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While medical imaging data have traditionally been viewed on two-dimensional (2D) displays, augmented reality (AR) allows physicians to project the medical imaging data on patient's bodies to locate important anatomy. We present a surgical AR application to plan the retrosigmoid craniotomy, a standard approach to access the posterior fossa and the internal auditory canal. As a simple and accurate alternative to surface landmarks and conventional surgical navigation systems, our AR application augments the surgeon's vision to guide the optimal location of cortical bone removal. In this work, two surgeons performed a retrosigmoid approach 14 times on eight cadaver heads. In each case, the surgeon manually aligned a computed tomography (CT)-derived virtual rendering of the sigmoid sinus on the real cadaveric heads using a see-through AR display, allowing the surgeon to plan and perform the craniotomy accordingly. Postprocedure CT scans were acquired to assess the accuracy of the retrosigmoid craniotomies with respect to their intended location relative to the dural sinuses. The two surgeons had a mean margin of davg = 0.6 ± 4.7 mm and davg = 3.7 ± 2.3 mm between the osteotomy border and the dural sinuses over all their cases, respectively, and only positive margins for 12 of the 14 cases. The intended surgical approach to the internal auditory canal was successfully achieved in all cases using the proposed method, and the relatively small and consistent margins suggest that our system has the potential to be a valuable tool to facilitate planning a variety of similar skull-base procedures.

The objective of this project was to identify and develop software for an augmented reality application that runs on the US Army Integrated Visual Augmentation System (IVAS) to support a medical caregiver during tactical combat casualty care scenarios. In this augmented reality tactical combat casualty care application, human anatomy of individual soldiers obtained predeployment is superimposed on the view of an injured war fighter through the IVAS. This offers insight into the anatomy of the injured war fighter to advance treatment in austere environments.In this article, we describe various software components required for an augmented reality tactical combat casualty care tool. These include a body pose tracking system to track the patient's body pose, a virtual rendering of a human anatomy avatar, speech input to control the application and rendering techniques to visualize the virtual anatomy, and treatment information on the augmented reality display. We then implemented speech commands and visualization for four common medical scenarios including injury of a limb, a blast to the pelvis, cricothyrotomy, and a pneumothorax on the Microsoft HoloLens 1 (Microsoft, Redmond, WA).The software is designed for a forward surgical care tool on the US Army IVAS, with the intention to provide the medical caregiver with a unique ability to quickly assess affected internal anatomy. The current software components still had some limitations with respect to speech recognition reliability during noise and body pose tracking. These will likely be improved with the improved hardware of the IVAS, which is based on a modified HoloLens 2.

Myelin insulates neuronal axons and enables fast signal transmission, constituting a key component of brain development, aging and disease. Yet, myelin-specific imaging of macroscopic samples remains a challenge. Here, we exploit myelin’s nanostructural periodicity, and use small-angle X-ray scattering tensor tomography (SAXS-TT) to simultaneously quantify myelin levels, nanostructural integrity and axon orientations in nervous tissue. Proof-of-principle is demonstrated in whole mouse brain, mouse spinal cord and human white and gray matter samples. Outcomes are validated by 2D/3D histology and compared to MRI measurements sensitive to myelin and axon orientations. Specificity to nanostructure is exemplified by concomitantly imaging different myelin types with distinct periodicities. Finally, we illustrate the method’s sensitivity towards myelin-related diseases by quantifying myelin alterations in dysmyelinated mouse brain. This non-destructive, stain-free molecular imaging approach enables quantitative studies of myelination within and across samples during development, aging, disease and treatment, and is applicable to other ordered biomolecules or nanostructures., Small-angle X-ray scattering (SAXS) combines the high tissue penetration of X-rays with specificity to periodic nanostructures. The authors use SAXS tensor tomography (SAXS-TT) on intact mouse and human brain tissue samples, to quantify myelin levels and determine myelin integrity, myelinated axon orientation, and fibre tracts non-destructively.

BACKGROUND External approaches to the frontal sinus such as osteoplastic flaps are challenging because they require blind entry into the sinus, posing risks of injury to the brain or orbit. Intraoperative computed tomography (CT)-based navigation is the current standard for planning the approach, but still necessitates blind entry into the sinus. The aim of this work was to describe a novel technique for external approaches to the frontal sinus using a holographic augmented reality (AR) application. METHODS Our team developed an AR system to create a 3-dimensional (3D) hologram of key anatomical structures, based on CT scans images. Using Magic Leap AR goggles for visualization, the frontal sinus hologram was aligned to the surface anatomy in 6 fresh cadaveric heads' anatomic boundaries, and the boundaries of the frontal sinus were demarcated based on the margins of the fused image. Trephinations and osteoplastic flap approaches were performed. The specimens were re-scanned to assess the accuracy of the osteotomy with respect to the actual frontal sinus perimeter. RESULTS Registration and surgery were completed successfully in all specimens. Registration required an average of 2 minutes. The postprocedure CT showed a mean difference of 1.4 ± 4.1 mm between the contour of the osteotomy and the contour of the frontal sinus. One surgical complication (posterior table perforation) occurred (16%). CONCLUSION We describe proof of concept of a novel technique utilizing AR to enhance external approaches to the frontal sinus. Holographic AR-enhanced surgical navigation holds promise for enhanced visualization of target structures during surgical approaches to the sinuses.

Only recently has the rodent hippocampus been implicated in orexigenic appetitive processing1,2. This function has been found to be mediated at least in part by lateral hypothalamic inputs involving an orexigenic neuropeptide, melanin-concentrating hormone3. This circuit remains elusive in humans. Here, we combine tractography, intracranial electrophysiology, cortico-subcortical evoked potentials, and brain-clearing 3D histology to identify an orexigenic circuit involving the lateral hypothalamus converging in a hippocampal subregion. We found that low-frequency power is modulated by sweet-fat food cues and this modulation was specific to the dorsolateral hippocampus. Lastly, structural and functional analyses of this circuit in a human cohort exhibiting dysregulated eating behavior revealed connectivity that was inversely related to body mass index. Collectively, this multimodal approach describes an orexigenic subnetwork within the human hippocampus implicated in obesity and related eating disorders.

To show that augmented reality (AR) visualization of single-photon emission computed tomography (SPECT)/computed tomography (CT) data in 3D can be used to accurately localize targets in the head and neck region.Eight head and neck styrofoam phantoms were painted with a mixture of radioactive solution (Tc-99m) detectable with a handheld gamma probe and fluorescent ink visible only under ultraviolet (UV) light to create 10-20 simulated lymph nodes on their surface. After obtaining SPECT/CT images of these phantoms, virtual renderings of the nodes were generated from the SPECT/CT data and displayed using a commercially available AR headset. For each of three physician evaluators, the time required to localize lymph node targets was recorded (1) using the gamma probe alone and (2) using the gamma probe while wearing the AR headset. In addition, the surface localization accuracy when using the AR headset was evaluated by measuring the misalignment between the locations visually marked by the evaluators and the ground truth locations identified using UV stimulation of the ink at the site of the nodes.For all three evaluators, using the AR headset significantly reduced the time to detect targets (P = 0.012, respectively) compared to using the gamma probe alone. The average misalignment between the location marked by the evaluators and the ground truth location was 8.6 mm.AR visualization of SPECT/CT data in 3D allows for accurate localization of targets in the head and neck region, and may reduce the localization time of targets.

3D histology, slice-based connectivity atlases, and diffusion MRI are common techniques to map brain wiring. While there are many modality-specific tools to process these data, there is a lack of integration across modalities. We develop an automated resource that combines histologically cleared volumes with connectivity atlases and MRI, enabling the analysis of histological features across multiple fiber tracts and networks, and their correlation with in-vivo biomarkers. We apply our pipeline in a murine stroke model, demonstrating not only strong correspondence between MRI abnormalities and CLARITY-tissue staining, but also uncovering acute cellular effects in areas connected to the ischemic core. We provide improved maps of connectivity by quantifying projection terminals from CLARITY viral injections, and integrate diffusion MRI with CLARITY viral tracing to compare connectivity maps across scales. Finally, we demonstrate tract-level histological changes of stroke through this multimodal integration. This resource can propel investigations of network alterations underlying neurological disorders., Many approaches exist to process data from individual imaging modalities, but integrating them is challenging. The authors develop an automated resource that enables co-registered network- and tract-level analysis of macroscopic in-vivo imaging and microscopic imaging of cleared tissue.

The objective of this project was to identify and develop software for an augmented reality application that runs on the US Army Integrated Visual Augmentation System (IVAS) to support a medical caregiver during tactical combat casualty care scenarios. In this augmented reality tactical combat casualty care application, human anatomy of individual soldiers obtained predeployment is superimposed on the view of an injured war fighter through the IVAS. This offers insight into the anatomy of the injured war fighter to advance treatment in austere environments.In this article, we describe various software components required for an augmented reality tactical combat casualty care tool. These include a body pose tracking system to track the patient's body pose, a virtual rendering of a human anatomy avatar, speech input to control the application and rendering techniques to visualize the virtual anatomy, and treatment information on the augmented reality display. We then implemented speech commands and visualization for four common medical scenarios including injury of a limb, a blast to the pelvis, cricothyrotomy, and a pneumothorax on the Microsoft HoloLens 1 (Microsoft, Redmond, WA).The software is designed for a forward surgical care tool on the US Army IVAS, with the intention to provide the medical caregiver with a unique ability to quickly assess affected internal anatomy. The current software components still had some limitations with respect to speech recognition reliability during noise and body pose tracking. These will likely be improved with the improved hardware of the IVAS, which is based on a modified HoloLens 2.

Introduction: Fragile X syndrome (FXS) is a debilitating neurogenetic disorder that can result in a multitude of impairments in cognition, memory, and learning. Case Presentation: a 25-year-old male with FXS participated in this study. The participant obtained scores in the non-spectrum range on the Autism Diagnostic Observation Scale and obtained an full scale IQ score of 57 (Verbal IQ = 23 and Nonverbal IQ = 34) on the Stanford-Binet Intelligence Scales (SB-5). On the Vineland Adaptive Behavior Scales, 2nd Edition (VABS-2) he obtained a composite score of 66. Pre-scan serum cortisol reactivity was 16.45 mcg/dL. Following a [18F]flumazenil (5mCi) intravenous bolus injection, the participant was scanned without sedation on a hybrid PET-MR system (Signa, GE Healthcare, Waukesha, WI) for 60 mins. List mode PET data, structural and diffusion MRI (DWI), and MR spectroscopy (MRS) were acquired simultaneously. Quantitative PET and DWI measures were extracted from 83 pre-defined regions of interest. MRS data was collected from two 20 cc voxels (thalamus and dorsolateral prefrontal cortex). Conclusion: This is the first study to investigate neuromolecular behavior in FXS without the use of sedation using PET-MR. Mapping the neuromolecular differences in FXS can lead to targeted treatments that can significantly improve quality of life for families and individuals with intellectual disabilities.

Many Augmented Reality (AR) applications require the alignment of virtual objects to the real world; this is particularly important in medical AR scenarios where medical imaging information may be displayed directly on a patient and is used to identify the exact locations of specific anatomical structures within the body. For optical see-through AR, alignment accuracy depends both on the optical parameters of the AR display as well as the visualization parameters of the virtual model. In this paper, we explore how different static visualization techniques influence users’ ability to perform perception-based alignment in AR for breast reconstruction surgery, where surgeons must accurately identify the locations of several perforator blood vessels while planning the procedure. We conducted a pilot study in which four subjects used four different visualization techniques with varying degrees of opaqueness and brightness as well as outline contrast to align virtual replicas of the relevant anatomy to their 3D-printed counterparts. We collected quantitative scores on spatial alignment accuracy using an external tracking system and qualitative scores on user preference and perceived performance. Results indicate that the highest source of alignment error was along the depth dimension, with users consistently overestimating depth when aligning the virtual renderings. The majority of subjects preferred visualization techniques rendered with lower levels of opaqueness and brightness as well as higher outline contrast, which were also found to support more accurate alignment.

Medical augmented reality (AR) applications where virtual renderings are aligned with the real world allow to visualize internal anatomy of the patient to a medical caregiver wearing an AR headset. Accurate alignment of virtual and real content is important for applications where the virtual rendering is used to guide the medical procedure such as a surgery. Compared to 2D AR applications, where the alignment accuracy can be directly measured on the 2D screen, 3D medical AR applications require alignment measurements using phantoms and external tracking systems. In this paper we present an approach for landmark-based alignment, validation and accuracy measurement of a 3D AR overlay of medical images on the real-world subject. This is done by performing an initial MRI of a subject’s head, an AR alignment task of the virtual rendering of the head MRI data to the subject’s real-world head using virtual fiducials, and a second MRI scan to test the accuracy of the AR alignment task. We have performed these 3D medical AR alignment measurements on seven volunteers using a MagicLeap AR head-mounted display. Across all seven volunteers we measured an alignment accuracy of $4.7 \pm 2.6$ mm. These results suggest that such an AR application can be a valuable tool for guiding non-invasive transcranial magnetic brain stimulation treatment. The presented MRI-based accuracy validation will furthermore be an important versatile tool to establish the safety of medical AR techniques. Index Terms: Mixed / augmented reality; Visualization design and evaluation methods

Purpose: Repetitive Transcranial Magnetic Stimulation (rTMS) is an important treatment option for medication resistant depression. It uses an electromagnetic coil that needs to be positioned accurately at a specific location and angle next to the head such that specific brain areas are stimulated. Existing image-guided neuronavigation systems allow accurate targeting but add cost, training and setup times, preventing their wide-spread use in the clinic. Mixed-reality neuronavigation can help mitigate these issues and thereby enable more widespread use of image-based neuronavigation by providing a much more intuitive and streamlined visualization of the target. A mixed-reality neuronavigation system requires two core functionalities: 1) tracking of the patient's head and 2) visualization of targeting-related information. Here we focus on the head tracking functionality and compare three different head tracking methods for a mixed-reality neuronavigation system. Methods: We integrated three head tracking methods into the mixed reality neuronavigation framework and measured their accuracy. Specifically, we experimented with (a) marker-based tracking with a mixed reality headset (optical see-through head-mounted display (OST-HMD)) camera, (b) marker-based tracking with a world-anchored camera and (c) markerless RGB-depth (RGB-D) tracking with a world-anchored camera. To measure the accuracy of each approach, we measured the distance between real-world and virtual target points on a mannequin head. Results: The mean tracking error for the initial head pose and the head rotated by 10° and 30° for the three methods respectively was: (a) 3.54±1.10 mm, 3.79±1.78 mm and 4.08±1.88 mm, (b) 3.97±1.41 mm, 6.01±2.51 mm and 6.84±3.48 mm, (c) 3.16±2.26 mm, 4.46±2.30 mm and 5.83±3.70 mm. Conclusion: For the initial head pose, all three methods achieved the required accuracy of < 5 mm for TMS treatment. For smaller head rotations of 10°, only the marker-based (a) and markerless method (c) delivered sufficient accuracy for TMS treatment. For larger head rotations of 30°, only the marker-based method (a) achieved sufficient accuracy. While the markerless method (c) did not provide sufficient accuracy for TMS at the larger head rotations, it offers significant advantages such as occlusion-handling and stability and could potentially meet the accuracy requirements with further methodological refinements.

Purpose The purpose of this study is to develop double diffusion encoding (DDE) MRI methods for clinical use. Microscopic diffusion anisotropy measurements from DDE promise greater specificity to changes in tissue microstructure compared with conventional diffusion tensor imaging, but implementation of DDE sequences on whole-body MRI scanners is challenging because of the limited gradient strengths and lengthy acquisition times. Methods A custom single-refocused DDE sequence was implemented on a 3T whole-body scanner. The DDE gradient orientation scheme and sequence parameters were optimized based on a Gaussian diffusion assumption. Using an optimized 5-min DDE acquisition, microscopic fractional anisotropy (μFA) maps were acquired for the first time in multiple sclerosis patients. Results Based on simulations and in vivo human measurements, six parallel and six orthogonal diffusion gradient pairs were found to be the minimum number of diffusion gradient pairs necessary to produce a rotationally invariant measurement of μFA. Simulations showed that optimal precision and accuracy of μFA measurements were obtained using b-values between 1500 and 3000 s/mm2 . The μFA maps showed improved delineation of multiple sclerosis lesions compared with conventional fractional anisotropy and distinct contrast from T2 -weighted fluid attenuated inversion recovery and T1 -weighted imaging. Conclusion The μFA maps can be measured using DDE in a clinical setting and may provide new opportunities for characterizing multiple sclerosis lesions and other types of tissue degeneration. Magn Reson Med 80:507-520, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Dysregulation of the nucleus accumbens (NAc) is implicated in numerous neuropsychiatric disorders. Treatments targeting this area directly (e.g. deep brain stimulation) demonstrate variable efficacy, perhaps owing to non-specific targeting of a functionally heterogeneous nucleus. Here we provide support for this notion, first observing disparate behavioral effects in response to direct simulation of different locations within the NAc in a human patient. These observations motivate a segmentation of the NAc into subregions, which we produce from a diffusion-tractography based analysis of 245 young, unrelated healthy subjects. We further explore the mechanism of these stimulation-induced behavioral responses by identifying the most probable subset of axons activated using a patient-specific computational model. We validate our diffusion-based segmentation using evidence from several modalities, including MRI-based measures of function and microstructure, human post-mortem immunohistochemical staining, and cross-species comparison of cortical-NAc projections that are known to be conserved. Finally, we visualize the passage of individual axon bundles through one NAc subregion in a post-mortem human sample using CLARITY 3D histology corroborated by 7T tractography. Collectively, these findings extensively characterize human NAc subregions and provide insight into their structural and functional distinctions with implications for stereotactic treatments targeting this region.

Diffusion MRI (dMRI) represents one of the few methods for mapping brain fiber orientations non-invasively. Unfortunately, dMRI fiber mapping is an indirect method that relies on inference from measured diffusion patterns. Comparing dMRI results with other modalities is a way to improve the interpretation of dMRI data and help advance dMRI technologies. Here, we present methods for comparing dMRI fiber orientation estimates with optical imaging of fluorescently labeled neurofilaments and vasculature in 3D human and primate brain tissue cuboids cleared using CLARITY. The recent advancements in tissue clearing provide a new opportunity to histologically map fibers projecting in 3D, which represents a captivating complement to dMRI measurements. In this work, we demonstrate the capability to directly compare dMRI and CLARITY in the same human brain tissue and assess multiple approaches for extracting fiber orientation estimates from CLARITY data. We estimate the three-dimensional neuronal fiber and vasculature orientations from neurofilament and vasculature stained CLARITY images by calculating the tertiary eigenvector of structure tensors. We then extend CLARITY orientation estimates to an orientation distribution function (ODF) formalism by summing multiple sub-voxel structure tensor orientation estimates. In a sample containing part of the human thalamus, there is a mean angular difference of 19o±15o between the primary eigenvectors of the dMRI tensors and the tertiary eigenvectors from the CLARITY neurofilament stain. We also demonstrate evidence that vascular compartments do not affect the dMRI orientation estimates by showing an apparent lack of correspondence (mean angular difference = 49o±23o) between the orientation of the dMRI tensors and the structure tensors in the vasculature stained CLARITY images. In a macaque brain dataset, we examine how the CLARITY feature extraction depends on the chosen feature extraction parameters. By varying the volume of tissue over which the structure tensor estimates are derived, we show that orientation estimates are noisier with more spurious ODF peaks for sub-voxels below 30 µm3 and that, for our data, the optimal gray matter sub-voxel size is between 62.5 µm3 and 125 µm3. The example experiments presented here represent an important advancement towards robust multi-modal MRI-CLARITY comparisons.

Background and Purpose— Many restorative therapies have been used to study brain repair after stroke. These therapeutic-induced changes have revealed important insights on brain repair and recovery mechanisms; however, the intrinsic changes that occur in spontaneously recovery after stroke is less clear. The goal of this study is to elucidate the intrinsic changes in spontaneous recovery after stroke, by directly investigating the transcriptome of primary motor cortex in mice that naturally recovered after stroke. Methods— Male C57BL/6J mice were subjected to transient middle cerebral artery occlusion. Functional recovery was evaluated using the horizontal rotating beam test. A novel in-depth lesion mapping analysis was used to evaluate infarct size and locations. Ipsilesional and contralesional primary motor cortices (iM1 and cM1) were processed for RNA-sequencing transcriptome analysis. Results— Cluster analysis of the stroke mice behavior performance revealed 2 distinct recovery groups: a spontaneously recovered and a nonrecovered group. Both groups showed similar lesion profile, despite their differential recovery outcome. RNA-sequencing transcriptome analysis revealed distinct biological pathways in the spontaneously recovered stroke mice, in both iM1 and cM1. Correlation analysis revealed that 38 genes in the iM1 were significantly correlated with improved recovery, whereas 74 genes were correlated in the cM1. In particular, ingenuity pathway analysis highlighted the involvement of cAMP signaling in the cM1, with selective reduction of Adora2a (adenosine receptor A2A), Drd2 (dopamine receptor D2), and Pde10a (phosphodiesterase 10A) expression in recovered mice. Interestingly, the expressions of these genes in cM1 were negatively correlated with behavioral recovery. Conclusions— Our RNA-sequencing data revealed a panel of recovery-related genes in the motor cortex of spontaneously recovered stroke mice and highlighted the involvement of contralesional cortex in spontaneous recovery, particularly Adora2a, Drd2, and Pde10a-mediated cAMP signaling pathway. Developing drugs targeting these candidates after stroke may provide beneficial recovery outcome.

Depression affects more than 16 million American adults and more than half do not respond to medication. Transcranial magnetic stimulation (TMS) is an important anti-depressant treatment that targets specific brain circuits responsible for mood and behavior. TMS efficacy and risk is strongly linked to correct TMS coil placement and can be significantly improved by accurate neuronavigation. In this paper, we present tools for the development of a novel mixed reality neuronavigation setup that allows the TMS operator to view the patient's brain anatomy directly overlaid on the head. This is performed by integrating patient tracking and visualization of brain magnetic resonance imaging (MRI) to provide a streamlined visualization of the patient's anatomy in a single immersive environment.

Diffusion spectrum MRI (DSI) provides model-free estimation of the diffusion ensemble average propagator (EAP) and orientation distribution function (ODF) but requires the diffusion data to be acquired on a Cartesian q-space grid. Multi-shell diffusion acquisitions are more flexible and more commonly acquired but have, thus far, only been compatible with model-based analysis methods. Here, we propose a generalized DSI (GDSI) framework to recover the EAP from multi-shell diffusion MRI data. The proposed GDSI approach corrects for q-space sampling density non-uniformity using a fast geometrical approach. The EAP is directly calculated in a preferable coordinate system by multiplying the sampling density corrected q-space signals by a discrete Fourier transform matrix, without any need for gridding. The EAP is demonstrated as a way to map diffusion patterns in brain regions such as the thalamus, cortex and brainstem where the tissue microstructure is not as well characterized as in white matter. Scalar metrics such as the zero displacement probability and displacement distances at different fractions of the zero displacement probability were computed from the recovered EAP to characterize the diffusion pattern within each voxel. The probability averaged across directions at a specific displacement distance provides a diffusion property based image contrast that clearly differentiates tissue types. The displacement distance at the first zero crossing of the EAP averaged across directions orthogonal to the primary fiber orientation in the corpus callosum is found to be larger in the body (5.65 ± 0.09 μm) than in the genu (5.55 ± 0.15 μm) and splenium (5.4 ± 0.15 μm) of the corpus callosum, which corresponds well to prior histological studies. The EAP also provides model-free representations of angular structure such as the diffusion ODF, which allows estimation and comparison of fiber orientations from both the model-free and model-based methods on the same multi-shell data. For the model-free methods, detection of crossing fibers is found to be strongly dependent on the maximum b-value and less sensitive compared to the model-based methods. In conclusion, our study provides a generalized DSI approach that allows flexible reconstruction of the diffusion EAP and ODF from multi-shell diffusion data and data acquired with other sampling patterns.

Aim To assess the acute effect of 1,2-dichloroethane (1,2-DCE) on rat brain using diffusion magnetic resonance imaging (dMRI). Materials and methods We performed dMRI on 30 male Sprague-Dawley rats, microstructural alterations were investigated by calculating the mean fractional anisotropy (FA) and apparent diffusion coefficient (ADC) changes in eight selected brain regions of interest. For the whole brain, clusters of 20+ voxels that differed significantly in FA and ADC between groups were marked. Hematoxylin-eosin staining was performed to confirm pathological changes. Results Brain images showed lesions with brain edema in the white matter in both hemispheres in all groups exposed to 1,2-DCE. Diffusivity values were significantly different after 1,2-DCE inhalation (p Conclusion Primarily cytotoxic edema occurred in acute 1,2-DCE-induced brain edema in rats. dMRI could be used for the early non-invasive detection of acute 1,2-DCE-induced toxic encephalopathy.

The ability of manganese ions (Mn(2+)) to enter cells through calcium ion (Ca(2+)) channels has been used for depolarization dependent brain functional imaging with manganese-enhanced MRI (MEMRI). The purpose of this study was to quantify changes to Mn(2+) uptake in rat brain using a dynamic manganese-enhanced MRI (dMEMRI) scanning protocol with the Patlak and Logan graphical analysis methods. The graphical analysis was based on a three-compartment model describing the tissue and plasma concentration of Mn. Mn(2+) uptake was characterized by the total distribution volume of manganese (Mn) inside tissue (V(T)) and the unidirectional influx constant of Mn(2+) from plasma to tissue (K(i)). The measurements were performed on the anterior (APit) and posterior (PPit) parts of the pituitary gland, a region with an incomplete blood brain barrier. Modulation of Ca(2+) channel activity was performed by administration of the stimulant glutamate and the inhibitor verapamil. It was found that the APit and PPit showed different Mn(2+) uptake characteristics. While the influx of Mn(2+) into the PPit was reversible, Mn(2+) was found to be irreversibly trapped in the APit during the course of the experiment. In the PPit, an increase of Mn(2+) uptake led to an increase in V(T) (from 2.8±0.3 ml/cm(3) to 4.6±1.2 ml/cm(3)) while a decrease of Mn(2+) uptake corresponded to a decrease in V(T) (from 2.8±0.3 ml/cm(3) to 1.4±0.3 ml/cm(3)). In the APit, an increase of Mn(2+) uptake led to an increase in K(i) (from 0.034±0.009 min(-1) to 0.049±0.012 min(-1)) while a decrease of Mn(2+) uptake corresponded to a decrease in K(i) (from 0.034±0.009 min(-1) to 0.019±0.003 min(-1)). This work demonstrates that graphical analysis applied to dMEMRI data can quantitatively measure changes to Mn(2+) uptake following modulation of neural activity.