Magnetic resonance spectroscopy shows associations between neurometabolite levels and perivascular space volume in Parkinson's disease: a pilot and feasibility study

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.

Increased perivascular space volume in white matter and basal ganglia is associated with cognition in Parkinson's Disease

Perivascular spaces (PVS), fluid-filled compartments surrounding brain vasculature, are an essential component of the glymphatic system responsible for transport of waste and nutrients. Glymphatic system impairment may underlie cognitive deficits in Parkinson's disease (PD). Studies have focused on the role of basal ganglia PVS with cognition in PD, but the role of white matter PVS is unknown. This study examined the relationship of white matter and basal ganglia PVS with domain-specific and global cognition in individuals with PD. Fifty individuals with PD underwent 3T T1w magnetic resonance imaging (MRI) to determine PVS volume fraction, defined as PVS volume normalized to total regional volume, within (i) centrum semiovale, (ii) prefrontal white matter (medial orbitofrontal, rostral middle frontal, superior frontal), and (iii) basal ganglia. A neuropsychological battery included assessment of global cognitive function (Montreal Cognitive Assessment, and global cognitive composite score), and cognitive-specific domains (executive function, memory, visuospatial function, attention, and language). Higher white matter rostral middle frontal PVS was associated with lower scores in both global cognitive and visuospatial function. In the basal ganglia higher PVS was associated with lower scores for memory with a trend towards lower global cognitive composite score. While previous reports have shown that greater amount of PVS in the basal ganglia is associated with decline in global cognition in PD, our findings suggest that increased white matter PVS volume may also underlie changes in cognition.

Alcohol Craving and Severity are Associated with Dorsal Anterior Cingulate Choline Levels in Individuals with an Alcohol Use Disorder

AIMS

Magnetic resonance spectroscopy (MRS) has been used to probe inflammation in the brain. While altered MRS metabolite levels have previously been found in individuals with alcohol use disorder (AUD), the relationship between potential metabolite markers of inflammation and the clinical correlates of AUD remains understudied. Therefore, this exploratory study sought to elucidate the clinical significance of inflammation in AUD by examining relationships between metabolites, AUD severity, alcohol consumption, and craving in individuals with AUD.

METHODS

Data for this secondary analysis are derived from a two-week clinical trial of ibudilast to treat AUD. Forty-three non-treatment-seeking individuals with an AUD (26M/17F) completed an MRS scan and alcohol-related questionnaires. MRS was performed using a multi-voxel array placed above the corpus callosum, extending from the pregnenual anterior cingulate to premotor cortex. The dorsal anterior cingulate was selected as the volume of interest. Metabolite levels of choline-compounds (Cho), myo-inositol (mI), and creatine+phosphocreatine (Cr) were quantified. Separate hierarchical regression models were used to evaluate the independent effects of metabolite levels on alcohol craving, alcohol problem severity, and alcohol consumption.

RESULTS

Dorsal anterior cingulate Cho predicted alcohol craving and alcohol problem severity over and above demographics, medication, and alcohol consumption measures. mI and Cr did not predict alcohol craving or alcohol problem severity. Metabolite markers were not predictive of alcohol consumption.

CONCLUSIONS

This preliminary study indicates that dACC Cho is sensitive to clinical characteristics of AUD. This is a further step in advancing neurometabolites, particularly Cho, as potential biomarkers and treatment targets for AUD.

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.