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

Dose-Dependent Effect of Current Smoking on Enlarged Perivascular Space Identified on Brain Magnetic Resonance Imaging

INTRODUCTION

Cerebral small-vessel disease (CSVD) is a common cause of cognitive decline and stroke. Several studies have shown that smoking is a risk factor for CSVD progression. However, the extent to which smoking exacerbates CSVD lesions remains unclear. In this study, we aimed to clarify the association between total smoking exposure and the severity of CSVD in healthy participants.

METHODS

We analyzed the data of participants aged ≥50 years who underwent brain screening. The participants' age, sex, body mass index, alcohol consumption history, and medical history (hypertension, diabetes mellitus, and dyslipidemia) were investigated. Smoking status was assessed in pack-years, and smokers were classified as current or past smokers. CSVD findings on magnetic resonance imaging were used to evaluate the severity of periventricular hyperintensity (PVH), deep subcortical white matter hyperintensity (DSWMH), and enlarged perivascular spaces (EPVSs). The EPVSs were measured in the basal ganglia and centrum semiovale regions. Multivariable ordinal logistic regression analyses were performed to evaluate the effect of smoking, adjusted for the participants' baseline characteristics.

RESULTS

A total of 2,137 participants were included in this study. The mean age of the participants was 58.7 years. The mean pack-years were 20.5 for past smokers and 26.8 for current smokers. Among current smokers, increased pack-years were significantly associated with a high EPVS burden in the basal ganglia (odds ratio: 1.14, 95% confidence interval: 1.00-1.28), whereas no such significant association was found for past smokers. No statistically significant association was found between pack-years and the risks of PVH, DSWMH, or EPVS in the centrum semiovale.

CONCLUSION

Current smoking was associated with a dose-dependent risk of EPVS in the basal ganglia in healthy participants.

Lifetime history of head injury is associated with reduced perivascular space number in acute mild traumatic brain injury

Traumatic brain injury impairs function of the glymphatic system, a perivascular network involved in waste clearance. Enlarged perivascular spaces visible on MRI are an emerging biomarker of glymphatic function. This study characterized enlarged perivascular spaces in acute head injury with 7 T MRI. Healthy controls (n = 8) and patients (n = 11) with mild traumatic brain injury underwent MRI within 7 days of injury and were evaluated for lifetime history of head injury, neurobehavioral symptoms and sleep disturbances. MRI-visible perivascular spaces were quantified and assessed according to published criteria. The number of enlarged perivascular spaces was significantly higher in traumatic brain injury patients than controls (P = 0.015). Among healthy controls, 6/8 scored 'none' or 'mild' on the perivascular space rating scale, while 10/11 patients scored 'moderate', 'frequent' or 'severe'. There was an inverse relationship between perivascular space number and number of lifetime head injuries. Patients with more prior head injuries exhibited fewer enlarged perivascular spaces (P = 0.014). These results indicate that mild head injury results in acute alterations in perivascular space number, and this effect is mediated by previous head injury history. Enlarged perivascular spaces may reflect a glymphatic response that is diminished after multiple head injuries, although this will require further study.

Development and validation of a perivascular space segmentation method in multi-center datasets

BACKGROUND

Perivascular spaces (PVS) visible on magnetic resonance imaging (MRI) are significant markers associated with various neurological diseases. Although quantitative analysis of PVS may enhance sensitivity and improve consistency across studies, the field lacks a universally validated method for analyzing images from multi-center studies.

METHODS

We annotated PVS on multi-center 3D T1-weighted (T1w) images acquired using scanners from three major vendors (Siemens, General Electric, and Philips). A neural network, mcPVS-Net (multi-center PVS segmentation network), was trained using data from 40 subjects and then tested in a separate cohort of 15 subjects. We assessed segmentation accuracy against ground truth masks tailored for each scanner vendor. Additionally, we evaluated the agreement between segmented PVS volumes and visual scores for each scanner. We also explored correlations between PVS volumes and various clinical factors such as age, hypertension, and white matter hyperintensities (WMH) in a larger sample of 1020 subjects. Furthermore, mcPVS-Net was applied to a new dataset comprising both T1w and T2-weighted (T2w) images from a United Imaging scanner to investigate if PVS volumes could discriminate between subjects with differing visual scores. We also compared the mcPVS-Net with a previously published method that segments PVS from T1 images.

RESULTS

In the test dataset, mcPVS-Net achieved a mean DICE coefficient of 0.80, with an average Precision of 0.81 and Recall of 0.79, indicating good specificity and sensitivity. The segmented PVS volumes were significantly associated with visual scores in both the basal ganglia (r = 0.541, p < 0.001) and white matter regions (r = 0.706, p < 0.001), and PVS volumes were significantly different among subjects with varying visual scores. Segmentation performance was consistent across different scanner vendors. PVS volumes exhibited significant associations with age, hypertension, and WMH. In the United Imaging scanner dataset, PVS volumes showed good associations with PVS visual scores evaluated on either T1w or T2w images. Compared to a previously published method, mcPVS-Net showed a higher accuracy and improved PVS segmentation in the basal ganglia region.

CONCLUSION

The mcPVS-Net demonstrated good accuracy for segmenting PVS from 3D T1w images. It may serve as a useful tool for future PVS research.

Understanding central nervous system fluid networks: Historical perspectives and a revised model for clinical neurofluid imaging

The central nervous system (CNS) lacks traditionally defined lymphatic vasculature. However, CNS tissues and barriers compartmentalize the brain, spinal cord, and adjacent spaces, facilitating the transmittal of fluids, metabolic wastes, immune cells, and vital signals, while more conventional lymphatic pathways in the meninges, cervicofacial and paraspinal regions transmit efflux fluid and molecules to peripheral lymph and lymph nodes. Thus, a unique and highly organized fluid circulation network encompassing intraparenchymal, subarachnoid, dural, and extradural segments functions in unison to maintain CNS homeostasis. Pathways involved in this system have been under investigation for centuries and continue to be the source of considerable interest and debate. Modern imaging and microscopy technologies have led to important breakthroughs pertaining to various elements of CNS fluid circuitry and exchange over the past decade, thus enhancing knowledge on mechanisms of mammalian CNS maintenance and disease. Yet, to better understand precise anatomical routes, the physiology and clinical significance of these CNS pathways, and potential therapeutic targets in humans, fluid conduits, flow-regulating factors, and tissue effects must be analyzed systematically and in a global manner in persons across age, demographical factors, and disease states. Here, we illustrate the system-wide nature of intermixing CNS fluid networks, summarize historical and clinical studies, and discuss anatomical and physiological similarities and differences that are relevant for translation of evidence from mice to humans. We also review Cushing's classical model of cerebrospinal fluid flow and present a new framework of this "third circulation" that emphasizes previously unexplained complexities of CNS fluid circulation in humans. Finally, we review future directions in the field, including emerging theranostic techniques and MRI studies required in humans.

Deep-learning-based segmentation of perivascular spaces on T2-Weighted 3T magnetic resonance images

Purpose

Studying perivascular spaces (PVSs) is important for understanding the pathogenesis and pathological changes of neurological disorders. Although some methods for automated segmentation of PVSs have been proposed, most of them were based on 7T MR images that were majorly acquired in healthy young people. Notably, 7T MR imaging is rarely used in clinical practice. Herein, we propose a deep-learning-based method that enables automatic segmentation of PVSs on T2-weighted 3T MR images.

Method

Twenty patients with Parkinson's disease (age range, 42-79 years) participated in this study. Specifically, we introduced a multi-scale supervised dense nested attention network designed to segment the PVSs. This model fosters progressive interactions between high-level and low-level features. Simultaneously, it utilizes multi-scale foreground content for deep supervision, aiding in refining segmentation results at various levels.

Result

Our method achieved the best segmentation results compared with the four other deep-learning-based methods, achieving a dice similarity coefficient (DSC) of 0.702. The results of the visual count of the PVSs in our model correlated extremely well with the expert scoring results on the T2-weighted images (basal ganglia: rs = 0.845, P < 0.001; rs = 0.868, P < 0.001; centrum semiovale: rs = 0.845, P < 0.001; rs = 0.823, P < 0.001 for raters 1 and 2, respectively). Experimental results show that the proposed method performs well in the segmentation of PVSs.

Conclusion

The proposed method can accurately segment PVSs; it will facilitate practical clinical applications and is expected to replace the method of visual counting directly on T1-weighted images or T2-weighted images.

A "glympse" into neurodegeneration: Diffusion MRI and cerebrospinal fluid aquaporin-4 for the assessment of glymphatic system in Alzheimer's disease and other dementias

The glymphatic system (GS) is a whole-brain perivascular network, consisting of three compartments: the periarterial and perivenous spaces and the interposed brain parenchyma. GS dysfunction has been implicated in neurodegenerative diseases, particularly Alzheimer's disease (AD). So far, comprehensive research on GS in humans has been limited by the absence of easily accessible biomarkers. Recently, promising non-invasive methods based on magnetic resonance imaging (MRI) along with aquaporin-4 (AQP4) quantification in the cerebrospinal fluid (CSF) were introduced for an indirect assessment of each of the three GS compartments. We recruited 111 consecutive subjects presenting with symptoms suggestive of degenerative cognitive decline, who underwent 3 T MRI scanning including multi-shell diffusion-weighted images. Forty nine out of 111 also underwent CSF examination with quantification of CSF-AQP4. CSF-AQP4 levels and MRI measures-including perivascular spaces (PVS) counts and volume fraction (PVSVF), white matter free water fraction (FW-WM) and mean kurtosis (MK-WM), diffusion tensor imaging analysis along the perivascular spaces (DTI-ALPS) (mean, left and right)-were compared among patients with AD (n = 47) and other neurodegenerative diseases (nAD = 24), patients with stable mild cognitive impairment (MCI = 17) and cognitively unimpaired (CU = 23) elderly people. Two runs of analysis were conducted, the first including all patients; the second after dividing both nAD and AD patients into two subgroups based on gray matter atrophy as a proxy of disease stage. Age, sex, years of education, and scanning time were included as confounding factors in the analyses. Considering the whole cohort, patients with AD showed significantly higher levels of CSF-AQP4 (exp(b) = 2.05, p = .005) and FW-WM FW-WM (exp(b) = 1.06, p = .043) than CU. AQP4 levels were also significantly higher in nAD in respect to CU (exp(b) = 2.98, p < .001). CSF-AQP4 and FW-WM were significantly higher in both less atrophic AD (exp(b) = 2.20, p = .006; exp(b) = 1.08, p = .019, respectively) and nAD patients (exp(b) = 2.66, p = .002; exp(b) = 1.10, p = .019, respectively) compared to CU subjects. Higher total (exp(b) = 1.59, p = .013) and centrum semiovale PVS counts (exp(b) = 1.89, p = .016), total (exp(b) = 1.50, p = .036) and WM PVSVF (exp(b) = 1.89, p = .005) together with lower MK-WM (exp(b) = 0.94, p = .006), mean and left ALPS (exp(b) = 0.91, p = .043; exp(b) = 0.88, p = .010 respectively) were observed in more atrophic AD patients in respect to CU. In addition, more atrophic nAD patients exhibited higher levels of AQP4 (exp(b) = 3.39, p = .002) than CU. Our results indicate significant changes in putative MRI biomarkers of GS and CSF-AQP4 levels in AD and in other neurodegenerative dementias, suggesting a close interaction between glymphatic dysfunction and neurodegeneration, particularly in the case of AD. However, the usefulness of some of these biomarkers as indirect and standalone indices of glymphatic activity may be hindered by their dependence on disease stage and structural brain damage.

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.

Change in Enlarged Perivascular Spaces over Time and Associations with Outcomes After Traumatic Brain Injury

Enlarged perivascular spaces (EPVs) can be seen on magnetic resonance imaging (MRI) scans in various neurological diseases, including traumatic brain injury (TBI). EPVs have been associated with cognitive dysfunction and sleep disturbances; however, their clinical significance remains unclear. The goal of this study was to identify MRI burden of EPVs over time following TBI and to explore their relationship with postinjury outcomes. Individuals with TBI underwent postinjury data collection at Day 1 (blood), 2 weeks (blood, MRI, outcomes), and 6 months (blood, MRI, outcomes). EPV burden was assessed using T1 and FLAIR sequences on representative slices in the centrum semiovale, basal ganglia, and midbrain. Serum blood was assayed to measure concentrations of neurofilament light (NfL) and glial fibrillary acidic protein (GFAP). Thirty-two participants with TBI were included (mean age 36.8 years, 78% male, 50% White). Total EPVs count did not significantly change from 2 weeks (23.5 [95% confidence interval or CI = 22.0-32.0]) to 6 months (26.0 [95% CI = 22.0-30.0], p = 0.16). For self-reported measures of sleep, there were no significant associations between EPVs count and Insomnia Severity Index (2 weeks: β = -0.004; 95% CI = -0.094, 0.086; 6 months: β = 0.002; 95% CI = -0.122, 0.125) or the subset of sleep questions on the Rivermead Post-Concussion Symptoms Questionnaire (2 weeks: β = -0.005; 95% CI = -0.049, 0.039; 6 months: β = -0.019; 95% CI = -0.079, 0.042). Functional outcome, determined by 6 months incomplete recovery (Glasgow Outcome Scale-Extended [GOS-E < 8]) versus complete recovery (GOS-E = 8), was significantly associated with a higher number of EPVs at 2 weeks (odds ratio = 0.94, 95% CI = 0.88-0.99). Spearman correlations showed no significant relationship between EPVs count and GFAP or NfL. This study used commonly acquired MRI sequences to quantify EPVs and investigated their utility as a potential imaging biomarker in TBI. Given the minimal change in EPVs over time, this period may not be long enough for potential recovery or may indicate that EPVs are structural findings that do not significantly change over time.

A novel perspective for exploring the relationship between cerebral small vessel disease and deep medullary veins with automatic segmentation

BACKGROUND

This study aimed to establish an intelligent segmentation algorithm to count the number of deep medullary veins (DMVs) and analyze the relationship between DMVs and imaging markers of cerebral small vessel disease (CSVD).

METHODS

DMVs on magnetic resonance imaging (MRI) of patients with CSVD were counted by intelligent segmentation and manual counting. The dice coefficient and intraclass correlation coefficient (ICC) were used to evaluate their consistency and correlation. Structural MR images were used to assess imaging markers and total burden of CSVD. A multivariate linear regression model was used to evaluate the correlation between the number of DMVs counted by intelligent segmentation and imaging markers of CSVD, including white matter hyperintensities of the presumed vascular origin, lacune, perivascular spaces, cerebral microbleeds, and total CSVD burden.

RESULTS

A total of 305 patients with CSVD were enrolled. An intelligent segmentation algorithm was established to calculate the number of DMVs, and it was validated and tested. The number of DMVs counted intelligently significantly correlated with the manual counting method (r = 0.761, P< 0.001). The number of smart-counted DMVs negatively correlated with the imaging markers and total burden of CSVD (P< 0.001), and the correlation remained after adjusting for age and hypertension (P< 0.05).

CONCLUSIONS

The proposed intelligent segmentation algorithm, which was established to count DMVs, can provide objective and quantitative imaging information for the follow-up of patients with CSVD. DMVs are involved in CSVD pathogenesis and a likely new imaging marker for CSVD.

Time-of-Flight MRA of Intracranial Aneurysms with Interval Surveillance, Clinical Segmentation and Annotations

Intracranial aneurysms (IAs) are present in 2-6% of the global population and can be catastrophic upon rupture with a mortality rate of 30-50%. IAs are commonly detected through time-of-flight magnetic resonance angiography (TOF-MRA), however, this data is rarely available for research and training purposes. The provision of imaging resources such as TOF-MRA images is imperative to develop new strategies for IA detection, rupture prediction, and surgical training. To support efforts in addressing data availability bottlenecks, we provide an open-access TOF-MRA dataset comprising 63 patients, of which 24 underwent interval surveillance imaging by TOF-MRA. Patient scans were evaluated by a neuroradiologist, providing aneurysm and vessel segmentations, clinical annotations, 3D models, in addition to 3D Slicer software environments containing all this data for each patient. This dataset is the first to provide interval surveillance imaging for supporting the understanding of IA growth and stability. This dataset will support computational and experimental research into IA dynamics and assist surgical and radiology training in IA treatment.

Detectability and accuracy of computational measurements of in-silico and physical representations of enlarged perivascular spaces from magnetic resonance images

BACKGROUND

Magnetic Resonance Imaging (MRI) visible perivascular spaces (PVS) have been associated with age, decline in cognitive abilities, interrupted sleep, and markers of small vessel disease. But the limits of validity of their quantification have not been established.

NEW METHOD

We use a purpose-built digital reference object to construct an in-silico phantom for addressing this need, and validate it using a physical phantom. We use cylinders of different sizes as models for PVS. We also evaluate the influence of 'PVS' orientation, and different sets of parameters of the two vesselness filters that have been used for enhancing tubular structures, namely Frangi and RORPO filters, in the measurements' accuracy.

RESULTS

PVS measurements in MRI are only a proxy of their true dimensions, as the boundaries of their representation are consistently overestimated. The success in the use of the Frangi filter relies on a careful tuning of several parameters. Alpha= 0.5, beta= 0.5 and c= 500 yielded the best results. RORPO does not have these requirements and allows detecting smaller cylinders in their entirety more consistently in the absence of noise and confounding artefacts. The Frangi filter seems to be best suited for voxel sizes equal or larger than 0.4 mm-isotropic and cylinders larger than 1 mm diameter and 2 mm length. 'PVS' orientation did not affect measurements in data with isotropic voxels.

COMPARISON WITH EXISTENT METHODS

Does not apply.

CONCLUSIONS

The in-silico and physical phantoms presented are useful for establishing the validity of quantification methods of tubular small structures.

Influence of threshold selection and image sequence in in-vivo segmentation of enlarged perivascular spaces

BACKGROUND

Growing interest surrounds perivascular spaces (PVS) as a clinical biomarker of brain dysfunction given their association with cerebrovascular risk factors and disease. Neuroimaging techniques allowing quick and reliable quantification are being developed, but, in practice, they require optimisation as their limits of validity are usually unspecified.

NEW METHOD

We evaluate modifications and alternatives to a state-of-the-art (SOTA) PVS segmentation method that uses a vesselness filter to enhance PVS discrimination, followed by thresholding of its response, applied to brain magnetic resonance images (MRI) from patients with sporadic small vessel disease acquired at 3 T.

RESULTS

The method is robust against inter-observer differences in threshold selection, but separate thresholds for each region of interest (i.e., basal ganglia, centrum semiovale, and midbrain) are required. Noise needs to be assessed prior to selecting these thresholds, as effect of noise and imaging artefacts can be mitigated with a careful optimisation of these thresholds. PVS segmentation from T1-weighted images alone, misses small PVS, therefore, underestimates PVS count, may overestimate individual PVS volume especially in the basal ganglia, and is susceptible to the inclusion of calcified vessels and mineral deposits. Visual analyses indicated the incomplete and fragmented detection of long and thin PVS as the primary cause of errors, with the Frangi filter coping better than the Jerman filter.

COMPARISON WITH EXISTING METHODS

Limits of validity to a SOTA PVS segmentation method applied to 3 T MRI with confounding pathology are given.

CONCLUSIONS

Evidence presented reinforces the STRIVE-2 recommendation of using T2-weighted images for PVS assessment wherever possible. The Frangi filter is recommended for PVS segmentation from MRI, offering robust output against variations in threshold selection and pathology presentation.

A Closer Look at the Perivascular Unit in the Development of Enlarged Perivascular Spaces in Obesity, Metabolic Syndrome, and Type 2 Diabetes Mellitus

The recently described perivascular unit (PVU) resides immediately adjacent to the true capillary neurovascular unit (NVU) in the postcapillary venule and contains the normal-benign perivascular spaces (PVS) and pathological enlarged perivascular spaces (EPVS). The PVS are important in that they have recently been identified to be the construct and the conduit responsible for the delivery of metabolic waste from the interstitial fluid to the ventricular cerebrospinal fluid for disposal into the systemic circulation, termed the glymphatic system. Importantly, the outermost boundary of the PVS is lined by protoplasmic perivascular astrocyte endfeet (pvACef) that communicate with regional neurons. As compared to the well-recognized and described neurovascular unit (NVU) and NVU coupling, the PVU is less well understood and remains an emerging concept. The primary focus of this narrative review is to compare the similarities and differences between these two units and discuss each of their structural and functional relationships and how they relate not only to brain homeostasis but also how they may relate to the development of multiple clinical neurological disease states and specifically how they may relate to obesity, metabolic syndrome, and type 2 diabetes mellitus. Additionally, the concept and importance of a perisynaptic astrocyte coupling to the neuronal synapses with pre- and postsynaptic neurons will also be considered as a perisynaptic unit to provide for the creation of the information transfer in the brain via synaptic transmission and brain homeostasis. Multiple electron microscopic images and illustrations will be utilized in order to help explain these complex units.

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.

The CADA-MRIT: An MRI Inventory Tool for Evaluating Cerebral Lesions in CADASIL Across Cohorts

BACKGROUND AND OBJECTIVES

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most frequent genetic cerebrovascular disease. The clinical aspects of the disease in relation to the various types of lesions on MRI vary widely not only within families but also between different cohorts reported worldwide. Many limitations prevent comparison of imaging data obtained with different scanners and sequences in different patient cohorts. We aimed to develop and validate a simple tool to inventory quickly the key MRI features in CADASIL to compare imaging data across different populations.

METHODS

The Inventory Tool (CADA-MRIT) was designed by consensus after repeated expert meetings. It consists of 11 imaging items to assess periventricular, deep, and superficial white matter hyperintensity (WMH), lacunes, cerebral microbleeds (CMB), centrum semiovale and basal ganglia dilated perivascular spaces (dPVS), superficial and deep atrophy, large infarcts, and macrobleeds. The reliability, clinical relevance, and time-effectiveness of CADA-MRIT were assessed using data from 3 independent patient cohorts.

RESULTS

Imaging data from 671 patients with CADASIL (440 from France, 119 from Germany, and 112 from Taiwan) were analyzed. Their mean age was 53.4 ± 12.2 years, 54.5% were women, 56.2% had stroke, and 31.1% had migraine with aura. Any lacune was present in at least 70% of individuals, whereas CMB occurred in 83% of patients from the Asian cohort and in only 35% of European patients. CADA-MRIT scores obtained for WMH, CMB, and dPVS were comparable regardless of the scanner or sequence used (weighted κ > 0.60). Intrarater and interrater agreements were from good to very good (weighted κ > 0.60). Global WMH and atrophy scores correlated strongly with accurate volumetric quantification of WMH or brain parenchymal fraction (Pearson r > 0.60). Different imaging scores were significantly associated with the main clinical manifestations of the disease. The time for evaluating 1 patient was approximately 2-3 minutes.

DISCUSSION

The CADA-MRIT is an easy-to-use tool for analyzing and comparing the most frequent MRI lesions of CADASIL across different populations. This instrument is reliable. It can be used with different imaging sequences or scanners. It also provides clinically relevant scores in a very short time for completion.

A trichotomy method for defining homogeneous subgroups in a dementia population

INTRODUCTION

Diagnosis of dementia in the aging brain is confounded by the presence of multiple pathologies. Mixed dementia (MX), a combination of Alzheimer's disease (AD) proteins with vascular disease (VD), is frequently found at autopsy, and has been difficult to diagnose during life. This report develops a method for separating the MX group and defining preclinical AD (presence of AD factors with normal cognition) and preclinical VD subgroups (presence of white matter damage with normal cognition).

METHODS

Clustering was based on three diagnostic axes: (1) AD factor (ADF) derived from cerebrospinal fluid proteins (Aβ42 and pTau), (2) VD factor (VDF) calculated from mean free water and peak width of skeletonized mean diffusivity in the white matter, and (3) Cognition (Cog) based on memory and executive function. The trichotomy method was applied to an Alzheimer's Disease Neuroimaging Initiative cohort (N = 538).

RESULTS

Eight biologically defined subgroups were identified which included the MX group with both high ADF and VDF (9.3%) and a preclinical VD group (3.9%), and a preclinical AD group (13.6%). Cog is significantly associated with both ADF and VDF, and the partial-correlation remains significant even when the effect of the other variable is removed (r(Cog, ADF/VDF removed) = 0.46, p < 10-28 and r(Cog, VDF/ADF removed) = 0.24, p < 10-7 ).

DISCUSSION

The trichotomy method creates eight biologically characterized patient groups, which includes MX, preclinical AD, and preclinical VD subgroups. Further longitudinal studies are needed to determine the utility of the 3-way clustering method with multimodal biological biomarkers.