Evaluation of cerebral microcirculation in a mouse model of systemic inflammation.

Significance: Perturbations in the microcirculatory system have been observed in neurological conditions, such as Alzheimer's disease or systemic inflammation. However, changes occurring at the level of the capillary are difficult to translate to biomarkers that could be measured macroscopically. Aim: We aim to evaluate whether transit time changes reflect capillary stalling and to what degree. Approach: We employ a combined spectral optical coherence tomography (OCT) and fluorescence optical imaging (FOI) system to investigate the relation between capillary stalling and transit time in a mouse model of systemic inflammation induced by intraperitoneal injection of lipopolysaccharide. Angiograms are obtained using OCT, and fluorescence signal images are acquired by the FOI system upon intravenous injection of fluorescein isothiocyanate via a catheter inserted into the tail vein. Results: Our findings reveal that lipopolysaccharide (LPS) administration significantly increases both the percentage and duration of capillary stalling compared to mice receiving a 0.9% saline injection. Moreover, LPS-induced mice exhibit significantly prolonged arteriovenous transit time compared to control mice. Conclusions: These observations suggest that capillary stalling, induced by inflammation, modulates cerebral mean transit time, a measure that has translational potential.

Phase contrast reflectance confocal brain imaging at 1650 nm.

Significance: The imaging depth of microscopy techniques is limited by the ability of light to penetrate biological tissue. Recent research has addressed this limitation by combining a reflectance confocal microscope with the NIR-II (or shortwave infrared) spectrum. This approach offers significant imaging depth, is straightforward in design, and remains cost-effective. However, the imaging system, which relies on intrinsic signals, could benefit from adjustments in its optical design and post-processing methods to differentiate cortical cells, such as neurons and small blood vessels. Aim: We implemented a phase contrast detection scheme to a reflectance confocal microscope using NIR-II spectral range as illumination. Approach: We analyzed the features retrieved in the images while testing the imaging depth. Moreover, we introduce an acquisition method for distinguishing dynamic signals from the background, allowing the creation of vascular maps similar to those produced by optical coherence tomography. Results: The phase contrast implementation is successful to retrieve deep images in the cortex up to 800 µm using a cranial window. Vascular maps were retrieved at similar cortical depth and the possibility of combining multiple images can provide a vessel network. Conclusions: Phase contrast reflectance confocal microscopy can improve the outlining of cortical cell bodies. With the presented framework, angiograms can be retrieved from the dynamic signal in the biological tissue. Our work presents an optical implementation and analysis techniques from a former microscope design.

Impact of stalling events on microcirculatory hemodynamics in the aged brain.

OBJECTIVE: The role of cerebral microvasculature in cognitive dysfunction can be investigated by identifying the impact of blood flow on cortical tissue oxygenation. In this paper, the impact of capillary stalls on microcirculatory characteristics such as flow and hematocrit (Ht) in the cortical angioarchitecture is studied. METHODS: Using a deterministic mathematical model to simulate blood flow in a realistic mouse cortex, hemodynamics parameters, including pressure, flow, vessel diameter-adjustable hematocrit, and transit time are calculated as a function of stalling events. RESULTS: Using a non-linear plasma skimming model, it is observed that Ht increases in the penetrating arteries from the pial vessels as a function of cortical depth. The incidence of stalling on Ht distribution along the blood network vessels shows reduction of RBCs around the tissue near occlusion sites and decreased Ht concentration downstream from the blockage points. Moreover, upstream of the occlusion, there is a noticeable increase of the Ht, leading to larger flow resistance due to higher blood viscosity. We predicted marked changes in transit time behavior due to stalls which match trends observed in mice in vivo. CONCLUSIONS: These changes to blood cell quantity and quality may be implicated in the development of Alzheimer's disease and contribute to the course of the illness.

Vascular synthesis based on hemodynamic efficiency principle recapitulates measured cerebral circulation properties in the human brain.

Quantifying anatomical and hemodynamical properties of the brain vasculature in vivo is difficult due to limited spatiotemporal resolution neuroimaging, variability between subjects, and bias between acquisition techniques. This work introduces a metabolically inspired vascular synthesis algorithm for creating a digital representation of the cortical blood supply in humans. Spatial organization and segment resistances of a cortical vascular network were generated. Cortical folding and macroscale arterial and venous vessels were reconstructed from anatomical MRI and MR angiography. The remaining network, including ensembles representing the parenchymal capillary bed, were synthesized following a mechanistic principle based on hydrodynamic efficiency of the cortical blood supply. We evaluated the digital model by comparing its simulated values with in vivo healthy human brain measurements of macrovessel blood velocity from phase contrast MRI and capillary bed transit times and bolus arrival times from dynamic susceptibility contrast. We find that measured and simulated values reasonably agree and that relevant neuroimaging observables can be recapitulated in silico. This work provides a basis for describing and testing quantitative aspects of the cerebrovascular circulation that are not directly observable. Future applications of such digital brains include the investigation of the organ-wide effects of simulated vascular and metabolic pathologies.

Determination of spinal tracer dispersion after intrathecal injection in a deformable CNS model.

Background: Traditionally, there is a widely held belief that drug dispersion after intrathecal (IT) delivery is confined locally near the injection site. We posit that high-volume infusions can overcome this perceived limitation of IT administration. Methods: To test our hypothesis, subject-specific deformable phantom models of the human central nervous system were manufactured so that tracer infusion could be realistically replicated in vitro over the entire physiological range of pulsating cerebrospinal fluid (CSF) amplitudes and frequencies. The distribution of IT injected tracers was studied systematically with high-speed optical methods to determine its dependence on injection parameters (infusion volume, flow rate, and catheter configurations) and natural CSF oscillations in a deformable model of the central nervous system (CNS). Results: Optical imaging analysis of high-volume infusion experiments showed that tracers spread quickly throughout the spinal subarachnoid space, reaching the cervical region in less than 10 min. The experimentally observed biodispersion is much slower than suggested by the Taylor-Aris dispersion theory. Our experiments indicate that micro-mixing patterns induced by oscillatory CSF flow around microanatomical features such as nerve roots significantly accelerate solute transport. Strong micro-mixing effects due to anatomical features in the spinal subarachnoid space were found to be active in intrathecal drug administration but were not considered in prior dispersion theories. Their omission explains why prior models developed in the engineering community are poor predictors for IT delivery. Conclusion: Our experiments support the feasibility of targeting large sections of the neuroaxis or brain utilizing high-volume IT injection protocols. The experimental tracer dispersion profiles acquired with an anatomically accurate, deformable, and closed in vitro human CNS analog informed a new predictive model of tracer dispersion as a function of physiological CSF pulsations and adjustable infusion parameters. The ability to predict spatiotemporal dispersion patterns is an essential prerequisite for exploring new indications of IT drug delivery that targets specific regions in the CNS or the brain.

A mechanistic pharmacokinetic model for intrathecal administration of antisense oligonucleotides.

Intrathecal administration is an important mode for delivering biological agents targeting central nervous system (CNS) diseases. However, current clinical practices lack a sound theorical basis for a quantitative understanding of the variables and conditions that govern the delivery efficiency and specific tissue targeting especially in the brain. This work presents a distributed mechanistic pharmacokinetic model (DMPK) for predictive analysis of intrathecal drug delivery to CNS. The proposed DMPK model captures the spatiotemporal dispersion of antisense oligonucleotides (ASO) along the neuraxis over clinically relevant time scales of days and weeks as a function of infusion, physiological and molecular properties. We demonstrate its prediction capability using biodistribution data of antisense oligonucleotide (ASO) administration in non-human primates. The results are in close agreement with the observed ASO pharmacokinetics in all key compartments of the central nervous system. The model enables determination of optimal injection parameters such as intrathecal infusion volume and duration for maximum ASO delivery to the brain. Our quantitative model-guided analysis is suitable for identifying optimal parameter settings to target specific brain regions with therapeutic drugs such as ASOs.

Mesh-free high-resolution simulation of cerebrocortical oxygen supply with fast Fourier preconditioning.

Oxygen transfer from blood vessels to cortical brain tissue is representative of a class of problems with mixed-domain character. Large-scale efficient computation of tissue oxygen concentration is dependent on the manner in which the tubular network of blood vessels is coupled to the tissue mesh. Models which explicitly resolve the interface between the tissue and vasculature with a contiguous mesh are prohibitively expensive for very dense cerebral microvasculature. We propose a mixed-domain mesh-free technique whereby a vascular anatomical network (VAN) represented as a thin directed graph serves for convection of blood oxygen, and the surrounding extravascular tissue is represented as a Cartesian grid of 3D voxels throughout which oxygen is transported by diffusion. We split the network and tissue meshes by the Schur complement method of domain decomposition to obtain a reduced set of system equations for the tissue oxygen concentration at steady state. The use of a Cartesian grid allows the corresponding matrix equation to be solved approximately with a fast Fourier transform-based Poisson solver, which serves as an effective preconditioner for Krylov subspace iteration. The performance of this method enables the steady-state simulation of cortical oxygen perfusion for anatomically accurate vascular networks down to single micron resolution without the need for supercomputers.

MESH-FREE HIGH-RESOLUTION SIMULATION OF CEREBROCORTICAL OXYGEN SUPPLY WITH FAST FOURIER PRECONDITIONING.

Oxygen transfer from blood vessels to cortical brain tissue is representative of a class of problems with mixed-domain character. Large-scale efficient computation of tissue oxygen concentration is dependent on the manner in which the tubular network of blood vessels is coupled to the tissue mesh. Models which explicitly resolve the interface between the tissue and vasculature with a contiguous mesh are prohibitively expensive for very dense cerebral microvasculature. We propose a mixed-domain mesh-free technique whereby a vascular anatomical network (VAN) represented as a thin directed graph serves for convection of blood oxygen, and the surrounding extravascular tissue is represented as a Cartesian grid of 3D voxels throughout which oxygen is transported by diffusion. We split the network and tissue meshes by the Schur complement method of domain decomposition to obtain a reduced set of system equations for the tissue oxygen concentration. The use of a Cartesian grid allows the corresponding matrix equation to be solved approximately with a fast Fourier transform based Poisson solver, which serves as an effective preconditioner for Krylov subspace iteration. The performance of this method enables the steady state simulation of cortical oxygen perfusion for anatomically accurate vascular networks down to single micron resolution without the need for supercomputers. Practitioner Points: We present a novel mixed-domain framework for efficiently modeling O 2 extraction kinetics in the brain. Model equations are generated by graph-theoretic methods for mixed domains.Dual mesh domain decomposition with FFT preconditioning yields very fast simulation times for extremely high spatial resolution.

A Suite of Neurophotonic Tools to Underpin the Contribution of Internal Brain States in fMRI.

Recent developments in optical microscopy, applicable for large-scale and longitudinal imaging of cortical activity in behaving animals, open unprecedented opportunities to gain a deeper understanding of neurovascular and neurometabolic coupling during different brain states. Future studies will leverage these tools to deliver foundational knowledge about brain state-dependent regulation of cerebral blood flow and metabolism as well as regulation as a function of brain maturation and aging. This knowledge is of critical importance to interpret hemodynamic signals observed with functional magnetic resonance imaging (fMRI).

Mathematical synthesis of the cortical circulation for the whole mouse brain-part II: Microcirculatory closure.

Recent advancements in multiphoton imaging and vascular reconstruction algorithms have increased the amount of data on cerebrovascular circulation for statistical analysis and hemodynamic simulations. Experimental observations offer fundamental insights into capillary network topology but mainly within a narrow field of view typically spanning a small fraction of the cortical surface (less than 2%). In contrast, larger-resolution imaging modalities, such as computed tomography (CT) or magnetic resonance imaging (MRI), have whole-brain coverage but capture only larger blood vessels, overlooking the microscopic capillary bed. To integrate data acquired at multiple length scales with different neuroimaging modalities and to reconcile brain-wide macroscale information with microscale multiphoton data, we developed a method for synthesizing hemodynamically equivalent vascular networks for the entire cerebral circulation. This computational approach is intended to aid in the quantification of patterns of cerebral blood flow and metabolism for the entire brain. In part I, we described the mathematical framework for image-guided generation of synthetic vascular networks covering the large cerebral arteries from the circle of Willis through the pial surface network leading back to the venous sinuses. Here in part II, we introduce novel procedures for creating microcirculatory closure that mimics a realistic capillary bed. We demonstrate our capability to synthesize synthetic vascular networks whose morphometrics match empirical network graphs from three independent state-of-the-art imaging laboratories using different image acquisition and reconstruction protocols. We also successfully synthesized twelve vascular networks of a complete mouse brain hemisphere suitable for performing whole-brain blood flow simulations. Synthetic arterial and venous networks with microvascular closure allow whole-brain hemodynamic predictions. Simulations across all length scales will potentially illuminate organ-wide supply and metabolic functions that are inaccessible to models reconstructed from image data with limited spatial coverage.

Constitutive relationship and governing physical properties for magnetophoresis.

Magnetophoresis is an important physical process with application to drug delivery, biomedical imaging, separation, and mixing. Other than empirically, little is known about how the magnetic field and magnetic properties of a solution affect the flux of magnetic particles. A comprehensive explanation of these effects on the transport of magnetic particles has not been developed yet. Here we formulate a consistent, constitutive equation for the magnetophoretic flux of magnetic nanoparticles suspended in a medium exposed to a stationary magnetic field. The constitutive relationship accounts for contributions from magnetic diffusion, magnetic convection, residual magnetization, and electromagnetic drift. We discovered that the key physical properties governing the magnetophoresis are magnetic diffusion coefficient, magnetic velocity, and activity coefficient, which depend on relative magnetic energy and the molar magnetic susceptibility of particles. The constitutive equation also reveals previously unknown ballistic and diffusive limits for magnetophoresis wherein the paramagnetic particles either aggregate near the magnet or diffusive away from the magnet, respectively. In the diffusive limit, the particle concentration is linearly proportional to the relative magnetic energy of the suspension of paramagnetic particles. The region of the localization of paramagnetic particles near the magnet decreases with increasing the strength of the magnet. The dynamic accumulation of nanoparticles, measured as the thickness of the nanoparticle aggregate, near the magnet compares well with the theoretical prediction. The effect of convective mixing on the rate of magnetophoresis is also discussed for the magnetic targeting applications.

Quantification of blood flow patterns in the cerebral arterial circulation of individual (human) subjects.

There is a growing research interest in quantifying blood flow distribution for the entire cerebral circulation to sharpen diagnosis and improve treatment options for cerebrovascular disease of individual patients. We present a methodology to reconstruct subject-specific cerebral blood flow patterns in accordance with physiological and fluid mechanical principles and optimally informed by in vivo neuroimage data of cerebrovascular anatomy and arterial blood flow rates. We propose an inverse problem to infer blood flow distribution across the visible portion of the arterial network that best matches subject-specific anatomy and a given set of volumetric flow measurements. The optimization technique also mitigates the effect of uncertainties by reconciling incomplete flow data and by dissipating unavoidable acquisition errors associated with medical imaging data.

In Vivo Intrathecal Tracer Dispersion in Cynomolgus Monkey Validates Wide Biodistribution Along Neuraxis.

OBJECTIVE: It is commonly believed that in intrathecal (IT) drug delivery, agent distribution is confined to a narrow region close to the injection site, thereby undermining the efficacy of the method. METHODS: To test the claim, multimodal in vivo imaging was used to experimentally observe the effects of IT infusion in cynomolgus monkey, looking at cerebrospinal fluid flow, anatomy, and dispersion of a radiolabeled tracer. RESULTS: At high infusion rates, the tracer reached the cervical region after only 2 h, demonstrating rapid and wide distribution. The same in vivo nonhuman primate imaging data also provided evidence in support of a computational fluid dynamic model for the prediction of drug distribution following IT injection. Tracer dispersion was predicted in two specimens matching the distribution acquired with positron emission tomography (PET). For the third specimen, tracer dispersion simulations were conducted as a blind study: predictions were made before in vivo biodistribution data was known. In all cases, the computational fluid dynamics (CFD) predictions of drug dispersion after IT administration showed close spatio-temporal agreement with tracer biodistribution in vivo. CONCLUSION: Validation by in vivo nonhuman primate data confirms our ability to predict the biodistribution of intrathecally administered agents in subject-specific models of the central nervous system from first principles. SIGNIFICANCE: The experiments reinstate IT delivery as a viable administration method when targeting molecules to the whole spine or the brain. The proposed computational methodology enables rational design of novel therapies for neurological diseases that require reliable, efficient, and safe delivery of therapeutic agents to specific target sites in the central nervous system.

Drug Delivery Applications of Nanoparticles in the Spine.

Nanoparticles offer several applications in the field of medicine such as targeted drug delivery, controlled drug release, and imaging applications. The central nervous system (CNS), in particular, has remained a challenge for drug delivery. This is mainly due to barriers such as the blood-brain barrier (BBB) and blood-cerebrospinal fluid barrier (BCSFB), which hinder drug molecules from reaching the brain and spinal cord tissue. Although researchers have mainly focused on applying nanotechnology in the brain, there is an increase in applications of nanomaterials in the spine as well. This chapter focuses on the potential of nanomedicine for medical applications in the spine, including unique drug delivery systems and gene therapy applications, and for enhancement of medical imaging. We look at the problems and recent advances in the development of nanoparticles for spine-related applications and provide a comprehensive review on recent research work.

An efficient full space-time discretization method for subject-specific hemodynamic simulations of cerebral arterial blood flow with distensible wall mechanics.

A computationally inexpensive mathematical solution approach using orthogonal collocations for space discretization with temporal Fourier series is proposed to compute subject-specific blood flow in distensible vessels of large cerebral arterial networks. Several models of wall biomechanics were considered to assess their impact on hemodynamic predictions. Simulations were validated against in vivo blood flow measurements in six human subjects. The average root-mean-square relative differences were found to be less than 4.3% for all subjects with a linear elastic wall model. This discrepancy decreased further in a viscoelastic Kelvin-Voigt biomechanical wall. The results provide support for the use of collocation-Fourier series approach to predict clinically relevant blood flow distribution and collateral blood supply in large portions of the cerebral circulation at reasonable computational costs. It thus opens the possibility of performing computationally inexpensive subject-specific simulations that are robust and fast enough to predict clinical results in real time on the same day.

Validation of parametric mesh generation for subject-specific cerebroarterial trees using modified Hausdorff distance metrics.

Accurate subject-specific vascular network reconstruction is a critical task for the hemodynamic analysis of cerebroarterial circulation. Vascular skeletonization and computational mesh generation for large sections of cerebrovascular trees from magnetic resonance angiography (MRA) is an error-prone, operator-dependent, and very time-consuming task. Validation of reconstructed computational models is essential to ascertain their accuracy and precision, which directly relates to the confidence of CFD computations performed on these meshes. The aim of this study is to generate an imaging segmentation pipeline to validate and quantify the spatial accuracy of computational models of subject-specific cerebral arterial trees. We used a recently introduced parametric structured mesh (PSM) generation method to automatically reconstruct six subject-specific cerebral arterial trees containing 1364 vessels and 571 bifurcations. By automatically extracting sampling frames for all vascular segments and bifurcations, we quantify the spatial accuracy of PSM against the original MRA images. Our comprehensive study correlates lumen area, pixel-based statistical analysis, area overlap and centerline accuracy measurements. In addition, we propose a new metric, the pointwise offset surface distance metric (PSD), to quantify the spatial alignment between dimensions of reconstructed arteries and bifurcations with in-vivo data with the ability to quantify the over- and under-approximation of the reconstructed models. Accurate reconstruction of vascular trees can a practical process tool for morphological analysis of large patient data banks, such as medical record files in hospitals, or subject-specific hemodynamic simulations of the cerebral arterial circulation.

Quantification of near-wall hemodynamic risk factors in large-scale cerebral arterial trees.

Detailed hemodynamic analysis of blood flow in pathological segments close to aneurysm and stenosis has provided physicians with invaluable information about the local flow patterns leading to vascular disease. However, these diseases have both local and global effects on the circulation of the blood within the cerebral tree. The aim of this paper is to demonstrate the importance of extending subject-specific hemodynamic simulations to the entire cerebral arterial tree with hundreds of bifurcations and vessels, as well as evaluate hemodynamic risk factors and waveform shape characteristics throughout the cerebral arterial trees. Angioarchitecture and in vivo blood flow measurement were acquired from healthy subjects and in cases with symptomatic intracranial aneurysm and stenosis. A global map of cerebral arterial blood flow distribution revealed regions of low to high hemodynamic risk that may significantly contribute to the development of intracranial aneurysms or atherosclerosis. Comparison of pre-intervention and post-intervention of pathological cases further shows large angular phase shift (~33.8°), and an augmentation of the peak-diastolic velocity. Hemodynamic indexes of waveform analysis revealed on average a 16.35% reduction in the pulsatility index after treatment from lesion site to downstream distal vessels. The lesion regions not only affect blood flow streamlines of the proximal sites but also generate pulse wave shift and disturbed flow in downstream vessels. This network effect necessitates the use of large-scale simulation to visualize both local and global effects of pathological lesions.

Large-scale subject-specific cerebral arterial tree modeling using automated parametric mesh generation for blood flow simulation.

In this paper, we present a novel technique for automatic parametric mesh generation of subject-specific cerebral arterial trees. This technique generates high-quality and anatomically accurate computational meshes for fast blood flow simulations extending the scope of 3D vascular modeling to a large portion of cerebral arterial trees. For this purpose, a parametric meshing procedure was developed to automatically decompose the vascular skeleton, extract geometric features and generate hexahedral meshes using a body-fitted coordinate system that optimally follows the vascular network topology. To validate the anatomical accuracy of the reconstructed vasculature, we performed statistical analysis to quantify the alignment between parametric meshes and raw vascular images using receiver operating characteristic curve. Geometric accuracy evaluation showed an agreement with area under the curves value of 0.87 between the constructed mesh and raw MRA data sets. Parametric meshing yielded on-average, 36.6% and 21.7% orthogonal and equiangular skew quality improvement over the unstructured tetrahedral meshes. The parametric meshing and processing pipeline constitutes an automated technique to reconstruct and simulate blood flow throughout a large portion of the cerebral arterial tree down to the level of pial vessels. This study is the first step towards fast large-scale subject-specific hemodynamic analysis for clinical applications.

Corticosteroid Treatment for Metastatic Spinal Cord Compression: A Review.

STUDY DESIGN: Narrative review. OBJECTIVE: Metastatic spinal cord compression (MSCC) is a very frequent complication among cancer patients. Presenting commonly as nocturnal back pain, MSCC typically progresses to lower extremity paresis, loss of ambulatory capabilities, and paraplegia. In addition to standard treatment modalities, corticosteroid administration has been utilized in preclinical and clinical settings as adjunctive therapy to reduce local spinal cord edema and improve clinical symptoms. This article serves as a review of existing literature regarding corticosteroid management of MSCC and seeks to provide potential avenues of research on the topic. METHODS: A literature search was performed using PubMed in order to consolidate existing information regarding dexamethasone treatment of MSCC. Of all search results, 7 articles are reviewed, establishing the current understanding of metastatic spine disease and dexamethasone treatment in both animal models and in clinical trials. RESULTS: Treatment with high-dose corticosteroids is associated with an increased rate of potentially serious systemic side effects. For this reason, definitive guidelines for the use of dexamethasone in the management of MSCC are unavailable. CONCLUSIONS: It is still unclear what role dexamethasone plays in the treatment of MSCC. It is evident that new, more localizable therapies may provide more acceptable treatment strategies using corticosteroids. Looking forward, the potential for more targeted, localized application of the steroid through the use of nanotechnology would decrease the incidence of adverse effects while maintaining the drug's efficacy.

Computational and In Vitro Experimental Investigation of Intrathecal Drug Distribution: Parametric Study of the Effect of Injection Volume, Cerebrospinal Fluid Pulsatility, and Drug Uptake.

BACKGROUND: Intrathecal drug delivery is an attractive option to circumvent the blood-brain barrier for pain management through its increased efficacy of pain relief, reduction in adverse side effects, and cost-effectiveness. Unfortunately, there are limited guidelines for physicians to choose infusion or drug pump settings to administer therapeutic doses to specific regions of the spine or the brain. Although empiric trialing of intrathecal drugs is critical to determine the sustained side effects, currently there is no inexpensive in vitro method to guide the selection of spinal drug delivery parameters. The goal of this study is to demonstrate current computational capabilities to predict drug biodistribution while varying 3 parameters: (1) infusion settings, (2) drug chemistry, and (3) subject-specific anatomy and cerebrospinal fluid dynamics. We will discuss strategies to systematically optimize these 3 parameters to administer drug molecules to targeted tissue locations in the central nervous system. METHODS: We acquired anatomical data from magnetic resonance imaging (MRI) and velocity measurements in the spinal cerebrospinal fluid with CINE-MRI for 2 subjects. A bench-top surrogate of the subject-specific central nervous system was constructed to match measured anatomical dimensions and volumes. We generated a computational mesh for the bench-top model. Idealized simulations of tracer distribution were compared with bench-top measurements for validation. Using reconstructions from MRI data, we also introduced a subject-specific computer model for predicting drug spread for the human volunteer. RESULTS: MRI velocity measurements at 3 spinal regions of interest reasonably matched the simulated flow fields in a subject-specific computer mesh. Comparison between the idealized spine computations and bench-top tracer distribution experiments demonstrate agreement of our drug transport predictions to this physical model. Simulated multibolus drug infusion theoretically localizes drug to the cervical and thoracic region. Continuous drug pump and single bolus injection were successful to target the lumbar spine in the simulations. The parenchyma might be targeted suitably by multiple boluses followed by a flush infusion. We present potential guidelines that take into account drug specific kinetics for tissue uptake, which influence the speed of drug dispersion in the model and potentially influence tissue targeting. CONCLUSIONS: We present potential guidelines considering drug-specific kinetics of tissue uptake, which determine the speed of drug dispersion and influence tissue targeting. However, there are limitations to this analysis in that the parameters were obtained from an idealized healthy patient in a supine position. The proposed methodology could assist physicians to select clinical infusion parameters for their patients and provide guidance to optimize treatment algorithms. In silico optimization of intrathecal drug delivery therapies presents the first steps toward a possible care paradigm in the future that is specific to personalized patient anatomy and diseases.