Influence of arterial transit time delays on the differentiation between tumor progression and pseudoprogression in glioblastoma by arterial spin labeling magnetic resonance imaging.

Arterial spin labeling (ASL) and dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) have shown potential for differentiating tumor progression from pseudoprogression. For pseudocontinuous ASL with a single postlabeling delay, the presence of delayed arterial transit times (ATTs) could affect the evaluation of ASL-MRI perfusion data. In this study, the influence of ATT artifacts on the perfusion assessment and differentiation between tumor progression and pseudoprogression were studied. This study comprised 66 adult patients (mean age 60 ± 13 years; 40 males) with a histologically confirmed glioblastoma who received postoperative radio (chemo)therapy. ASL-MRI and DSC-MRI scans were acquired at 3 months postradiotherapy as part of the standard clinical routine. These scans were visually scored regarding (i) the severity of ATT artifacts (%) on the ASL-MRI scans only, scored by two neuroradiologists; (ii) perfusion of the enhancing tumor lesion; and (iii) radiological evaluation of tumor progression versus pseudoprogression by one neuroradiologist. The final outcome was based on combined clinical and radiological follow-up until 9 months postradiotherapy. ATT artifacts were identified in all patients based on the mean scores of two raters. A significant difference between the radiological evaluation of ASL-MRI and DSC-MRI was observed only for ASL images with moderate ATT severity (30%-65%). The perfusion assessment showed ASL-MRI tending more towards hyperperfusion than DSC-MRI in the case of moderate ATT artifacts. In addition, there was a significant difference between the prediction of tumor progression with ASL-MRI and the final outcome in the case of severe ATT artifacts (McNemar test, p = 0.041). Despite using ASL imaging parameters close to the recommended settings, ATT artifacts frequently occur in patients with treated brain tumors. Those artifacts could hinder the radiological evaluation of ASL-MRI data and the detection of true disease progression, potentially affecting treatment decisions for patients with glioblastoma.

Human brain clearance imaging: Pathways taken by magnetic resonance imaging contrast agents after administration in cerebrospinal fluid and blood.

Over the last decade, it has become evident that cerebrospinal fluid (CSF) plays a pivotal role in brain solute clearance through perivascular pathways and interactions between the brain and meningeal lymphatic vessels. Whereas most of this fundamental knowledge was gained from rodent models, human brain clearance imaging has provided important insights into the human system and highlighted the existence of important interspecies differences. Current gold standard techniques for human brain clearance imaging involve the injection of gadolinium-based contrast agents and monitoring their distribution and clearance over a period from a few hours up to 2 days. With both intrathecal and intravenous injections being used, which each have their own specific routes of distribution and thus clearance of contrast agent, a clear understanding of the kinetics associated with both approaches, and especially the differences between them, is needed to properly interpret the results. Because it is known that intrathecally injected contrast agent reaches the blood, albeit in small concentrations, and that similarly some of the intravenously injected agent can be detected in CSF, both pathways are connected and will, in theory, reach the same compartments. However, because of clear differences in relative enhancement patterns, both injection approaches will result in varying sensitivities for assessment of different subparts of the brain clearance system. In this opinion review article, the "EU Joint Programme - Neurodegenerative Disease Research (JPND)" consortium on human brain clearance imaging provides an overview of contrast agent pharmacokinetics in vivo following intrathecal and intravenous injections and what typical concentrations and concentration-time curves should be expected. This can be the basis for optimizing and interpreting contrast-enhanced MRI for brain clearance imaging. Furthermore, this can shed light on how molecules may exchange between blood, brain, and CSF.

Recommendations for quantitative cerebral perfusion MRI using multi-timepoint arterial spin labeling: Acquisition, quantification, and clinical applications.

Accurate assessment of cerebral perfusion is vital for understanding the hemodynamic processes involved in various neurological disorders and guiding clinical decision-making. This guidelines article provides a comprehensive overview of quantitative perfusion imaging of the brain using multi-timepoint arterial spin labeling (ASL), along with recommendations for its acquisition and quantification. A major benefit of acquiring ASL data with multiple label durations and/or post-labeling delays (PLDs) is being able to account for the effect of variable arterial transit time (ATT) on quantitative perfusion values and additionally visualize the spatial pattern of ATT itself, providing valuable clinical insights. Although multi-timepoint data can be acquired in the same scan time as single-PLD data with comparable perfusion measurement precision, its acquisition and postprocessing presents challenges beyond single-PLD ASL, impeding widespread adoption. Building upon the 2015 ASL consensus article, this work highlights the protocol distinctions specific to multi-timepoint ASL and provides robust recommendations for acquiring high-quality data. Additionally, we propose an extended quantification model based on the 2015 consensus model and discuss relevant postprocessing options to enhance the analysis of multi-timepoint ASL data. Furthermore, we review the potential clinical applications where multi-timepoint ASL is expected to offer significant benefits. This article is part of a series published by the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group, aiming to guide and inspire the advancement and utilization of ASL beyond the scope of the 2015 consensus article.

Serum and cerebrospinal fluid neurofilament light chain and glial fibrillary acid protein levels in early and advanced stages of cerebral amyloid Angiopathy.

BACKGROUND: Neurofilament light chain (NFL) is a biomarker for neuroaxonal damage and glial fibrillary acidic protein (GFAP) for reactive astrocytosis. Both processes occur in cerebral amyloid angiopathy (CAA), but studies investigating the potential of NFL and GFAP as markers for CAA are lacking. We aimed to investigate NFL and GFAP as biomarkers for neuroaxonal damage and astrocytosis in CAA. METHODS: For this cross-sectional study serum and cerebrospinal fluid (CSF) samples were collected between 2010 and 2020 from controls, (pre)symptomatic Dutch-type hereditary (D-CAA) mutation-carriers and participants with sporadic CAA (sCAA) from two prospective CAA studies at two University hospitals in the Netherlands. NFL and GFAP levels were measured with Simoa-assays. The association between NFL and GFAP levels and age, cognitive performance (MoCA), CAA-related MRI markers (CAA-CSVD-burden) and Aß40 and Aß42 levels in CSF were assessed with linear regression adjusted for confounders. The control group was divided in age < 55 and ≥55 years to match the specific groups. RESULTS: We included 187 participants: 28 presymptomatic D-CAA mutation-carriers (mean age 40 years), 29 symptomatic D-CAA participants (mean age 58 years), 59 sCAA participants (mean age 72 years), 33 controls < 55 years (mean age 42 years) and 38 controls ≥ 55 years (mean age 65 years). In presymptomatic D-CAA, only GFAP in CSF (7.7*103pg/mL vs. 4.4*103pg/mL in controls; P<.001) was increased compared to controls. In symptomatic D-CAA, both serum (NFL:26.2pg/mL vs. 12.5pg/mL; P=0.008, GFAP:130.8pg/mL vs. 123.4pg/mL; P=0.027) and CSF (NFL:16.8*102pg/mL vs. 7.8*102pg/mL; P=0.01 and GFAP:11.4*103pg/mL vs. 7.5*103pg/mL; P<.001) levels were higher than in controls and serum levels (NFL:26.2pg/mL vs. 6.7pg/mL; P=0.05 and GFAP:130.8pg/mL vs. 66.0pg/mL; P=0.004) were higher than in pre-symptomatic D-CAA. In sCAA, only NFL levels were increased compared to controls in both serum (25.6pg/mL vs. 12.5pg/mL; P=0.005) and CSF (20.0*102pg/mL vs 7.8*102pg/mL; P=0.008). All levels correlated with age. Serum NFL correlated with MoCA (P=0.008) and CAA-CSVD score (P<.001). NFL and GFAP in CSF correlated with Aß42 levels (P=0.01/0.02). CONCLUSIONS: GFAP level in CSF is an early biomarker for CAA and is increased years before symptom onset. NFL and GFAP levels in serum and CSF are biomarkers for advanced CAA.

Identification of Distinct Brain MRI Phenotypes and Their Association With Long-Term Dementia Risk in Community-Dwelling Older Adults.

BACKGROUND AND OBJECTIVES: Individual brain MRI markers only show at best a modest association with long-term occurrence of dementia. Therefore, it is challenging to accurately identify individuals at increased risk for dementia. We aimed to identify different brain MRI phenotypes by hierarchical clustering analysis based on combined neurovascular and neurodegenerative brain MRI markers and to determine the long-term dementia risk within the brain MRI phenotype subgroups. METHODS: Hierarchical clustering analysis based on 32 combined neurovascular and neurodegenerative brain MRI markers in community-dwelling individuals of the Age-Gene/Environment Susceptibility Reykjavik Study was applied to identify brain MRI phenotypes. A Cox proportional hazards regression model was used to determine the long-term risk for dementia per subgroup. RESULTS: We included 3,056 participants and identified 15 subgroups with distinct brain MRI phenotypes. The phenotypes ranged from limited burden, mostly irregular white matter hyperintensity (WMH) shape and cerebral atrophy, mostly irregularly WMHs and microbleeds, mostly cortical infarcts and atrophy, mostly irregularly shaped WMH and cerebral atrophy to multiburden subgroups. Each subgroup showed different long-term risks for dementia (min-max range hazard ratios [HRs] 1.01-6.18; mean time to follow-up 9.9 ± 2.6 years); especially the brain MRI phenotype with mainly WMHs and atrophy showed a large increased risk (HR 6.18, 95% CI 3.37-11.32). DISCUSSION: Distinct brain MRI phenotypes can be identified in community-dwelling older adults. Our results indicate that distinct brain MRI phenotypes are related to varying long-term risks of developing dementia. Brain MRI phenotypes may in the future assist in an improved understanding of the structural correlates of dementia predisposition.

Temporal Ordering of Biomarkers in Dutch-Type Hereditary Cerebral Amyloid Angiopathy.

BACKGROUND: The temporal ordering of biomarkers for cerebral amyloid angiopathy (CAA) is important for their use in trials and for the understanding of the pathological cascade of CAA. We investigated the presence and abnormality of the most common biomarkers in the largest (pre)symptomatic Dutch-type hereditary CAA (D-CAA) cohort to date. METHODS: We included cross-sectional data from participants with (pre)symptomatic D-CAA and controls without CAA. We investigated CAA-related cerebral small vessel disease markers on 3T-MRI, cerebrovascular reactivity with functional 7T-MRI (fMRI) and amyloid-ß40 and amyloid-ß42 levels in cerebrospinal fluid. We calculated frequencies and plotted biomarker abnormality according to age to form scatterplots. RESULTS: We included 68 participants with D-CAA (59% presymptomatic, mean age, 50 [range, 26-75] years; 53% women), 53 controls (mean age, 51 years; 42% women) for cerebrospinal fluid analysis and 36 controls (mean age, 53 years; 100% women) for fMRI analysis. Decreased cerebrospinal fluid amyloid-ß40 and amyloid-ß42 levels were the earliest biomarkers present: all D-CAA participants had lower levels of amyloid-ß40 and amyloid-ß42 compared with controls (youngest participant 30 years). Markers of nonhemorrhagic injury (>20 enlarged perivascular spaces in the centrum semiovale and white matter hyperintensities Fazekas score, ≥2, present in 83% [n=54]) and markers of impaired cerebrovascular reactivity (abnormal BOLD amplitude, time to peak and time to baseline, present in 56% [n=38]) were present from the age of 30 years. Finally, markers of hemorrhagic injury were present in 64% (n=41) and only appeared after the age of 41 years (first microbleeds and macrobleeds followed by cortical superficial siderosis). CONCLUSIONS: Our results suggest that amyloid biomarkers in cerebrospinal fluid are the first to become abnormal in CAA, followed by MRI biomarkers for cerebrovascular reactivity and nonhemorrhagic injury and lastly hemorrhagic injury. This temporal ordering probably reflects the pathological stages of CAA and should be taken into account when future therapeutic trials targeting specific stages are designed.

The relation between vascular risk factors and flow in cerebral perforating arteries. A 7 Tesla MRI study.

INTRODUCTION: Cerebral perforating arteries provide blood supply to the deep regions of the brain. Recently, it became possible to measure blood flow velocity and pulsatility in these small arteries. It is unknown if vascular risk factors are related to these measures. METHODS: We measured perforating artery flow with 2D phase contrast 7 Tesla MRI at the level of the centrum semiovale (CSO) and the basal ganglia (BG) in seventy participants from the Heart Brain Connection study with carotid occlusive disease (COD), vascular cognitive impairment (VCI), or no actual cerebrovascular disease. Vascular risk factors included hypertension, diabetes, hyperlipidemia and smoking. RESULTS: No consistent relations were found between any of the vascular risk factors and either flow velocity or flow pulsatility, although there was a relation between lower diastolic blood pressure and higher pulse pressure and higher cerebral perforator pulsatility (p=0,045 and p=0,044, respectively) at the BG level. Results were similar in stratified analyses for patients with and without a history of cardiovascular disease, or only COD or VCI. CONCLUSION: We conclude that, cross-sectionally, cerebral perforating artery flow velocity and pulsatility are largely independent of the presence of common vascular risk factors in a population with a mixed vascular burden.

Fluid biomarkers in cerebral amyloid angiopathy.

Cerebral amyloid angiopathy (CAA) is a type of cerebrovascular disorder characterised by the accumulation of amyloid within the leptomeninges and small/medium-sized cerebral blood vessels. Typically, cerebral haemorrhages are one of the first clinical manifestations of CAA, posing a considerable challenge to the timely diagnosis of CAA as the bleedings only occur during the later disease stages. Fluid biomarkers may change prior to imaging biomarkers, and therefore, they could be the future of CAA diagnosis. Additionally, they can be used as primary outcome markers in prospective clinical trials. Among fluid biomarkers, blood-based biomarkers offer a distinct advantage over cerebrospinal fluid biomarkers as they do not require a procedure as invasive as a lumbar puncture. This article aimed to provide an overview of the present clinical data concerning fluid biomarkers associated with CAA and point out the direction of future studies. Among all the biomarkers discussed, amyloid ß, neurofilament light chain, matrix metalloproteinases, complement 3, uric acid, and lactadherin demonstrated the most promising evidence. However, the field of fluid biomarkers for CAA is an under-researched area, and in most cases, there are only one or two studies on each of the biomarkers mentioned in this review. Additionally, a small sample size is a common limitation of the discussed studies. Hence, it is hard to reach a solid conclusion on the clinical significance of each biomarker at different stages of the disease or in various subpopulations of CAA. In order to overcome this issue, larger longitudinal and multicentered studies are needed.

Investigation of metabolite correlates of CEST in the human brain at 7 T.

Metabolite-weighted chemical exchange saturation transfer MRI can be used to indirectly image metabolites such as creatine and glutamate. This study aims to further explore the contrast of CEST at 2 ppm in the human brain at 7T and investigate the metabolite correlates of CEST at 2 ppm via correlations with magnetic resonance spectroscopy (MRS). Simulations were performed to establish the optimal acquisition parameters, such as total saturation time (tsat) and B1 root mean squared (B1rms) for CEST at 2 ppm in the human brain. Parameters were validated via in vitro phantom studies at 7T using concentrations, pH and temperature comparable to what is found in the human brain. Finally, 10 healthy volunteers were scanned at 7T for comparison with MRS. Our results show that the optimal parameters to acquire CEST at 2 ppm images are: B1rms = 2.14 µT & tsat = 1500 ms, respectively. Comparison with MRS showed no significant correlation between CEST at 2 ppm and total Creatine measured by MRS (R = 0.19; p-value = 0.273). However, a significant correlation was found between CEST at 2 ppm and Glu (R = 0.39; p-value = 0.033), indicating the broad Glutamate-weighted CEST as the main measurable contributor to CEST at 2 ppm. We identified and confirmed optimal CEST at 2 ppm sequence parameters and validated CEST at 2 ppm measurements in a controlled in vitro environment. Our findings suggest that glutamate is a substantial contributor to the CEST at 2 ppm contrast observed in the human brain, whereas the creatine contribution to CEST at 2 ppm in the brain did not show a measurable contribution.

AI-based motion artifact severity estimation in undersampled MRI allowing for selection of appropriate reconstruction models.

BACKGROUND: Magnetic Resonance acquisition is a time consuming process, making it susceptible to patient motion during scanning. Even motion in the order of a millimeter can introduce severe blurring and ghosting artifacts, potentially necessitating re-acquisition. Magnetic Resonance Imaging (MRI) can be accelerated by acquiring only a fraction of k-space, combined with advanced reconstruction techniques leveraging coil sensitivity profiles and prior knowledge. Artificial intelligence (AI)-based reconstruction techniques have recently been popularized, but generally assume an ideal setting without intra-scan motion. PURPOSE: To retrospectively detect and quantify the severity of motion artifacts in undersampled MRI data. This may prove valuable as a safety mechanism for AI-based approaches, provide useful information to the reconstruction method, or prompt for re-acquisition while the patient is still in the scanner. METHODS: We developed a deep learning approach that detects and quantifies motion artifacts in undersampled brain MRI. We demonstrate that synthetically motion-corrupted data can be leveraged to train the convolutional neural network (CNN)-based motion artifact estimator, generalizing well to real-world data. Additionally, we leverage the motion artifact estimator by using it as a selector for a motion-robust reconstruction model in case a considerable amount of motion was detected, and a high data consistency model otherwise. RESULTS: Training and validation were performed on 4387 and 1304 synthetically motion-corrupted images and their uncorrupted counterparts, respectively. Testing was performed on undersampled in vivo motion-corrupted data from 28 volunteers, where our model distinguished head motion from motion-free scans with 91% and 96% accuracy when trained on synthetic and on real data, respectively. It predicted a manually defined quality label ('Good', 'Medium' or 'Bad' quality) correctly in 76% and 85% of the time when trained on synthetic and real data, respectively. When used as a selector it selected the appropriate reconstruction network 93% of the time, achieving near optimal SSIM values. CONCLUSIONS: The proposed method quantified motion artifact severity in undersampled MRI data with high accuracy, enabling real-time motion artifact detection that can help improve the safety and quality of AI-based reconstructions.

Model-based super-resolution reconstruction for pseudo-continuous Arterial Spin Labeling.

Arterial spin labeling (ASL) is a promising, non-invasive perfusion magnetic resonance imaging technique for quantifying cerebral blood flow (CBF). Unfortunately, ASL suffers from an inherently low signal-to-noise ratio (SNR) and spatial resolution, undermining its potential. Increasing spatial resolution without significantly sacrificing SNR or scan time represents a critical challenge towards routine clinical use. In this work, we propose a model-based super-resolution reconstruction (SRR) method with joint motion estimation that breaks the traditional SNR/resolution/scan-time trade-off. From a set of differently oriented 2D multi-slice pseudo-continuous ASL images with a low through-plane resolution, 3D-isotropic, high resolution, quantitative CBF maps are estimated using a Bayesian approach. Experiments on both synthetic whole brain phantom data, and on in vivo brain data, show that the proposed SRR Bayesian estimation framework outperforms state-of-the-art ASL quantification.

Current Understanding of the Anatomy, Physiology, and Magnetic Resonance Imaging of Neurofluids: Update From the 2022 "ISMRM Imaging Neurofluids Study group" Workshop in Rome.

Neurofluids is a term introduced to define all fluids in the brain and spine such as blood, cerebrospinal fluid, and interstitial fluid. Neuroscientists in the past millennium have steadily identified the several different fluid environments in the brain and spine that interact in a synchronized harmonious manner to assure a healthy microenvironment required for optimal neuroglial function. Neuroanatomists and biochemists have provided an incredible wealth of evidence revealing the anatomy of perivascular spaces, meninges and glia and their role in drainage of neuronal waste products. Human studies have been limited due to the restricted availability of noninvasive imaging modalities that can provide a high spatiotemporal depiction of the brain neurofluids. Therefore, animal studies have been key in advancing our knowledge of the temporal and spatial dynamics of fluids, for example, by injecting tracers with different molecular weights. Such studies have sparked interest to identify possible disruptions to neurofluids dynamics in human diseases such as small vessel disease, cerebral amyloid angiopathy, and dementia. However, key differences between rodent and human physiology should be considered when extrapolating these findings to understand the human brain. An increasing armamentarium of noninvasive MRI techniques is being built to identify markers of altered drainage pathways. During the three-day workshop organized by the International Society of Magnetic Resonance in Medicine that was held in Rome in September 2022, several of these concepts were discussed by a distinguished international faculty to lay the basis of what is known and where we still lack evidence. We envision that in the next decade, MRI will allow imaging of the physiology of neurofluid dynamics and drainage pathways in the human brain to identify true pathological processes underlying disease and to discover new avenues for early diagnoses and treatments including drug delivery. Evidence level: 1 Technical Efficacy: Stage 3.

Improved reliability of perfusion estimation in dynamic susceptibility contrast MRI by using the arterial input function from dynamic contrast enhanced MRI.

The arterial input function (AIF) plays a crucial role in estimating quantitative perfusion properties from dynamic susceptibility contrast (DSC) MRI. An important issue, however, is that measuring the AIF in absolute contrast-agent concentrations is challenging, due to uncertainty in relation to the measured R 2 ∗ -weighted signal, signal depletion at high concentration, and partial-volume effects. A potential solution could be to derive the AIF from separately acquired dynamic contrast enhanced (DCE) MRI data. We aim to compare the AIF determined from DCE MRI with the AIF from DSC MRI, and estimated perfusion coefficients derived from DSC data using a DCE-driven AIF with perfusion coefficients determined using a DSC-based AIF. AIFs were manually selected in branches of the middle cerebral artery (MCA) in both DCE and DSC data in each patient. In addition, a semi-automatic AIF-selection algorithm was applied to the DSC data. The amplitude and full width at half-maximum of the AIFs were compared statistically using the Wilcoxon rank-sum test, applying a 0.05 significance level. Cerebral blood flow (CBF) was derived with different AIF approaches and compared further. The results showed that the AIFs extracted from DSC scans yielded highly variable peaks across arteries within the same patient. The semi-automatic DSC-AIF had significantly narrower width compared with the manual AIFs, and a significantly larger peak than the manual DSC-AIF. Additionally, the DCE-based AIF provided a more stable measurement of relative CBF and absolute CBF values estimated with DCE-AIFs that were compatible with previously reported values. In conclusion, DCE-based AIFs were reproduced significantly better across vessels, showed more realistic profiles, and delivered more stable and reasonable CBF measurements. The DCE-AIF can, therefore, be considered as an alternative AIF source for quantitative perfusion estimations in DSC MRI.

MRI phenotypes of glioblastomas early after treatment are suggestive of overall patient survival.

Background: Distinguishing true tumor progression (TP) from treatment-induced abnormalities (eg, pseudo-progression (PP) after radiotherapy) on conventional MRI scans remains challenging in patients with a glioblastoma. We aimed to establish brain MRI phenotypes of glioblastomas early after treatment by combined analysis of structural and perfusion tumor characteristics and assessed the relation with recurrence rate and overall survival time. Methods: Structural and perfusion MR images of 67 patients at 3 months post-radiotherapy were visually scored by a neuroradiologist. In total 23 parameters were predefined and used for hierarchical clustering analysis. Progression status was assessed based on the clinical course of each patient 9 months after radiotherapy (or latest available). Multivariable Cox regression models were used to determine the association between the phenotypes, recurrence rate, and overall survival. Results: We established 4 subgroups with significantly different tumor MRI characteristics, representing distinct MRI phenotypes of glioblastomas: TP and PP rates did not differ significantly between subgroups. Regression analysis showed that patients in subgroup 1 (characterized by having mostly small and ellipsoid nodular enhancing lesions with some hyper-perfusion) had a significant association with increased mortality at 9 months (HR: 2.6 (CI: 1.1-6.3); P = .03) with a median survival time of 13 months (compared to 22 months of subgroup 2). Conclusions: Our study suggests that distinct MRI phenotypes of glioblastomas at 3 months post-radiotherapy can be indicative of overall survival, but does not aid in differentiating TP from PP. The early prognostic information our method provides might in the future be informative for prognostication of glioblastoma patients.

White matter changes measured by multi-component MR Fingerprinting in multiple sclerosis.

T2-hyperintense lesions are the key imaging marker of multiple sclerosis (MS). Previous studies have shown that the white matter surrounding such lesions is often also affected by MS. Our aim was to develop a new method to visualize and quantify the extent of white matter tissue changes in MS based on relaxometry properties. We applied a fast, multi-parametric quantitative MRI approach and used a multi-component MR Fingerprinting (MC-MRF) analysis. We assessed the differences in the MRF component representing prolongedrelaxation time between patients with MS and controls and studied the relation between this component's volume and structural white matter damage identified on FLAIR MRI scans in patients with MS. A total of 48 MS patients at two different sites and 12 healthy controls were scanned with FLAIR and MRF-EPI MRI scans. MRF scans were analyzed with a joint-sparsity multi-component analysis to obtain magnetization fraction maps of different components, representing tissues such as myelin water, white matter, gray matter and cerebrospinal fluid. In the MS patients, an additional component was identified with increased transverse relaxation times compared to the white matter, likely representing changes in free water content. Patients with MS had a higher volume of the long- component in the white matter of the brain compared to healthy controls (B (95%-CI) = 0.004 (0.0006-0.008), p = 0.02). Furthermore, this MRF component had a moderate correlation (correlation coefficient R 0.47) with visible structural white matter changes on the FLAIR scans. Also, the component was found to be more extensive compared to structural white matter changes in 73% of MS patients. In conclusion, our MRF acquisition and analysis captured white matter tissue changes in MS patients compared to controls. In patients these tissue changes were more extensive compared to visually detectable white matter changes on FLAIR scans. Our method provides a novel way to quantify the extent of white matter changes in MS patients, which is underestimated using only conventional clinical MRI scans.

Preliminary cross-sectional investigations into the human glymphatic system using multiple novel non-contrast MRI methods.

We discuss two potential non-invasive MRI methods to cross-sectionally study two distinct facets of the glymphatic system and its association with sleep and aging. We apply diffusion-based intravoxel incoherent motion (IVIM) imaging to evaluate pseudodiffusion coefficient, D*, or cerebrospinal fluid (CSF) movement across large spaces like the subarachnoid space (SAS). We also performed perfusion-based multi-echo, Hadamard encoded multi-delay arterial spin labeling (ASL) to evaluate whole brain cortical cerebral blood flow (CBF) and transendothelial exchange (Tex) of water from the vasculature into the perivascular space and parenchyma. Both methods were used in young adults (N=9, 6F, 23±3 years old) in the setting of sleep and sleep deprivation. To study aging, 10 older adults, (6F, 67±3 years old) were imaged after a night of normal sleep only and compared with the young adults. D* in SAS was significantly (p<0.05) lesser after sleep deprivation (0.014±0.001 mm2/s) than after normal sleep (0.016±0.001 mm2/s), but was unchanged with aging. Cortical CBF and Tex on the other hand, were unchanged after sleep deprivation but were significantly lower in older adults (37±3 ml/100g/min, 476±66 ms) than young adults (42±2 ml/100g/min, 624±66 ms). IVIM was thus, sensitive to sleep physiology and multi-echo, multi-delay ASL was sensitive to aging.

Lower haemoglobin concentrations are associated with impaired cognition in patients with carotid artery occlusion.

Background: Patients with carotid artery occlusion (CAO) are vulnerable to cognitive impairment (CI). Anaemia is associated with CI in the general population. We hypothesized that lower haemoglobin is associated with cognitive impairment (CI) in patients with CAO and that this association is accentuated by cerebral blood flow (CBF). Methods: 104 patients (mean age 66±8 years, 77% men) with complete CAO from the Heart-Brain Connection study were included. Anaemia was defined as haemoglobin < 12 g/dL for women and < 13 g/dL for men. Cognitive test results were standardized into z-scores (using a reference group) in four cognitive domains. Patients were classified as cognitively impaired when ≥ one domain was impaired. The association between lower haemoglobin and both cognitive domain z-scores and the presence of CI was assessed with adjusted (age, sex, education and ischaemic stroke) regression models. Total CBF (measured with phase contrast MRI) and the interaction term haemoglobin*CBF were additionally added to the analyses. Results: Anaemia was present in 6 (6%) patients and was associated with CI (RR 2.54, 95% CI 1.36; 4.76). Lower haemoglobin was associated with the presence of CI (RR per minus 1 g/dL haemoglobin 1.15, 95% CI 1.02; 1.30). This association was strongest for the attention-psychomotor speed domain (RR for impaired attention-psychomotor speed functioning per minus 1 g/dL haemoglobin 1.27, 95% CI 1.09;1.47) and ß for attention-psychomotor speed z-scores per minus 1 g/dL haemoglobin -0.19, 95% CI -0.33; -0.05). Adjustment for CBF did not affect these results and we found no interaction between haemoglobin and CBF in relation to cognition. Conclusion: Lower haemoglobin concentrations are associated with CI in patients with complete CAO, particularly in the domain attention-psychomotor speed. CBF did not accentuate this association. If validated in longitudinal studies, haemoglobin might be a viable target to prevent cognitive deterioration in patients with CAO.

Plasma amyloid beta 42 is a biomarker for patients with hereditary, but not sporadic, cerebral amyloid angiopathy.

BACKGROUND: The diagnosis of probable cerebral amyloid angiopathy (CAA) is currently mostly based on characteristics of brain MRI. Blood biomarkers would be a cost-effective, easily accessible diagnostic method that may complement diagnosis by MRI and aid in monitoring disease progression. We studied the diagnostic potential of plasma Aß38, Aß40, and Aß42 in patients with hereditary Dutch-type CAA (D-CAA) and sporadic CAA (sCAA). METHODS: All Aß peptides were quantified in the plasma by immunoassays in a discovery cohort (11 patients with presymptomatic D-CAA and 24 patients with symptomatic D-CAA, and 16 and 24 matched controls, respectively) and an independent validation cohort (54 patients with D-CAA, 26 presymptomatic and 28 symptomatic, and 39 and 46 matched controls, respectively). In addition, peptides were quantified in the plasma in a group of 61 patients with sCAA and 42 matched controls. We compared Aß peptide levels between patients and controls using linear regression adjusting for age and sex. RESULTS: In the discovery cohort, we found significantly decreased levels of all Aß peptides in patients with presymptomatic D-CAA (Aß38: p < 0.001; Aß40: p = 0.009; Aß42: p < 0.001) and patients with symptomatic D-CAA (Aß38: p < 0.001; Aß40: p = 0.01; Aß42: p < 0.001) compared with controls. In contrast, in the validation cohort, plasma Aß38, Aß40, and Aß42 were similar in patients with presymptomatic D-CAA and controls (Aß38: p = 0.18; Aß40: p = 0.28; Aß42: p = 0.63). In patients with symptomatic D-CAA and controls, plasma Aß38 and Aß40 were similar (Aß38: p = 0.14; Aß40: p = 0.38), whereas plasma Aß42 was significantly decreased in patients with symptomatic D-CAA (p = 0.033). Plasma Aß38, Aß40, and Aß42 levels were similar in patients with sCAA and controls (Aß38: p = 0.092; Aß40: p = 0.64. Aß42: p = 0.68). CONCLUSIONS: Plasma Aß42 levels, but not plasma Aß38 and Aß40, may be used as a biomarker for patients with symptomatic D-CAA. In contrast, plasma Aß38, Aß40, and Aß42 levels do not appear to be applicable as a biomarker in patients with sCAA.