Connectome 2.0: Developing the next-generation ultra-high gradient strength human MRI scanner for bridging studies of the micro-, meso- and macro-connectome.

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.

Reverse Janssen Effect in Narrow Granular Columns.

When grains are added to a cylinder, the weight at the bottom is smaller than the total weight of the column, which is partially supported by the lateral walls through frictional interactions with the grains. This is known as the Janssen effect. Via a combined experimental and numerical investigation, here we demonstrate a reverse Jansen effect whereby the fraction of the weight supported by the base overcomes one. We characterize the dependence of this phenomenon on the various control parameters involved, rationalize the physical process causing the emergence of the compressional frictional forces responsible for the anomaly, and introduce a model to reproduce our findings. Contrary to prior assumptions, our results demonstrate that the constitutive relation on a material element can depend on the applied stress.

Assessment of quantitative magnetic resonance imaging metrics in the brain through the use of a novel phantom.

OBJECTIVE: Multisite and longitudinal neuroimaging studies are important in uncovering trajectories of recovery and neurodegeneration following traumatic brain injury (TBI) and concussion through the use of diffusion tensor imaging (DTI) and other imaging modalities. This study assessed differences in anisotropic diffusion measurement across four scanners using a human and a novel phantom developed in conjunction with the Chronic Effects of Neurotrauma Consortium. METHOD: Human scans provided measurement within biological tissue, and the novel physical phantom provided measures of anisotropic intra-tubular diffusion to serve as a model for intra-axonal water diffusion. Intra- and inter-scanner measurement variances were compared, and the impact on effect size was calculated. RESULTS: Intra-scanner test-retest reliability estimates for fractional anisotropy (FA) demonstrated relative stability over testing intervals. The human tissue and phantom showed similar FA ranges, high linearity and large within-device effect sizes. However, inter-scanner measures of FA indicated substantial differences, some of which exceeded typical DTI effect sizes in mild TBI. CONCLUSION: The diffusion phantom may be used to better elucidate inter-scanner variability in DTI-based measurement and provides an opportunity to better calibrate results obtained from scanners used in multisite and longitudinal studies. Novel solutions are being evaluated to understand and potentially overcome these differences.

A diffusion spectrum imaging-based tractographic study into the anatomical subdivision and cortical connectivity of the ventral external capsule: uncinate and inferior fronto-occipital fascicles.

PURPOSE: The inferior fronto-occipital fasciculus (IFOF) and uncinate fasciculus (UF) are major fronto-capsular white matter pathways. IFOF connects frontal areas of the brain to parieto-occipital areas. UF connects ventral frontal areas to anterior temporal areas. Both fascicles are thought to subserve higher language and emotion roles. Controversy pertaining to their connectivity and subdivision persists in the literature, however. METHODS: High-definition fiber tractography (HDFT) is a non-tensor tractographic method using diffusion spectrum imaging data. Its major advantage over tensor-based tractography is its ability to trace crossing fiber pathways. We used HDFT to investigate subdivisions and cortical connectivity of IFOF and UF in 30 single subjects and in an atlas comprising averaged data from 842 individuals. A per-subject aligned, atlas-based approach was employed to seed fiber tracts and to study cortical terminations. RESULTS: For IFOF, we observed a tripartite arrangement corresponding to ventrolateral, ventromedial, and dorsomedial frontal origins. IFOF volume was not significantly lateralized to either hemisphere. UF fibers arose from ventromedial and ventrolateral frontal areas on the left and from ventromedial frontal areas on the right. UF volume was significantly lateralized to the left hemisphere. The data from the averaged atlas was largely in concordance with subject-specific findings. IFOF connected to parietal, occipital, but not temporal, areas. UF connected predominantly to temporal poles. CONCLUSION: Both IFOF and UF possess subdivided arrangements according to their frontal origin. Our connectivity results indicate the multifunctional involvement of IFOF and UF in language tasks. We discuss our findings in context of the tractographic literature.

Magnetoencephalography-based identification of functional connectivity network disruption following mild traumatic brain injury.

Mild traumatic brain injury (mTBI) leads to long-term cognitive sequelae in a significant portion of patients. Disruption of normal neural communication across functional brain networks may explain the deficits in memory and attention observed after mTBI. In this study, we used magnetoencephalography (MEG) to examine functional connectivity during a resting state in a group of mTBI subjects (n = 9) compared with age-matched control subjects (n = 15). We adopted a data-driven, exploratory analysis in source space using phase locking value across different frequency bands. We observed a significant reduction in functional connectivity in band-specific networks in mTBI compared with control subjects. These networks spanned multiple cortical regions involved in the default mode network (DMN). The DMN is thought to subserve memory and attention during periods when an individual is not engaged in a specific task, and its disruption may lead to cognitive deficits after mTBI. We further applied graph theoretical analysis on the functional connectivity matrices. Our data suggest reduced local efficiency in different brain regions in mTBI patients. In conclusion, MEG can be a potential tool to investigate and detect network alterations in patients with mTBI. The value of MEG to reveal potential neurophysiological biomarkers for mTBI patients warrants further exploration.

Advanced diffusion MRI fiber tracking in neurosurgical and neurodegenerative disorders and neuroanatomical studies: A review.

Diffusion MRI enabled in vivo microstructural imaging of the fiber tracts in the brain resulting in its application in a wide range of settings, including in neurological and neurosurgical disorders. Conventional approaches such as diffusion tensor imaging (DTI) have been shown to have limited applications due to the crossing fiber problem and the susceptibility of their quantitative indices to partial volume effects. To overcome these limitations, the recent focus has shifted to the advanced acquisition methods and their related analytical approaches. Advanced white matter imaging techniques provide superior qualitative data in terms of demonstration of multiple crossing fibers in their spatial orientation in a three dimensional manner in the brain. In this review paper, we discuss the advancements in diffusion MRI and introduce their roles. Using examples, we demonstrate the role of advanced diffusion MRI-based fiber tracking in neuroanatomical studies. Results from its preliminary application in the evaluation of intracranial space occupying lesions, including with respect to future directions for prognostication, are also presented. Building upon the previous DTI studies assessing white matter disease in Huntington's disease and Amyotrophic lateral sclerosis; we also discuss approaches which have led to encouraging preliminary results towards developing an imaging biomarker for these conditions.

Energy decay in three-dimensional freely cooling granular gas.

The kinetic energy of a freely cooling granular gas decreases as a power law t(-θ) at large times t. Two theoretical conjectures exist for the exponent θ. One based on ballistic aggregation of compact spherical aggregates predicts θ=2d/(d+2) in d dimensions. The other based on Burgers equation describing anisotropic, extended clusters predicts θ=d/2 when 2≤d≤4. We do extensive simulations in three dimensions to find that while θ is as predicted by ballistic aggregation, the cluster statistics and velocity distribution differ from it. Thus, the freely cooling granular gas fits to neither the ballistic aggregation or a Burgers equation description.

Rethinking the role of the middle longitudinal fascicle in language and auditory pathways.

The middle longitudinal fascicle (MdLF) was originally described in the monkey brain as a pathway that interconnects the superior temporal and angular gyri. Only recently have diffusion tensor imaging studies provided some evidence of its existence in humans, with a connectivity pattern similar to that in monkeys and a potential role in the language system. In this study, we combine high-angular-resolution fiber tractography and fiber microdissection techniques to determine the trajectory, cortical connectivity, and a quantitative analysis of the MdLF. Here, we analyze diffusion spectrum imaging (DSI) studies in 6 subjects (subject-specific approach) and in a template of 90 DSI studies (NTU-90 Atlas). Our tractography and microdissection results show that the human MdLF differs significantly from the monkey. Indeed, the human MdLF interconnects the superior temporal gyrus with the superior parietal lobule and parietooccipital region, and has only minor connections with the angular gyrus. On the basis of the roles of these interconnected cortical regions, we hypothesize that, rather than a language-related tract, the MdLF may contribute to the dorsal "where" pathway of the auditory system.

Integration of magnetoencephalography-generated functional brain maps into dose planning during arteriovenous malformation radiosurgery.

BACKGROUND: Magnetoencephalography (MEG) can delineate critical regions of the cortex and facilitate conformal stereotactic radiosurgery (SRS) dose planning. Despite the substantial role of Gamma Knife® SRS in arteriovenous malformation (AVM) management, MEG-generated maps of critical regions have never been utilized to improve dose planning. PURPOSE: To assess the value of integrating functional brain mapping using MEG with dose planning during treatment of brain AVMs with SRS. METHODS: This case series encompassed 5 patients with motor region AVMs. Noninvasive eloquent cortex mapping was achieved using a whole-head 306-channel Neuromag® Vectorview MEG System 5-10 days before SRS. On the day of SRS, the functional brain maps were integrated onto the intraoperative dose planning magnetic resonance imaging for Leksell GammaPlan® version 10. The median AVM volume treated was 12.7 cm(3), and 18 Gy was the median margin dose. RESULTS: Functional image integration of MEG improved the recognition of critical brain structures adjacent to the AVM. This facilitated anatomical planning designed to reduce the dose to adjacent critical structures while maintaining a therapeutic dose to the AVM target. The 5 patients had no adverse radiation effects during the follow-up. CONCLUSION: Coregistration of MEG data improves the accuracy and dose sparing needed for optimal planning during Gamma Knife SRS.

Undersampled single-shell to MSMT fODF reconstruction using CNN-based ODE solver.

BACKGROUND AND OBJECTIVE: Diffusion MRI (dMRI) has been considered one of the most popular non-invasive techniques for studying the human brain's white matter (WM). dMRI is used to delineate the brain's microstructure by approximating the WM region's fiber tracts. The achieved fiber tracts can be utilized to assess mental diseases like Multiple sclerosis, ADHD, Seizures, Intellectual disability, and others. New techniques such as high angular resolution diffusion-weighted imaging (HARDI) have been developed, providing precise fiber directions, and overcoming the limitation of traditional DTI. Unlike Single-shell, Multi-shell HARDI provides tissue fractions for white matter, gray matter, and cerebrospinal fluid, resulting in a Multi-shell Multi-tissue fiber orientation distribution function (MSMT fODF). This MSMT fODF comes up with more precise fiber directions than a Single-shell, which helps to get correct fiber tracts. In addition, various multi-compartment diffusion models, including as CHARMED and NODDI, have been developed to describe the brain tissue microstructural information. This type of model requires multi-shell data to obtain more specific tissue microstructural information. However, a major concern with multi-shell is that it takes a longer scanning time restricting its use in clinical applications. In addition, most of the existing dMRI scanners with low gradient strengths commonly acquire a single b-value (shell) upto b=1000s/mm2 due to SNR (Signal-to-noise ratio) reasons and severe imaging artifacts. METHODS: To address this issue, we propose a CNN-based ordinary differential equations solver for the reconstruction of MSMT fODF from under-sampled and fully sampled Single-shell (b=1000s/mm2) dMRI. The proposed architecture consists of CNN-based Adams-Bash-forth and Runge-Kutta modules along with two loss functions, including L1 and total variation. RESULTS: We have shown quantitative results and visualization of fODF, fiber tracts, and structural connectivity for several brain regions on the publicly available HCP dataset. In addition, the obtained angular correlation coefficients for white matter and full brain are high, showing the proposed network's utility.Finally, we have also demonstrated the effect of noise by adjusting SNR from 5 to 50 and observed the network robustness. CONCLUSION: We can conclude that our model can accurately predict MSMT fODF from under-sampled or fully sampled Single-shell dMRI volumes.

TrGANet: Transforming 3T to 7T dMRI using Trapezoidal Rule and Graph based Attention Modules.

Diffusion MRI (dMRI) is a non-invasive tool for assessing the white matter region of the brain by approximating the fiber streamlines, structural connectivity, and estimation of microstructure. This modality can yield useful information for diagnosing several mental diseases as well as for surgical planning. The higher angular resolution diffusion imaging (HARDI) technique is helpful in obtaining more robust fiber tracts by getting a good approximation of regions where fibers cross. Moreover, HARDI is more sensitive to tissue changes and can accurately represent anatomical details in the human brain at higher magnetic strengths. In other words, magnetic strengths affect the quality of the image, and hence high magnetic strength has good tissue contrast with better spatial resolution. However, a higher magnetic strength scanner (like 7T) is costly and unaffordable to most hospitals. Hence, in this work, we have proposed a novel CNN architecture for the transformation of 3T to 7T dMRI. Additionally, we have also reconstructed the multi-shell multi-tissue fiber orientation distribution function (MSMT fODF) at 7T from single-shell 3T. The proposed architecture consists of a CNN-based ODE solver utilizing the Trapezoidal rule and graph-based attention layer alongwith L1 and total variation loss. Finally, the model has been validated on the HCP data set quantitatively and qualitatively.

VRfRNet: Volumetric ROI fODF reconstruction network for estimation of multi-tissue constrained spherical deconvolution with only single shell dMRI.

Diffusion MRI (dMRI) is one of the most popular techniques for studying the brain structure, mainly the white matter region. Among several sampling methods in dMRI, the high angular resolution diffusion imaging (HARDI) technique has attracted researchers due to its more accurate fiber orientation estimation. However, the current single-shell HARDI makes the intravoxel structure challenging to estimate accurately. While multi-shell acquisition can address this problem, it takes a longer scanning time, restricting its use in clinical applications. In addition, most existing dMRI scanners with low gradient-strengths often acquire single-shell up to b=1000s/mm2 because of signal-to-noise ratio issues and severe image artefacts. Hence, we propose a novel generative adversarial network, VRfRNet, for the reconstruction of multi-shell multi-tissue fiber orientation distribution function from single-shell HARDI volumes. Such a transformation learning is performed in the spherical harmonics (SH) space, as raw input HARDI volume is transformed to SH coefficients to soften gradient directions. The proposed VRfRNet consists of several modules, such as multi-context feature enrichment module, feature level attention, and softmax level attention. In addition, three loss functions have been used to optimize network learning, including L1, adversarial, and total variation. The network is trained and tested using standard qualitative and quantitative performance metrics on the publicly available HCP data-set.

In vivo mapping of microstructural somatotopies in the human corticospinal pathways.

The human corticospinal pathway is organized in a body-centric (i.e., somatotopic) manner that begins in cortical cell bodies and is maintained in the axons as they project through the midbrain on their way to spinal motor neurons. The subcortical segment of this somatotopy has been described using histological methods on non-human primates but only coarsely validated from lesion studies in human patient populations. Using high definition fiber tracking (HDFT) techniques, we set out to provide the first in vivo quantitative description of the midbrain somatotopy of corticospinal fibers in humans. Multi-shell diffusion imaging and deterministic fiber tracking were used to map white matter bundles that originate in the neocortex, navigate complex fiber crossings, and project through the midbrain. These fiber bundles were segmented into premotor (dorsal premotor, ventral premotor, and supplementary motor area) and primary motor sections based on the cortical origin of each fiber streamline. With HDFT, we were able to reveal several unique corticospinal patterns, including the cortical origins of ventral premotor fibers and small (∼ 1-2 mm) shifts in the midbrain location of premotor versus primary motor cortex fibers. More importantly, within the relatively small diameter of the pyramidal tracts (∼ 5 mm), we were able to map and quantify the direction of the corticospinal somatotopy. These results show how an HDFT approach to white matter mapping provides the first in vivo, quantitative mapping of subcortical corticospinal topographies at resolutions previously only available with postmortem histological techniques.

Identifying the brain's most globally connected regions.

Recent advances in brain connectivity methods have made it possible to identify hubs-the brain's most globally connected regions. Such regions are essential for coordinating brain functions due to their connectivity with numerous regions with a variety of specializations. Current structural and functional connectivity methods generally agree that default mode network (DMN) regions have among the highest global brain connectivity (GBC). We developed two novel statistical approaches using resting state functional connectivity MRI-weighted and unweighted GBC (wGBC and uGBC)-to test the hypothesis that the highest global connectivity also occurs in the cognitive control network (CCN), a network anti-correlated with the DMN across a variety of tasks. High global connectivity was found in both CCN and DMN. The newly developed wGBC approach improves upon existing methods by quantifying inter-subject consistency, quantifying the highest GBC values by percentage, and avoiding arbitrarily connection strength thresholding. The uGBC approach is based on graph theory and includes many of these improvements, but still requires an arbitrary connection threshold. We found high GBC in several subcortical regions (e.g., hippocampus, basal ganglia) only with wGBC despite the regions' extensive anatomical connectivity. These results demonstrate the complementary utility of wGBC and uGBC analyses for the characterization of the most highly connected, and thus most functionally important, regions of the brain. Additionally, the high connectivity of both the CCN and the DMN demonstrates that brain regions outside primary sensory-motor networks are highly involved in coordinating information throughout the brain.

Multi-Shell D-MRI Reconstruction via Residual Learning utilizing Encoder-Decoder Network with Attention (MSR-Net).

Contemporary diffusion MRI based analysis with HARDI, which provides more accurate fiber orientation, can be performed using single or multiple b-values (single or multi-shell). Single shell HARDI cannot provide volume fraction for different tissue types, which can produce bias and noisier results in estimation of fiber ODF. Multi-shell acquisition can resolve this issue. However, it requires more scanning time and is therefore not very well suited in clinical setting. Considering this, we propose a novel deep learning architecture, MSR-Net, for reconstruction of diffusion MRI volumes for some b-value using acquisitions at another b-value. In this work, we demonstrate this for b = 2000 s/mm2 and b = 1000 s/mm2. We learn such a transformation in the space of spherical harmonic coefficients. The proposed network consists of encoder-decoder along-with an attention module and a feature module. We have considered L2 and Content loss for optimizing and improving the performance. We have trained and validated the network using the HCP data-set with standard qualitative and quantitative performance measures.

Deep Learning Estimation of Multi-Tissue Constrained Spherical Deconvolution with Limited Single Shell DW-MRI.

Diffusion-weighted magnetic resonance imaging (DW-MRI) is the only non-invasive approach for estimation of intra-voxel tissue microarchitecture and reconstruction of in vivo neural pathways for the human brain. With improvement in accelerated MRI acquisition technologies, DW-MRI protocols that make use of multiple levels of diffusion sensitization have gained popularity. A well-known advanced method for reconstruction of white matter microstructure that uses multi-shell data is multi-tissue constrained spherical deconvolution (MT-CSD). MT-CSD substantially improves the resolution of intra-voxel structure over the traditional single shell version, constrained spherical deconvolution (CSD). Herein, we explore the possibility of using deep learning on single shell data (using the b=1000 s/mm2 from the Human Connectome Project (HCP)) to estimate the information content captured by 8th order MT-CSD using the full three shell data (b=1000, 2000, and 3000 s/mm2 from HCP). Briefly, we examine two network architectures: 1.) Sequential network of fully connected dense layers with a residual block in the middle (ResDNN), 2.) Patch based convolutional neural network with a residual block (ResCNN). For both networks an additional output block for estimation of voxel fraction was used with a modified loss function. Each approach was compared against the baseline of using MT-CSD on all data on 15 subjects from the HCP divided into 5 training, 2 validation, and 8 testing subjects with a total of 6.7 million voxels. The fiber orientation distribution function (fODF) can be recovered with high correlation (0.77 vs 0.74 and 0.65) and low root mean squared error ResCNN:0.0124, ResDNN:0.0168 and sCSD:0.0323 as compared to the ground truth of MT-CST, which was derived from the multi-shell DW-MRI acquisitions. The mean squared error between the MT-CSD estimates for white matter tissue fraction and for the predictions are ResCNN:0.0249 vs ResDNN:0.0264. We illustrate the applicability of high definition fiber tractography on a single testing subject with arcuate and corpus callosum Tractography. In summary, the proposed approach provides a promising framework to estimate MT-CSD with limited single shell data. Source code and models have been made publicly available.

A Preliminary High-Definition Fiber Tracking Study of the Executive Control Network in Blast-Induced Traumatic Brain Injury.

Blast-induced traumatic brain injury (bTBI) is common in veterans of the Iraq- and Afghanistan-era conflicts. However, the typical subtlety of neural alterations and absence of definitive biomarkers impede clinical detection on conventional imaging. This preliminary study examined the structure and functional correlates of executive control network (ECN) white matter in veterans to investigate the clinical utility of using high-definition fiber tracking (HDFT) to detect chronic bTBI. Demographically similar male veterans (N = 38) with and without bTBI (ages 24 to 50 years) completed standardized neuropsychological testing and magnetic resonance imaging. Quantitative HDFT metrics of subcortical-dorsolateral prefrontal cortex (DLPFC) tracts were derived. Moderate-to-large group effects were observed on HDFT metrics. Relative to comparisons, bTBI demonstrated elevated quantitative anisotropy (QA) and reduced right hemisphere volume of all examined tracts, and reduced fiber count and increased generalized fractional anisotropy in the right DLPFC-putamen tract and DLPFC-thalamus, respectively. The Group × Age interaction effect on DLPFC-caudate tract volume was large; age negatively related to volume in the bTBI group, but not comparison group. Groups performed similarly on the response inhibition measure. Performance (reaction time and commission errors) robustly correlated with HDFT tract metrics (QA and tract volume) in the comparison group, but not bTBI group. Results support anomalous density and integrity of ECN connectivity, particularly of the right DLPFC-putamen pathway, in bTBI. Results also support exacerbated aging in veterans with bTBI. Similar ECN function despite anomalous microstructure could reflect functional compensation in bTBI, although alternate interpretations are explored.

[18F]FDG, [11C]PiB, and [18F]AV-1451 PET Imaging of Neurodegeneration in Two Subjects With a History of Repetitive Trauma and Cognitive Decline.

Background: Trauma-related neurodegeneration can be difficult to differentiate from multifactorial neurodegenerative syndromes, both clinically and radiographically. We have initiated a protocol for in vivo imaging of patients with suspected TBI-related neurodegeneration utilizing volumetric MRI and PET studies, including [18F]FDG indexing cerebral glucose metabolism, [11C]PiB for Aß deposition, and [18F]AV-1451 for tau deposition. Objective: To present results from a neuroimaging protocol for in vivo evaluation of TBI-related neurodegeneration in patients with early-onset cognitive decline and a history of TBI. Methods: Patients were enrolled in parallel TBI studies and underwent a comprehensive neuropsychological test battery as well as an imaging protocol of volumetric MRI and PET studies. Findings from two patients were compared with two age-matched control subjects without a history of TBI. Results: Both chronic TBI patients demonstrated cognitive deficits consistent with early-onset dementia on neuropsychological testing, and one patient self-reported a diagnosis of probable early-onset AD. Imaging studies demonstrated significant [18F]AV-1451 uptake in the bilateral occipital lobes, substantial [11C]PiB uptake throughout the cortex in both TBI patients, and abnormally decreased [18F]FDG uptake in the posterior temporoparietal areas of the brain. One TBI patient also had subcortical volume loss. Control subjects demonstrated no appreciable [18F]AV-1451 or [11C]PiB uptake, had normal cortical volumes, and had normal cognition profiles on neuropsychological testing. Conclusions: In the two patients presented, the [11C]PiB and [18F]FDG PET scans demonstrate uptake patterns characteristic of AD. [11C]PiB PET scans showed widespread neocortical uptake with less abnormal uptake in the occipital lobes, whereas there was significant [18F]AV-1451 uptake in both occipital lobes.

Shock propagation in locally driven granular systems.

We study shock propagation in a system of initially stationary hard spheres that is driven by a continuous injection of particles at the origin. The disturbance created by the injection of energy spreads radially outward through collisions between particles. Using scaling arguments, we determine the exponent characterizing the power-law growth of this disturbance in all dimensions. The scaling functions describing the various physical quantities are determined using large-scale event-driven simulations in two and three dimensions for both elastic and inelastic systems. The results are shown to describe well the data from two different experiments on granular systems that are similarly driven.