Semi-automated Segmentation and Quantification of Perivascular Spaces at 7 Tesla in COVID-19

Systematic review and meta-analysis of automated methods for quantifying enlarged perivascular spaces in the brain

Research into magnetic resonance imaging (MRI)-visible perivascular spaces (PVS) has recently increased, as results from studies in different diseases and populations are cementing their association with sleep, disease phenotypes, and overall health indicators. With the establishment of worldwide consortia and the availability of large databases, computational methods that allow to automatically process all this wealth of information are becoming increasingly relevant. Several computational approaches have been proposed to assess PVS from MRI, and efforts have been made to summarise and appraise the most widely applied ones. We systematically reviewed and meta-analysed all publications available up to September 2023 describing the development, improvement, or application of computational PVS quantification methods from MRI. We analysed 67 approaches and 60 applications of their implementation, from 112 publications. The two most widely applied were the use of a morphological filter to enhance PVS-like structures, with Frangi being the choice preferred by most, and the use of a U-Net configuration with or without residual connections. Older adults or population studies comprising adults from 18 years old onwards were, overall, more frequent than studies using clinical samples. PVS were mainly assessed from T2-weighted MRI acquired in 1.5T and/or 3T scanners, although combinations using it with T1-weighted and FLAIR images were also abundant. Common associations researched included age, sex, hypertension, diabetes, white matter hyperintensities, sleep and cognition, with occupation-related, ethnicity, and genetic/hereditable traits being also explored. Despite promising improvements to overcome barriers such as noise and differentiation from other confounds, a need for joined efforts for a wider testing and increasing availability of the most promising methods is now paramount.

On the merits and potential of advanced neuroimaging techniques in COVID-19: A scoping review

Many Coronavirus Disease 2019 (COVID-19) patients are suffering from long-term neuropsychological sequelae. These patients may benefit from a better understanding of the underlying neuropathophysiological mechanisms and identification of potential biomarkers and treatment targets. Structural clinical neuroimaging techniques have limited ability to visualize subtle cerebral abnormalities and to investigate brain function. This scoping review assesses the merits and potential of advanced neuroimaging techniques in COVID-19 using literature including advanced neuroimaging or postmortem analyses in adult COVID-19 patients published from the start of the pandemic until December 2023. Findings were summarized according to distinct categories of reported cerebral abnormalities revealed by different imaging techniques. Although no unified COVID-19-specific pattern could be subtracted, a broad range of cerebral abnormalities were revealed by advanced neuroimaging (likely attributable to hypoxic, vascular, and inflammatory pathology), even in absence of structural clinical imaging findings. These abnormalities are validated by postmortem examinations. This scoping review emphasizes the added value of advanced neuroimaging compared to structural clinical imaging and highlights implications for brain functioning and long-term consequences in COVID-19.

Estimation of the Ischemic Lesion in the Experimental Stroke Studies Using Magnetic Resonance Imaging (Review)

In translational animal study aimed at evaluation of the effectiveness of innovative methods for treating cerebral stroke, including regenerative cell technologies, of particular importance is evaluation of the dynamics of changes in the volume of the cerebral infarction in response to therapy. Among the methods for assessing the focus of infarction, MRI is the most effective and convenient tool for use in preclinical studies. This review provides a description of MR pulse sequences used to visualize cerebral ischemia at various stages of its development, and a detailed description of the MR semiotics of cerebral infarction. A comparison of various methods for morphometric analysis of the focus of a cerebral infarction, including systems based on artificial intelligence for a more objective measurement of the volume of the lesion, is also presented.

Assessment of Perivascular Space Morphometry Across the White Matter in Huntington's Disease Using MRI

Background

Perivascular spaces (PVS) are fluid-filled cavities surrounding small cerebral blood vessels. There are limited reports of enlarged PVS across the grey matter in manifest Huntington's disease (HD). Little is known about how PVS morphometry in the white matter may contribute to HD. Enlarged PVS have the potential to both contribute to HD pathology and affect the distribution and success of intraparenchymal and intrathecally administered huntingtin-lowering therapies.

Objective

To investigate PVS morphometry in the global white matter across the spectrum of HD. Relationships between PVS morphometry and disease burden and severity measures were examined.

Methods

White matter PVS were segmented on 3T T2 W MRI brain scans of 33 healthy controls, 30 premanifest HD (pre-HD), and 32 early manifest HD (early-HD) participants from the Vancouver site of the TRACK-HD study. PVS count and total PVS volume were measured.

Results

PVS total count slightly increased in pre-HD (p = 0.004), and early-HD groups (p = 0.005), compared to healthy controls. PVS volume, as a percentage of white matter volume, increased subtly in pre-HD compared to healthy controls (p = 0.044), but not in early-HD. No associations between PVS measures and HD disease burden or severity were found.

Conclusions

This study reveals relatively preserved PVS morphometry across the global white matter of pre-HD and early-HD. Subtle morphometric abnormalities are implied but require confirmation in a larger cohort. However, in conjunction with previous publications, further investigation of PVS in HD and its potential impact on future treatments, with a focus on subcortical grey matter, is warranted.

Gut microbiome association with brain imaging markers, APOE genotype, calcium and vegetable intakes, and obesity in healthy aging adults

Introduction

Advanced age is a significant factor in changes to brain physiology and cognitive functions. Recent research has highlighted the critical role of the gut microbiome in modulating brain functions during aging, which can be influenced by various factors such as apolipoprotein E (APOE) genetic variance, body mass index (BMI), diabetes, and dietary intake. However, the associations between the gut microbiome and these factors, as well as brain structural, vascular, and metabolic imaging markers, have not been well explored.

Methods

We recruited 30 community dwelling older adults between age 55-85 in Kentucky. We collected the medical history from the electronic health record as well as the Dietary Screener Questionnaire. We performed APOE genotyping with an oral swab, gut microbiome analysis using metagenomics sequencing, and brain structural, vascular, and metabolic imaging using MRI.

Results

Individuals with APOE e2 and APOE e4 genotypes had distinct microbiota composition, and higher level of pro-inflammatory microbiota were associated higher BMI and diabetes. In contrast, calcium- and vegetable-rich diets were associated with microbiota that produced short chain fatty acids leading to an anti-inflammatory state. We also found that important gut microbial butyrate producers were correlated with the volume of the thalamus and corpus callosum, which are regions of the brain responsible for relaying and processing information. Additionally, putative proinflammatory species were negatively correlated with GABA production, an inhibitory neurotransmitter. Furthermore, we observed that the relative abundance of bacteria from the family Eggerthellaceae, equol producers, was correlated with white matter integrity in tracts connecting the brain regions related to language, memory, and learning.

Discussion

These findings highlight the importance of gut microbiome association with brain health in aging population and could have important implications aimed at optimizing healthy brain aging through precision prebiotic, probiotic or dietary interventions.

Perivascular network segmentations derived from high-field MRI and their implications for perivascular and parenchymal mass transport in the rat brain

A custom segmentation workflow was applied to ex vivo high-field MR images of rat brains acquired following in vivo intraventricular contrast agent infusion to generate maps of the perivascular spaces (PVS). The resulting perivascular network segmentations enabled analysis of perivascular connections to the ventricles, parenchymal solute clearance, and dispersive solute transport within PVS. Numerous perivascular connections between the brain surface and the ventricles suggest the ventricles integrate into a PVS-mediated clearance system and raise the possibility of cerebrospinal fluid (CSF) return from the subarachnoid space to the ventricles via PVS. Assuming rapid solute exchange between the PVS and CSF spaces primarily by advection, the extensive perivascular network decreased the mean clearance distance from parenchyma to the nearest CSF compartment resulting in an over 21-fold reduction in the estimated diffusive clearance time scale, irrespective of solute diffusivity. This corresponds to an estimated diffusive clearance time scale under 10 min for amyloid-beta which suggests that the widespread distribution of PVS may render diffusion an effective parenchymal clearance mechanism. Additional analysis of oscillatory solute dispersion within PVS indicates that advection rather than dispersion is likely the primary transport mechanism for dissolved compounds greater than 66 kDa in the long (> 2 mm) perivascular segments identified here, although dispersion may be significant for smaller compounds in shorter perivascular segments.

Possible post-COVID epilepsy: A review of epilepsy monitoring unit admissions during the two years of COVID-19 pandemic

Large scale healthcare data shows that new-onset epilepsy is noted in 0.3 % patients within 6 months of COVID-19 infection. We analyzed diagnostic epilepsy monitoring unit (EMU) evaluations to identify and report such cases. We thoroughly reviewed our EMU database and identified patients having "COVID" or "Corona" virus mention in their medical record from 03/15/2020 to 02/28/2022. Patients with epilepsy prior to COVID infection were excluded. Among 62 patients without prior epilepsy evaluated in the EMU for new-onset spells after confirmed COVID-19 infection, three patients were diagnosed with focal epilepsy. These three women without epilepsy risk factors had seizure onset at the time of, or within one to three months of, COVID-19 diagnosis. Their 3 T MRI imaging was non-lesional but revealed bilateral enlarged perivascular spaces. The video EEG monitoring was consistent with temporal or fronto-temporal lobe epilepsy in all three patients. Two of them developed drug-resistant epilepsy within six months of seizure onset. Our thorough analysis of diagnostic EMU evaluations during the two years of pandemic reveals three cases of post-COVID-19 epilepsy after non-symptomatic to mild disease. Although coincidental epilepsy onset cannot be ruled out, larger multicenter or national database investigations are needed to further analyze the possibility of post-COVID epilepsy.

A critical guide to the automated quantification of perivascular spaces in magnetic resonance imaging

The glymphatic system is responsible for waste clearance in the brain. It is comprised of perivascular spaces (PVS) that surround penetrating blood vessels. These spaces are filled with cerebrospinal fluid and interstitial fluid, and can be seen with magnetic resonance imaging. Various algorithms have been developed to automatically label these spaces in MRI. This has enabled volumetric and morphological analyses of PVS in healthy and disease cohorts. However, there remain inconsistencies between PVS measures reported by different methods of automated segmentation. The present review emphasizes that importance of voxel-wise evaluation of model performance, mainly with the Sørensen Dice similarity coefficient. Conventional count correlations for model validation are inadequate if the goal is to assess volumetric or morphological measures of PVS. The downside of voxel-wise evaluation is that it requires manual segmentations that require large amounts of time to produce. One possible solution is to derive these semi-automatically. Additionally, recommendations are made to facilitate rigorous development and validation of automated PVS segmentation models. In the application of automated PVS segmentation tools, publication of image quality metrics, such as the contrast-to-noise ratio, alongside descriptive statistics of PVS volumes and counts will facilitate comparability between studies. Lastly, a head-to-head comparison between two algorithms, applied to two cohorts of astronauts reveals how results can differ substantially between techniques.

Larger gray matter volumes in neuropsychiatric long-COVID syndrome

Neuropsychiatric symptoms are the most common sequelae of long-COVID. As accumulating evidence suggests an impact of survived SARS-CoV-2-infection on brain physiology, it is necessary to further investigate brain structural changes in relation to course and neuropsychiatric symptom burden in long-COVID. To this end, the present study investigated 3T-MRI scans from long-COVID patients suffering from neuropsychiatric symptoms (n = 30), and healthy controls (n = 20). Whole-brain comparison of gray matter volume (GMV) was conducted by voxel-based morphometry. To determine whether changes in GMV are predicted by neuropsychiatric symptom burden and/or initial severity of symptoms of COVID-19 and time since onset of COVID-19 stepwise linear regression analysis was performed. Significantly enlarged GMV in long-COVID patients was present in several clusters (spanning fronto-temporal areas, insula, hippocampus, amygdala, basal ganglia, and thalamus in both hemispheres) when compared to controls. Time since onset of COVID-19 was a significant regressor in four of these clusters with an inverse relationship. No associations with clinical symptom burden were found. GMV alterations in limbic and secondary olfactory areas are present in long-COVID patients and might be dynamic over time. Larger samples and longitudinal data in long-COVID patients are required to further clarify the mediating mechanisms between COVID-19, GMV and neuropsychiatric symptoms.