Peripheral neuronal activation shapes the microbiome and alters gut physiology.

The gastrointestinal (GI) tract is innervated by intrinsic neurons of the enteric nervous system (ENS) and extrinsic neurons of the central nervous system and peripheral ganglia. The GI tract also harbors a diverse microbiome, but interactions between the ENS and the microbiome remain poorly understood. Here, we activate choline acetyltransferase (ChAT)-expressing or tyrosine hydroxylase (TH)-expressing gut-associated neurons in mice to determine effects on intestinal microbial communities and their metabolites as well as on host physiology. The resulting multi-omics datasets support broad roles for discrete peripheral neuronal subtypes in shaping microbiome structure, including modulating bile acid profiles and fungal colonization. Physiologically, activation of either ChAT+ or TH+ neurons increases fecal output, while only ChAT+ activation results in increased colonic contractility and diarrhea-like fluid secretion. These findings suggest that specific subsets of peripherally activated neurons differentially regulate the gut microbiome and GI physiology in mice without involvement of signals from the brain.

Lumbar Spinal Canal Segmentation in Cases with Lumbar Stenosis Using Deep-U-Net Ensembles.

BACKGROUND: Narrowing of the lumbar spinal canal, or lumbar stenosis (LS), may cause debilitating radicular pain or muscle weakness. It is the most frequent indication for spinal surgery in the elderly population. Modern diagnosis relies on magnetic resonance imaging and its inherently subjective interpretation. Diagnostic rigor, accuracy, and speed may be improved by automation. In this work, we aimed to determine whether a deep-U-Net ensemble trained to segment spinal canals on a heterogeneous mix of clinical data is comparable to radiologists' segmentation of these canals in patients with LS. METHODS: The deep U-nets were trained on spinal canals segmented by physicians on 100 axial T2 lumbar magnetic resonance imaging selected randomly from our institutional database. Test data included a total of 279 elderly patients with LS that were separate from the training set. RESULTS: Machine-generated segmentations (MA) were qualitatively similar to expert-generated segmentations (ME1, ME2). Machine- and expert-generated segmentations were quantitatively similar, as evidenced by Dice scores (MA vs. ME1: 0.88 ± 0.04, MA vs. ME2: 0.89 ± 0.04), the Hausdorff distance (MA vs. ME1: 11.7 mm ± 13.8, MA vs. ME2: 13.1 mm ± 16.3), and average surface distance (MAvs. ME1: 0.18 mm ± 0.13, MA vs. ME2 0.18 mm ± 0.16) metrics. These metrics are comparable to inter-rater variation (ME1 vs. ME2 Dice scores: 0.94 ± 0.02, the Hausdorff distances: 9.3 mm ± 15.6, average surface distances: 0.08 mm ± 0.09). CONCLUSION: We conclude that machine learning algorithms can segment lumbar spinal canals in LS patients, and automatic delineations are both qualitatively and quantitatively comparable to expert-generated segmentations.

Identifying acute ischemic stroke patients within the thrombolytic treatment window using deep learning.

BACKGROUND AND PURPOSE: Treatment of acute ischemic stroke is heavily contingent upon time, as there is a strong relationship between time clock and tissue progression. Work has established imaging biomarker assessments as surrogates for time since stroke (TSS), namely, by comparing signal mismatch between diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) imaging. Our goal was to develop an automatic technique for determining TSS from imaging that does not require subspecialist radiology expertise. METHODS: Using 772 patients (66 ± 9 years, 319 women), we developed and externally evaluated a deep learning network for classifying TSS from MR images and compared algorithm predictions to neuroradiologist assessments of DWI-FLAIR mismatch. Models were trained to classify TSS within 4.5 hours and performance metrics with confidence intervals were reported on both internal and external evaluation sets. RESULTS: Three board-certified neuroradiologists' DWI-FLAIR mismatch assessments, based on majority vote, yielded a sensitivity of .62, a specificity of .86, and a Fleiss' kappa of .46 when used to classify TSS. The deep learning method performed similarly to radiologists and outperformed previously reported methods, with the best model achieving an average evaluation accuracy, sensitivity, and specificity of .726, .712, and .741, respectively, on an internal cohort and .724, .757, and .679, respectively, on an external cohort. CONCLUSION: Our model achieved higher generalization performance on external evaluation datasets than the current state-of-the-art for TSS classification. These results demonstrate the potential of automatic assessment of onset time from imaging without the need for expertly trained radiologists.

MR Angiography in Assessment of Collaterals in Patients with Acute Ischemic Stroke: A Comparative Analysis with Digital Subtraction Angiography.

Collateral status has prognostic and treatment implications in acute ischemic stroke (AIS) patients. Unlike CTA, grading collaterals on MRA is not well studied. We aimed to evaluate the accuracy of assessing collaterals on pretreatment MRA in AIS patients against DSA. AIS patients with anterior circulation proximal arterial occlusion with baseline MRA and subsequent endovascular treatment were included. MRA collaterals were evaluated by two neuroradiologists independently using the Tan and Maas scoring systems. DSA collaterals were evaluated by using the American Society of Interventional and Therapeutic Neuroradiology grading system and were used as the reference for comparative analysis against MRA. A total of 104 patients met the inclusion criteria (59 female, age (mean ± SD): 70.8 ± 18.1). The inter-rater agreement (k) for collateral scoring was 0.49, 95% CI 0.37-0.61 for the Tan score and 0.44, 95% CI 0.26-0.62 for the Maas score. Total number (%) of sufficient vs. insufficient collaterals based on DSA was 49 (47%) and 55 (53%) respectively. Using the Tan score, 45% of patients with sufficient collaterals and 64% with insufficient collaterals were correctly identified in comparison to DSA, resulting in a poor agreement (0.09, 95% CI 0.1-0.28). Using the Maas score, only 4% of patients with sufficient collaterals and 93% with insufficient collaterals were correctly identified against DSA, resulting in poor agreement (0.03, 95% CI 0.06-0.13). Pretreatment MRA in AIS patients has limited concordance with DSA when grading collaterals using the Tan and Maas scoring systems.

Volumetric measurements are preferred in the evaluation of mutant IDH inhibition in non-enhancing diffuse gliomas: Evidence from a phase I trial of ivosidenib.

BACKGROUND: Since IDH-mutant (mIDH) low-grade gliomas (LGGs) progress slowly and have a relatively long survival, there is a significant need for earlier measurements of clinical benefit. Guidance using the LGG RANO criteria recommends serial bidirectional (2D) measurements on a single slice; however, questions remain as to whether volumetric (3D) measurements are better, since they would allow for more accurate measurements in irregular shaped lesions and allow readers to better assess areas of subtle change. METHODS: Twenty-one (out of 24) non-enhancing, recurrent mIDH1 LGGs were enrolled in a phase I, multicenter, open-label study of oral ivosidenib (NCT02073994), and with imaging pre- and post-treatment as part of this exploratory ad hoc analysis. 2D and 3D measurements on T2-weighted FLAIR images were centrally evaluated at an imaging contract research organization using a paired read and forced adjudication paradigm. The effects of 2D vs 3D measurements on progression-free survival (PFS), growth rate measurement variability, and reader concordance and adjudication rates were quantified. RESULTS: 3D volumetric measurements showed significantly longer estimated PFS (P = .0181), more stable (P = .0063) and considerably slower measures of tumor growth rate (P = .0037), the highest inter-reader agreement (weighted kappa = 0.7057), and significantly lower reader discordance rates (P = .0002) with 2D LGG RANO. CONCLUSION: 3D volumetric measurements are better for determining response assessment in LGGs due to more stable measures of tumor growth rates (ie, less "yo-yo-ing" of measurements over time), highest inter-reader agreement, and lowest reader discordance rates. Continued evaluation in future studies is warranted to determine whether these measurements reflect clinical benefit.

Imaging Biomarker Development for Lower Back Pain Using Machine Learning: How Image Analysis Can Help Back Pain.

State-of-the-art diagnosis of radiculopathy relies on "highly subjective" radiologist interpretation of magnetic resonance imaging of the lower back. Currently, the treatment of lumbar radiculopathy and associated lower back pain lacks coherence due to an absence of reliable, objective diagnostic biomarkers. Using emerging machine learning techniques, the subjectivity of interpretation may be replaced by the objectivity of automated analysis. However, training computer vision methods requires a curated database of imaging data containing anatomical delineations vetted by a team of human experts. In this chapter, we outline our efforts to develop such a database of curated imaging data alongside the required delineations. We detail the processes involved in data acquisition and subsequent annotation. Then we explain how the resulting database can be utilized to develop a machine learning-based objective imaging biomarker. Finally, we present an explanation of how we validate our machine learning-based anatomy delineation algorithms. Ultimately, we hope to allow validated machine learning models to be used to generate objective biomarkers from imaging data-for clinical use to diagnose lumbar radiculopathy and guide associated treatment plans.

A Semi-Supervised Learning Framework to Leverage Proxy Information for Stroke MRI Analysis.

Treating acute ischemic stroke (AIS) patients is a time-sensitive endeavor, as therapies target areas experiencing ischemia to prevent irreversible damage to brain tissue. Depending on how an AIS is progressing, thrombolytics such as tissue-plasminogen activator (tPA) may be administered within a short therapeutic window. The underlying conditions for optimal treatment are varied. While previous clinical guidelines only permitted tPA to be administered to patients with a known onset within 4.5 hours, clinical trials demonstrated that patients with signal intensity differences between diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) sequences in an MRI study can benefit from thrombolytic therapy. This intensity difference, known as DWI-FLAIR mismatch, is prone to high inter-reader variability. Thus, a paradigm exists where onset time serves as a weak proxy for DWI-FLAIR mismatch. In this study, we sought to detect DWI-FLAIR mismatch in an automated fashion, and we compared this to assessments done by three expert neuroradiologists. Our approach involved training a deep learning model on MRI to classify tissue clock and leveraging time clock as a weak proxy label to supplement training in a semi-supervised learning (SSL) framework. We evaluate our deep learning model by testing it on an unseen dataset from an external institution. In total, our proposed framework was able to improve detection of DWI-FLAIR mismatch, achieving a top ROC-AUC of 74.30%. Our study illustrated that incorporating clinical proxy information into SSL can improve model optimization by increasing the fidelity of unlabeled samples included in the training process.

Intra-domain task-adaptive transfer learning to determine acute ischemic stroke onset time.

Treatment of acute ischemic strokes (AIS) is largely contingent upon the time since stroke onset (TSS). However, TSS may not be readily available in up to 25% of patients with unwitnessed AIS. Current clinical guidelines for patients with unknown TSS recommend the use of MRI to determine eligibility for thrombolysis, but radiology assessments have high inter-reader variability. In this work, we present deep learning models that leverage MRI diffusion series to classify TSS based on clinically validated thresholds. We propose an intra-domain task-adaptive transfer learning method, which involves training a model on an easier clinical task (stroke detection) and then refining the model with different binary thresholds of TSS. We apply this approach to both 2D and 3D CNN architectures with our top model achieving an ROC-AUC value of 0.74, with a sensitivity of 0.70 and a specificity of 0.81 for classifying TSS < 4.5 h. Our pretrained models achieve better classification metrics than the models trained from scratch, and these metrics exceed those of previously published models applied to our dataset. Furthermore, our pipeline accommodates a more inclusive patient cohort than previous work, as we did not exclude imaging studies based on clinical, demographic, or image processing criteria. When applied to this broad spectrum of patients, our deep learning model achieves an overall accuracy of 75.78% when classifying TSS < 4.5 h, carrying potential therapeutic implications for patients with unknown TSS.

Frequency, Determinants, and Outcomes of Emboli to Distal and New Territories Related to Mechanical Thrombectomy for Acute Ischemic Stroke.

Background and Purpose: Clot fragmentation and distal embolization during endovascular thrombectomy for acute ischemic stroke may produce emboli downstream of the target occlusion or in previously uninvolved territories. Susceptibility-weighted magnetic resonance imaging can identify both emboli to distal territories (EDT) and new territories (ENT) as new susceptibility vessel signs (SVS). Diffusion-weighted imaging (DWI) can identify infarcts in new territories (INT). Methods: We studied consecutive acute ischemic stroke patients undergoing magnetic resonance imaging before and after thrombectomy. Frequency, predictors, and outcomes of EDT and ENT detected on gradient-recalled echo imaging (EDT-SVS and ENT-SVS) and INT detected on DWI (INT-DWI) were analyzed. Results: Among 50 thrombectomy-treated acute ischemic stroke patients meeting study criteria, mean age was 70 (±16) years, 44% were women, and presenting National Institutes of Health Stroke Scale score 15 (interquartile range, 8­19). Overall, 21 of 50 (42%) patients showed periprocedural embolic events, including 10 of 50 (20%) with new EDT-SVS, 10 of 50 (20%) with INT-DWI, and 1 of 50 (2%) with both. No patient showed ENT-SVS. On multivariate analysis, model-selected predictors of EDT-SVS were lower initial diastolic blood pressure (odds ratio, 1.09 [95% CI, 1.02­1.16]), alteplase pretreatment (odds ratio, 5.54 [95% CI, 0.94­32.49]), and atrial fibrillation (odds ratio, 7.38 [95% CI, 1.02­53.32]). Classification tree analysis identified pretreatment target occlusion SVS as an additional predictor. On univariate analysis, INT-DWI was less common with internal carotid artery (5%), intermediate with middle cerebral artery (25%), and highest with vertebrobasilar (57%) target occlusions (P=0.02). EDT-SVS was not associated with imaging/functional outcomes, but INT-DWI was associated with reduced radiological hemorrhagic transformation (0% versus 54%; P<0.01). Conclusions: Among acute ischemic stroke patients treated with thrombectomy, imaging evidence of distal emboli, including EDT-SVS beyond the target occlusion and INT-DWI in novel territories, occur in about 2 in every 5 cases. Predictors of EDT-SVS are pretreatment intravenous fibrinolysis, potentially disrupting thrombus structural integrity; atrial fibrillation, possibly reflecting larger target thrombus burden; lower diastolic blood pressure, suggestive of impaired embolic washout; and pretreatment target occlusion SVS sign, indicating erythrocyte-rich, friable target thrombus.

Acute Ischemic Stroke: MR Imaging-Based Paradigms.

Multimodal MR imaging provides valuable information in the management of patients with acute ischemic stroke (AIS), with diagnostic, therapeutic, and prognostic implications. MR imaging plays a critical role in treatment decision making for (1) thrombolytic treatment of AIS patients with unknown symptom-onset and (2) endovascular treatment of patients with large vessel occlusion presenting beyond 6 hours from the symptom onset. MR imaging provides the most accurate information for detection of ischemic brain and is invaluable for differentiating AIS from stroke mimics.

Predicting Spinal Surgery Candidacy From Imaging Data Using Machine Learning.

BACKGROUND: The referral process for consultation with a spine surgeon remains inefficient, given a substantial proportion of referrals to spine surgeons are nonoperative. OBJECTIVE: To develop a machine-learning-based algorithm which accurately identifies patients as candidates for consultation with a spine surgeon, using only magnetic resonance imaging (MRI). METHODS: We trained a deep U-Net machine learning model to delineate spinal canals on axial slices of 100 normal lumbar MRI scans which were previously delineated by expert radiologists and neurosurgeons. We then tested the model against lumbar MRI scans for 140 patients who had undergone lumbar spine MRI at our institution (60 of whom ultimately underwent surgery, and 80 of whom did not). The model generated automated segmentations of the lumbar spinal canals and calculated a maximum degree of spinal stenosis for each patient, which served as our biomarker for surgical pathology warranting expert consultation. RESULTS: The machine learning model correctly predicted surgical candidacy (ie, whether patients ultimately underwent lumbar spinal decompression) with high accuracy (area under the curve = 0.88), using only imaging data from lumbar MRI scans. CONCLUSION: Automated interpretation of lumbar MRI scans was sufficient to correctly determine surgical candidacy in nearly 90% of cases. Given that a significant proportion of referrals placed for spine surgery evaluation fail to meet criteria for surgical intervention, our model could serve as a valuable tool for patient triage and thereby address some of the inefficiencies within the outpatient surgical referral process.

A Machine Learning Approach to Predict Acute Ischemic Stroke Thrombectomy Reperfusion using Discriminative MR Image Features.

Mechanical thrombectomy (MTB) is one of the two standard treatment options for Acute Ischemic Stroke (AIS) patients. Current clinical guidelines instruct the use of pretreatment imaging to characterize a patient's cerebrovascular flow, as there are many factors that may underlie a patient's successful response to treatment. There is a critical need to leverage pretreatment imaging, taken at admission, to guide potential treatment avenues in an automated fashion. The aim of this study is to develop and validate a fully automated machine learning algorithm to predict the final modified thrombolysis in cerebral infarction (mTICI) score following MTB. A total 321 radiomics features were computed from segmented pretreatment MRI scans for 141 patients. Successful recanalization was defined as mTICI score >= 2c. Different feature selection methods and classification models were examined in this study. Our best performance model achieved 74.42±2.52% AUC, 75.56±4.44% sensitivity, and 76.75±4.55% specificity, showing a good prediction of reperfusion quality using pretreatment MRI. Results suggest that MR images can be informative to predicting patient response to MTB, and further validation with a larger cohort can determine the clinical utility.

Eigenrank by committee: Von-Neumann entropy based data subset selection and failure prediction for deep learning based medical image segmentation.

Manual delineation of anatomy on existing images is the basis of developing deep learning algorithms for medical image segmentation. However, manual segmentation is tedious. It is also expensive because clinician effort is necessary to ensure correctness of delineation. Consequently most algorithm development is based on a tiny fraction of the vast amount of imaging data collected at a medical center. Thus, selection of a subset of images from hospital databases for manual delineation - so that algorithms trained on such data are accurate and tolerant to variation, becomes an important challenge. We address this challenge using a novel algorithm. The proposed algorithm named 'Eigenrank by Committee' (EBC) first computes the degree of disagreement between segmentations generated by each DL model in a committee. Then, it iteratively adds to the committee, a DL model trained on cases where the disagreement is maximal. The disagreement between segmentations is quantified by the maximum eigenvalue of a Dice coefficient disagreement matrix a measure closely related to the Von Neumann entropy. We use EBC for selecting data subsets for manual labeling from a larger database of spinal canal segmentations as well as intervertebral disk segmentations. U-Nets trained on these subsets are used to generate segmentations on the remaining data. Similar sized data subsets are also randomly sampled from the respective databases, and U-Nets are trained on these random subsets as well. We found that U-Nets trained using data subsets selected by EBC, generate segmentations with higher average Dice coefficients on the rest of the database than U-Nets trained using random sampling (p < 0.05 using t-tests comparing averages). Furthermore, U-Nets trained using data subsets selected by EBC generate segmentations with a distribution of Dice coefficients that demonstrate significantly (p < 0.05 using Bartlett's test) lower variance in comparison to U-Nets trained using random sampling for all datasets. We believe that this lower variance indicates that U-Nets trained with EBC are more robust than U-Nets trained with random sampling.

Efficient Multimodal MRI Evaluation for Endovascular Thrombectomy of Anterior Circulation Large Vessel Occlusion.

BACKGROUND: MRI and CT modalities are both current standard-of-care options for initial imaging in patients with acute ischemic stroke due to large vessel occlusion (AIS-LVO). MR provides greater lesion conspicuity and spatial resolution, but few series have demonstrated multimodal MR may be performed efficiently. METHODS: In a prospective comprehensive stroke center registry, we analyzed all anterior circulation LVO thrombectomy patients between 2012-2017 who: (1) arrived directly by EMS from the field, and (2) had initial NIHSS ≥6. Center imaging policy was multimodal MRI (including DWI/GRE/MRA w/wo PWI) as the initial evaluation in all patients without contraindications, and multimodal CT (including CT with CTA, w/wo CTP) in the remainder. RESULTS: Among 106 EMS-arriving endovascular thrombectomy patients, initial imaging was MRI 62.3%, CT in 37.7%. MRI and CT patients were similar in age (72.5 vs 71.3), severity (NIHSS 16.4 v 18.2), and medical history, though MRI patients had longer onset-to-door times. Overall, door-to-needle (DTN) and door-to-puncture (DTP) times did not differ among MR and CT patients, and were faster for both modalities in 2015-2017 versus 2012-2014. In the 2015-2017 period, for MR-imaged patients, the median DTN 42m (IQR 34-55) surpassed standard (60m) and advanced (45m) national targets and the median DTP 86m (IQR 71-106) surpassed the standard national target (90m). CONCLUSIONS: AIS-LVO patients can be evaluated by multimodal MR imaging with care speeds faster than national recommendations for door-to-needle and door-to-puncture times. With its more sensitive lesion identification and spatial resolution, MRI remains a highly viable primary imaging strategy in acute ischemic stroke patients, though further workflow efficiency improvements are desirable.

Focal cortical dysplasia imaging discrepancies between MRI and FDG-PET: Unique association with temporal lobe location.

PURPOSE: Although magnetic resonance imaging (MRI) and 18F-2-fluorodeoxyglucose-positron emission tomography (FDG-PET) are used for pre-surgical assessment of focal cortical dysplasia (FCD), they often disagree. This study aimed to identify factors that contribute to discrepancies in FCD imaging between MRI and FDG-PET. METHODS: Sixty-two patients (mean age, 18.9 years) with a FCD type I or II were retrospectively selected. These patients were visually categorized into two groups: 1) extent of PET abnormality larger than MRI abnormality and 2) vice versa or equivalent. Predictive factors of these two groups were analyzed by multivariate logistic regression. The extent of hypometabolic transient zone surrounding FCDs and their mean standardized uptake values were measured and compared by the Mann-Whitney U-test. RESULTS: FCDs were detected on MRI and PET in 46 and 55 patients, respectively, whereas no abnormality was detected in 4 patients. The PET hypometabolic areas were larger than the MRI abnormal areas in 26 patients (88 % in the temporal lobe), whereas the PET hypometabolic areas were equivalent or smaller than the MRI abnormal areas in 32 patients (69 % in the frontal lobe). The temporal lobe location was an independent predictor for differentiating the two groups (OR = 35.2, 95 % CI = 6.81-168.0, P < .001). The temporal lobe lesions had significantly wider transient zones and lower standardized uptake values than those in the other lobes (P < .001, both). CONCLUSION: The discrepancies between MRI and FDG-PET findings of FCD were associated with temporal lobe location.

Predictors and Functional Outcomes of Fast, Intermediate, and Slow Progression Among Patients With Acute Ischemic Stroke.

BACKGROUND AND PURPOSE: We aimed to delineate the determinants of the initial speed of infarct progression and the association of speed of infarct progression (SIP) with procedural and functional outcomes. METHODS: From a prospectively maintained stroke center registry, consecutive anterior circulation ischemic stroke patients with large artery occlusion, National Institutes of Health Stroke Scale score ≥4, and multimodal vessel, ischemic core, and tissue-at-risk imaging within 24 hours of onset were included. Initial SIP was calculated as ischemic core volume at first imaging divided by the time from stroke onset to imaging. RESULTS: Among the 88 patients, SIP was median 2.2 cc/h (interquartile range, 0-8.7), ranging most widely within the first 6 hours after onset. Faster SIP was positively independently associated with a low collateral score (odds ratio [OR], 3.30 [95% CI, 1.25-10.49]) and arrival by emergency medical services (OR, 3.34 [95% CI, 1.06-10.49]) and negatively associated with prior ischemic stroke (OR, 0.12 [95% CI, 0.03-0.50]) and coronary artery disease (OR, 0.32 [95% CI, 0.10-1.00]). Among the 67 patients who underwent endovascular thrombectomy, slower SIP was associated with a shift to reduced levels of disability at discharge (OR, 3.26 [95% CI, 1.02-10.45]), increased substantial reperfusion by thrombectomy (OR, 8.30 [95% CI, 0.97-70.87]), and reduced radiological hemorrhagic transformation (OR, 0.34 [95% CI, 0.12-0.94]). CONCLUSIONS: Slower SIP is associated with a high collateral score, prior ischemic stroke, and coronary artery disease, supporting roles for both collateral robustness and ischemic preconditioning in fostering tissue resilience to ischemia. Among patients undergoing endovascular thrombectomy, the speed of infarct progression is a major determinant of clinical outcome.

Gut-seeded α-synuclein fibrils promote gut dysfunction and brain pathology specifically in aged mice.

Parkinson's disease is a synucleinopathy that is characterized by motor dysfunction, death of midbrain dopaminergic neurons and accumulation of α-synuclein (α-Syn) aggregates. Evidence suggests that α-Syn aggregation can originate in peripheral tissues and progress to the brain via autonomic fibers. We tested this by inoculating the duodenal wall of mice with α-Syn preformed fibrils. Following inoculation, we observed gastrointestinal deficits and physiological changes to the enteric nervous system. Using the AAV-PHP.S capsid to target the lysosomal enzyme glucocerebrosidase for peripheral gene transfer, we found that α-Syn pathology is reduced due to the increased expression of this protein. Lastly, inoculation of α-Syn fibrils in aged mice, but not younger mice, resulted in progression of α-Syn histopathology to the midbrain and subsequent motor defects. Our results characterize peripheral synucleinopathy in prodromal Parkinson's disease and explore cellular mechanisms for the gut-to-brain progression of α-Syn pathology.

Evaluation of a Workflow to Define Low Specific Absorption Rate MRI Protocols for Patients With Active Implantable Medical Devices.

BACKGROUND: MRI exams for patients with MR-conditional active implantable medical devices (AIMDs) are contraindicated unless specific conditions are met. This limits the maximum specific absorption rate (SAR, W/kg). Currently, there is no general framework to guide meeting a lower SAR limit. PURPOSE: To design and evaluate a workflow for modifying MRI protocols to whole-body SAR (WB-SAR ≤0.1 W/kg) and local-head SAR (LH-SAR ≤0.3 W/kg) limits while mitigating the impact on image quality and exam time. STUDY TYPE: Prospective. POPULATION: Twenty healthy volunteers on head (n = 5), C-spine (n = 5), T-spine (n = 5), and L-spine (n = 5) with IRB consent. ASSESSMENT: Vendor-provided head, C-spine, T-spine, and L-spine protocols (SARRT ) were modified to meet both low SAR targets (SARLOW ) using the proposed workflow. in vitro SNR and CNR were evaluated with a T1 -T2 phantom. in vivo image quality and clinical acceptability were scored using a 5-point Likert scale for two blinded readers. FIELD STRENGTH/SEQUENCES: 1.5T/spin-echoes, gradient-echoes. STATISTICAL ANALYSIS: In vitro SNR and CNR values were evaluated with a repeated measures general linear model. in vivo image quality and clinical acceptability were evaluated using a generalized estimating equation analysis (GEE). The two reader's level of agreement was analyzed using Cohen's kappa statistical analysis. RESULTS: Using the workflow, SAR limits were met. LH-SAR: 0.12 ± 0.02 W/kg, median (SD) values for LH-SAR were 0.12 (0.02) W/kg and WB-SAR: 0.09 (0.01) W/kg. Examination time did not increase ≤2x the initial time. SARRT SNR values were higher and significantly different than SARLOW (P < 0.05). However, no significant difference was observed between the CNR values (value = 0.21). Median (IQR) CNR values were 14.2 (25.0) vs. 15.1 (9.2) for head, 12.1 (16.9) vs. 25.3 (14.2) for C-spine, 81.6 (70.1) vs. 71.0 (26.6) for T-spine, and 51.4 (52.6) vs. 37.7 (27.3) for L-spine. Image quality scores were not significantly different between SARRT and SARLOW (median [SD] scores were 4.0 [0.01] vs. 4.3 [0.2], P > 0.05). DATA CONCLUSION: The proposed workflow provides guidance for modifying routine MRI exams to achieve low SAR limits. This can benefit patients referred for an MRI exam with low SAR MR-conditional AIMDs. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2020;52:91-102.

Impaired Notch Signaling Leads to a Decrease in p53 Activity and Mitotic Catastrophe in Aged Muscle Stem Cells.

The decline of tissue regenerative potential with age correlates with impaired stem cell function. However, limited strategies are available for therapeutic modulation of stem cell function during aging. Using skeletal muscle stem cells (MuSCs) as a model system, we identify cell death by mitotic catastrophe as a cause of impaired stem cell proliferative expansion in aged animals. The mitotic cell death is caused by a deficiency in Notch activators in the microenvironment. We discover that ligand-dependent stimulation of Notch activates p53 in MuSCs via inhibition of Mdm2 expression through Hey transcription factors during normal muscle regeneration and that this pathway is impaired in aged animals. Pharmacologic activation of p53 promotes the expansion of aged MuSCs in vivo. Altogether, these findings illuminate a Notch-p53 signaling axis that plays an important role in MuSC survival during activation and is dysregulated during aging, contributing to the age-related decline in muscle regenerative potential.