An improved spectral clustering method for accurate detection of brain resting-state networks.

This paper proposes a data-driven analysis method to accurately partition large-scale resting-state functional brain networks from fMRI data. The method is based on a spectral clustering algorithm and combines eigenvector direction selection with Pearson correlation clustering in the spectral space. The method is an improvement on available spectral clustering methods, capable of robustly identifying active brain networks consistent with those from model-driven methods at different noise levels, even at the noise level of real fMRI data.

ASL lexicon and reporting recommendations: A consensus report from the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI).

The 2015 consensus statement published by the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group and the European Cooperation in Science and Technology ( COST) Action ASL in Dementia aimed to encourage the implementation of robust arterial spin labeling (ASL) perfusion MRI for clinical applications and promote consistency across scanner types, sites, and studies. Subsequently, the recommended 3D pseudo-continuous ASL sequence has been implemented by most major MRI manufacturers. However, ASL remains a rapidly and widely developing field, leading inevitably to further divergence of the technique and its associated terminology, which could cause confusion and hamper research reproducibility. On behalf of the ISMRM Perfusion Study Group, and as part of the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI), the ASL Lexicon Task Force has been working on the development of an ASL Lexicon and Reporting Recommendations for perfusion imaging and analysis, aiming to (1) develop standardized, consensus nomenclature and terminology for the broad range of ASL imaging techniques and parameters, as well as for the physiological constants required for quantitative analysis; and (2) provide a community-endorsed recommendation of the imaging parameters that we encourage authors to include when describing ASL methods in scientific reports/papers. In this paper, the sequences and parameters in (pseudo-)continuous ASL, pulsed ASL, velocity-selective ASL, and multi-timepoint ASL for brain perfusion imaging are included. However, the content of the lexicon is not intended to be limited to these techniques, and this paper provides the foundation for a growing online inventory that will be extended by the community as further methods and improvements are developed and established.

Recommendations for quantitative cerebral perfusion MRI using multi-timepoint arterial spin labeling: Acquisition, quantification, and clinical applications.

Accurate assessment of cerebral perfusion is vital for understanding the hemodynamic processes involved in various neurological disorders and guiding clinical decision-making. This guidelines article provides a comprehensive overview of quantitative perfusion imaging of the brain using multi-timepoint arterial spin labeling (ASL), along with recommendations for its acquisition and quantification. A major benefit of acquiring ASL data with multiple label durations and/or post-labeling delays (PLDs) is being able to account for the effect of variable arterial transit time (ATT) on quantitative perfusion values and additionally visualize the spatial pattern of ATT itself, providing valuable clinical insights. Although multi-timepoint data can be acquired in the same scan time as single-PLD data with comparable perfusion measurement precision, its acquisition and postprocessing presents challenges beyond single-PLD ASL, impeding widespread adoption. Building upon the 2015 ASL consensus article, this work highlights the protocol distinctions specific to multi-timepoint ASL and provides robust recommendations for acquiring high-quality data. Additionally, we propose an extended quantification model based on the 2015 consensus model and discuss relevant postprocessing options to enhance the analysis of multi-timepoint ASL data. Furthermore, we review the potential clinical applications where multi-timepoint ASL is expected to offer significant benefits. This article is part of a series published by the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group, aiming to guide and inspire the advancement and utilization of ASL beyond the scope of the 2015 consensus article.

Reproducibility and Sensitivity of Resting-State fMRI in Patients With Parkinson's Disease Using Cross Validation-Based Data Censoring.

BACKGROUND: Uncontrollable body movements are typical symptoms of Parkinson's disease (PD), which results in inconsistent findings regarding resting-state functional connectivity (rsFC) networks, especially for group difference clusters. Systematically identifying the motion-associated data was highly demanded. PURPOSE: To determine data censoring criteria using a quantitative cross validation-based data censoring (CVDC) method and to improve the detection of rsFC deficits in PD. STUDY TYPE: Prospective. SUBJECTS: Forty-one PD patients (68.63 ± 9.17 years, 44% female) and 20 healthy controls (66.83 ± 12.94 years, 55% female). FIELD STRENGTH/SEQUENCE: 3-T, T1-weighted gradient echo and EPI sequences. ASSESSMENT: Clusters with significant differences between groups were found in three visual networks, default network, and right sensorimotor network. Five-fold cross-validation tests were performed using multiple motion exclusion criteria, and the selected criteria were determined based on cluster sizes, significance values, and Dice coefficients among the cross-validation tests. As a reference method, whole brain rsFC comparisons between groups were analyzed using a FMRIB Software Library (FSL) pipeline with default settings. STATISTICAL TESTS: Group difference clusters were calculated using nonparametric permutation statistics of FSL-randomize. The family-wise error was corrected. Demographic information was evaluated using independent sample t-tests and Pearson's Chi-squared tests. The level of statistical significance was set at P < 0.05. RESULTS: With the FSL processing pipeline, the mean Dice coefficient of the network clusters was 0.411, indicating a low reproducibility. With the proposed CVDC method, motion exclusion criteria were determined as frame-wise displacement >0.55 mm. Group-difference clusters showed a mean P-value of 0.01 and a 72% higher mean Dice coefficient compared to the FSL pipeline. Furthermore, the CVDC method was capable of detecting subtle rsFC deficits in the medial sensorimotor network and auditory network that were unobservable using the conventional pipeline. DATA CONCLUSION: The CVDC method may provide superior sensitivity and improved reproducibility for detecting rsFC deficits in PD. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Affine image registration of arterial spin labeling MRI using deep learning networks.

Convolutional neural networks (CNN) have demonstrated good accuracy and speed in spatially registering high signal-to-noise ratio (SNR) structural magnetic resonance imaging (sMRI) images. However, some functional magnetic resonance imaging (fMRI) images, e.g., those acquired from arterial spin labeling (ASL) perfusion fMRI, are of intrinsically low SNR and therefore the quality of registering ASL images using CNN is not clear. In this work, we aimed to explore the feasibility of a CNN-based affine registration network (ARN) for registration of low-SNR three-dimensional ASL perfusion image time series and compare its performance with that from the state-of-the-art statistical parametric mapping (SPM) algorithm. The six affine parameters were learned from the ARN using both simulated motion and real acquisitions from ASL perfusion fMRI data and the registered images were generated by applying the transformation derived from the affine parameters. The speed and registration accuracy were compared between ARN and SPM. Several independent datasets, including meditation study (10 subjects × 2), bipolar disorder study (26 controls, 19 bipolar disorder subjects), and aging study (27 young subjects, 33 older subjects), were used to validate the generality of the trained ARN model. The ARN method achieves superior image affine registration accuracy (total translation/total rotation errors of ARN vs. SPM: 1.17 mm/1.23° vs. 6.09 mm/12.90° for simulated images and reduced MSE/L1/DSSIM/Total errors of 18.07% / 19.02% / 0.04% / 29.59% for real ASL test images) and 4.4 times (ARN vs. SPM: 0.50 s vs. 2.21 s) faster speed compared to SPM. The trained ARN can be generalized to align ASL perfusion image time series acquired with different scanners, and from different image resolutions, and from healthy or diseased populations. The results demonstrated that our ARN markedly outperforms the iteration-based SPM both for simulated motion and real acquisitions in terms of registration accuracy, speed, and generalization.

Recent Technical Developments in ASL: A Review of the State of the Art.

This review article provides an overview of a range of recent technical developments in advanced arterial spin labeling (ASL) methods that have been developed or adopted by the community since the publication of a previous ASL consensus paper by Alsop et al. It is part of a series of review/recommendation papers from the International Society for Magnetic Resonance in Medicine Perfusion Study Group. Here, we focus on advancements in readouts and trajectories, image reconstruction, noise reduction, partial volume correction, quantification of nonperfusion parameters, fMRI, fingerprinting, vessel selective ASL, angiography, deep learning, and ultrahigh field ASL. We aim to provide a high level of understanding of these new approaches and some guidance for their implementation, with the goal of facilitating the adoption of such advances by research groups and by MRI vendors. Topics outside the scope of this article that are reviewed at length in separate articles include velocity selective ASL, multiple-timepoint ASL, body ASL, and clinical ASL recommendations.

Quantitative transport mapping (QTM) of the kidney with an approximate microvascular network.

PURPOSE: Proof-of-concept study of mapping renal blood flow vector field according to the inverse solution to a mass transport model of time resolved tracer-labeled MRI data. THEORY AND METHODS: To determine tissue perfusion according to the underlying physics of spatiotemporal tracer concentration variation, the mass transport equation is integrated over a voxel with an approximate microvascular network for fitting time-resolved tracer imaging data. The inverse solution to the voxelized transport equation provides the blood flow vector field, which is referred to as quantitative transport mapping (QTM). A numerical microvascular network modeling the kidney with computational fluid dynamics reference was used to verify the accuracy of QTM and the current Kety's method that uses a global arterial input function. Multiple post-label delay arterial spin labeling (ASL) of the kidney on seven subjects was used to assess QTM in vivo feasibility. RESULTS: Against the ground truth in the numerical model, the error in flow estimated by QTM (18.6%) was smaller than that in Kety's method (45.7%, 2.5-fold reduction). The in vivo kidney perfusion quantification by QTM (cortex: 443 ± 58 mL/100 g/min and medulla: 190 ± 90 mL/100 g/min) was in the range of that by Kety's method (482 ± 51 mL/100 g/min in the cortex and 242 ± 73 mL/100 g/min in the medulla), and QTM provided better flow homogeneity in the cortex region. CONCLUSIONS: QTM flow velocity mapping is feasible from multi-delay ASL MRI data based on inverting the transport equation. In a numerical simulation, QTM with deconvolution in space and time provided more accurate perfusion quantification than Kety's method with deconvolution in time only.

Altered cerebral perfusion in bipolar disorder: A pCASL MRI study.

BACKGROUND: Neurovascular abnormalities are relevant to the pathophysiology of bipolar disorder (BD), which can be assessed using cerebral blood flow (CBF) imaging. CBF alterations have been identified in BD, but studies to date have been small and inconclusive. We aimed to determine cortical gray matter CBF (GM-CBF) differences between BD and healthy controls (HC) and to identify relationships between CBF and clinical or cognitive measures. METHODS: Cortical GM-CBF maps were generated using Pseudo-Continuous Arterial Spin Labeling (pCASL) for 109 participants (BD, n = 61; HC, n = 48). We used SnPM13 to perform non-parametric voxel-wise two-sample t-tests comparing CBF between groups. We performed multiple linear regression to relate GM-CBF with clinical and cognitive measures. Analysis was adjusted for multiple comparisons with 10,000 permutations. Significance was set at a voxel level threshold of P < .001 followed by AlphaSim cluster-wise correction of P < .05. RESULTS: Compared to HCs, BD patients had greater GM-CBF in the left lateral occipital cortex, superior division and lower CBF in the right lateral occipital, angular and middle temporal gyrus. Greater GM-CBF in the left lateral occipital cortex correlated with worse working memory, verbal memory, attention and speed of processing. We found using voxel-wise regression that decreased gray matter CBF in the bilateral thalamus and cerebellum, and increased right fronto-limbic CBF were associated with worse working memory. No clusters were associated with clinical variables after FDR correction. CONCLUSIONS: Cortical GM-CBF alterations are seen in BD and may be related to cognitive function, which suggest neurovascular unit dysfunction as a possible pathophysiologic mechanism.

Identifying cardiovascular risk factors that impact cerebrovascular reactivity: An ASL MRI study.

BACKGROUND: To maintain cerebral blood flow (CBF), cerebral blood vessels dilate and contract in response to blood supply through cerebrovascular reactivity (CR). PURPOSE: Cardiovascular (CV) disease is associated with increased stroke risk, but which risk factors specifically impact CR is unknown. STUDY TYPE: Prospective longitudinal. SUBJECTS: Fifty-three subjects undergoing carotid endarterectomy or stenting. FIELD STRENGTH/SEQUENCE: 3T, 3D pseudo-continuous arterial spin labeling (PCASL) ASL, and T1 3D fast spoiled gradient echo (FSPGR). ASSESSMENT: We evaluated group differences in CBF changes for multiple cardiovascular risk factors in patients undergoing carotid revascularization surgery. STATISTICAL TESTS: PRE (baseline), POST (48-hour postop), and 6MO (6 months postop) whole-brain CBF measurements, as 129 CBF maps from 53 subjects were modeled as within-subject analysis of variance (ANOVA). To identify CV risk factors associated with CBF change, the CBF change from PRE to POST, POST to 6MO, and PRE to 6MO were modeled as multiple linear regression with each CV risk factor as an independent variable. Statistical models were performed controlling for age on a voxel-by-voxel basis using SPM8. Significant clusters were reported if familywise error (FWE)-corrected cluster-level was P < 0.05, while the voxel-level significance threshold was set for P < 0.001. RESULTS: The entire group showed significant (cluster-level P < 0.001) CBF increase from PRE to POST, decrease from POST to 6MO, and no significant difference (all voxels with P > 0.001) from PRE to 6MO. Of multiple CV risk factors evaluated, only elevated systolic blood pressure (SBP, P = 0.001), chronic renal insufficiency (CRI, P = 0.026), and history of prior stroke (CVA, P < 0.001) predicted lower increases in CBF PRE to POST. Over POST to 6MO, obesity predicted lower (P > 0.001) and cholesterol greater CBF decrease (P > 0.001). DATA CONCLUSION: The CV risk factors of higher SBP, CRI, CVA, BMI, and cholesterol may indicate altered CR, and may warrant different stroke risk mitigation and special consideration for CBF change evaluation. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2020;51:734-747.

Abnormal perfusion fluctuation and perfusion connectivity in bipolar disorder measured by dynamic arterial spin labeling.

OBJECTIVES: We sought to evaluate whether dynamic Arterial Spin Labeling (dASL), a novel quantitative technique robust to artifacts and noise that especially arise in inferior brain regions, could characterize neural substrates of BD pathology and symptoms. METHODS: Forty-five subjects (19 BD patients, 26 controls) were imaged using a dASL sequence. Maps of average perfusion, perfusion fluctuation, and perfusion connectivity with anterior cingulate cortex (ACC) were derived. Patient symptoms were quantified along four symptom dimensions determined using factor analysis of the subjects from the Bipolar and Schizophrenia Network on Intermediate Phenotypes (BSNIP) study. Maps of the perfusion measures were compared between BD patients and controls and correlated with the symptom dimensions in the BD patients only by voxel-level and region-level analyses. RESULTS: BD patients exhibited (i) significantly increased perfusion fluctuations in the left fusiform and inferior temporal regions (P = .020, voxel-level corrected) and marginally increased perfusion fluctuations in the right temporal pole and inferior temporal regions (P = .063, cluster-level corrected), (ii) significantly increased perfusion connectivity between ACC and the occipitoparietal cortex (P = .050, cluster-level corrected). In BD patients, positive symptoms were negatively associated with ACC perfusion connectivity to the right orbitofrontal and superior frontal regions (P = .002, cluster-level corrected) and right orbitofrontal and inferior frontal regions (P = .023, cluster-level corrected). CONCLUSION: The abnormal perfusion fluctuations and connectivity alterations may underlie the mood fluctuations and cognitive and emotional dysregulation that characterize BD.

Improved perfusion pattern score association with type 2 diabetes severity using machine learning pipeline: Pilot study.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is associated with alterations in the blood-brain barrier, neuronal damage, and arterial stiffness, thus affecting cerebral metabolism and perfusion. There is a need to implement machine-learning methodologies to identify a T2DM-related perfusion pattern and possible relationship between the pattern and cognitive performance/disease severity. PURPOSE: To develop a machine-learning pipeline to investigate the method's discriminative value between T2DM patients and normal controls, the T2DM-related network pattern, and association of the pattern with cognitive performance/disease severity. STUDY TYPE: A cross-sectional study and prospective longitudinal study with a 2-year time interval. POPULATION: Seventy-three subjects (41 T2DM patients and 32 controls) aged 50-85 years old at baseline, and 42 subjects (19 T2DM and 23 controls) aged 53-88 years old at 2-year follow-up. FIELD STRENGTH/SEQUENCE: 3T pseudocontinuous arterial spin-labeling MRI. ASSESSMENT: Machine-learning-based pipeline (principal component analysis, feature selection, and logistic regression classifier) to generate the T2DM-related network pattern and the individual scores associated with the pattern. STATISTICAL TESTS: Linear regression analysis with gray matter volume and education years as covariates. RESULTS: The machine-learning-based method is superior to the widely used univariate group comparison method with increased test accuracy, test area under the curve, test positive predictive value, adjusted McFadden's R square of 4%, 12%, 7%, and 24%, respectively. The pattern-related individual scores are associated with diabetes severity variables, mobility, and cognitive performance at baseline (P < 0.05, |r| > 0.3). More important, the longitudinal change of individual pattern scores is associated with the longitudinal change of HbA1c (P = 0.0053, r = 0.64), and baseline cholesterol (P = 0.037, r = 0.51). DATA CONCLUSION: The individual perfusion diabetes pattern score is a highly promising perfusion imaging biomarker for tracing the disease progression of individual T2DM patients. Further validation is needed from a larger study. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:834-844.

Postoperative Delirium and Postoperative Cognitive Dysfunction: Overlap and Divergence.

BACKGROUND: Postoperative delirium and postoperative cognitive dysfunction share risk factors and may co-occur, but their relationship is not well established. The primary goals of this study were to describe the prevalence of postoperative cognitive dysfunction and to investigate its association with in-hospital delirium. The authors hypothesized that delirium would be a significant risk factor for postoperative cognitive dysfunction during follow-up. METHODS: This study used data from an observational study of cognitive outcomes after major noncardiac surgery, the Successful Aging after Elective Surgery study. Postoperative delirium was evaluated each hospital day with confusion assessment method-based interviews supplemented by chart reviews. Postoperative cognitive dysfunction was determined using methods adapted from the International Study of Postoperative Cognitive Dysfunction. Associations between delirium and postoperative cognitive dysfunction were examined at 1, 2, and 6 months. RESULTS: One hundred thirty-four of 560 participants (24%) developed delirium during hospitalization. Slightly fewer than half (47%, 256 of 548) met the International Study of Postoperative Cognitive Dysfunction-defined threshold for postoperative cognitive dysfunction at 1 month, but this proportion decreased at 2 months (23%, 123 of 536) and 6 months (16%, 85 of 528). At each follow-up, the level of agreement between delirium and postoperative cognitive dysfunction was poor (kappa less than .08) and correlations were small (r less than .16). The relative risk of postoperative cognitive dysfunction was significantly elevated for patients with a history of postoperative delirium at 1 month (relative risk = 1.34; 95% CI, 1.07-1.67), but not 2 months (relative risk = 1.08; 95% CI, 0.72-1.64), or 6 months (relative risk = 1.21; 95% CI, 0.71-2.09). CONCLUSIONS: Delirium significantly increased the risk of postoperative cognitive dysfunction in the first postoperative month; this relationship did not hold in longer-term follow-up. At each evaluation, postoperative cognitive dysfunction was more common among patients without delirium. Postoperative delirium and postoperative cognitive dysfunction may be distinct manifestations of perioperative neurocognitive deficits.

Effects of arterial transit delay on cerebral blood flow quantification using arterial spin labeling in an elderly cohort.

PURPOSE: To investigate whether measurement of arterial transit time (ATT) can improve the accuracy of arterial spin labeling (ASL) cerebral blood flow (CBF) quantification in an elderly cohort due to the potentially prolonged ATT in the cohort. MATERIALS AND METHODS: We employed a 1-minute, low-resolution (12 mm in-plane), sequential multidelay ATT measurement (both with and without vessel suppression) approach to characterize and correct ATT errors in CBF imaging of an elderly, clinical cohort. In all, 140 nondemented subjects greater than 70 years old were imaged at 3T with a single delay, volumetric continuous ASL sequence and also with the fast ATT measurement method. Nine healthy young subjects (28 ± 6 years old) were also imaged. RESULTS: ATTs measured without vessel suppression (superior frontal: 1.51 ± 0.27 sec) in the elderly were significantly shorter than those with suppression (P < 0.0001). Correction of CBF for ATT significantly increased average CBF in multiple brain regions where ATT was longer than the postlabeling delay (P < 0.01) and decreased intersubject variability of CBF in frontal, parietal, and occipital regions (P < 10-8 ). Measured ATT with vessel suppression was significantly longer in the elderly subjects (eg, superior frontal: 1.76 ± 0.25 sec) compared to the younger adults (superior frontal: 1.59 ± 0.19 sec) in basal ganglia and frontal cortical regions (P < 0.05). CONCLUSION: The ATT measurement is beneficial for imaging of elderly clinical populations. If ATT mapping is not feasible or available, postlabeling delays of 2-2.3 seconds should be used for elderly populations based on longest measured regional ATTs. LEVEL OF EVIDENCE: 1 J. Magn. Reson. Imaging 2017;45:472-481.

Neural substrates of vulnerability to postsurgical delirium as revealed by presurgical diffusion MRI.

Despite the significant impact of postoperative delirium on surgical outcomes and the long-term prognosis of older patients, its neural basis has not yet been clarified. In this study we investigated the impact of premorbid brain microstructural integrity, as measured by diffusion tensor imaging before surgery, on postoperative delirium incidence and severity, as well as the relationship among presurgical cognitive performance, diffusion tensor imaging abnormalities and postoperative delirium. Presurgical diffusion tensor imaging scans of 136 older (≥70 years), dementia-free subjects from the prospective Successful Aging after Elective Surgery study were analysed blind to the clinical data and delirium status. Primary outcomes were postoperative delirium incidence and severity during the hospital stay, as assessed by the Confusion Assessment Method. We measured cognition before surgery using general cognitive performance, a composite score based on a battery of neuropsychological tests. We investigated the association between presurgical diffusion tensor imaging parameters of brain microstructural integrity (i.e. fractional anisotropy, axial, mean and radial diffusivity) with postoperative delirium incidence and severity. Analyses were adjusted for the following potential confounders: age, gender, vascular comorbidity status, and general cognitive performance. Postoperative delirium occurred in 29 of 136 subjects (21%) during hospitalization. Presurgical diffusion tensor imaging abnormalities of the cerebellum, cingulum, corpus callosum, internal capsule, thalamus, basal forebrain, occipital, parietal and temporal lobes, including the hippocampus, were associated with delirium incidence and severity, after controlling for age, gender and vascular comorbidities. After further controlling for general cognitive performance, diffusion tensor imaging abnormalities of the cerebellum, hippocampus, thalamus and basal forebrain still remained associated with delirium incidence and severity. This study raises the intriguing possibility that structural dysconnectivity involving interhemispheric and fronto-thalamo-cerebellar networks, as well as microstructural changes of structures involved in limbic and memory functions predispose to delirium under the stress of surgery. While the diffusion tensor imaging abnormalities observed in the corpus callosum, cingulum, and temporal lobe likely constitute the neural substrate for the association between premorbid cognition, as measured by general cognitive performance, and postoperative delirium, the microstructural changes observed in the cerebellum, hippocampus, thalamus and basal forebrain seem to constitute a separate phenomenon that predisposes to postsurgical delirium independent of presurgical cognitive status.

Associations between the choroid plexus and tau in Alzheimer's disease using an active learning segmentation pipeline.

BACKGROUND: The cerebrospinal fluid (CSF), primarily generated by the choroid plexus (ChP), is the major carrier of the glymphatic system. The alternations of CSF production and the ChP can be associated with the Alzheimer's disease (AD). The present work investigated the roles of the ChP in the AD based on a proposed ChP image segmentation pipeline. METHODS: A human-in-the-loop ChP image segmentation pipeline was implemented with intermediate and active learning datasets. The performance of the proposed pipeline was evaluated on manual contours by five radiologists, compared to the FreeSurfer and FastSurfer toolboxes. The ChP volume and blood flow were investigated among AD groups. The correlations between the ChP volume and AD CSF biomarkers including phosphorylated tau (p-tau), total tau (t-tau), amyloid-ß42 (Aß42), and amyloid-ß40 (Aß40) was investigated using three models (univariate, multiple variables, and stepwise regression) on two datasets with 806 and 320 subjects. RESULTS: The proposed ChP segmentation pipeline achieved superior performance with a Dice coefficient of 0.620 on the test dataset, compared to the FreeSurfer (0.342) and FastSurfer (0.371). Significantly larger volumes (p < 0.001) and higher perfusion (p = 0.032) at the ChP were found in AD compared to CN groups. Significant correlations were found between the tau and the relative ChP volume (the ChP volume and ChP/parenchyma ratio) in each patient groups and in the univariate regression analysis (p < 0.001), the multiple regression model (p < 0.05 except for the t-tau in the LMCI), and in the step-wise regression model (p < 0.021). In addition, the correlation coefficients changed from - 0.32 to - 0.21 along with the AD progression in the multiple regression model. In contrast, the Aß42 and Aß40 shows consistent and significant associations with the lateral ventricle related measures in the step-wise regression model (p < 0.027). CONCLUSIONS: The proposed pipeline provided accurate ChP segmentation which revealed the associations between the ChP and tau level in the AD. The proposed pipeline is available on GitHub ( https://github.com/princeleeee/ChP-Seg ).

Diffusion restriction in the human spinal cord characterized in vivo with high b-value STEAM diffusion imaging.

Restricted or hindered motion of water across axonal membranes as characterized with diffusion-weighted (DW) imaging may be a potential marker of axonal damage in white matter (WM) injury due to trauma, neurodegeneration, or other causes. This study sought to determine whether high b-value DW imaging with a stimulated echo (STEAM) sequence could improve the spatially resolved assessment of tissue architecture in the human spinal cord in vivo. Diffusion times from 76 ms to 1000 ms and b-values of up to 14,750 s/mm(2) were used to acquire axial DW images in six healthy volunteers, and four additional healthy volunteers were studied with a protocol focused on high b-value, higher-resolution imaging. Mono-exponential, diffusional kurtosis, and mono-exponential with an additive constant (MEC) models were fit individually to diffusion decay curves obtained at different diffusion times. Diffusion restriction, characterized with the diffusional kurtosis and MEC models, was measured more precisely using higher b-value ranges. DW images at high b-value and fitting parameters using the large range of b-values available at the diffusion time of 1000 ms demonstrated signal and restriction differences between gray and white matter and even across white matter regions. These white matter differences may reflect variations in axonal density, diameter, or alignment. We conclude that high b-value DW imaging with a STEAM sequence on a conventional clinical scanner can provide accurate measures of diffusion hindrance and restriction in human spinal cord in vivo.

Volumetric measurement of perfusion and arterial transit delay using hadamard encoded continuous arterial spin labeling.

Creating images of the transit delay from the labeling location to image tissue can aid the optimization and quantification of arterial spin labeling perfusion measurements and may provide diagnostic information independent of perfusion. Unfortunately, measuring transit delay requires acquiring a series of images with different labeling timing that adds to the time cost and increases the noise of the arterial spin labeling study. Here, we implement and evaluate a proposed Hadamard encoding of labeling that speeds the imaging and improves the signal-to-noise ratio efficiency. Volumetric images in human volunteers confirmed the theoretical advantages of Hadamard encoding over sequential acquisition of images with multiple labeling timing. Perfusion images calculated from Hadamard encoded acquisition had reduced signal-to-noise ratio relative to a dedicated perfusion acquisition with either assumed or separately measured transit delays, however.

[Design and application of a B-ultrasound-guided deep vein puncture precise positioning device].

Deep venipuncture catheterization is a routine and basic operation in the treatment of critically ill patients, and it is the most effective way to quickly correct the shock. Clinical B-ultrasound guided deep vein catheters can improve the success rate of puncture, but in the process of operation, the short axis needs to be replaced by the long axis. In the replacement process, the stability of the novice is insufficient, the positioning is difficult, and the operation time is too long. If only short axis puncture is used, it is impossible to know whether the current position of the puncture needle, and the puncture may be too deep and stray into the artery. The accuracy of the 45 degree angle of the injection point requires a very experienced operator. In view of the above shortcomings, doctors in the department of critical care medicine of Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine designed a B-ultrasound puncture equipment, which has obtained the National Invention Patent of China (ZL 2016 1 0571557.X). The device is composed of B-ultrasound probe fixing frame, sliding scale plate, simulation slide rule, puncture needle, sliding device. By sliding device the angle of the pinhole channel, it is conducive to the accurate positioning of the puncture target, optimizing the operation procedure, improving the puncture speed and accuracy, effectively reducing the occurrence of puncture complications, ensuring patient safety, reducing unnecessary waste of human and material resources. It can reduce the workload of medical staff and is worthy of clinical practice.

Longitudinal Patterns of Brain Changes in a Community Sample in Relation to Aging and Cognitive Status.

BACKGROUND: Aging and Alzheimer's disease (AD) are characterized by widespread cortical and subcortical atrophy. Though atrophy patterns between aging and AD overlap considerably, regional differences between these two conditions may exist. Few studies, however, have investigated these patterns in large community samples. OBJECTIVE: Elaborate longitudinal changes in brain morphometry in relation to aging and cognitive status in a well-characterized community cohort. METHODS: Clinical and neuroimaging data were compiled from 72 participants from the Cardiovascular Health Study-Cognition Study, a community cohort of healthy aging and probable AD participants. Two time points were identified for each participant with a mean follow-up time of 5.36 years. MRI post-processing, morphometric measurements, and statistical analyses were performed using FreeSurfer, Version 7.1.1. RESULTS: Cortical volume was significantly decreased in the bilateral superior frontal, bilateral inferior parietal, and left superior parietal regions, among others. Cortical thickness was significantly reduced in the bilateral superior frontal and left inferior parietal regions, among others. Overall gray and white matter volumes and hippocampal subfields also demonstrated significant reductions. Cortical volume atrophy trajectories between cognitively stable and cognitively declined participants were significantly different in the right postcentral region. CONCLUSION: Observed volume reductions were consistent with previous studies investigating morphometric brain changes. Patterns of brain atrophy between AD and aging may be different in magnitude but exhibit widespread spatial overlap. These findings help characterize patterns of brain atrophy that may reflect the general population. Larger studies may more definitively establish population norms of aging and AD-related neuroimaging changes.

Mechanical hierarchy in the formation and modulation of cortical folding patterns.

The important mechanical parameters and their hierarchy in the growth and folding of the human brain have not been thoroughly understood. In this study, we developed a multiscale mechanical model to investigate how the interplay between initial geometrical undulations, differential tangential growth in the cortical plate, and axonal connectivity form and regulate the folding patterns of the human brain in a hierarchical order. To do so, different growth scenarios with bilayer spherical models that features initial undulations on the cortex and uniform or heterogeneous distribution of axonal fibers in the white matter were developed, statistically analyzed, and validated by the imaging observations. The results showed that the differential tangential growth is the inducer of cortical folding, and in a hierarchal order, high-amplitude initial undulations on the surface and axonal fibers in the substrate regulate the folding patterns and determine the location of gyri and sulci. The locations with dense axonal fibers after folding settle in gyri rather than sulci. The statistical results also indicated that there is a strong correlation between the location of positive (outward) and negative (inward) initial undulations and the locations of gyri and sulci after folding, respectively. In addition, the locations of 3-hinge gyral folds are strongly correlated with the initial positive undulations and locations of dense axonal fibers. As another finding, it was revealed that there is a correlation between the density of axonal fibers and local gyrification index, which has been observed in imaging studies but not yet fundamentally explained. This study is the first step in understanding the linkage between abnormal gyrification (surface morphology) and disruption in connectivity that has been observed in some brain disorders such as Autism Spectrum Disorder. Moreover, the findings of the study directly contribute to the concept of the regularity and variability of folding patterns in individual human brains.