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The contributions of this author are as follows: conceived and designed the experiments, performed the experiments, and analyzed the data. 1. (2013) White Matter Injury Due to Experimental Chronic Cerebral Hypoperfusion Is Associated with C5 Deposition.

•Non-invasive mALPS-index was closely related to the classical detected glymphatic clearance function, providing an alternative method in researches on glymphatic system.•WMHs, lacunas, microbleeds and EPVS in basal ganglia were related to glymphatic clearance function.•Glymphatic clearance function was related to cognitive function in CSVD patients. Few studies have focused on the connection between glymphatic dysfunction and cerebral small vessel disease (CSVD), partially due to the lack of non-invasive methods to measure glymphatic function. We established modified index for diffusion tensor image analysis along the perivascular space (mALPS-index), which was calculated on diffusion tensor image (DTI), compared it with the classical detection of glymphatic clearance function calculated on Glymphatic MRI after intrathecal administration of gadolinium (study 1), and analyzed the relationship between CSVD imaging markers and mALPS-index in CSVD patients from the CIRCLE study (ClinicalTrials.gov ID: NCT03542734) (study 2). Among 39 patients included in study 1, mALPS-index were significantly related to glymphatic clearance function calculated on Glymphatic MRI ( r  = -0.772~-0.844, p < 0.001). A total of 330 CSVD patients were included in study 2. Severer periventricular and deep white matter hyperintensities (β = -0.332, p < 0.001; β = -0.293, p < 0.001), number of lacunas (β = -0.215, p < 0.001), number of microbleeds (β = -0.152, p = 0.005), and severer enlarged perivascular spaces in basal ganglia (β = -0.223, p < 0.001) were related to mALPS-index. Our results indicated that non-invasive mALPS-index might represent glymphatic clearance function, which could be applied in clinic in future. Glymphatic clearance function might play a role in the development of CSVD.

There is an error in Equation 18 which appears under the sub-heading “Log-normal Distribution” in the “Introduction” section. Please view the correct Table S1 here Download corrected item. https://doi.org/10.1371/annotation/295b3621-1d29-43ac-8180-6fad3d2de593.s001.cn Citation: Pajevic S, Basser PJ (2013) Correction: An Optimum Principle Predicts the Distribution of Axon Diameters in Normal White Matter.

Cerebral amyloid angiopathy (CAA) is an age-related small vessel disease, characterised pathologically by progressive deposition of amyloid β in the cerebrovascular wall. The Boston criteria are used worldwide for the in-vivo diagnosis of CAA but have not been updated since 2010, before the emergence of additional MRI markers. We report an international collaborative study aiming to update and externally validate the Boston diagnostic criteria across the full spectrum of clinical CAA presentations. In this multicentre, hospital-based, retrospective, MRI and neuropathology diagnostic accuracy study, we did a retrospective analysis of clinical, radiological, and histopathological data available to sites participating in the International CAA Association to formulate updated Boston criteria and establish their diagnostic accuracy across different populations and clinical presentations. Ten North American and European academic medical centres identified patients aged 50 years and older with potential CAA-related clinical presentations (ie, spontaneous intracerebral haemorrhage, cognitive impairment, or transient focal neurological episodes), available brain MRI, and histopathological assessment for CAA diagnosis. MRI scans were centrally rated at Massachusetts General Hospital (Boston, MA, USA) for haemorrhagic and non-haemorrhagic CAA markers, and brain tissue samples were rated by neuropathologists at the contributing sites. We derived the Boston criteria version 2.0 (v2.0) by selecting MRI features to optimise diagnostic specificity and sensitivity in a prespecified derivation cohort (Boston cases 1994–2012, n=159), then externally validated the criteria in a prespecified temporal validation cohort (Boston cases 2012–18, n=59) and a geographical validation cohort (non-Boston cases 2004–18; n=123), comparing accuracy of the new criteria to the currently used modified Boston criteria with histopathological assessment of CAA as the diagnostic standard. We also assessed performance of the v2.0 criteria in patients across all cohorts who had the diagnostic gold standard of brain autopsy. The study protocol was finalised on Jan 15, 2017, patient identification was completed on Dec 31, 2018, and imaging analyses were completed on Sept 30, 2019. Of 401 potentially eligible patients presenting to Massachusetts General Hospital, 218 were eligible to be included in the analysis; of 160 patient datasets from other centres, 123 were included. Using the derivation cohort, we derived provisional criteria for probable CAA requiring the presence of at least two strictly lobar haemorrhagic lesions (ie, intracerebral haemorrhages, cerebral microbleeds, or foci of cortical superficial siderosis) or at least one strictly lobar haemorrhagic lesion and at least one white matter characteristic (ie, severe visible perivascular spaces in centrum semiovale or white matter hyperintensities in a multispot pattern). The sensitivity and specificity of these criteria were 74·8% (95% CI 65·4–82·7) and 84·6% (71·9–93·1) in the derivation cohort, 92·5% (79·6–98·4) and 89·5% (66·9–98·7) in the temporal validation cohort, 80·2% (70·8–87·6) and 81·5% (61·9–93·7) in the geographical validation cohort, and 74·5% (65·4–82·4) and 95·0% (83·1–99·4) in all patients who had autopsy as the diagnostic standard. The area under the receiver operating characteristic curve (AUC) was 0·797 (0·732–0·861) in the derivation cohort, 0·910 (0·828–0·992) in the temporal validation cohort, 0·808 (0·724–0·893) in the geographical validation cohort, and 0·848 (0·794–0·901) in patients who had autopsy as the diagnostic standard. The v2.0 Boston criteria for probable CAA had superior accuracy to the current Boston criteria (sensitivity 64·5% [54·9–73·4]; specificity 95·0% [83·1–99·4]; AUC 0·798 [0·741–0854]; p=0·0005 for comparison of AUC) across all individuals who had autopsy as the diagnostic standard. The Boston criteria v2.0 incorporate emerging MRI markers of CAA to enhance sensitivity without compromising their specificity in our cohorts of patients aged 50 years and older presenting with spontaneous intracerebral haemorrhage, cognitive impairment, or transient focal neurological episodes. Future studies will be needed to determine generalisability of the v.2.0 criteria across the full range of patients and clinical presentations. US National Institutes of Health (R01 AG26484).

•A high-level overview of how tractography is used to enable quantitative analysis of the brains structural connectivity.•Review methodology involved for tractography correction, segmentation and quantification.•Review studies using quantitative tractography approaches to study the brains white matter in health and disease. Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain’s white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain’s structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain’s structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain’s white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the “best” methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

Small vessel disease is a disorder of cerebral microvessels that causes white matter hyperintensities and several other common abnormalities (eg, recent small subcortical infarcts and lacunes) seen on brain imaging. Despite being a common cause of stroke and vascular dementia, the underlying pathogenesis is poorly understood. Research in humans has identified several manifestations of cerebral microvessel endothelial dysfunction including blood–brain barrier dysfunction, impaired vasodilation, vessel stiffening, dysfunctional blood flow and interstitial fluid drainage, white matter rarefaction, ischaemia, inflammation, myelin damage, and secondary neurodegeneration. These brain abnormalities are more dynamic and widespread than previously thought. Relationships between lesions and symptoms are highly variable but poorly understood. Major challenges are the determination of which vascular dysfunctions are most important in pathogenesis, which abnormalities are reversible, and why lesion progression and symptomatology are so variable. This knowledge will help to identify potential targets for intervention and improve risk prediction for individuals with small vessel disease.

Biophysical models of diffusion in white matter have been center-stage over the past two decades and are essentially based on what is now commonly referred to as the “Standard Model” (SM) of non-exchanging anisotropic compartments with Gaussian diffusion. In this work, we focus on diffusion MRI in gray matter, which requires rethinking basic microstructure modeling blocks. In particular, at least three contributions beyond the SM need to be considered for gray matter: water exchange across the cell membrane – between neurites and the extracellular space; non-Gaussian diffusion along neuronal and glial processes – resulting from structural disorder; and signal contribution from soma. For the first contribution, we propose Neurite Exchange Imaging (NEXI) as an extension of the SM of diffusion, which builds on the anisotropic Kärger model of two exchanging compartments. Using datasets acquired at multiple diffusion weightings (b) and diffusion times (t) in the rat brain in vivo, we investigate the suitability of NEXI to describe the diffusion signal in the gray matter, compared to the other two possible contributions. Our results for the diffusion time window 20–45 ms show minimal diffusivity time-dependence and more pronounced kurtosis decay with time, which is well fit by the exchange model. Moreover, we observe lower signal for longer diffusion times at high b. In light of these observations, we identify exchange as the mechanism that best explains these signal signatures in both low-b and high-b regime, and thereby propose NEXI as the minimal model for gray matter microstructure mapping. We finally highlight multi-b multi-t acquisition protocols as being best suited to estimate NEXI model parameters reliably. Using this approach, we estimate the inter-compartment water exchange time to be 15 – 60 ms in the rat cortex and hippocampus in vivo, which is of the same order or shorter than the diffusion time in typical diffusion MRI acquisitions. This suggests water exchange as an essential component for interpreting diffusion MRI measurements in gray matter.

•The fixel-based analysis framework was proposed for fibre-specific statistical analysis of diffusion MRI data.•A “fixel” represents an individual fibre population in a voxel, allowing for increased specificity over voxel-wise measures.•A state-of-the-art fixel-based analysis pipeline consists of several bespoke steps, but is conceptually similar to a voxel-based analysis.•Fixel-based analysis has seen increased adoption recently, with 75 published studies to date.•The framework has unique benefits and future opportunities, but specific challenges and limitations exist as well. Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organization. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple “crossing” fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the “Fixel-Based Analysis” (FBA) framework, which implements bespoke solutions to this end. It has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to Fixel-Based Analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of all current FBA studies, categorized across a broad range of neuro-scientific domains, listing key design choices and summarizing their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the FBA framework, and outline some directions and future opportunities. [Display omitted]

Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of ‘brain-predicted age’ as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. CNN accurately predicted chronological age using GM (correlation between brain-predicted age and chronological age r = 0.96, mean absolute error [MAE] = 4.16 years) and raw (r = 0.94, MAE = 4.65 years) data. This was comparable to GPR brain-predicted age using GM data (r = 0.95, MAE = 4.66 years). Brain-predicted age was a heritable phenotype for all models and input data (h2 ≥ 0.5). Brain-predicted age showed high test-retest reliability (intraclass correlation coefficient [ICC] = 0.90–0.99). Multi-centre reliability was more variable within high ICCs for GM (0.83–0.96) and poor-moderate levels for WM and raw data (0.51–0.77). Brain-predicted age represents an accurate, highly reliable and genetically-influenced phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings. •Chronological age can be accurately predicted using convolutional neural networks.•Age predicted is accurate even using raw structural neuroimaging data.•Brain-predicted age can be generated in a clinically applicable timeframe.•Brain-predicted age is significantly heritable.•Brain-predicted age is highly reliable, both within and between scanners.