different regions often overlap, hence traditional approaches based on thresholds might not provide reliable results. In this study, we used multi-layered perceptron (MLP) neural network to segment rat brain MR images into 3 different regions namely white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF).

Recent advances in the treatment of cancer involving therapeutic agents have shown promising results. However, treatment efficacy can be limited due to inadequate and uneven uptake in solid tumors, thereby making the prediction of drug transport important for developing effective therapeutic strategies. In this study, a patient-specific computational porous media model (voxelized model) was developed for predicting the interstitial flow field and distribution of a systemically delivered magnetic resonance (MR) visible tracer in a tumor. The benefits of a voxel approach include less labor and less computational time (approximately an order of magnitude reduction compared to the traditional computational fluid dynamics (CFD) approach developed earlier by our group). The model results were compared with that obtained from a previous approach based on unstructured meshes along with MR-measured tracer concentration data within tumors, using statistical analysis and qualitative representations. The statistical analysis indicated the similarity between the structured and unstructured models' results with a low root mean square error (RMS) and a high correlation coefficient. The voxelized model captured features of the flow field and tracer distribution such as high interstitial fluid pressure inside the tumor and the heterogeneous distribution of the tracer. Predictions of tracer distribution by the voxelized approach also resulted in low RMS error when compared with MR-measured data over a 1 h time course. The similarity in the voxelized model results with experiment and the nonvoxelized model predictions were maintained across three different tumors. Overall, the voxelized model serves as a reliable and swift alternative to approaches using unstructured meshes in predicting extracellular transport within tumors.

We present a computational model that highlights the role of basal ganglia (BG) in generating simple reaching movements. The model is cast within the reinforcement learning (RL) framework with correspondence between RL components and neuroanatomy as follows: dopamine signal of substantia nigra pars compacta as the temporal difference error, striatum as the substrate for the critic, and the motor cortex as the actor. A key feature of this neurobiological interpretation is our hypothesis that the indirect pathway is the explorer. Chaotic activity, originating from the indirect pathway part of the model, drives the wandering, exploratory movements of the arm. Thus, the direct pathway subserves exploitation, while the indirect pathway subserves exploration. The motor cortex becomes more and more independent of the corrective influence of BG as training progresses. Reaching trajectories show diminishing variability with training. Reaching movements associated with Parkinson's disease (PD) are simulated by reducing dopamine and degrading the complexity of indirect pathway dynamics by switching it from chaotic to periodic behavior. Under the simulated PD conditions, the arm exhibits PD motor symptoms like tremor, bradykinesia and undershooting. The model echoes the notion that PD is a dynamical disease.