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Cerebrovascular reactivity (CVR), which measures the ability of cerebral blood vessels to dilate or constrict in response to vasoactive stimuli such as CO2 inhalation, is an important index of the brain's vascular health. Quantification of CVR using BOLD MRI with hypercapnia challenge has shown great promises in research and clinical studies. However, in order for it to be used as a potential imaging biomarker in large-scale and multi-site studies, the reliability of CO2-CVR quantification across different MRI acquisition platforms and researchers/raters must be examined. The goal of this report from the MarkVCID small vessel disease biomarkers consortium is to evaluate the reliability of CO2-CVR quantification in three studies. First, the inter-rater reliability of CO2-CVR data processing was evaluated by having raters from 5 MarkVCID sites process the same 30 CVR datasets using a cloud-based CVR data processing pipeline. Second, the inter-scanner reproducibility of CO2-CVR quantification was assessed in 10 young subjects across two scanners of different vendors. Third, test-retest repeatability was evaluated in 20 elderly subjects from 4 sites with a scan interval of less than 2 weeks. In all studies, the CO2 CVR measurements were performed using the fixed inspiration method, where the subjects wore a nose clip and a mouthpiece and breathed room air and 5% CO2 air contained in a Douglas bag alternatively through their mouth. The results showed that the inter-rater CoV of CVR processing was 0.08 ± 0.08% for whole-brain CVR values and ranged from 0.16% to 0.88% in major brain regions, with ICC of absolute agreement above 0.9959 for all brain regions. Inter-scanner CoV was found to be 6.90 ± 5.08% for whole-brain CVR values, and ranged from 4.69% to 12.71% in major brain regions, which are comparable to intra-session CoVs obtained from the same scanners on the same day. ICC of consistency between the two scanners was 0.8498 for whole-brain CVR and ranged from 0.8052 to 0.9185 across major brain regions. In the test-retest evaluation, test-retest CoV across different days was found to be 18.29 ± 17.12% for whole-brain CVR values, and ranged from 16.58% to 19.52% in major brain regions, with ICC of absolute agreement ranged from 0.6480 to 0.7785. These results demonstrated good inter-rater, inter-scanner, and test-retest reliability in healthy volunteers, and suggested that CO2-CVR has suitable instrumental properties for use as an imaging biomarker of cerebrovascular function in multi-site and longitudinal observational studies and clinical trials.

Introduction To describe the protocol and findings of the instrumental validation of three imaging‐based biomarker kits selected by the MarkVCID consortium: free water (FW) and peak width of skeletonized mean diffusivity (PSMD), both derived from diffusion tensor imaging (DTI), and white matter hyperintensity (WMH) volume derived from fluid attenuation inversion recovery and T1‐weighted imaging. Methods The instrumental validation of imaging‐based biomarker kits included inter‐rater reliability among participating sites, test–retest repeatability, and inter‐scanner reproducibility across three types of magnetic resonance imaging (MRI) scanners using intra‐class correlation coefficients (ICC). Results The three biomarkers demonstrated excellent inter‐rater reliability (ICC >0.94, P‐values < .001), very high agreement between test and retest sessions (ICC >0.98, P‐values < .001), and were extremely consistent across the three scanners (ICC >0.98, P‐values < .001). Discussion The three biomarker kits demonstrated very high inter‐rater reliability, test–retest repeatability, and inter‐scanner reproducibility, offering robust biomarkers suitable for future multi‐site observational studies and clinical trials in the context of vascular cognitive impairment and dementia (VCID).

Accumulating evidence suggests that altered cerebral white matter (WM) influences normal aging, and further that WM degeneration may modulate the clinical expression of Alzheimer's disease (AD). Here we conducted a study of differences in WM volume across the adult age span and in AD employing a newly developed, automated method for regional parcellation of the subcortical WM that uses curvature landmarks and gray matter (GM)/WM surface boundary information. This procedure measures the volume of gyral WM, utilizing a distance constraint to limit the measurements from extending into the centrum semiovale. Regional estimates were first established to be reliable across two scan sessions in 20 young healthy individuals. Next, the method was applied to a large clinically-characterized sample of 299 individuals including 73 normal older adults and 91 age-matched participants with very mild to mild AD. The majority of measured regions showed a decline in volume with increasing age, with strong effects found in bilateral fusiform, lateral orbitofrontal, superior frontal, medial orbital frontal, inferior temporal, and middle temporal WM. The association between WM volume and age was quadratic in many regions suggesting that WM volume loss accelerates in advanced aging. A number of WM regions were further reduced in AD with parahippocampal, entorhinal, inferior parietal and rostral middle frontal WM showing the strongest AD-associated reductions. There were minimal sex effects after correction for intracranial volume, and there were associations between ventricular volume and regional WM volumes in the older adults and AD that were not apparent in the younger adults. Certain results, such as the loss of WM in the fusiform region with aging, were unexpected and provide novel insight into patterns of age associated neural and cognitive decline. Overall, these results demonstrate the utility of automated regional WM measures in revealing the distinct patterns of age and AD associated volume loss that may contribute to cognitive decline.

INTRODUCTION Diffusion tensor image analysis along the perivascular space (DTI‐ALPS) index was proposed for assessing glymphatic clearance function. This study evaluated DTI‐ALPS as a biomarker for cerebral small vessel disease (cSVD) related vascular cognitive impairment and dementia (VCID). METHODS Four independent cohorts were examined. A composite score of executive function (UDS3‐EF) was used to evaluate EF status. The association between the ALPS index and UDS3‐EF scores and the mediator effect of free water in white matter (WM‐FW) on such association was analyzed. RESULTS The ALPS index was significantly associated with UDS3‐EF scores in all cohorts. Additionally, WM‐FW mediates the relationship between the ALPS index and UDS3‐EF scores. DISCUSSION Lower ALPS index may be a surrogate marker of glymphatic dysfunction, which is associated with impaired EF, and this association is mediated by the interstitial fluid (ISF) drainage ISF in WM, providing a clinical rationale for using ALPS index as a biomarker for cSVD‐related VCID. Highlights This is the first study to investigate the mediation role of interstitial FW fraction (WM‐FW) on the relationship between glymphatic clearance (ALPS index) and EF (UDS3‐EF scores) in four independent middle to aged cohorts at risk for cSVD. This study identified that ALPS index was independently associated with UDS3‐EF scores after adjusting for demographics, VRFs, and WM hyperintensity burden and that WM‐FW mediated this association in all middle to aged cohorts. Our findings suggest that in middle to aged individuals, glymphatic dysfunction (reflected by ALPS index) is strongly associated with EF and that this association is mediated by the ISF drainage in WM. This study provides a strong clinical rationale for the use of the ALPS index as a marker of cognitive function in multi‐site observational studies and clinical trials to monitor and prevent VCID.

The biomedical research community is motivated to share and reuse data from studies and projects by funding agencies and publishers. Effectively combining and reusing neuroimaging data from publicly available datasets, requires the capability to query across datasets in order to identify cohorts that match both neuroimaging and clinical/behavioral data criteria. Critical barriers to operationalizing such queries include, in part, the broad use of undefined study variables with limited or no annotations that make it difficult to understand the data available without significant interaction with the original authors. Using the Brain Imaging Data Structure (BIDS) to organize neuroimaging data has made querying across studies for specific image types possible at scale. However, in BIDS, beyond file naming and tightly controlled imaging directory structures, there are very few constraints on ancillary variable naming/meaning or experiment-specific metadata. In this work, we present NIDM-Terms, a set of user-friendly terminology management tools and associated software to better manage individual lab terminologies and help with annotating BIDS datasets. Using these tools to annotate BIDS data with a Neuroimaging Data Model (NIDM) semantic web representation, enables queries across datasets to identify cohorts with specific neuroimaging and clinical/behavioral measurements. This manuscript describes the overall informatics structures and demonstrates the use of tools to annotate BIDS datasets to perform integrated cross-cohort queries.

INTRODUCTION Vascular contributions to cognitive impairment and dementia (VCID) represent a major factor in cognitive decline in older adults. The present study examined the relationship between cerebrovascular reactivity (CVR) measured by magnetic resonance imaging (MRI) and cognitive function in a multi‐site study, using a predefined hypothesis. METHODS We conducted the study in a total of three analysis sites and 263 subjects. Each site performed an identical CVR MRI procedure using 5% carbon dioxide inhalation. A global cognitive measure of Montreal Cognitive Assessment (MoCA) and an executive function measure of item response theory (IRT) score were used as outcomes. RESULTS CVR and MoCA were positively associated, and this relationship was reproduced at all analysis sites. CVR was found to be positively associated with executive function. DISCUSSION The predefined hypothesis on the association between CVR and a global cognitive score was validated in three independent analysis sites, providing support for CVR as a biomarker in VCID. Highlights This study measured a novel functional index of small arteries referred to as cerebrovascular reactivity (CVR). CVR was positively associated with global cognition in older adults. This finding was observed in three independent cohorts at three sites. Our statistical analysis plan was predefined before beginning data collection.

Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging.

Disorders of the central nervous system, including neurodegenerative diseases, frequently affect the brainstem and can present with focal atrophy. This study aimed to (1) optimize deep learning‐based brainstem segmentation for a wide range of pathologies and T1‐weighted image acquisition parameters, (2) conduct a systematic technical and clinical validation, (3) improve segmentation quality in the presence of brainstem lesions, and (4) make an optimized brainstem segmentation tool available for public use. An intentionally heterogeneous ground truth dataset (n = 257) was employed in the training of deep learning models based on multi‐dimensional gated recurrent units (MD‐GRU) or the nnU‐Net method. Segmentation performance was evaluated against ground truth labels. FreeSurfer was used for benchmarking in subsequent validation. Technical validation, including scan‐rescan repeatability (n = 46) and inter‐scanner reproducibility (n = 20, 3 different scanners) in unseen data, was conducted in patients with cerebral small vessel disease. Clinical validation in unseen data was performed in 1‐year follow‐up data of 16 patients with multiple system atrophy, evaluating the annual percentage volume change. Two lesion filling algorithms were investigated to improve segmentation performance in 23 patients with multiple sclerosis. The MD‐GRU and nnU‐Net models demonstrated very good segmentation performance (median Dice coefficients ≥ 0.95 each) and outperformed a previously published model trained on a narrower dataset. Scan–rescan repeatability and inter‐scanner reproducibility yielded similar Bland–Altman derived limits of agreement for longitudinal FreeSurfer (total brainstem volume repeatability/reproducibility 0.68/1.85), MD‐GRU (0.72/1.46), and nnU‐Net (0.48/1.52). All methods showed comparable performance in the detection of atrophy in the total brainstem (atrophy detected in 100% of patients) and its substructures. In patients with multiple sclerosis, lesion filling further improved the accuracy of brainstem segmentation. We enhanced and systematically validated two fully automated deep learning brainstem segmentation methods and released them publicly. This enables a broader evaluation of brainstem volume as a candidate biomarker for neurodegeneration. We systematically validated new brainstem segmentation algorithms, including clinical and technical validation in patients. Our training strategy, which uses intentionally heterogeneous data, will allow to apply these algorithms to a wide range of datasets and without the need for re‐training.

BACKGROUND Peak‐width of skeletonized mean diffusivity (PSMD), a neuroimaging marker of cerebral small vessel disease (SVD), has shown excellent instrumental properties. Here, we extend our work to perform a biological validation of PSMD. METHODS We included 396 participants from the Biomarkers for Vascular Contributions to Cognitive Impairment and Dementia (MarkVCID‐1) Consortium and three replication samples (Cohorts for Heart and Aging Research in Genomic Epidemiology = 6172, Rush University Medical Center = 287, University of California Davis Alzheimer's Disease Research Center = 567). PSMD was derived from diffusion tensor imaging using an automated algorithm. We related PSMD to a composite measure of general cognitive function using linear regression models adjusting for confounders. RESULTS Higher PSMD was associated with lower general cognition in MarkVCID‐1 independent of age, sex, education, and intracranial volume (Beta [95% confidence interval], −0.8 [−1.2, −0.4], P < 0.001). These findings were replicated in independent samples. Furthermore, PSMD explained cognitive status above and beyond white matter hyperintensities. DISCUSSION Our biological validation work supports the pursuit of larger clinical validation studies evaluating PSMD as a susceptibility/risk biomarker of small vessel disease contributing to cognitive impairment and dementia. Highlights Peak‐width of skeletonized mean diffusivity (PSMD) is a novel small vessel disease neuroimaging biomarker. A prior instrumental validation study demonstrated that PSMD is a robust biomarker. This biological validation study shows that high PSMD relates to worse cognition. PSMD explains cognitive function above and beyond white matter hyperintensities. Future clinical validation will assess PSMD as a vascular contribution to cognitive impairment and dementia biomarker in clinical trials.

As the sharing of data is mandated by funding agencies and journals, reuse of data has become more prevalent. It becomes imperative, therefore, to develop methods to characterize the similarity of data. While users can group data based on the acquisition parameters stored in the file headers, these gives no indication whether a file can be combined with other data without increasing the variance in the data set. Methods have been implemented that characterize the signal-to-noise ratio or identify signal drop-outs in the raw image files, but potential users of data often have access to calculated metric maps and these are more difficult to characterize and compare. Here we describe a histogram-distance-based method applied to diffusion metric maps of fractional anisotropy and mean diffusivity that were generated using data extracted from a repository of clinically-acquired MRI data. We describe the generation of the data set, the pitfalls specific to diffusion MRI data, and the results of the histogram distance analysis. We find that, in general, data from GE scanners are less similar than are data from Siemens scanners. We also find that the distribution of distance metric values is not Gaussian at any selection of the acquisition parameters considered here (field strength, number of gradient directions, -value, and vendor).