Harmonizing brain magnetic resonance imaging methods for vascular contributions to neurodegeneration

Imaging Biomarkers of VCI: A Focused Update

Vascular cognitive impairment is common after stroke, in memory clinics, medicine for the elderly services, and undiagnosed in the community. Vascular disease is said to be the second most common cause of dementia after Alzheimer disease, yet vascular dysfunction is now known to predate cognitive decline in Alzheimer disease, and most dementias at older ages are mixed. Neuroimaging has a major role in identifying the proportion of vascular versus other likely pathologies in patients with cognitive impairment. Here, we aim to provide a pragmatic but evidence-based summary of the current state of potential imaging biomarkers, focusing on magnetic resonance imaging and computed tomography, which are relevant to diagnosing, estimating prognosis, monitoring vascular cognitive impairment, and incorporating our own experiences. We focus on markers that are well-established, with a known profile of association with cognitive measures, but also consider more recently described, including quantitative tissue markers of vascular injury. We highlight the gaps in accessibility and translation to more routine clinical practice.

Assessment of Small Vessel Function Using 7T MRI in Patients With Sporadic Cerebral Small Vessel Disease: The ZOOM@SVDs Study

BACKGROUND AND OBJECTIVES

Cerebral small vessel disease (cSVD) is a major cause of stroke and dementia, but little is known about disease mechanisms at the level of the small vessels. 7T-MRI allows assessing small vessel function in vivo in different vessel populations. We hypothesized that multiple aspects of small vessel function are altered in patients with cSVD and that these abnormalities relate to disease burden.

METHODS

Patients and controls participated in a prospective observational cohort study, the ZOOM@SVDs study. Small vessel function measures on 7T-MRI included perforating artery blood flow velocity and pulsatility index in the basal ganglia and centrum semiovale, vascular reactivity to visual stimulation in the occipital cortex, and reactivity to hypercapnia in the gray and white matter. Lesion load on 3T-MRI and cognitive function were used to assess disease burden.

RESULTS

Forty-six patients with sporadic cSVD (mean age ± SD 65 ± 9 years) and 22 matched controls (64 ± 7 years) participated in the ZOOM@SVDs study. Compared with controls, patients had increased pulsatility index (mean difference 0.09, p = 0.01) but similar blood flow velocity in basal ganglia perforating arteries and similar flow velocity and pulsatility index in centrum semiovale perforating arteries. The duration of the vascular response to brief visual stimulation in the occipital cortex was shorter in patients than in controls (mean difference -0.63 seconds, p = 0.02), whereas reactivity to hypercapnia was not significantly affected in the gray and total white matter. Among patients, reactivity to hypercapnia was lower in white matter hyperintensities compared with normal-appearing white matter (blood-oxygen-level dependent mean difference 0.35%, p = 0.001). Blood flow velocity and pulsatility index in basal ganglia perforating arteries and reactivity to brief visual stimulation correlated with disease burden.

DISCUSSION

We observed abnormalities in several aspects of small vessel function in patients with cSVD indicative of regionally increased arteriolar stiffness and decreased reactivity. Worse small vessel function also correlated with increased disease burden. These functional measures provide new mechanistic markers of sporadic cSVD.

Plasma homocysteine level, estradiol level, and brain atrophy: a Mendelian randomization study

OBJECTIVES

Observational studies link elevated plasma homocysteine (Hcy) with vascular disease. Our aim was to assess the gender difference in the association between the plasma tHcy level and brain atrophy and identify the possible influencer. We employed Mendelian randomization (MR) to explore the causal relationship between plasma tHcy level, estradiol level, and brain atrophy.

METHODS

A total of 687 patients with brain atrophy were included, and gender-specific subgroup analyses in association between tHcy and brain atrophy are conducted. From genome-wide association studies, we selected genetic variants (P < 5 × 10-8) for the plasma tHcy level and estradiol level. We investigated the degree of brain atrophy (including gray matter volume and total brain volume) in the UK biobank (n = 7,916). The inverse variance-weighted and several sensitivity MR regression analyses were carried out.

RESULTS

The plasma tHcy level was significantly associated with brain atrophy for females, but not for males. An MR study showed that there was little evidence of the causal link between elevated plasma tHcy and brain atrophy. On the other hand, we found evidence to support causality for genetically decreased estradiol with higher risk of brain atrophy. Furthermore, genetic predisposition to elevated plasma tHcy was associated with a lower estradiol level.

CONCLUSIONS

The influence of estradiol on the association between tHcy and brain atrophy deserves further investigation.

What have clinical trials taught us about brain health?

The Global Burden of Disease Study projects an almost tripling of dementia cases worldwide in the next 30 years making it important to recognize and understand modifiable risks and preventatives for cognitive impairment. Recent studies suggest that prevention or treatment of cardiovascular risks may be an important strategy to prevent or slow the progression of cognitive impairment. In 2017, the American Heart Association and American Stroke Association introduced metrics for "optimal brain health". These metrics defined brain health in terms of ideal health behaviors and factors. Since then and leading up to 2017, a number of clinical trials have been conducted to investigate the potential of modification of cardiovascular risks on prevention of dementia or cognitive impairment and thus, enhancement of brain health. This discussion is a review of findings from clinical trials focusing on interventions, including antihypertensive agents, glycemic control and lipid-lowering therapies, multidomain approaches, and antithrombotic medications. Notably, the results highlight the promise of intensive blood pressure lowering strategies and multidomain approaches, as evidenced by the FINGER trial. The review also discusses the potential of treatment or prevention of cerebral small vessel disease (cSVD) and the application of Mendelian randomization as a strategy to preserve brain structure and function.

National and international collaborations to advance research into vascular contributions to cognitive decline

Cerebrovascular disease is the second most common cause of cognitive disorders, usually referred to as vascular contributions to cognitive impairment and dementia (VCID) and makes some contribution to about 70 % of all dementias. Despite its importance, research into VCID has lagged as compared to cognitive impairment due to Alzheimer's disease. There is an increasing appreciation that closing this gap requires large national and international collaborations. This paper highlights 24 notable large-scale national and international efforts to advance research into VCID (MarkVCID, DiverseVCID, DISCOVERY, COMPASS-ND, HBC, RHU SHIVA, UK DRI Vascular Theme, STROKOG, Meta VCI Map, ISGC, ENIGMA-Stroke Recovery, CHARGE, SVDs@target, BRIDGET, CADASIL Consortium, CADREA, AusCADASIL, DPUK, DPAU, STRIVE, HARNESS, FINESSE, VICCCS, VCD-CRE Delphi). These collaborations aim to investigate the effects on cognition from cerebrovascular disease or impaired cerebral blood flow, the mechanisms of action, means of prevention and avenues for treatment. Consensus groups have been developed to harmonise global approaches to VCID, standardise terminology and inform management and treatment, and data sharing is becoming the norm. VCID research is increasingly a global collaborative enterprise which bodes well for rapid advances in this field.

Using a dual-stream attention neural network to characterize mild cognitive impairment based on retinal images

Mild cognitive impairment (MCI) is a critical transitional stage between normal cognition and dementia, for which early detection is crucial for timely intervention. Retinal imaging has been shown as a promising potential biomarker for MCI. This study aimed to develop a dual-stream attention neural network to classify individuals with MCI based on multi-modal retinal images. Our approach incorporated a cross-modality fusion technique, a variable scale dense residual model, and a multi-classifier mechanism within the dual-stream network. The model utilized a residual module to extract image features and employed a multi-level feature aggregation method to capture complex context information. Self-attention and cross-attention modules were utilized at each convolutional layer to fuse features from optical coherence tomography (OCT) and fundus modalities, resulting in multiple output losses. The neural network was applied to classify individuals with MCI, Alzheimer's disease, and control participants with normal cognition. Through fine-tuning the pre-trained model, we classified community-dwelling participants into two groups based on cognitive impairment test scores. To identify retinal imaging biomarkers associated with accurate prediction, we used the Gradient-weighted Class Activation Mapping technique. The proposed method achieved high precision rates of 84.96% and 80.90% in classifying MCI and positive test scores for cognitive impairment, respectively. Notably, changes in the optic nerve head on fundus photographs or OCT images among patients with MCI were not used to discriminate patients from the control group. These findings demonstrate the potential of our approach in identifying individuals with MCI and emphasize the significance of retinal imaging for early detection of cognitive impairment.

Factors affecting progression of non-Alzheimer dementia: a retrospective analysis with long-term follow-up

Background

Non-Alzheimer's dementias, including vascular dementia (VaD), frontotemporal dementia (FTD), Lewy body dementia (LBD), and Parkinson's disease dementia (PDD), possess unique characteristics and prognostic factors that remain poorly understood. This study aims to investigate the temporal course of these subtypes and identify the impact of functional, neuropsychiatric, and comorbid medical conditions on prognosis. Additionally, the relationship between hippocampal atrophy, white matter intensities, and disease progression will be examined, along with the identification of key covariates influencing slow or fast progression in non-Alzheimer's dementias.

Methods

A total of 196 patients with non-Alzheimer's dementias who underwent at least three comprehensive evaluations were included, with proportions of VaD, FTD, LBD, and PDD being 50, 19.39, 19.90, and 10.71%, respectively. Patient demographics, comorbidities, neuropsychiatric and neuroimaging parameters, and global evaluation were analyzed using appropriate statistical methods. The study followed patients for a mean duration of 62.57 ± 33.45 months (ranging from 11 to 198 months).

Results

The results from three different visits for each non-AD dementia case demonstrated significant differences in various measures across visits, including functional capacity (BDLAS), cognition (MMSE), and other neuropsychological tests. Notably, certain genotypes and hippocampal atrophy grades were more prevalent in specific subtypes. The results indicate that Fazekas grading and hippocampal atrophy were significant predictors of disease progression, while epilepsy, extrapyramidal symptoms, thyroid dysfunction, coronary artery disease, diabetes mellitus, hypertension, stroke, hyperlipidemia, sleep disorders, smoking, and family history of dementia were not significant predictors. BDLAS and EDLAS scores at the first and second visits showed significant associations with disease progression, while scores at the third visit did not. Group-based trajectory analysis revealed that non-AD cases separated into two reliable subgroups with slow/fast prognosis, showing high reliability (Entropy = 0.790, 51.8 vs. 48.2%).

Conclusion

This study provides valuable insights into the temporal course and prognostic factors of non-Alzheimer's dementias. The findings underscore the importance of considering functional, neuropsychological, and comorbid medical conditions in understanding disease progression. The significant associations between hippocampal atrophy, white matter intensities, and prognosis highlight potential avenues for further research and therapeutic interventions.

Blood-brain barrier leakage hotspots collocating with brain lesions due to sporadic and monogenic small vessel disease

Blood-brain barrier (BBB) is known to be impaired in cerebral small vessel disease (SVD), and is measurable by dynamic-contrast enhancement (DCE)-MRI. In a cohort of 69 patients (42 sporadic, 27 monogenic SVD), who underwent 3T MRI, including DCE and cerebrovascular reactivity (CVR) sequences, we assessed the relationship of BBB-leakage hotspots to SVD lesions (lacunes, white matter hyperintensities (WMH), and microbleeds). We defined as hotspots the regions with permeability surface area product highest decile on DCE-derived maps within the white matter. We assessed factors associated with the presence and number of hotspots corresponding to SVD lesions in multivariable regression models adjusted for age, WMH volume, number of lacunes, and SVD type. We identified hotspots at lacune edges in 29/46 (63%) patients with lacunes, within WMH in 26/60 (43%) and at the WMH edges in 34/60 (57%) patients with WMH, and microbleed edges in 4/11 (36%) patients with microbleeds. In adjusted analysis, lower WMH-CVR was associated with presence and number of hotspots at lacune edges, and higher WMH volume with hotspots within WMH and at WMH edges, independently of the SVD type. In conclusion, SVD lesions frequently collocate with high BBB-leakage in patients with sporadic and monogenic forms of SVD.

Neuroimaging in Dementia: More than Typical Alzheimer Disease

Alzheimer disease (AD) is the most common cause of dementia. The prevailing theory of the underlying pathology assumes amyloid accumulation followed by tau protein aggregation and neurodegeneration. However, the current antiamyloid and antitau treatments show only variable clinical efficacy. Three relevant points are important for the radiologic assessment of dementia. First, besides various dementing disorders (including AD, frontotemporal dementia, and dementia with Lewy bodies), clinical variants and imaging subtypes of AD include both typical and atypical AD. Second, atypical AD has overlapping radiologic and clinical findings with other disorders. Third, the diagnostic process should consider mixed pathologies in neurodegeneration, especially concurrent cerebrovascular disease, which is frequent in older age. Neuronal loss is often present at, or even before, the onset of cognitive decline. Thus, for effective emerging treatments, early diagnosis before the onset of clinical symptoms is essential to slow down or stop subsequent neuronal loss, requiring molecular imaging or plasma biomarkers. Neuroimaging, particularly MRI, provides multiple imaging parameters for neurodegenerative and cerebrovascular disease. With emerging treatments for AD, it is increasingly important to recognize AD variants and other disorders that mimic AD. Describing the individual composition of neurodegenerative and cerebrovascular disease markers while considering overlapping and mixed diseases is necessary to better understand AD and develop efficient individualized therapies.

New horizons in cognitive and functional impairment as a consequence of cerebral small vessel disease

Cerebral small vessel disease (cSVD) is a frequent finding in imaging of the brain in older adults, especially in the concomitance of cardiovascular disease risk factors. Despite the well-established link between cSVD and (vascular) cognitive impairment (VCI), it remains uncertain how and when these vascular alterations lead to cognitive decline. The extent of acknowledged markers of cSVD is at best modestly associated with the severity of clinical symptoms, but technological advances increasingly allow to identify and quantify the extent and perhaps also the functional impact of cSVD more accurately. This will facilitate a more accurate diagnosis of VCI, against the backdrop of concomitant other neurodegenerative pathology, and help to identify persons with the greatest risk of cognitive and functional deterioration. In this study, we discuss how better assessment of cSVD using refined neuropsychological and comprehensive geriatric assessment as well as modern image analysis techniques may improve diagnosis and possibly the prognosis of VCI. Finally, we discuss new avenues in the treatment of cSVD and outline how these contemporary insights into cSVD can contribute to optimise screening and treatment strategies in older adults with cognitive impairment and multimorbidity.

Neuroimaging standards for research into small vessel disease-advances since 2013

Cerebral small vessel disease (SVD) is common during ageing and can present as stroke, cognitive decline, neurobehavioural symptoms, or functional impairment. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive and other symptoms and affect activities of daily living. Standards for Reporting Vascular Changes on Neuroimaging 1 (STRIVE-1) categorised and standardised the diverse features of SVD that are visible on structural MRI. Since then, new information on these established SVD markers and novel MRI sequences and imaging features have emerged. As the effect of combined SVD imaging features becomes clearer, a key role for quantitative imaging biomarkers to determine sub-visible tissue damage, subtle abnormalities visible at high-field strength MRI, and lesion-symptom patterns, is also apparent. Together with rapidly emerging machine learning methods, these metrics can more comprehensively capture the effect of SVD on the brain than the structural MRI features alone and serve as intermediary outcomes in clinical trials and future routine practice. Using a similar approach to that adopted in STRIVE-1, we updated the guidance on neuroimaging of vascular changes in studies of ageing and neurodegeneration to create STRIVE-2.

Peak width of skeletonized mean diffusivity and cognitive performance in cerebral amyloid angiopathy

Background

Cerebral Amyloid Angiopathy (CAA) is a cerebral small vessel disease that can lead to microstructural disruption of white matter (WM), which can be measured by the Peak Width of Skeletonized Mean Diffusivity (PSMD). We hypothesized that PSMD measures would be increased in patients with CAA compared to healthy controls (HC), and increased PSMD is associated with lower cognitive scores in patients with CAA.

Methods

Eighty-one probable CAA patients without cognitive impairment who were diagnosed with Boston criteria and 23 HCs were included. All subjects underwent an advanced brain MRI with high-resolution diffusion-weighted imaging (DWI). PSMD scores were quantified from a probabilistic skeleton of the WM tracts in the mean diffusivity (MD) image using a combination of fractional anisotropy (FA) and the FSL Tract-Based Spatial Statistics (TBSS) algorithm (www.psmd-marker.com). Within CAA cohort, standardized z-scores of processing speed, executive functioning and memory were obtained.

Results

The mean of age and sex were similar between CAA patients (69.6 ± 7.3, 59.3% male) and HCs (70.6 ± 8.5, 56.5% male) (p = 0.581 and p = 0.814). PSMD was higher in the CAA group [(4.13 ± 0.94) × 10-4 mm2/s] compared to HCs [(3.28 ± 0.51) × 10-4 mm2/s] (p < 0.001). In a linear regression model corrected for relevant variables, diagnosis of CAA was independently associated with increased PSMD compared to HCs (ß = 0.45, 95% CI 0.13-0.76, p = 0.006). Within CAA cohort, higher PSMD was associated with lower scores in processing speed (p < 0.001), executive functioning (p = 0.004), and memory (0.047). Finally, PSMD outperformed all other MRI markers of CAA by explaining most of the variance in models predicting lower scores in each cognitive domain.

Discussion

Peak Width of Skeletonized Mean Diffusivity is increased in CAA, and it is associated with worse cognitive scores supporting the view that disruption of white matter has a significant role in cognitive impairment in CAA. As a robust marker, PSMD can be used in clinical trials or practice.

Peak Width of Skeletonized Mean Diffusivity: A Neuroimaging Marker for White Matter Injury

A leading cause of white matter (WM) injury in older individuals is cerebral small vessel disease (SVD). Cerebral SVD is the most prevalent vascular contributor to cognitive impairment and dementia. Therapeutic progress for cerebral SVD and other WM disorders depends on the development and validation of neuroimaging markers suitable as outcome measures in future interventional trials. Diffusion-tensor imaging (DTI) is one of the best-suited MRI techniques for assessing the extent of WM damage in the brain. But the optimal method to analyze individual DTI data remains hindered by labor-intensive and time-consuming processes. Peak width of skeletonized mean diffusivity (PSMD), a recently developed fast, fully automated DTI marker, was designed to quantify the WM damage secondary to cerebral SVD and reflect related cognitive impairment. Despite its promising results, knowledge about PSMD is still limited in the radiologic community. This focused review provides an overview of the technical details of PSMD while synthesizing the available data on its clinical and neuroimaging associations. From a critical expert viewpoint, the authors discuss the limitations of PSMD and its current validation status as a neuroimaging marker for vascular cognitive impairment. Finally, they point out the gaps to be addressed to further advance the field.

Recent advances in arterial spin labeling perfusion MRI in patients with vascular cognitive impairment

Cognitive impairment (CI) is a major health concern in aging populations. It impairs patients' independent life and may progress to dementia. Vascular cognitive impairment (VCI) encompasses all cerebrovascular pathologies that contribute to cognitive impairment (CI). Moreover, the majority of CI subtypes involve various aspects of vascular dysfunction. Recent research highlights the critical role of reduced cerebral blood flow (CBF) in the progress of VCI, and the detection of altered CBF may help to detect or even predict the onset of VCI. Arterial spin labeling (ASL) is a non-invasive, non-ionizing perfusion MRI technique for assessing CBF qualitatively and quantitatively. Recent methodological advances enabling improved signal-to-noise ratio (SNR) and data acquisition have led to an increase in the use of ASL to assess CBF in VCI patients. Combined with other imaging modalities and biomarkers, ASL has great potential for identifying early VCI and guiding prediction and prevention strategies. This review focuses on recent advances in ASL-based perfusion MRI for identifying patients at high risk of VCI.

Older adults with perivascular spaces exhibit cerebrovascular reactivity deficits

BACKGROUND

Perivascular spaces on brain magnetic resonance imaging (MRI) may indicate poor fluid drainage in the brain and have been associated with numerous neurological conditions. Cerebrovascular reactivity (CVR) is a marker of cerebrovascular function and represents the ability of cerebral blood vessels to regulate cerebral blood flow in response to vasodilatory or vasoconstrictive stimuli. We aimed to examine whether pathological widening of the perivascular space in older adults may be associated with deficits in CVR.

METHODS

Independently living older adults free of dementia or clinical stroke were recruited from the community and underwent brain MRI. Pseudo-continuous arterial spin labeling MRI quantified whole brain cerebral perfusion at rest and during CVR to hypercapnia and hypocapnia induced by visually guided breathing exercises. Perivascular spaces were visually scored using existing scales.

RESULTS

Thirty-seven independently living older adults (mean age = 66.3 years; SD = 6.8; age range 55-84 years; 29.7% male) were included in the current analysis. Multiple linear regression analysis revealed a significant negative association between burden of perivascular spaces and global CVR to hypercapnia (B = -2.0, 95% CI (-3.6, -0.4), p = .015), adjusting for age and sex. Perivascular spaces were not related to CVR to hypocapnia.

DISCUSSION

Perivascular spaces are associated with deficits in cerebrovascular vasodilatory response, but not vasoconstrictive response. Enlargement of perivascular spaces could contribute to, or be influenced by, deficits in CVR. Additional longitudinal studies are warranted to improve our understanding of the relationship between cerebrovascular function and perivascular space enlargement.

Adapting UK Biobank imaging for use in a routine memory clinic setting: The Oxford Brain Health Clinic

The Oxford Brain Health Clinic (BHC) is a joint clinical-research service that provides memory clinic patients and clinicians access to high-quality assessments not routinely available, including brain MRI aligned with the UK Biobank imaging study (UKB). In this work we present how we 1) adapted the UKB MRI acquisition protocol to be suitable for memory clinic patients, 2) modified the imaging analysis pipeline to extract measures that are in line with radiology reports and 3) explored the alignment of measures from BHC patients to the largest brain MRI study in the world (ultimately 100,000 participants). Adaptations of the UKB acquisition protocol for BHC patients include dividing the scan into core and optional sequences (i.e., additional imaging modalities) to improve patients' tolerance for the MRI assessment. We adapted the UKB structural MRI analysis pipeline to take into account the characteristics of a memory clinic population (e.g., high amount of white matter hyperintensities and hippocampal atrophy). We then compared the imaging derived phenotypes (IDPs) extracted from the structural scans to visual ratings from radiology reports, non-imaging factors (age, cognition) and to reference distributions derived from UKB data. Of the first 108 BHC attendees (August 2020-November 2021), 92.5 % completed the clinical scans, 88.0 % consented to use of data for research, and 43.5 % completed the additional research sequences, demonstrating that the protocol is well tolerated. The high rates of consent to research makes this a valuable real-world quality research dataset routinely captured in a clinical service. Modified tissue-type segmentation with lesion masking greatly improved grey matter volume estimation. CSF-masking marginally improved hippocampal segmentation. The IDPs were in line with radiology reports and showed significant associations with age and cognitive performance, in line with the literature. Due to the age difference between memory clinic patients of the BHC (age range 65-101 years, average 78.3 years) and UKB participants (44-82 years, average 64 years), additional scans on elderly healthy controls are needed to improve reference distributions. Current and future work aims to integrate automated quantitative measures in the radiology reports and evaluate their clinical utility.

Peak width of skeletonized mean diffusivity in cerebral amyloid angiopathy: Spatial signature, cognitive, and neuroimaging associations

Background

Peak width of skeletonized mean diffusivity (PSMD) is a promising diffusion tensor imaging (DTI) marker that shows consistent and strong cognitive associations in the context of different cerebral small vessel diseases (cSVD).

Purpose

Investigate whether PSMD (1) is higher in patients with Cerebral Amyloid Angiopathy (CAA) than those with arteriolosclerosis; (2) can capture the anteroposterior distribution of CAA-related abnormalities; (3) shows similar neuroimaging and cognitive associations in comparison to other classical DTI markers, such as average mean diffusivity (MD) and fractional anisotropy (FA).

Materials and methods

We analyzed cross-sectional neuroimaging and neuropsychological data from 90 non-demented memory-clinic subjects from a single center. Based on MRI findings, we classified them into probable-CAA (those that fulfilled the modified Boston criteria), subjects with MRI markers of cSVD not attributable to CAA (presumed arteriolosclerosis; cSVD), and subjects without evidence of cSVD on MRI (non-cSVD). We compared total and lobe-specific (frontal and occipital) DTI metrics values across the groups. We used linear regression models to investigate how PSMD, MD, and FA correlate with conventional neuroimaging markers of cSVD and cognitive scores in CAA.

Results

PSMD was comparable in probable-CAA (median 4.06 × 10^–4 mm²/s) and cSVD (4.07 × 10^–4 mm²/s) patients, but higher than in non-cSVD (3.30 × 10^–4 mm²/s; p < 0.001) subjects. Occipital-frontal PSMD gradients were higher in probable-CAA patients, and we observed a significant interaction between diagnosis and region on PSMD values [F(2, 87) = 3.887, p = 0.024]. PSMD was mainly associated with white matter hyperintensity volume, whereas MD and FA were also associated with other markers, especially with the burden of perivascular spaces. PSMD correlated with worse executive function (β = −0.581, p < 0.001) and processing speed (β = −0.463, p = 0.003), explaining more variance than other MRI markers. MD and FA were not associated with performance in any cognitive domain.

Conclusion

PSMD is a promising biomarker of cognitive impairment in CAA that outperforms other conventional and DTI-based neuroimaging markers. Although global PSMD is similarly increased in different forms of cSVD, PSMD’s spatial variations could potentially provide insights into the predominant type of underlying microvascular pathology.

5-Year Associations among Cerebral Arterial Pulsatility, Perivascular Space Dilation, and White Matter Lesions

OBJECTIVE

High cerebral arterial pulsatility index (PI), white matter lesions (WMLs), enlarged perivascular spaces (PVSs), and lacunar infarcts are common findings in the elderly population, and considered indicators of small vessel disease (SVD). Here, we investigate the potential temporal ordering among these variables, with emphasis on determining whether high PI is an early or delayed manifestation of SVD.

METHODS

In a population-based cohort, 4D flow MRI data for cerebral arterial pulsatility was collected for 159 participants at baseline (age 64-68), and for 122 participants at follow-up 5 years later. Structural MRI was used for WML and PVS segmentation, and lacune identification. Linear mixed-effects (LME) models were used to model longitudinal changes testing for pairwise associations, and latent change score (LCS) models to model multiple relationships among variables simultaneously.

RESULTS

Longitudinal 5-year increases were found for WML, PVS, and PI. Cerebral arterial PI at baseline did not predict changes in WML or PVS volume. However, WML and PVS volume at baseline predicted 5-year increases in PI. This was shown for PI increases in relation to baseline WML and PVS volumes using LME models (R  ≥  0.24; p < 0.02 and R  ≥  0.23; p < 0.03, respectively) and LCS models ( β  = 0.28; p = 0.015 and β  = 0.28; p = 0.009, respectively). Lacunes at baseline were unrelated to PI.

INTERPRETATION

In healthy older adults, indicators of SVD are related in a lead-lag fashion, in which the expression of WML and PVS precedes increases in cerebral arterial PI. Hence, we propose that elevated PI is a relatively late manifestation, rather than a risk factor, for cerebral SVD. ANN NEUROL 2022;92:871-881.

Framework for Clinical Trials in Cerebral Small Vessel Disease (FINESSE): A Review

Importance

Cerebral small vessel disease (SVD) causes a quarter of strokes and is the most common pathology underlying vascular cognitive impairment and dementia. An important step to developing new treatments is better trial methodology. Disease mechanisms in SVD differ from other stroke etiologies; therefore, treatments need to be evaluated in cohorts in which SVD has been well characterized. Furthermore, SVD itself can be caused by a number of different pathologies, the most common of which are arteriosclerosis and cerebral amyloid angiopathy. To date, there have been few sufficiently powered high-quality randomized clinical trials in SVD, and inconsistent trial methodology has made interpretation of some findings difficult.

Observations

To address these issues and develop guidelines for optimizing design of clinical trials in SVD, the Framework for Clinical Trials in Cerebral Small Vessel Disease (FINESSE) was created under the auspices of the International Society of Vascular Behavioral and Cognitive Disorders. Experts in relevant aspects of SVD trial methodology were convened, and a structured Delphi consensus process was used to develop recommendations. Areas in which recommendations were developed included optimal choice of study populations, choice of clinical end points, use of brain imaging as a surrogate outcome measure, use of circulating biomarkers for participant selection and as surrogate markers, novel trial designs, and prioritization of therapeutic agents using genetic data via Mendelian randomization.

Conclusions and Relevance

The FINESSE provides recommendations for trial design in SVD for which there are currently few effective treatments. However, new insights into understanding disease pathogenesis, particularly from recent genetic studies, provide novel pathways that could be therapeutically targeted. In addition, whether other currently available cardiovascular interventions are specifically effective in SVD, as opposed to other subtypes of stroke, remains uncertain. FINESSE provides a framework for design of trials examining such therapeutic approaches.

Contrast-agent-free state-of-the-art MRI on cerebral small vessel disease-part 1. ASL, IVIM, and CVR

Cerebral small vessel disease (cSVD), a common cause of stroke and dementia, is traditionally considered the small vessel equivalent of large artery occlusion or rupture that leads to cortical and subcortical brain damage. Microvessel endothelial dysfunction can also contribute to it. Brain imaging, including MRI, is useful to show the presence of lesions of several types, although the association between conventional MRI measures and clinical features of cSVD is not always concordant. We assessed the additional contribution of contrast-agent-free, state-of-the-art MRI techniques such as arterial spin labeling (ASL), diffusion tensor imaging, functional MRI, and intravoxel incoherent motion (IVIM) applied to cSVD in the existing literature. We performed a review following the PICO Worksheet and Search Strategy, including original papers in English, published between 2000 and 2022. For each MRI method, we extracted information about their contributions, in addition to those established with traditional MRI methods and related information about the origins, pathology, markers, and clinical outcomes in cSVD. This paper presents the first part of the review, which includes 37 studies focusing on ASL, IVIM, and cerebrovascular reactivity (CVR) measures. In general, they have shown that, in addition to white matter hyperintensities, alterations in other neuroimaging parameters such as blood flow and CVR also indicate the presence of cSVD. Such quantitative parameters were also related to cSVD risk factors. Therefore, they are promising, noninvasive tools to explore questions that have not yet been clarified about this clinical condition. However, protocol standardization is essential to increase their clinical use.

Contrast agent-free state-of-the-art magnetic resonance imaging on cerebral small vessel disease - Part 2: Diffusion tensor imaging and functional magnetic resonance imaging

Cerebral small vessel disease (cSVD) has been widely studied using conventional magnetic resonance imaging (MRI) methods, although the association between MRI findings and clinical features of cSVD is not always concordant. We assessed the additional contribution of contrast agent-free, state-of-the-art MRI techniques, particularly diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), to understand brain damage and structural and functional connectivity impairment related to cSVD. We performed a review following the PICOS worksheet and Search Strategy, including 152 original papers in English, published from 2000 to 2022. For each MRI method, we extracted information about their contributions regarding the origins, pathology, markers, and clinical outcomes in cSVD. In general, DTI studies have shown that changes in mean, radial, and axial diffusivity measures are related to the presence of cSVD. In addition to the classical deficit in executive functions and processing speed, fMRI studies indicate connectivity dysfunctions in other domains, such as sensorimotor, memory, and attention. Neuroimaging metrics have been correlated with the diagnosis, prognosis, and rehabilitation of patients with cSVD. In short, the application of contrast agent-free, state-of-the-art MRI techniques has provided a complete picture of cSVD markers and tools to explore questions that have not yet been clarified about this clinical condition. Longitudinal studies are desirable to look for causal relationships between image biomarkers and clinical outcomes.

Distinct Brain Proteomic Signatures in Cerebral Small Vessel Disease Rat Models of Hypertension and Cerebral Amyloid Angiopathy

Cerebral small vessel diseases (CSVDs) are prominent contributors to vascular cognitive impairment and dementia and can arise from a range of etiologies. Cerebral amyloid angiopathy (CAA) and hypertension (HTN), both prevalent in the elderly population, lead to cerebral microhemorrhages, macrohemorrhages, and white matter damage. However, their respective underlying mechanisms and molecular events are poorly understood. Here, we show that the transgenic rat model of CAA type 1 (rTg-DI) exhibits perivascular inflammation that is lacking in the spontaneously hypertensive stroke-prone (SHR-SP) rat model of HTN. Alternatively, SHR-SP rats display notable dilation of arteriolar perivascular spaces. Comparative proteomics analysis revealed few shared altered proteins, with key proteins such as ANXA3, H2A, and HTRA1 unique to rTg-DI rats, and Nt5e, Flot-1 and Flot-2 unique to SHR-SP rats. Immunolabeling confirmed that upregulation of ANXA3, HTRA1, and neutrophil extracellular trap proteins were distinctly associated with rTg-DI rats. Pathway analysis predicted activation of TGF-β1 and TNFα in rTg-DI rat brain, while insulin signaling was reduced in the SHR-SP rat brain. Thus, we report divergent protein signatures associated with distinct cerebral vessel pathologies in the SHR-SP and rTg-DI rat models and provide new mechanistic insight into these different forms of CSVD.

Construction of a Medical Micro-Object Cascade Network for Automated Segmentation of Cerebral Microbleeds in Susceptibility Weighted Imaging

Aim: The detection and segmentation of cerebral microbleeds (CMBs) images are the focus of clinical diagnosis and treatment. However, segmentation is difficult in clinical practice, and missed diagnosis may occur. Few related studies on the automated segmentation of CMB images have been performed, and we provide the most effective CMB segmentation to date using an automated segmentation system.

Materials and Methods: From a research perspective, we focused on the automated segmentation of CMB targets in susceptibility weighted imaging (SWI) for the first time and then constructed a deep learning network focused on the segmentation of micro-objects. We collected and marked clinical datasets and proposed a new medical micro-object cascade network (MMOC-Net). In the first stage, U-Net was utilized to select the region of interest (ROI). In the second stage, we utilized a full-resolution network (FRN) to complete fine segmentation. We also incorporated residual atrous spatial pyramid pooling (R-ASPP) and a new joint loss function.

Results: The most suitable segmentation result was achieved with a ROI size of 32 × 32. To verify the validity of each part of the method, ablation studies were performed, which showed that the best segmentation results were obtained when FRN, R-ASPP and the combined loss function were used simultaneously. Under these conditions, the obtained Dice similarity coefficient (DSC) value was 87.93% and the F2-score (F2) value was 90.69%. We also innovatively developed a visual clinical diagnosis system that can provide effective support for clinical diagnosis and treatment decisions.

Conclusions: We created the MMOC-Net method to perform the automated segmentation task of CMBs in an SWI and obtained better segmentation performance; hence, this pioneering method has research significance.

Reproducibility and repeatability of magnetic resonance imaging in dementia

PURPOSE

Individualised predictive models of cognitive decline require disease-monitoring markers that are repeatable. For wide-spread adoption, such markers also need to be reproducible at different locations. This study assessed the repeatability and reproducibility of MRI markers derived from a dementia protocol.

METHODS

Six participants were scanned at three different sites with a 3T MRI scanner. The protocol employed: T1-weighted (T1w) imaging, resting state functional MRI (rsfMRI), arterial spin labelling (ASL), diffusion-weighted imaging (DWI), T2-weighted fluid attenuation inversion recovery (FLAIR), T2-weighted (T2w) imaging, and susceptibility weighted imaging (SWI). Participants were scanned repeatedly, up to six times over a maximum period of five years. One participant was also scanned a further three times on sequential days on one scanner. Fifteen derived metrics were computed from the seven different modalities.

RESULTS

Reproducibility (coefficient of variation; CoV, across sites) was best for T1w derived grey matter, white matter and hippocampal volume (CoV < 1.5%), compared to rsfMRI and SWI derived metrics (CoV, 19% and 21%). For a given metric, long-term repeatability (CoV across time) was comparable to reproducibility, with short-term repeatability considerably better.

CONCLUSIONS

Reproducibility and repeatability were assessed for a suite of markers calculated from a dementia MRI protocol. In general, structural markers were less variable than functional MRI markers. Variability over time on the same scanner was comparable to variability measured across different scanners. Overall, the results support the viability of multi-site longitudinal studies for monitoring cognitive decline.

Systematic validation of structural brain networks in cerebral small vessel disease

Cerebral small vessel disease (SVD) is considered a disconnection syndrome, which can be quantified using structural brain network analysis obtained from diffusion MRI. Network analysis is a demanding analysis approach and the added benefit over simpler diffusion MRI analysis is largely unexplored in SVD. In this pre-registered study, we assessed the clinical and technical validity of network analysis in two non-overlapping samples of SVD patients from the RUN DMC study (n = 52 for exploration and longitudinal analysis and n = 105 for validation). We compared two connectome pipelines utilizing single-shell or multi-shell diffusion MRI, while also systematically comparing different node and edge definitions. For clinical validation, we assessed the added benefit of network analysis in explaining processing speed and in detecting short-term disease progression. For technical validation, we determined test-retest repeatability.Our findings in clinical validation show that structural brain networks provide only a small added benefit over simpler global white matter diffusion metrics and do not capture short-term disease progression. Test-retest reliability was excellent for most brain networks. Our findings question the added value of brain network analysis in clinical applications in SVD and highlight the utility of simpler diffusion MRI based markers.

Prediction of Chronological Age in Healthy Elderly Subjects with Machine Learning from MRI Brain Segmentation and Cortical Parcellation

Normal aging is associated with changes in volumetric indices of brain atrophy. A quantitative understanding of age-related brain changes can shed light on successful aging. To investigate the effect of age on global and regional brain volumes and cortical thickness, 3514 magnetic resonance imaging scans were analyzed using automated brain segmentation and parcellation methods in elderly healthy individuals (69–88 years of age). The machine learning algorithm extreme gradient boosting (XGBoost) achieved a mean absolute error of 2 years in predicting the age of new subjects. Feature importance analysis showed that the brain-to-intracranial-volume ratio is the most important feature in predicting age, followed by the hippocampi volumes. The cortical thickness in temporal and parietal lobes showed a superior predictive value than frontal and occipital lobes. Insights from this approach that integrate model prediction and interpretation may help to shorten the current explanatory gap between chronological age and biological brain age.

Vascular cognitive impairment and dementia: An early career researcher perspective

The field of vascular contributions to cognitive impairment and dementia (VCID) is evolving rapidly. Research in VCID encompasses topics aiming to understand, prevent, and treat the detrimental effects of vascular disease burden in the human brain. In this perspective piece, early career researchers (ECRs) in the field provide an overview of VCID, discuss past and present efforts, and highlight priorities for future research. We emphasize the following critical points as the field progresses: (a) consolidate existing neuroimaging and fluid biomarkers, and establish their utility for pharmacological and non-pharmacological interventions; (b) develop new biomarkers, and new non-clinical models that better recapitulate vascular pathologies; (c) amplify access to emerging biomarker and imaging techniques; (d) validate findings from previous investigations in diverse populations, including those at higher risk of cognitive impairment (e.g., Black, Hispanic, and Indigenous populations); and (e) conduct randomized controlled trials within diverse populations with well-characterized vascular pathologies emphasizing clinically meaningful outcomes.

Instrumental validation of free water, peak-width of skeletonized mean diffusivity, and white matter hyperintensities: MarkVCID neuroimaging kits

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).

Imaging Markers of Vascular Brain Health: Quantification, Clinical Implications, and Future Directions

Cerebrovascular disease (CVD) manifests through a broad spectrum of mechanisms that negatively impact brain and cognitive health. Oftentimes, CVD changes (excluding acute stroke) are insufficiently considered in aging and dementia studies which can lead to an incomplete picture of the etiologies contributing to the burden of cognitive impairment. Our goal with this focused review is 3-fold. First, we provide a research update on the current magnetic resonance imaging methods that can measure CVD lesions as well as early CVD-related brain injury specifically related to small vessel disease. Second, we discuss the clinical implications and relevance of these CVD imaging markers for cognitive decline, incident dementia, and disease progression in Alzheimer disease, and Alzheimer-related dementias. Finally, we present our perspective on the outlook and challenges that remain in the field. With the increased research interest in this area, we believe that reliable CVD imaging biomarkers for aging and dementia studies are on the horizon.

The Potential Impact of Neuroimaging and Translational Research on the Clinical Management of Lacunar Stroke

Lacunar infarcts represent one of the most frequent subtypes of ischemic strokes and may represent the first recognizable manifestation of a progressive disease of the small perforating arteries, capillaries, and venules of the brain, defined as cerebral small vessel disease. The pathophysiological mechanisms leading to a perforating artery occlusion are multiple and still not completely defined, due to spatial resolution issues in neuroimaging, sparsity of pathological studies, and lack of valid experimental models. Recent advances in the endovascular treatment of large vessel occlusion may have diverted attention from the management of patients with small vessel occlusions, often excluded from clinical trials of acute therapy and secondary prevention. However, patients with a lacunar stroke benefit from early diagnosis, reperfusion therapy, and secondary prevention measures. In addition, there are new developments in the knowledge of this entity that suggest potential benefits of thrombolysis in an extended time window in selected patients, as well as novel therapeutic approaches targeting different pathophysiological mechanisms involved in small vessel disease. This review offers a comprehensive update in lacunar stroke pathophysiology and clinical perspective for managing lacunar strokes, in light of the latest insights from imaging and translational studies.

Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets

Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the training, validation, and testing of advanced deep learning (DL)-based automated tools, including structural magnetic resonance (MR) image-based diagnostic and treatment monitoring approaches. When assembling a number of smaller datasets to form a larger dataset, understanding the underlying variability between different acquisition and processing protocols across the aggregated dataset (termed “batch effects”) is critical. The presence of variation in the training dataset is important as it more closely reflects the true underlying data distribution and, thus, may enhance the overall generalizability of the tool. However, the impact of batch effects must be carefully evaluated in order to avoid undesirable effects that, for example, may reduce performance measures. Batch effects can result from many sources, including differences in acquisition equipment, imaging technique and parameters, as well as applied processing methodologies. Their impact, both beneficial and adversarial, must be considered when developing tools to ensure that their outputs are related to the proposed clinical or research question (i.e., actual disease-related or pathological changes) and are not simply due to the peculiarities of underlying batch effects in the aggregated dataset. We reviewed applications of DL in structural brain MR imaging that aggregated images from neuroimaging datasets, typically acquired at multiple sites. We examined datasets containing both healthy control participants and patients that were acquired using varying acquisition protocols. First, we discussed issues around Data Access and enumerated the key characteristics of some commonly used publicly available brain datasets. Then we reviewed methods for correcting batch effects by exploring the two main classes of approaches: Data Harmonization that uses data standardization, quality control protocols or other similar algorithms and procedures to explicitly understand and minimize unwanted batch effects; and Domain Adaptation that develops DL tools that implicitly handle the batch effects by using approaches to achieve reliable and robust results. In this narrative review, we highlighted the advantages and disadvantages of both classes of DL approaches, and described key challenges to be addressed in future studies.

Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets

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We harmonised measures of WMHs across two studies on healthy ageing.

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Specific pre-processing strategies can increase comparability across studies.

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Modelling of biological differences is crucial to provide calibrated measures.

Large scale neuroimaging datasets present the possibility of providing normative distributions for a wide variety of neuroimaging markers, which would vastly improve the clinical utility of these measures. However, a major challenge is our current poor ability to integrate measures across different large-scale datasets, due to inconsistencies in imaging and non-imaging measures across the different protocols and populations. Here we explore the harmonisation of white matter hyperintensity (WMH) measures across two major studies of healthy elderly populations, the Whitehall II imaging sub-study and the UK Biobank. We identify pre-processing strategies that maximise the consistency across datasets and utilise multivariate regression to characterise study sample differences contributing to differences in WMH variations across studies. We also present a parser to harmonise WMH-relevant non-imaging variables across the two datasets. We show that we can provide highly calibrated WMH measures from these datasets with: (1) the inclusion of a number of specific standardised processing steps; and (2) appropriate modelling of sample differences through the alignment of demographic, cognitive and physiological variables. These results open up a wide range of applications for the study of WMHs and other neuroimaging markers across extensive databases of clinical data.

White matter hyperintensities in patients with Parkinson's disease: A systematic review and meta-analysis

INTRODUCTION

Mechanisms driving neurodegeneration in Parkinson's disease (PD) are unclear and neurovascular dysfunction may be a contributing factor. White matter hyperintensities (WMH) are commonly found on brain MRI in patients with PD. It is controversial if they are more prevalent or more severe in PD compared with controls. This systematic review aims to answer this question.

METHODS

A systematic search of electronic databases was conducted for studies of WMH in patients with PD. A qualitative synthesis was done for studies reporting WMH prevalence or WMH scores on a visual rating scale (VRS). In studies reporting total WMH volume, the difference between patients with PD and controls was pooled using random effects meta-analysis.

RESULTS

Among 3860 subjects from 24 studies, 2360 were cases and 1500 controls. Fifteen studies reported WMH scores and four studies reported the prevalence of WMH. On VRS, five studies reported no difference in WMH scores, three found higher WMH scores in PD compared to controls, three reported increased WMH scores either in periventricular or deep white matter, and four reported higher scores only in PD with dementia. In studies reporting WMH volume, there was no difference between patients with PD and controls (pooled standardized mean difference = 0.1, 95%CI: -0.1-0.4, I² = 81%).

CONCLUSION

WMH are not more prevalent or severe in patients with PD than in age-matched controls. PD dementia may have more severe WMH compared to controls and PD with normal cognition. Prospective studies using standardized methods of WMH assessment are needed.

Diffuse white matter loss in a transgenic rat model of cerebral amyloid angiopathy

Diffuse white matter (WM) disease is highly prevalent in elderly with cerebral small vessel disease (cSVD). In humans, cSVD such as cerebral amyloid angiopathy (CAA) often coexists with Alzheimer's disease imposing a significant impediment for characterizing their distinct effects on WM. Here we studied the burden of age-related CAA pathology on WM disease in a novel transgenic rat model of CAA type 1 (rTg-DI). A cohort of rTg-DI and wild-type rats was scanned longitudinally using MRI for characterization of morphometry, cerebral microbleeds (CMB) and WM integrity. In rTg-DI rats, a distinct pattern of WM loss was observed at 9 M and 11 M. MRI also revealed manifestation of small CMB in thalamus at 6 M, which preceded WM loss and progressively enlarged until the moribund disease stage. Histology revealed myelin loss in the corpus callosum and thalamic CMB in all rTg-DI rats, the latter of which manifested in close proximity to occluded and calcified microvessels. The quantitation of CAA load in rTg-DI rats revealed that the most extensive microvascular Aβ deposition occurred in the thalamus. For the first time using in vivo MRI, we show that CAA type 1 pathology alone is associated with a distinct pattern of WM loss.

Structural Brain Magnetic Resonance Imaging to Rule Out Comorbid Pathology in the Assessment of Alzheimer's Disease Dementia: Findings from the Ontario Neurodegenerative Disease Research Initiative (ONDRI) Study and Clinical Trials Over the Past 10 Years

BACKGROUND/OBJECTIVE

Structural brain magnetic resonance imaging (MRI) is not mandatory in Alzheimer's disease (AD) research or clinical guidelines. We aimed to explore the use of structural brain MRI in AD/mild cognitive impairment (MCI) trials over the past 10 years and determine the frequency with which inclusion of standardized structural MRI acquisitions detects comorbid vascular and non-vascular pathologies.

METHODS

We systematically searched ClinicalTrials.gov for AD clinical trials to determine their neuroimaging criteria and then used data from an AD/MCI cohort who underwent standardized MRI protocols, to determine type and incidence of clinically relevant comorbid pathologies.

RESULTS

Of 210 AD clinical trials, 105 (50%) included structural brain imaging in their eligibility criteria. Only 58 (27.6%) required MRI. 16,479 of 53,755 (30.7%) AD participants were in trials requiring MRI. In the observational AD/MCI cohort, 141 patients met clinical criteria; 22 (15.6%) had relevant MRI findings, of which 15 (10.6%) were exclusionary for the study.

DISCUSSION

In AD clinical trials over the last 10 years, over two-thirds of participants could have been enrolled without brain MRI and half without even a brain CT. In a study sample, relevant comorbid pathology was found in 15% of participants, despite careful screening. Standardized structural MRI should be incorporated into NIA-AA diagnostic guidelines (when available) and research frameworks routinely to reduce diagnostic heterogeneity.

MarkVCID cerebral small vessel consortium: II. Neuroimaging protocols

The MarkVCID consortium was formed under cooperative agreements with the National Institute of Neurologic Diseases and Stroke (NINDS) and National Institute on Aging (NIA) in 2016 with the goals of developing and validating biomarkers for the cerebral small vessel diseases associated with the vascular contributions to cognitive impairment and dementia (VCID). Rigorously validated biomarkers have consistently been identified as crucial for multicenter studies to identify effective strategies to prevent and treat VCID, specifically to detect increased VCID risk, diagnose the presence of small vessel disease and its subtypes, assess prognosis for disease progression or response to treatment, demonstrate target engagement or mechanism of action for candidate interventions, and monitor disease progression during treatment. The seven project sites and central coordinating center comprising MarkVCID, working with NINDS and NIA, identified a panel of 11 candidate fluid- and neuroimaging-based biomarker kits and established harmonized multicenter study protocols (see companion paper "MarkVCID cerebral small vessel consortium: I. Enrollment, clinical, fluid protocols" for full details). Here we describe the MarkVCID neuroimaging protocols with specific focus on validating their application to future multicenter trials. MarkVCID procedures for participant enrollment; clinical and cognitive evaluation; and collection, handling, and instrumental validation of fluid samples are described in detail in a companion paper. Magnetic resonance imaging (MRI) has long served as the neuroimaging modality of choice for cerebral small vessel disease and VCID because of its sensitivity to a wide range of brain properties, including small structural lesions, connectivity, and cerebrovascular physiology. Despite MRI's widespread use in the VCID field, there have been relatively scant data validating the repeatability and reproducibility of MRI-based biomarkers across raters, scanner types, and time intervals (collectively defined as instrumental validity). The MRI protocols described here address the core MRI sequences for assessing cerebral small vessel disease in future research studies, specific sequence parameters for use across various research scanner types, and rigorous procedures for determining instrumental validity. Another candidate neuroimaging modality considered by MarkVCID is optical coherence tomography angiography (OCTA), a non-invasive technique for directly visualizing retinal capillaries as a marker of the cerebral capillaries. OCTA has theoretical promise as a unique opportunity to visualize small vessels derived from the cerebral circulation, but at a considerably earlier stage of development than MRI. The additional OCTA protocols described here address procedures for determining OCTA instrumental validity, evaluating sources of variability such as pupil dilation, and handling data to maintain participant privacy. MRI protocol and instrumental validation The core sequences selected for the MarkVCID MRI protocol are three-dimensional T1-weighted multi-echo magnetization-prepared rapid-acquisition-of-gradient-echo (ME-MPRAGE), three-dimensional T2-weighted fast spin echo fluid-attenuated-inversion-recovery (FLAIR), two-dimensional diffusion-weighted spin-echo echo-planar imaging (DWI), three-dimensional T2*-weighted multi-echo gradient echo (3D-GRE), three-dimensional T2 -weighted fast spin-echo imaging (T2w), and two-dimensional T2*-weighted gradient echo echo-planar blood-oxygenation-level-dependent imaging with brief periods of CO2 inhalation (BOLD-CVR). Harmonized parameters for each of these core sequences were developed for four 3 Tesla MRI scanner models in widespread use at academic medical centers. MarkVCID project sites are trained and certified for their instantiation of the consortium MRI protocols. Sites are required to perform image quality checks every 2 months using the Alzheimer's Disease Neuroimaging Initiative phantom. Instrumental validation for MarkVCID MRI-based biomarkers is operationally defined as inter-rater reliability, test-retest repeatability, and inter-scanner reproducibility. Assessments of these instrumental properties are performed on individuals representing a range of cerebral small vessel disease from mild to severe. Inter-rater reliability is determined by distribution of an independent dataset of MRI scans to each analysis site. Test-retest repeatability is determined by repeat MRI scans performed on individual participants on a single MRI scanner after a short (1- to 14-day) interval. Inter-scanner reproducibility is determined by repeat MRI scans performed on individuals performed across four MRI scanner models. OCTA protocol and instrumental validation The MarkVCID OCTA protocol uses a commercially available, Food and Drug Administration-approved OCTA apparatus. Imaging is performed on one dilated and one undilated eye to assess the need for dilation. Scans are performed in quadruplicate. MarkVCID project sites participating in OCTA validation are trained and certified by this biomarker's lead investigator. Inter-rater reliability for OCTA is assessed by distribution of OCTA datasets to each analysis site. Test-retest repeatability is assessed by repeat OCTA imaging on individuals on the same day as their baseline OCTA and a different-day repeat session after a short (1- to 14-day) interval. Methods were developed to allow the OCTA data to be de-identified by the sites before transmission to the central data management system. The MarkVCID neuroimaging protocols, like the other MarkVCID procedures, are designed to allow translation to multicenter trials and as a template for outside groups to generate directly comparable neuroimaging data. The MarkVCID neuroimaging protocols are available to the biomedical community and intended to be shared. In addition to the instrumental validation procedures described here, each of the neuroimaging MarkVCID kits will undergo biological validation to determine its ability to measure important aspects of VCID such as cognitive function. The analytic methods for the neuroimaging-based kits and the results of these validation studies will be published separately. The results will ultimately determine the neuroimaging kits' potential usefulness for multicenter interventional trials in small vessel disease-related VCID.

Cerebral small vessel disease and vascular cognitive impairment: from diagnosis to management

PURPOSE OF REVIEW

We present recent developments in the field of small vessel disease (SVD)-related vascular cognitive impairment, including pathological mechanisms, updated diagnostic criteria, cognitive profile, neuroimaging markers and risk factors. We further address available management and therapeutic strategies.

RECENT FINDINGS

Vascular and neurodegenerative pathologies often co-occur and share similar risk factors. The updated consensus criteria aim to standardize vascular cognitive impairment (VCI) diagnosis, relying strongly on cognitive profile and MRI findings. Aggressive blood pressure control and multidomain lifestyle interventions are associated with decreased risk of cognitive impairment, but disease-modifying treatments are still lacking. Recent research has led to a better understanding of mechanisms leading to SVD-related cognitive decline, such as blood-brain barrier dysfunction, reduced cerebrovascular reactivity and impaired perivascular clearance.

SUMMARY

SVD is the leading cause of VCI and is associated with substantial morbidity. Tackling cardiovascular risk factors is currently the most effective approach to prevent cognitive decline in the elderly. Advanced imaging techniques provide tools for early diagnosis and may play an important role as surrogate markers for cognitive endpoints in clinical trials. Designing and testing disease-modifying interventions for VCI remains a key priority in healthcare.

Peak Width of Skeletonized Mean Diffusivity as Neuroimaging Biomarker in Cerebral Amyloid Angiopathy

BACKGROUND AND PURPOSE

Whole-brain network connectivity has been shown to be a useful biomarker of cerebral amyloid angiopathy and related cognitive impairment. We evaluated an automated DTI-based method, peak width of skeletonized mean diffusivity, in cerebral amyloid angiopathy, together with its association with conventional MRI markers and cognitive functions.

MATERIALS AND METHODS

We included 24 subjects (mean age, 74.7 [SD, 6.0] years) with probable cerebral amyloid angiopathy and mild cognitive impairment and 62 patients with MCI not attributable to cerebral amyloid angiopathy (non-cerebral amyloid angiopathy-mild cognitive impairment). We compared peak width of skeletonized mean diffusivity between subjects with cerebral amyloid angiopathy-mild cognitive impairment and non-cerebral amyloid angiopathy-mild cognitive impairment and explored its associations with cognitive functions and conventional markers of cerebral small-vessel disease, using linear regression models.

RESULTS

Subjects with Cerebral amyloid angiopathy-mild cognitive impairment showed increased peak width of skeletonized mean diffusivity in comparison to those with non-cerebral amyloid angiopathy-mild cognitive impairment (P < .001). Peak width of skeletonized mean diffusivity values were correlated with the volume of white matter hyperintensities in both groups. Higher peak width of skeletonized mean diffusivity was associated with worse performance in processing speed among patients with cerebral amyloid angiopathy, after adjusting for other MRI markers of cerebral small vessel disease. The peak width of skeletonized mean diffusivity did not correlate with cognitive functions among those with non-cerebral amyloid angiopathy-mild cognitive impairment.

CONCLUSIONS

Peak width of skeletonized mean diffusivity is altered in cerebral amyloid angiopathy and is associated with performance in processing speed. This DTI-based method may reflect the degree of white matter structural disruption in cerebral amyloid angiopathy and could be a useful biomarker for cognition in this population.

Bringing AI to the clinic: blueprint for a vendor-neutral AI deployment infrastructure

AI provides tremendous opportunities for improving patient care, but at present there is little evidence of real-world uptake. An important barrier is the lack of well-designed, vendor-neutral and future-proof infrastructures for deployment. Because current AI algorithms are very narrow in scope, it is expected that a typical hospital will deploy many algorithms concurrently. Managing stand-alone point solutions for all of these algorithms will be unmanageable. A solution to this problem is a dedicated platform for deployment of AI. Here we describe a blueprint for such a platform and the high-level design and implementation considerations of such a system that can be used clinically as well as for research and development. Close collaboration between radiologists, data scientists, software developers and experts in hospital IT as well as involvement of patients is crucial in order to successfully bring AI to the clinic.

[Clustering of diagnostic MRI signs of cerebral microangiopathy and its relationship with markers of inflammation and angiogenesis]

OBJECTIVE

To perform cluster analysis of MRI signs of cerebral microangiopathy (small vessel disease, SVD) and to clarify the relationship between the isolated groups and circulating markers of inflammation and angiogenesis.

MATERIAL AND METHODS

The identification of groups of MRI signs (MRI types) using cluster hierarchical agglomerative analysis and iterative algorithm of k-means and assessment of their relationship with serum concentrations of tumor necrosis factor-α (TNF-α), transforming growth factor-β1 (TGF-β1), vascular endothelial growth factor-A (VEGF-A), hypoxia-inducible factor 1-α (HIF1-α) determined by ELISA were performed in 96 patients with SVD (STRIVE, 2013) (65 women, average age 60.91±6.57 years).

RESULTS

Cluster analysis of MRI signs identified two MRI types of SVD with Fazekas grade 3 of white matter hyperintensity (WMH). MRI type 1 (n=18; 6 women, mean age 59.1±6.8 years) and MRI type 2 (n=22, 15 f., mean age 63.5±6.2 years) did not differ by age, sex, severity of hypertension, presence of other risk factors. MRI type 1 had a statistically significantly more pronounced WMH in the periventricular regions, multiple lacunes and microbleeds, atrophy, severe cognitive impairment and gait disorders compared with MRI type 2. Its formation was associated with a decrease in VEGF-A level. MRI type 2 had the significantly more pronounced juxtacortical WMH, white matter lacunes, in the absence of microbleeds and atrophy, and less severe clinical manifestations compared with MRI type 1. Its formation was associated with an increase in TNF-α level.

CONCLUSION

Clustering of diagnostic MRI signs into MRI types of SVD with significant differences in the severity of clinical manifestations suggests the pathogenetic heterogeneity of age-related SVD. The relationship of MRI types with circulating markers of different mechanisms of vascular wall and brain damage indicates the dominant role of depletion of angiogenesis in the formation of MRI type 1 and increased inflammation in the formation of MRI type 2. Further studies are needed to clarify the criteria and diagnostic value of differentiation of MRI types of SVD, and also their mechanisms with the definition of pathogenetically justified prevention and treatment of various forms of SVD.

White Matter Hyperintensities Quantification in Healthy Adults: A Systematic Review and Meta-Analysis

BACKGROUND

Although white matter hyperintensities (WMH) volumetric assessment is now customary in research studies, inconsistent WMH measures among homogenous populations may prevent the clinical usability of this biomarker.

PURPOSE

To determine whether a point estimate and reference standard for WMH volume in the healthy aging population could be determined.

STUDY TYPE

Systematic review and meta-analysis.

POPULATION

In all, 9716 adult subjects from 38 studies reporting WMH volume were retrieved following a systematic search on EMBASE.

FIELD STRENGTH/SEQUENCE

1.0T, 1.5T, or 3.0T/fluid-attenuated inversion recovery (FLAIR) and/or proton density/T₂ -weighted fast spin echo sequences or gradient echo T₁ -weighted sequences.

ASSESSMENT

After a literature search, sample size, demographics, magnetic field strength, MRI sequences, level of automation in WMH assessment, study population, and WMH volume were extracted.

STATISTICAL TESTS

The pooled WMH volume with 95% confidence interval (CI) was calculated using the random-effect model. The I² statistic was calculated as a measure of heterogeneity across studies. Meta-regression analysis of WMH volume on age was performed.

RESULTS

Of the 38 studies analyzed, 17 reported WMH volume as the mean and standard deviation (SD) and were included in the meta-analysis. Mean and SD of age was 66.11 ± 10.92 years (percentage of men 50.45% ± 21.48%). Heterogeneity was very high (I²  = 99%). The pooled WMH volume was 4.70 cm³ (95% CI: 3.88-5.53 cm³ ). At meta-regression analysis, WMH volume was positively associated with subjects' age (β = 0.358 cm³ per year, P < 0.05, R²  = 0.27).

DATA CONCLUSION

The lack of standardization in the definition of WMH together with the high technical variability in assessment may explain a large component of the observed heterogeneity. Currently, volumes of WMH in healthy subjects are not comparable between studies and an estimate and reference interval could not be determined.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 1.

Magnetic resonance imaging manifestations of cerebral small vessel disease: automated quantification and clinical application

The common cerebral small vessel disease (CSVD) neuroimaging features visible on conventional structural magnetic resonance imaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. The CSVD neuroimaging features have shared and distinct clinical consequences, and the automatic quantification methods for these features are increasingly used in research and clinical settings. This review article explores the recent progress in CSVD neuroimaging feature quantification and provides an overview of the clinical consequences of these CSVD features as well as the possibilities of using these features as endpoints in clinical trials. The added value of CSVD neuroimaging quantification is also discussed for researches focused on the mechanism of CSVD and the prognosis in subjects with CSVD.

Microstructural Predictors of Cognitive Impairment in Cerebral Small Vessel Disease and the Conditions of Their Formation

Introduction: Cerebral small vessel disease (CSVD) is the leading cause of vascular and mixed degenerative cognitive impairment (CI). The variability in the rate of progression of CSVD justifies the search for sensitive predictors of CI. Materials: A total of 74 patients (48 women, average age 60.6 ± 6.9 years) with CSVD and CI of varying severity were examined using 3T MRI. The results of diffusion tensor imaging with a region of interest (ROI) analysis were used to construct a predictive model of CI using binary logistic regression, while phase-contrast magnetic resonance imaging and voxel-based morphometry were used to clarify the conditions for the formation of CI predictors. Results: According to the constructed model, the predictors of CI are axial diffusivity (AD) of the posterior frontal periventricular normal-appearing white matter (pvNAWM), right middle cingulum bundle (CB), and mid-posterior corpus callosum (CC). These predictors showed a significant correlation with the volume of white matter hyperintensity; arterial and venous blood flow, pulsatility index, and aqueduct cerebrospinal fluid (CSF) flow; and surface area of the aqueduct, volume of the lateral ventricles and CSF, and gray matter volume. Conclusion: Disturbances in the AD of pvNAWM, CB, and CC, associated with axonal damage, are a predominant factor in the development of CI in CSVD. The relationship between AD predictors and both blood flow and CSF flow indicates a disturbance in their relationship, while their location near the floor of the lateral ventricle and their link with indicators of internal atrophy, CSF volume, and aqueduct CSF flow suggest the importance of transependymal CSF transudation when these regions are damaged.

Calgary Normative Study: design of a prospective longitudinal study to characterise potential quantitative MR biomarkers of neurodegeneration over the adult lifespan

INTRODUCTION

A number of MRI methods have been proposed to be useful, quantitative biomarkers of neurodegeneration in ageing. The Calgary Normative Study (CNS) is an ongoing single-centre, prospective, longitudinal study that seeks to develop, test and assess quantitative magnetic resonance (MR) methods as potential biomarkers of neurodegeneration. The CNS has three objectives: first and foremost, to evaluate and characterise the dependence of the selected quantitative neuroimaging biomarkers on age over the adult lifespan; second, to evaluate the precision, variability and repeatability of quantitative neuroimaging biomarkers as part of biomarker validation providing proof-of-concept and proof-of-principle; and third, provide a shared repository of normative data for comparison to various disease cohorts.

METHODS AND ANALYSIS

Quantitative MR mapping of the brain including longitudinal relaxation time (T1), transverse relaxation time (T2), T2*, magnetic susceptibility (QSM), diffusion and perfusion measurements, as well as morphological assessments are performed. The Montreal Cognitive Assessment (MoCA) and a brief, self-report medical history will be collected. Mixed regression models will be used to characterise changes in quantitative MR biomarker measures over the adult lifespan. In this report, we describe the study design, strategies to recruit and perform changes to the acquisition protocol from inception to 31 December 2018, planned statistical approach and data sharing procedures for the study.

ETHICS AND DISSEMINATION

Participants provide signed informed consent. Changes in quantitative MR biomarkers measured over the adult lifespan as well as estimates of measurement variance and repeatability will be disseminated through peer-reviewed scientific publication.

MRI Types of Cerebral Small Vessel Disease and Circulating Markers of Vascular Wall Damage

The evaluation of the clustering of magnetic resonance imaging (MRI) signs into MRI types and their relationship with circulating markers of vascular wall damage were performed in 96 patients with cerebral small vessel disease (cSVD) (31 men and 65 women; mean age, 60.91 ± 6.57 years). The serum concentrations of the tumor necrosis factor-α (TNF-α), transforming growth factor-β1 (TGF-β1), vascular endothelial growth factor-A (VEGF-A), and hypoxia-inducible factor 1-α (HIF-1α) were investigated in 70 patients with Fazekas stages 2 and 3 of white matter hyperintensities (WMH) and 21 age- and sex-matched volunteers with normal brain MRI using ELISA. The cluster analysis excluded two patients from the further analysis due to restrictions in their scanning protocol. MRI signs of 94 patients were distributed into two clusters. In the first group there were 18 patients with Fazekas 3 stage WMH. The second group consisted of 76 patients with WMH of different stages. The uneven distribution of patients between clusters limited the subsequent steps of statistical analysis; therefore, a cluster comparison was performed in patients with Fazekas stage 3 WMH, designated as MRI type 1 and type 2 of Fazekas 3 stage. There were no differences in age, sex, degree of hypertension, or other risk factors. MRI type 1 had significantly more widespread WMH, lacunes in many areas, microbleeds, atrophy, severe cognitive and gait impairments, and was associated with downregulation of VEGF-A compared with MRI type 2. MRI type 2 had more severe deep WMH, lacunes in the white matter, no microbleeds or atrophy, and less severe clinical manifestations and was associated with upregulation of TNF-α compared with MRI type 1. The established differences reflect the pathogenetic heterogeneity of cSVD and explain the variations in the clinical manifestations observed in Fazekas stage 3 of this disease.

Brain atrophy in cerebral small vessel diseases: Extent, consequences, technical limitations and perspectives: The HARNESS initiative

Brain atrophy is increasingly evaluated in cerebral small vessel diseases. We aim at systematically reviewing the available data regarding its extent, correlates and cognitive consequences. Given that in this context, brain atrophy measures might be biased, the first part of the review focuses on technical aspects. Thereafter, data from the literature are analyzed in light of these potential limitations, to better understand the relationships between brain atrophy and other MRI markers of cerebral small vessel diseases. In the last part, we review the links between brain atrophy and cognitive alterations in patients with cerebral small vessel diseases.