Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients.

Introduction: White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating cognitive health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. We propose that lesion network mapping (LNM), enables to infer if brain networks are connected to lesions, and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed this approach to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. Methods & results: We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity across 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. The capacity of total and regional WMH volumes and LNM scores in predicting cognitive function was compared using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention and executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater disruptive effects of WMH on regional connectivity, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Conclusion: Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network effects, particularly in attentionrelated brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.

Amyloid pathology and vascular risk are associated with distinct patterns of cerebral white matter hyperintensities: A multicenter study in 3132 memory clinic patients.

INTRODUCTION: White matter hyperintensities (WMH) are associated with key dementia etiologies, in particular arteriolosclerosis and amyloid pathology. We aimed to identify WMH locations associated with vascular risk or cerebral amyloid-ß1-42 (Aß42)-positive status. METHODS: Individual patient data (n = 3,132; mean age 71.5 ± 9 years; 49.3% female) from 11 memory clinic cohorts were harmonized. WMH volumes in 28 regions were related to a vascular risk compound score (VRCS) and Aß42 status (based on cerebrospinal fluid or amyloid positron emission tomography), correcting for age, sex, study site, and total WMH volume. RESULTS: VRCS was associated with WMH in anterior/superior corona radiata (B = 0.034/0.038, p < 0.001), external capsule (B = 0.052, p < 0.001), and middle cerebellar peduncle (B = 0.067, p < 0.001), and Aß42-positive status with WMH in posterior thalamic radiation (B = 0.097, p < 0.001) and splenium (B = 0.103, p < 0.001). DISCUSSION: Vascular risk factors and Aß42 pathology have distinct signature WMH patterns. This regional vulnerability may incite future studies into how arteriolosclerosis and Aß42 pathology affect the brain's white matter. HIGHLIGHTS: Key dementia etiologies may be associated with specific patterns of white matter hyperintensities (WMH). We related WMH locations to vascular risk and cerebral Aß42 status in 11 memory clinic cohorts. Aß42 positive status was associated with posterior WMH in splenium and posterior thalamic radiation. Vascular risk was associated with anterior and infratentorial WMH. Amyloid pathology and vascular risk have distinct signature WMH patterns.

Spatial distributions of white matter hyperintensities on brain MRI: A pooled analysis of individual participant data from 11 memory clinic cohorts.

INTRODUCTION: The spatial distribution of white matter hyperintensities (WMH) on MRI is often considered in the diagnostic evaluation of patients with cognitive problems. In some patients, clinicians may classify WMH patterns as "unusual", but this is largely based on expert opinion, because detailed quantitative information about WMH distribution frequencies in a memory clinic setting is lacking. Here we report voxel wise 3D WMH distribution frequencies in a large multicenter dataset and also aimed to identify individuals with unusual WMH patterns. METHODS: Individual participant data (N = 3525, including 777 participants with subjective cognitive decline, 1389 participants with mild cognitive impairment and 1359 patients with dementia) from eleven memory clinic cohorts, recruited through the Meta VCI Map Consortium, were used. WMH segmentations were provided by participating centers or performed in Utrecht and registered to the Montreal Neurological Institute (MNI)-152 brain template for spatial normalization. To determine WMH distribution frequencies, we calculated WMH probability maps at voxel level. To identify individuals with unusual WMH patterns, region-of-interest (ROI) based WMH probability maps, rule-based scores, and a machine learning method (Local Outlier Factor (LOF)), were implemented. RESULTS: WMH occurred in 82% of voxels from the white matter template with large variation between subjects. Only a small proportion of the white matter (1.7%), mainly in the periventricular areas, was affected by WMH in at least 20% of participants. A large portion of the total white matter was affected infrequently. Nevertheless, 93.8% of individual participants had lesions in voxels that were affected in less than 2% of the population, mainly located in subcortical areas. Only the machine learning method effectively identified individuals with unusual patterns, in particular subjects with asymmetric WMH distribution or with WMH at relatively rarely affected locations despite common locations not being affected. DISCUSSION: Aggregating data from several memory clinic cohorts, we provide a detailed 3D map of WMH lesion distribution frequencies, that informs on common as well as rare localizations. The use of data-driven analysis with LOF can be used to identify unusual patterns, which might serve as an alert that rare causes of WMH should be considered.

Predicting amyloid-beta pathology in the general population.

INTRODUCTION: Reliable models to predict amyloid beta (Aß) positivity in the general aging population are lacking but could become cost-efficient tools to identify individuals at risk of developing Alzheimer's disease. METHODS: We developed Aß prediction models in the clinical Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) Study (n = 4,119) including a broad range of easily ascertainable predictors (demographics, cognition and daily functioning, health and lifestyle factors). Importantly, we determined the generalizability of our models in the population-based Rotterdam Study (n = 500). RESULTS: The best performing model in the A4 Study (area under the curve [AUC] = 0.73 [0.69-0.76]), including age, apolipoprotein E (APOE) ε4 genotype, family history of dementia, and subjective and objective measures of cognition, walking duration and sleep behavior, was validated in the independent Rotterdam Study with higher accuracy (AUC = 0.85 [0.81-0.89]). Yet, the improvement relative to a model including only age and APOE ε4 was marginal. DISCUSSION: Aß prediction models including inexpensive and non-invasive measures were successfully applied to a general population-derived sample more representative of typical older non-demented adults.

Strategic white matter hyperintensity locations for cognitive impairment: A multicenter lesion-symptom mapping study in 3525 memory clinic patients.

INTRODUCTION: Impact of white matter hyperintensities (WMH) on cognition likely depends on lesion location, but a comprehensive map of strategic locations is lacking. We aimed to identify these locations in a large multicenter study. METHODS: Individual patient data (n = 3525) from 11 memory clinic cohorts were harmonized. We determined the association of WMH location with attention and executive functioning, information processing speed, language, and verbal memory performance using voxel-based and region of interest tract-based analyses. RESULTS: WMH in the left and right anterior thalamic radiation, forceps major, and left inferior fronto-occipital fasciculus were significantly related to domain-specific impairment, independent of total WMH volume and atrophy. A strategic WMH score based on these tracts inversely correlated with performance in all domains. DISCUSSION: The data show that the impact of WMH on cognition is location-dependent, primarily involving four strategic white matter tracts. Evaluation of WMH location may support diagnosing vascular cognitive impairment. HIGHLIGHTS: We analyzed white matter hyperintensities (WMH) in 3525 memory clinic patients from 11 cohorts The impact of WMH on cognition depends on location We identified four strategic white matter tracts A single strategic WMH score was derived from these four strategic tracts The strategic WMH score was an independent determinant of four cognitive domains.

Diabetes and hypertension are related to amyloid-beta burden in the population-based Rotterdam Study.

Higher vascular disease burden increases the likelihood of developing dementia, including Alzheimer's disease. Better understanding the association between vascular risk factors and Alzheimer's disease pathology at the predementia stage is critical for developing effective strategies to delay cognitive decline. In this work, we estimated the impact of six vascular risk factors on the presence and severity of in vivo measured brain amyloid-beta (Aß) plaques in participants from the population-based Rotterdam Study. Vascular risk factors (hypertension, hypercholesterolaemia, diabetes, obesity, physical inactivity and smoking) were assessed 13 (2004-2008) and 7 years (2009-2014) prior to 18F-florbetaben PET (2018-2021) in 635 dementia-free participants. Vascular risk factors were associated with binary amyloid PET status or continuous PET readouts (standard uptake value ratios, SUVrs) using logistic and linear regression models, respectively, adjusted for age, sex, education, APOE4 risk allele count and time between vascular risk and PET assessment. Participants' mean age at time of amyloid PET was 69 years (range: 60-90), 325 (51.2%) were women and 190 (29.9%) carried at least one APOE4 risk allele. The adjusted prevalence estimates of an amyloid-positive PET status markedly increased with age [12.8% (95% CI 11.6; 14) in 60-69 years versus 35% (36; 40.8) in 80-89 years age groups] and APOE4 allele count [9.7% (8.8; 10.6) in non-carriers versus 38.4% (36; 40.8) to 60.4% (54; 66.8) in carriers of one or two risk allele(s)]. Diabetes 7 years prior to PET assessment was associated with a higher risk of a positive amyloid status [odds ratio (95% CI) = 3.68 (1.76; 7.61), P < 0.001] and higher standard uptake value ratios, indicating more severe Aß pathology [standardized beta = 0.40 (0.17; 0.64), P = 0.001]. Hypertension was associated with higher SUVr values in APOE4 carriers (mean SUVr difference of 0.09), but not in non-carriers (mean SUVr difference 0.02; P = 0.005). In contrast, hypercholesterolaemia was related to lower SUVr values in APOE4 carriers (mean SUVr difference -0.06), but not in non-carriers (mean SUVr difference 0.02). Obesity, physical inactivity and smoking were not related to amyloid PET measures. The current findings suggest a contribution of diabetes, hypertension and hypercholesterolaemia to the pathophysiology of Alzheimer's disease in a general population of older non-demented adults. As these conditions respond well to lifestyle modification and drug treatment, further research should focus on the preventative effect of early risk management on the development of Alzheimer's disease neuropathology.

Longitudinal Brain Atrophy Rates in Presymptomatic Carriers of Genetic Frontotemporal Dementia.

BACKGROUND AND OBJECTIVES: It is important to identify at what age brain atrophy rates in genetic frontotemporal dementia (FTD) start to accelerate and deviate from normal aging effects to find the optimal starting point for treatment. We investigated longitudinal brain atrophy rates in the presymptomatic stage of genetic FTD, using normative brain volumetry software. METHODS: Presymptomatic GRN, MAPT, and C9orf72 pathogenic variant carriers underwent longitudinal volumetric T1-weighted magnetic resonance imaging of the brain as part of a prospective cohort study. Images were automatically analyzed with Quantib® ND which consisted of volume measurements (CSF and sum of gray and white matter) of lobes, cerebellum, and hippocampus. All volumes were compared to reference centile curves based on a large population-derived sample of non-demented individuals (n=4951). Mixed-effects models were fitted to analyze atrophy rates of the different gene groups as a function of age. RESULTS: 34 GRN, eight MAPT, and 14 C9orf72 pathogenic variant carriers were included (mean age=52.1, standard deviation=7.2; 66% female). Mean follow-up duration of the study was 64±33 months (median=52; range 13-108). GRN pathogenic variant carriers showed faster decline than the reference centile curves for all brain areas, though relative volumes remained between 5th and 75th percentile between the ages of 45-70. In MAPT pathogenic variant carriers, frontal lobe volume was already at the 5th percentile at age 45, and showed further decline between the ages 50-60. Temporal lobe volume started in the 50th percentile at age 45, but showed fastest decline over time compared to other brain structures. Frontal, temporal, parietal and cerebellar volume already started below the 5th percentile compared to the reference centile curves at age 45 for C9orf72 pathogenic variant carriers, but there was minimal decline over time until the age of 60. DISCUSSION: We provide evidence for longitudinal brain atrophy in the presymptomatic stage of genetic FTD. The affected brain areas and the age after which atrophy rates start to accelerate and diverge from normal aging slopes differed between gene groups. These results highlight the value of normative volumetry software for disease-tracking and staging biomarkers in genetic FTD. These techniques could help in identifying the optimal time window for starting treatment and monitoring treatment response.

Comparing two artificial intelligence software packages for normative brain volumetry in memory clinic imaging.

PURPOSE: To compare two artificial intelligence software packages performing normative brain volumetry and explore whether they could differently impact dementia diagnostics in a clinical context. METHODS: Sixty patients (20 Alzheimer's disease, 20 frontotemporal dementia, 20 mild cognitive impairment) and 20 controls were included retrospectively. One MRI per subject was processed by software packages from two proprietary manufacturers, producing two quantitative reports per subject. Two neuroradiologists assigned forced-choice diagnoses using only the normative volumetry data in these reports. They classified the volumetric profile as "normal," or "abnormal", and if "abnormal," they specified the most likely dementia subtype. Differences between the packages' clinical impact were assessed by comparing (1) agreement between diagnoses based on software output; (2) diagnostic accuracy, sensitivity, and specificity; and (3) diagnostic confidence. Quantitative outputs were also compared to provide context to any diagnostic differences. RESULTS: Diagnostic agreement between packages was moderate, for distinguishing normal and abnormal volumetry (K = .41-.43) and for specific diagnoses (K = .36-.38). However, each package yielded high inter-observer agreement when distinguishing normal and abnormal profiles (K = .73-.82). Accuracy, sensitivity, and specificity were not different between packages. Diagnostic confidence was different between packages for one rater. Whole brain intracranial volume output differed between software packages (10.73%, p < .001), and normative regional data interpreted for diagnosis correlated weakly to moderately (rs = .12-.80). CONCLUSION: Different artificial intelligence software packages for quantitative normative assessment of brain MRI can produce distinct effects at the level of clinical interpretation. Clinics should not assume that different packages are interchangeable, thus recommending internal evaluation of packages before adoption.

Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review.

Developments in neuroradiological MRI analysis offer promise in enhancing objectivity and consistency in dementia diagnosis through the use of quantitative volumetric reporting tools (QReports). Translation into clinical settings should follow a structured framework of development, including technical and clinical validation steps. However, published technical and clinical validation of the available commercial/proprietary tools is not always easy to find and pathways for successful integration into the clinical workflow are varied. The quantitative neuroradiology initiative (QNI) framework highlights six necessary steps for the development, validation and integration of quantitative tools in the clinic. In this paper, we reviewed the published evidence regarding regulatory-approved QReports for use in the memory clinic and to what extent this evidence fulfils the steps of the QNI framework. We summarize unbiased technical details of available products in order to increase the transparency of evidence and present the range of reporting tools on the market. Our intention is to assist neuroradiologists in making informed decisions regarding the adoption of these methods in the clinic. For the 17 products identified, 11 companies have published some form of technical validation on their methods, but only 4 have published clinical validation of their QReports in a dementia population. Upon systematically reviewing the published evidence for regulatory-approved QReports in dementia, we concluded that there is a significant evidence gap in the literature regarding clinical validation, workflow integration and in-use evaluation of these tools in dementia MRI diagnosis.

Resistance to developing brain pathology due to vascular risk factors: the role of educational attainment.

Brain pathology develops at different rates between individuals with similar burden of risk factors, possibly explained by brain resistance. We examined if education contributes to brain resistance by studying its influence on the association between vascular risk factors and brain pathology. In 4111 stroke-free and dementia-free community-dwelling participants (62.9 ± 10.7 years), we explored the association between vascular risk factors (hypertension and the Framingham Stroke Risk Profile [FRSP]) and imaging markers of brain pathology (markers of cerebral small vessel disease and brain volumetry), stratified by educational attainment level. Associations of hypertension and FSRP with markers of brain pathology were not significantly different between levels of educational attainment. Certain associations appeared weaker in those with higher compared to lower educational attainment, particularly for white matter hyperintensities (WMH). Supplementary residual analyses showed significant associations between higher educational attainment and stronger resistance to WMH among others. Our results suggest a role for educational attainment in resistance to vascular brain pathology. Yet, further research is needed to better characterize determinants of brain resistance.

Modelling the cascade of biomarker changes in GRN-related frontotemporal dementia.

OBJECTIVE: Progranulin-related frontotemporal dementia (FTD-GRN) is a fast progressive disease. Modelling the cascade of multimodal biomarker changes aids in understanding the aetiology of this disease and enables monitoring of individual mutation carriers. In this cross-sectional study, we estimated the temporal cascade of biomarker changes for FTD-GRN, in a data-driven way. METHODS: We included 56 presymptomatic and 35 symptomatic GRN mutation carriers, and 35 healthy non-carriers. Selected biomarkers were neurofilament light chain (NfL), grey matter volume, white matter microstructure and cognitive domains. We used discriminative event-based modelling to infer the cascade of biomarker changes in FTD-GRN and estimated individual disease severity through cross-validation. We derived the biomarker cascades in non-fluent variant primary progressive aphasia (nfvPPA) and behavioural variant FTD (bvFTD) to understand the differences between these phenotypes. RESULTS: Language functioning and NfL were the earliest abnormal biomarkers in FTD-GRN. White matter tracts were affected before grey matter volume, and the left hemisphere degenerated before the right. Based on individual disease severities, presymptomatic carriers could be delineated from symptomatic carriers with a sensitivity of 100% and specificity of 96.1%. The estimated disease severity strongly correlated with functional severity in nfvPPA, but not in bvFTD. In addition, the biomarker cascade in bvFTD showed more uncertainty than nfvPPA. CONCLUSION: Degeneration of axons and language deficits are indicated to be the earliest biomarkers in FTD-GRN, with bvFTD being more heterogeneous in disease progression than nfvPPA. Our data-driven model could help identify presymptomatic GRN mutation carriers at risk of conversion to the clinical stage.

Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging.

Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized from brain diffusion MRI. In addition, to enable analysis of WM tracts in large datasets and in clinical practice it is essential to have methodology that is fast and easy to apply. This work therefore presents a new approach for WM tract segmentation: Neuro4Neuro, that is capable of direct extraction of WM tracts from diffusion tensor images using convolutional neural network (CNN). This 3D end-to-end method is trained to segment 25 WM tracts in aging individuals from a large population-based study (N â€‹= â€‹9752, 1.5T MRI). The proposed method showed good segmentation performance and high reproducibility, i.e., a high spatial agreement (Cohen's kappa, κ=0.72-0.83) and a low scan-rescan error in tract-specific diffusion measures (e.g., fractional anisotropy: ε=1%-5%). The reproducibility of the proposed method was higher than that of a tractography-based segmentation algorithm, while being orders of magnitude faster (0.5s to segment one tract). In addition, we showed that the method successfully generalizes to diffusion scans from an external dementia dataset (N â€‹= â€‹58, 3T MRI). In two proof-of-principle experiments, we associated WM microstructure obtained using the proposed method with age in a normal elderly population, and with disease subtypes in a dementia cohort. In concordance with the literature, results showed a widespread reduction of microstructural organization with aging and substantial group-wise microstructure differences between dementia subtypes. In conclusion, we presented a highly reproducible and fast method for WM tract segmentation that has the potential of being used in large-scale studies and clinical practice.

Implementation and validation of ASL perfusion measurements for population imaging.

PURPOSE: Pseudocontinuous arterial spin labeling (pCASL) allows for noninvasive measurement of regional cerebral blood flow (CBF), which has the potential to serve as biomarker for neurodegenerative and cardiovascular diseases. This work aimed to implement and validate pCASL on the dedicated MRI system within the population-based Rotterdam Study, which was installed in 2005 and for which software and hardware configurations have remained fixed. METHODS: Imaging was performed on two 1.5T MRI systems (General Electric); (I) the Rotterdam Study system, and (II) a hospital-based system with a product pCASL sequence. An in-house implementation of pCASL was created on scanner I. A flow phantom and three healthy volunteers (<27 years) were scanned on both systems for validation purposes. The data of the first 30 participants (86 ± 4 years) of the Rotterdam Study undergoing pCASL scans on scanner I only were analyzed with and without partial volume correction for gray matter. RESULTS: The validation study showed a difference in blood flow velocity, sensitivity, and spatial coefficient of variation of the perfusion-weighted signal between the two scanners, which was accounted for during post-processing. Gray matter CBF for the Rotterdam Study participants was 52.4 ± 8.2 ml/100 g/min, uncorrected for partial volume effects of gray matter. In this elderly cohort, partial volume correction for gray matter had a variable effect on measured CBF in a range of cortical and sub-cortical regions of interest. CONCLUSION: Regional CBF measurements are now included to investigate novel biomarkers in the Rotterdam Study. This work highlights that when it is not feasible to purchase a novel ASL sequence, an in-house implementation is valuable.

Qualitative Assessment of Longitudinal Changes in Phenocopy Frontotemporal Dementia.

Phenocopy frontotemporal dementia (phFTD) shares core characteristics with behavioral variant frontotemporal dementia (bvFTD), yet without associated cognitive deficits and brain abnormalities on conventional magnetic resonance imaging (MRI), and without progression. Using advanced MRI techniques, we previously observed subtle structural and functional brain changes in phFTD similar to bvFTD. The aim of the current study was to follow these as well as cognition in phFTD over time, by means of a descriptive case series. Cognition, gray matter (GM) volume and white matter (WM) microstructure, and perfusion of 6 phFTD patients were qualitatively compared longitudinally (3-years follow-up), and cross-sectionally with baseline data from 9 bvFTD patients and 17 controls. For functional brain changes, arterial spin labeling (ASL) was performed to assess GM perfusion. For structural brain changes, diffusion tensor imaging was performed to assess WM microstructure and T1w imaging to assess GM volume. MRI acquisition was performed at 3T (General Electric, US). Clinical profiles of phFTD cases at follow-up are described. At follow-up phFTD patients showed clinical symptomatology similar to bvFTD, but had a relatively stable clinical profile. Longitudinal qualitative comparisons in phFTD showed some deterioration of language and memory function, a stable pattern of structural brain abnormalities and increased perfusion over time. Additionally, both baseline and follow-up cognitive scores and structural values in phFTD were generally in between those of controls and bvFTD. Although a descriptive case series does not allow for strong conclusions, these observations in a unique longitudinal phFTD patient cohort are suggestive of the notion that phFTD and bvFTD may belong to the same disease spectrum. They may also provide a basis for further longitudinal studies in phFTD, specifically exploring the structural vs. functional brain changes. Such studies are essential for improved insight, accurate diagnosis, and appropriate treatment of phFTD.

Normative brain volumetry derived from different reference populations: impact on single-subject diagnostic assessment in dementia.

Brain imaging data are increasingly made publicly accessible, and volumetric imaging measures derived from population-based cohorts may serve as normative data for individual patient diagnostic assessment. Yet, these normative cohorts are usually not a perfect reflection of a patient's base population, nor are imaging parameters such as field strength or scanner type similar. In this proof of principle study, we assessed differences between reference curves of subcortical structure volumes of normal controls derived from two population-based studies and a case-control study. We assessed the impact of any differences on individual assessment of brain structure volumes. Percentile curves were fitted on the three healthy cohorts. Next, percentile values for these subcortical structures for individual patients from these three cohorts, 91 mild cognitive impairment and 95 Alzheimer's disease cases and patients from the Alzheimer Center, were calculated, based on the distributions of each of the three cohorts. Overall, we found that the subcortical volume normative data from these cohorts are highly interchangeable, suggesting more flexibility in clinical implementation.

Clinical value of cerebrospinal fluid neurofilament light chain in semantic dementia.

BACKGROUND: Semantic dementia (SD) is a neurodegenerative disorder characterised by progressive language problems falling within the clinicopathological spectrum of frontotemporal lobar degeneration (FTLD). The development of disease-modifying agents may be facilitated by the relative clinical and pathological homogeneity of SD, but we need robust monitoring biomarkers to measure their efficacy. In different FTLD subtypes, neurofilament light chain (NfL) is a promising marker, therefore we investigated the utility of cerebrospinal fluid (CSF) NfL in SD. METHODS: This large retrospective multicentre study compared cross-sectional CSF NfL levels of 162 patients with SD with 65 controls. CSF NfL levels of patients were correlated with clinical parameters (including survival), neuropsychological test scores and regional grey matter atrophy (including longitudinal data in a subset). RESULTS: CSF NfL levels were significantly higher in patients with SD (median: 2326 pg/mL, IQR: 1628-3593) than in controls (577 (446-766), p<0.001). Higher CSF NfL levels were moderately associated with naming impairment as measured by the Boston Naming Test (rs =-0.32, p=0.002) and with smaller grey matter volume of the parahippocampal gyri (rs =-0.31, p=0.004). However, cross-sectional CSF NfL levels were not associated with progression of grey matter atrophy and did not predict survival. CONCLUSION: CSF NfL is a promising biomarker in the diagnostic process of SD, although it has limited cross-sectional monitoring or prognostic abilities.

Harmonizing brain magnetic resonance imaging methods for vascular contributions to neurodegeneration.

INTRODUCTION: Many consequences of cerebrovascular disease are identifiable by magnetic resonance imaging (MRI), but variation in methods limits multicenter studies and pooling of data. The European Union Joint Program on Neurodegenerative Diseases (EU JPND) funded the HARmoNizing Brain Imaging MEthodS for VaScular Contributions to Neurodegeneration (HARNESS) initiative, with a focus on cerebral small vessel disease. METHODS: Surveys, teleconferences, and an in-person workshop were used to identify gaps in knowledge and to develop tools for harmonizing imaging and analysis. RESULTS: A framework for neuroimaging biomarker development was developed based on validating repeatability and reproducibility, biological principles, and feasibility of implementation. The status of current MRI biomarkers was reviewed. A website was created at www.harness-neuroimaging.org with acquisition protocols, a software database, rating scales and case report forms, and a deidentified MRI repository. CONCLUSIONS: The HARNESS initiative provides resources to reduce variability in measurement in MRI studies of cerebral small vessel disease.

Automatic normative quantification of brain tissue volume to support the diagnosis of dementia: A clinical evaluation of diagnostic accuracy.

Objectives: To assesses whether automated brain image analysis with quantification of structural brain changes improves diagnostic accuracy in a memory clinic setting. Methods: In 42 memory clinic patients, we evaluated whether automated quantification of brain tissue volumes, hippocampal volume and white matter lesion volume improves diagnostic accuracy for Alzheimer's disease (AD) and frontotemporal dementia (FTD), compared to visual interpretation. Reference data were derived from a dementia-free aging population (n = 4915, aged >45 years), and were expressed as age- and sex-specific percentiles. Experienced radiologists determined the most likely imaging-based diagnosis based on structural brain MRI using three strategies (visual assessment of MRI only, quantitative normative information only, or a combination of both). Diagnostic accuracy of each strategy was calculated with the clinical diagnosis as the reference standard. Results: Providing radiologists with only quantitative data decreased diagnostic accuracy both for AD and FTD compared to conventional visual rating. The combination of quantitative with visual information, however, led to better diagnostic accuracy compared to only visual ratings for AD. This was not the case for FTD. Conclusion: Quantitative assessment of structural brain MRI combined with a reference standard in addition to standard visual assessment may improve diagnostic accuracy in a memory clinic setting.