Reconstruction of major fibers using 7T multi-shell Hybrid Diffusion Imaging in mice

Diffusion weighted imaging (DWI) can reveal the orientation of the underlying fiber populations in the brain. High angular resolution diffusion imaging (HARDI) is increasingly used to better resolve the orientation and mixing of fibers. Here, we assessed the added value of multi-shell q-space sampling on the reconstruction of major fibers using mathematical frameworks from q-ball imaging (QBI) and generalized q-sampling imaging (GQI), as compared to diffusion tensor imaging (DTI). We scanned a healthy mouse brain using 7-Tesla 5-shell HARDI (b=1000, 3000, 4000, 8000, 12000 s/mm2), also known as hybrid diffusion imaging (HYDI). We found that QBI may provide greater reconstruction accuracy for major fibers, which improves with the addition of higher b-value shells, unlike GQI or DTI (as expected). Although QBI is a special case of GQI, the major fiber orientation in QBI was more closely related to the orientation in DTI, rather than GQI. HYDI can aid the clinical outcomes of research and especially – more advanced human and animal connectomics projects to map the brain’s neural pathways and networks

Imaging subtle leaks in the blood–brain barrier in the aging human brain: potential pitfalls, challenges, and possible solutions

Recent studies using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with gadolinium-based contrast agents (GBCA) have demonstrated subtle blood–brain barrier (BBB) leaks in the human brain during normal aging, in individuals with age-related cognitive dysfunction, genetic risk for Alzheimer’s disease (AD), mild cognitive impairment, early AD, cerebral small vessel disease (SVD), and other neurodegenerative disorders. In these neurological conditions, the BBB leaks, quantified by the unidirectional BBB GBCA tracer’s constant K(trans) maps, are typically orders of magnitude lower than in brain tumors, after stroke and/or during relapsing episodes of multiple sclerosis. This puts extra challenges for the DCE-MRI technique by pushing calculations towards its lower limits of detectability. In addition, presently, there are no standardized multivendor protocols or evidence of repeatability and reproducibility. Nevertheless, subtle BBB leaks may critically contribute to the pathophysiology of cognitive impairment and dementia associated with AD or SVD, and therefore, efforts to improve sensitivity of detection, reliability, and reproducibility are warranted. A larger number of participants scanned by different MR scanners at different clinical sites are sometimes required to detect differences in BBB integrity between control and at-risk groups, which impose additional challenges. Here, we focus on these new challenges and propose some approaches to normalize and harmonize DCE data between different scanners. In brief, we recommend specific regions to be used for the tracer’s vascular input function and DCE data processing and how to find and correct negative K(trans) values that are physiologically impossible. We hope this information will prove helpful to new investigators wishing to study subtle BBB damage in neurovascular and neurodegenerative conditions and in the aging human brain. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11357-022-00571-x

Blood-brain barrier-associated pericytes internalize and clear aggregated amyloid-β42 by LRP1-dependent apolipoprotein E isoform-specific mechanism

Table S1. Demographic and clinical features of human subjects used in this study. Figure S1. Aβ deposition in microvessels in AD patients and APPSw/0 mice. Figure S2. Biochemical analysis of Aβ42 aggregates. Figure S3. Cy3-Aβ42 cellular uptake in wild type mouse brain slices within 30 min. Figure S4. Pericyte coverages in Lrp1lox/lox and Lrp1lox/lox; Cspg4-Cre mice. Figure S5.. LRP1 and apoE suppression with siRNA. (DOCX 1454 kb

ROCKETSHIP: a flexible and modular software tool for the planning, processing and analysis of dynamic MRI studies

Background: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising technique to characterize pathology and evaluate treatment response. However, analysis of DCE-MRI data is complex and benefits from concurrent analysis of multiple kinetic models and parameters. Few software tools are currently available that specifically focuses on DCE-MRI analysis with multiple kinetic models. Here, we developed ROCKETSHIP, an open-source, flexible and modular software for DCE-MRI analysis. ROCKETSHIP incorporates analyses with multiple kinetic models, including data-driven nested model analysis. Results: ROCKETSHIP was implemented using the MATLAB programming language. Robustness of the software to provide reliable fits using multiple kinetic models is demonstrated using simulated data. Simulations also demonstrate the utility of the data-driven nested model analysis. Applicability of ROCKETSHIP for both preclinical and clinical studies is shown using DCE-MRI studies of the human brain and a murine tumor model. Conclusion: A DCE-MRI software suite was implemented and tested using simulations. Its applicability to both preclinical and clinical datasets is shown. ROCKETSHIP was designed to be easily accessible for the beginner, but flexible enough for changes or additions to be made by the advanced user as well. The availability of a flexible analysis tool will aid future studies using DCE-MRI

Protein S blocks the extrinsic apoptotic cascade in tissue plasminogen activator/N-methyl D-aspartate-treated neurons via Tyro3-Akt-FKHRL1 signaling pathway

<p>Abstract</p> <p>Background</p> <p>Thrombolytic therapy with tissue plasminogen activator (tPA) benefits patients with acute ischemic stroke. However, tPA increases the risk for intracerebral bleeding and enhances post-ischemic neuronal injury if administered 3-4 hours after stroke. Therefore, combination therapies with tPA and neuroprotective agents have been considered to increase tPA's therapeutic window and reduce toxicity. The anticoagulant factor protein S (PS) protects neurons from hypoxic/ischemic injury. PS also inhibits N-methyl-D-aspartate (NMDA) excitotoxicity by phosphorylating Bad and Mdm2 which blocks the downstream steps in the intrinsic apoptotic cascade. To test whether PS can protect neurons from tPA toxicity we studied its effects on tPA/NMDA combined injury which in contrast to NMDA alone kills neurons by activating the extrinsic apoptotic pathway. Neither Bad nor Mdm2 which are PS's targets and control the intrinsic apoptotic pathway can influence the extrinsic cascade. Thus, based on published data one cannot predict whether PS can protect neurons from tPA/NMDA injury by blocking the extrinsic pathway. Neurons express all three TAM (Tyro3, Axl, Mer) receptors that can potentially interact with PS. Therefore, we studied whether PS can activate TAM receptors during a tPA/NMDA insult.</p> <p>Results</p> <p>We show that PS protects neurons from tPA/NMDA-induced apoptosis by suppressing Fas-ligand (FasL) production and FasL-dependent caspase-8 activation within the extrinsic apoptotic pathway. By transducing neurons with adenoviral vectors expressing the kinase-deficient Akt mutant <it>Akt^K179A</it>and a triple FKHRL1 Akt phosphorylation site mutant (FKHRL1-TM), we show that Akt activation and Akt-mediated phosphorylation of FKHRL1, a member of the Forkhead family of transcription factors, are critical for FasL down-regulation and caspase-8 inhibition. Using cultured neurons from Tyro3, Axl and Mer mutants, we show that Tyro3, but not Axl and Mer, mediates phosphorylation of FHKRL1 that is required for PS-mediated neuronal protection after tPA/NMDA-induced injury.</p> <p>Conclusions</p> <p>PS blocks the extrinsic apoptotic cascade through a novel mechanism mediated by Tyro3-dependent FKHRL1 phosphorylation which inhibits FasL-dependent caspase-8 activation and can control tPA-induced neurotoxicity associated with pathologic activation of NMDA receptors. The present findings should encourage future studies in animal stroke models to determine whether PS can increase the therapeutic window of tPA by reducing its post-ischemic neuronal toxicity.</p

Prenatal disruption of blood–brain barrier formation via cyclooxygenase activation leads to lifelong brain inflammation

Gestational maternal immune activation (MIA) in mice induces persistent brain microglial activation and a range of neuropathologies in the adult offspring. Although long-term phenotypes are well documented, how MIA in utero leads to persistent brain inflammation is not well understood. Here, we found that offspring of mothers treated with polyriboinosinic–polyribocytidylic acid [poly(I:C)] to induce MIA at gestational day 13 exhibit blood–brain barrier (BBB) dysfunction throughout life. Live MRI in utero revealed fetal BBB hyperpermeability 2 d after MIA. Decreased pericyte–endothelium coupling in cerebral blood vessels and increased microglial activation were found in fetal and 1- and 6-mo-old offspring brains. The long-lasting disruptions result from abnormal prenatal BBB formation, driven by increased proliferation of cyclooxygenase-2 (COX2; Ptgs2)-expressing microglia in fetal brain parenchyma and perivascular spaces. Targeted deletion of the Ptgs2 gene in fetal myeloid cells or treatment with the inhibitor celecoxib 24 h after immune activation prevented microglial proliferation and disruption of BBB formation and function, showing that prenatal COX2 activation is a causal pathway of MIA effects. Thus, gestational MIA disrupts fetal BBB formation, inducing persistent BBB dysfunction, which promotes microglial overactivation and behavioral alterations across the offspring life span. Taken together, the data suggest that gestational MIA disruption of BBB formation could be an etiological contributor to neuropsychiatric disorders

7T multi-shell hybrid diffusion imaging (HYDI) for mapping brain connectivity in mice

Diffusion weighted imaging (DWI) is widely used to study microstructural characteristics of the brain. High angular resolution diffusion imaging (HARDI) samples diffusivity at a large number of spherical angles, to better resolve neural fibers that mix or cross. Here, we implemented a framework for advanced mathematical analysis of mouse 5-shell HARDI (b=1000, 3000, 4000, 8000, 12000 s/mm^2), also known as hybrid diffusion imaging (HYDI). Using q-ball imaging (QBI) at ultra-high field strength (7 Tesla), we computed diffusion and fiber orientation distribution functions (dODF, fODF) to better detect crossing fibers. We also computed a quantitative anisotropy (QA) index, and deterministic tractography, from the peak orientation of the fODFs. We found that the signal to noise ratio (SNR) of the QA was significantly higher in single and multi-shell reconstructed data at the lower b-values (b=1000, 3000, 4000 s/mm^2) than at higher b-values (b=8000, 12000 s/mm2); the b=1000 s/mm^2 shell increased the SNR of the QA in all multi-shell reconstructions, but when used alone or in <5-shell reconstruction, it led to higher angular error for the major fibers, compared to 5-shell HYDI. Multi-shell data reconstructed major fibers with less error than single-shell data, and was most successful at reducing the angular error when the lowest shell was excluded (b=1000 s/mm2). Overall, high-resolution connectivity mapping with 7T HYDI offers great potential for understanding unresolved changes in mouse models of brain disease

Optimal Acquisition and Modeling Parameters for Accurate Assessment of Low K_(trans) Blood–Brain Barrier Permeability Using Dynamic Contrast-Enhanced MRI

Purpose: To determine optimal parameters for acquisition and processing of dynamic contrast-enhanced MRI (DCE-MRI) to detect small changes in near normal low blood–brain barrier (BBB) permeability. Methods: Using a contrast-to-noise ratio metric (K-CNR) for K_(trans) precision and accuracy, the effects of kinetic model selection, scan duration, temporal resolution, signal drift, and length of baseline on the estimation of low permeability values was evaluated with simulations. Results: The Patlak model was shown to give the highest K-CNR at low K_(trans). The K_(trans) transition point, above which other models yielded superior results, was highly dependent on scan duration and tissue extravascular extracellular volume fraction (v_e). The highest K-CNR for low K_(trans) was obtained when Patlak model analysis was combined with long scan times (10–30 min), modest temporal resolution (<60 s/image), and long baseline scans (1–4 min). Signal drift as low as 3% was shown to affect the accuracy of K_(trans) estimation with Patlak analysis. Conclusion: DCE acquisition and modeling parameters are interdependent and should be optimized together for the tissue being imaged. Appropriately optimized protocols can detect even the subtlest changes in BBB integrity and may be used to probe the earliest changes in neurodegenerative diseases such as Alzheimer's disease and multiple sclerosis

Pericyte degeneration leads to neurovascular uncoupling and limits oxygen supply to brain

Pericytes are perivascular mural cells of brain capillaries. They are positioned centrally in the neurovascular unit between endothelial cells, astrocytes and neurons. This position allows them to regulate key neurovascular functions of the brain. The role of pericytes in the regulation of cerebral blood flow (CBF) and neurovascular coupling remains, however, under debate. Using loss-of-function pericyte-deficient mice, here we show that pericyte degeneration diminishes global and individual capillary CBF responses to neuronal stimuli, resulting in neurovascular uncoupling, reduced oxygen supply to the brain and metabolic stress. Neurovascular deficits lead over time to impaired neuronal excitability and neurodegenerative changes. Thus, pericyte degeneration as seen in neurological disorders such as Alzheimer's disease may contribute to neurovascular dysfunction and neurodegeneration associated with human disease.R01 AG023084 - NIA NIH HHS; P50 AG005142 - NIA NIH HHS; R01 AG039452 - NIA NIH HHS; R24 NS092986 - NINDS NIH HHS; R01 EB000790 - NIBIB NIH HHS; R01 NS034467 - NINDS NIH HHS; R01 NS091230 - NINDS NIH HHS; R01 NS100459 - NINDS NIH HHS; P01 NS055104 - NINDS NIH HHS; P01 AG052350 - NIA NIH HHS; R01 MH111359 - NIMH NIH HHSAccepted manuscrip