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Background: Children with trigonocephaly are at risk for neurodevelopmental disorders. The aim of this study is to investigate white matter properties of the frontal lobes in young, unoperated patients with metopic synostosis as compared to healthy controls using diffusion tension imaging (DTI). Methods: Preoperative DTI data sets of 46 patients with trigonocephaly with a median age of 0.49 (interquartile range: 0.38) years were compared with 21 controls with a median age of 1.44 (0.98) years. White matter metrics of the tracts in the frontal lobe were calculated using FMRIB Software Library (FSL). The mean value of tract-specific fractional anisotropy (FA) and mean diffusivity (MD) were estimated for each subject and compared to healthy controls. By linear regression, FA and MD values per tract were assessed by trigonocephaly, sex, and age. Results: The mean FA and MD values in the frontal lobe tracts of untreated trigonocephaly patients, younger than 3 years, were not significantly different in comparison to controls, where age showed to be a significant associated factor. Conclusions: Microstructural parameters of white matter tracts of the frontal lobe of patients with trigonocephaly are comparable to those of controls aged 0-3 years.

© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/ by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com, Several CSF and blood biomarkers for genetic frontotemporal dementia have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain and phosphorylated neurofilament heavy chain), synapse dysfunction [neuronal pentraxin 2 (NPTX2)], astrogliosis (glial fibrillary acidic protein) and complement activation (C1q, C3b). Determining the sequence in which biomarkers become abnormal over the course of disease could facilitate disease staging and help identify mutation carriers with prodromal or early-stage frontotemporal dementia, which is especially important as pharmaceutical trials emerge. We aimed to model the sequence of biomarker abnormalities in presymptomatic and symptomatic genetic frontotemporal dementia using cross-sectional data from the Genetic Frontotemporal dementia Initiative (GENFI), a longitudinal cohort study. Two-hundred and seventy-five presymptomatic and 127 symptomatic carriers of mutations in GRN, C9orf72 or MAPT, as well as 247 non-carriers, were selected from the GENFI cohort based on availability of one or more of the aforementioned biomarkers. Nine presymptomatic carriers developed symptoms within 18 months of sample collection ('converters'). Sequences of biomarker abnormalities were modelled for the entire group using discriminative event-based modelling (DEBM) and for each genetic subgroup using co-initialized DEBM. These models estimate probabilistic biomarker abnormalities in a data-driven way and do not rely on previous diagnostic information or biomarker cut-off points. Using cross-validation, subjects were subsequently assigned a disease stage based on their position along the disease progression timeline. CSF NPTX2 was the first biomarker to become abnormal, followed by blood and CSF neurofilament light chain, blood phosphorylated neurofilament heavy chain, blood glial fibrillary acidic protein and finally CSF C3b and C1q. Biomarker orderings did not differ significantly between genetic subgroups, but more uncertainty was noted in the C9orf72 and MAPT groups than for GRN. Estimated disease stages could distinguish symptomatic from presymptomatic carriers and non-carriers with areas under the curve of 0.84 (95% confidence interval 0.80-0.89) and 0.90 (0.86-0.94) respectively. The areas under the curve to distinguish converters from non-converting presymptomatic carriers was 0.85 (0.75-0.95). Our data-driven model of genetic frontotemporal dementia revealed that NPTX2 and neurofilament light chain are the earliest to change among the selected biomarkers. Further research should investigate their utility as candidate selection tools for pharmaceutical trials. The model's ability to accurately estimate individual disease stages could improve patient stratification and track the efficacy of therapeutic interventions., This study was supported in the Netherlands by two Memorabel grants from Deltaplan Dementie (The Netherlands Organisation for Health Research and Development and Alzheimer Nederland; grant numbers 733050813,733050103 and 733050513), the Bluefield Project to Cure Frontotemporal Dementia, the Dioraphte foundation (grant number 1402 1300), the European Joint Programme—Neurodegenerative Disease Research and the Netherlands Organisation for Health Research and Development (PreFrontALS: 733051042, RiMod-FTD: 733051024); V.V. and S.K. have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 666992 (EuroPOND). E.B. was supported by the Hartstichting (PPP Allowance, 2018B011); in Belgium by the Mady Browaeys Fonds voor Onderzoek naar Frontotemporale Degeneratie; in the UK by the MRC UK GENFI grant (MR/M023664/1); J.D.R. is supported by an MRC Clinician Scientist Fellowship (MR/M008525/1) and has received funding from the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH); I.J.S. is supported by the Alzheimer’s Association; J.B.R. is supported by the Wellcome Trust (103838); in Spain by the Fundació Marató de TV3 (20143810 to R.S.V.); in Germany by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy—ID 390857198) and by grant 779357 ‘Solve-RD’ from the Horizon 2020 Research and Innovation Programme (to MS); in Sweden by grants from the Swedish FTD Initiative funded by the Schörling Foundation, grants from JPND PreFrontALS Swedish Research Council (VR) 529–2014-7504, Swedish Research Council (VR) 2015–02926, Swedish Research Council (VR) 2018–02754, Swedish Brain Foundation, Swedish Alzheimer Foundation, Stockholm County Council ALF, Swedish Demensfonden, Stohnes foundation, Gamla Tjänarinnor, Karolinska Institutet Doctoral Funding and StratNeuro. H.Z. is a Wallenberg Scholar.

Background The prevalence of end-stage renal disease (ESRD) is increasing worldwide, with the majority of new ESRD cases diagnosed in patients >60 years of age. These older patients are at increased risk for impaired cognitive functioning, potentially through cerebral small vessel disease (SVD). Novel markers of vascular integrity may be of clinical value for identifying patients at high risk for cognitive impairment. Methods We aimed to associate the levels of angiopoietin-2 (Ang-2), asymmetric dimethylarginine and a selection of eight circulating angiogenic microRNAs (miRNAs) with SVD and cognitive impairment in older patients reaching ESRD that did not yet initiate renal replacement therapy (n = 129; mean age 75.3 years, mean eGFR 16.4 mL/min). We assessed brain magnetic resonance imaging changes of SVD (white matter hyperintensity volume, microbleeds and the presence of lacunes) and measures of cognition in domains of memory, psychomotor speed and executive function in a neuropsychological test battery. Results Older patients reaching ESRD showed an unfavourable angiogenic profile, as indicated by aberrant levels of Ang-2 and five angiogenic miRNAs (miR-27a, miR-126, miR-132, miR-223 and miR-326), compared with healthy persons and patients with diabetic nephropathy. Moreover, Ang-2 was associated with SVD and with the domains of psychomotor speed and executive function, while miR-223 and miR-29a were associated with memory function. Conclusions Taken together, these novel angiogenic markers might serve to identify older patients with ESRD at risk of cognitive decline, as well as provide insights into the underlying (vascular) pathophysiology.

Introduction: Sporadic Creutzfeldt-Jakob disease (sCJD) comprises multiple subtypes (MM1, MM2, MV1, MV2C, MV2K, VV1, and VV2) with distinct disease durations and spatiotemporal cascades of brain lesions. Our goal was to establish the ante mortem diagnosis of sCJD subtype, based on patient-specific estimates of the spatiotemporal cascade of lesions detected by diffusion-weighted magnetic resonance imaging (DWI). Methods: We included 488 patients with autopsy-confirmed diagnosis of sCJD subtype and 50 patients with exclusion of prion disease. We applied a discriminative event-based model (DEBM) to infer the spatiotemporal cascades of lesions, derived from the DWI scores of 12 brain regions assigned by three neuroradiologists. Based on the DEBM cascades and the prion protein genotype at codon 129, we developed and validated a novel algorithm for the diagnosis of the sCJD subtype. Results: Cascades of MM1, MM2, MV1, MV2C, and VV1 originated in the parietal cortex and, following subtype-specific orderings of propagation, went toward the striatum, thalamus, and cerebellum; conversely, VV2 and MV2K cascades showed a striatum-to-cortex propagation. The proposed algorithm achieved 76.5% balanced accuracy for the sCJD subtype diagnosis, with low rater dependency (differences in accuracy of ± 1% among neuroradiologists). Discussion: Ante mortem diagnosis of sCJD subtype is feasible with this novel data-driven approach, and it may be valuable for patient prognostication, stratification in targeted clinical trials, and future therapeutics. Highlights: Subtype diagnosis of sporadic Creutzfeldt-Jakob disease (sCJD) is achievable with diffusion MRI. Cascades of diffusion MRI abnormalities in the brain are subtype-specific in sCJD. We proposed a diagnostic algorithm based on cascades of diffusion MRI abnormalities and demonstrated that it is accurate. Our method may aid early diagnosis, prognosis, stratification in clinical trials, and future therapeutics. The present approach is applicable to other neurodegenerative diseases, enhancing the differential diagnoses.

BACKGROUND: Transcatheter aortic valve implantation (TAVI) is a minimally invasive, life-saving treatment for patients with severe aortic valve stenosis that improves quality of life. We examined cardiac output and cerebral blood flow in patients undergoing TAVI to test the hypothesis that improved cardiac output after TAVI is associated with an increase in cerebral blood flow. DESIGN: Prospective cohort study. SETTING: European high-volume tertiary multidisciplinary cardiac care. PARTICIPANTS: Thirty-one patients (78.3 ± 4.6 years; 61% female) with severe symptomatic aortic valve stenosis. MEASUREMENTS: Noninvasive prospective assessment of cardiac output (L/min) by inert gas rebreathing and cerebral blood flow of the total gray matter (mL/100 g per min) using arterial spin labeling magnetic resonance imaging in resting state less than 24 hours before TAVI and at 3-month follow-up. Cerebral blood flow change was defined as the difference relative to baseline. RESULTS: On average, cardiac output in patients with severe aortic valve stenosis increased from 4.0 ± 1.1 to 5.4 ± 2.4 L/min after TAVI (P =.003). The increase in cerebral blood flow after TAVI strongly varied between patients (7% ± 24%; P =.41) and related to the increase in cardiac output, with an 8.2% (standard error = 2.3%; P =.003) increase in cerebral blood flow per every additional liter of cardiac output following the TAVI procedure. CONCLUSION: Following TAVI, there was an association of increase in cardiac output with increase in cerebral blood flow. These findings encourage future larger studies to determine the influence of TAVI on cerebral blood flow and cognitive function.

Background: Patients with heart failure (HF) are at risk for vascular brain injury. Cerebral cortical microinfarcts (CMIs) are a novel MRI marker of vascular brain injury. This study aims to determine the occurrence of CMIs in patient with HF and their clinical correlates, including haemodynamic status.Methods: From the Heart-Brain Study, a multicenter prospective cohort study, 154 patients with clinically stable HF without concurrent atrial fibrillation (mean age 69.5 +/- 10.1, 32% female) and 124 reference participants without HF (mean age 65.6 +/- 7.4, 47% females) were evaluated for CMIs on 3 T MRI. CMI presence in HF was tested for associations with vascular risk profile, cardiac function and history, MRI markers of vascular brain injury and cognitive profile.Results: CMI occurrence was higher in patient with HF (17%) than reference participants (7%); after correction for age and sex OR 2.5 [95% CI 1.1-6.0] p=.032; after additional correction for vascular risk factors OR 2.7 [1.0-7.1] p=.052. In patients with HF, CMI presence was associated with office hypertension (OR 2.7 [1.2-6.5] p =.021) and a lower cardiac index (B = -0.29 [-0.55--0.04] p =.023 independent of vascular risk factors), but not with cause or duration of HF. Presence of CMIs was not associated with cognitive performance in patients with HF.Conclusions: CMIs are a common occurrence in patients with HF and related to an adverse vascular risk factor profile and severity of cardiac dysfunction. CMIs thus represent a novel marker of vascular brain injury in these patients. (c) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Objectives This study explored group-wise quantitative measures of tract-specific white matter (WM) microstructure and functional default mode network (DMN) connectivity to establish an initial indication of their clinical applicability for early-stage and follow-up differential diagnosis of Alzheimer’s disease (AD) and behavioural variant frontotemporal dementia (bvFTD). Methods Eleven AD and 12 bvFTD early-stage patients and 18 controls underwent diffusion tensor imaging and resting state functional magnetic resonance imaging at 3 T. All AD and 6 bvFTD patients underwent the same protocol at 1-year follow-up. Functional connectivity measures of DMN and WM tract-specific diffusivity measures were determined for all groups. Exploratory analyses were performed to compare all measures between the three groups at baseline and between patients at follow-up. Additionally, the difference between baseline and follow-up diffusivity measures in AD and bvFTD patients was compared. Results Functional connectivity of the DMN was not different between groups at baseline and at follow-up. Diffusion abnormalities were observed widely in bvFTD and regionally in the hippocampal cingulum in AD. The extent of the differences between bvFTD and AD was diminished at follow-up, yet abnormalities were still more pronounced in bvFTD. The rate of change was similar in bvFTD and AD. Conclusions This study provides a tentative indication that quantitative tract-specific microstructural WM abnormalities, but not quantitative functional connectivity of the DMN, may aid early-stage and follow-up differential diagnosis of bvFTD and AD. Specifically, pronounced microstructural changes in anterior WM tracts may characterise bvFTD, whereas microstructural abnormalities of the hippocampal cingulum may characterise AD. Key Points • The clinical applicability of quantitative brain imaging measures for early-stage and follow-up differential diagnosis of dementia subtypes was explored using a group-wise approach. • Quantitative tract-specific microstructural white matter abnormalities, but not quantitative functional connectivity of the default mode network, may aid early-stage and follow-up differential diagnosis of behavioural variant frontotemporal dementia and Alzheimer’s disease. • Pronounced microstructural white matter (WM) changes in anterior WM tracts characterise behavioural variant frontotemporal dementia, whereas microstructural WM abnormalities of the hippocampal cingulum in the absence of other WM changes characterise Alzheimer’s disease. Electronic supplementary material The online version of this article (10.1007/s00330-019-06061-7) contains supplementary material, which is available to authorized users.

Introduction: Nonfocal transient neurological attacks (TNAs) are associated with an increased risk of cardiac events, stroke and dementia. Their etiology is still unknown. Global cerebral hypoperfusion has been suggested to play a role in their etiology, but this has not been investigated. We assessed whether lower total brain perfusion is associated with a higher occurrence of TNAs. Methods: Between 2015 and 2018, patients with heart failure were included in the Heart Brain Connection study. Patients underwent brain magnetic resonance imaging, including quantitative magnetic resonance angiography (QMRA) to measure cerebral blood flow (CBF). We calculated total brain perfusion of each participant by dividing total CBF by brain volume. Patients were interviewed with a standardized questionnaire on the occurrence of TNAs by physicians who were blinded to QMRA flow status. We assessed the relation between total brain perfusion and the occurrence of TNAs with Poisson regression analysis. Results: Of 136 patients (mean age 70 years, 68% men), 29 (21%) experienced ≥1 TNAs. Nonrotatory dizziness was the most common subtype of TNA. Patients with TNAs were more often female and more often had angina pectoris than patients without TNAs, but total CBF and total brain perfusion were not different between both groups. Total brain perfusion was not associated with the occurrence of TNAs (adjusted risk ratio 1.12, 95% CI 0.88–1.42). Conclusion: We found no association between total brain perfusion and the occurrence of TNAs in patients with heart failure.

Background: Emerging evidence shows sex differences in manifestations of vascular brain injury in memory clinic patients. We hypothesize that this is explained by sex differences in cardiovascular function. Objective: To assess the relation between sex and manifestations of vascular brain injury in patients with cognitive complaints, in interaction with cardiovascular function. Methods: 160 outpatient clinic patients (68.8±8.5 years, 38% female) with cognitive complaints and vascular brain injury from the Heart-Brain Connection study underwent a standardized work-up, including heart-brain MRI. We calculated sex differences in vascular brain injury (lacunar infarcts, non-lacunar infarcts, white matter hyperintensities [WMHs], and microbleeds) and cardiovascular function (arterial stiffness, cardiac index, left ventricular [LV] mass index, LV mass-to-volume ratio and cerebral blood flow). In separate regression models, we analyzed the interaction effect between sex and cardiovascular function markers on manifestations of vascular brain injury with interaction terms (sex*cardiovascular function marker). Results: Males had more infarcts, whereas females tended to have larger WMH-volumes. Males had higher LV mass indexes and LV mass-to-volume ratios and lower CBF values compared to females. Yet, we found no interaction effect between sex and individual cardiovascular function markers in relation to the different manifestations of vascular brain injury (p-values interaction terms > 0.05). Conclusion: Manifestations of vascular brain injury in patients with cognitive complaints differed by sex. There was no interaction between sex and cardiovascular function, warranting further studies to explain the observed sex differences in injury patterns.

This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this method concurrently learns a multi-class segmentation and nonlinear registration. Segmentation and registration are modeled using a convolutional neural network and optimized simultaneously for their mutual benefit. An objective function that optimizes spatial correspondence for the segmented structures across time-points is proposed. We applied Segis-Net to the analysis of white matter tracts from N=8045 longitudinal brain MRI datasets of 3249 elderly individuals. Segis-Net approach showed a significant increase in registration accuracy, spatio-temporal segmentation consistency, and reproducibility comparing with two multistage pipelines. This also led to a significant reduction in the sample-size that would be required to achieve the same statistical power in analyzing tract-specific measures. Thus, we expect that Segis-Net can serve as a new reliable tool to support longitudinal imaging studies to investigate macro- and microstructural brain changes over time., To appear in NeuroImage

Machine learning methods exploiting multi-parametric biomarkers, especially based on neuroimaging, have huge potential to improve early diagnosis of dementia and to predict which individuals are at-risk of developing dementia. To benchmark algorithms in the field of machine learning and neuroimaging in dementia and assess their potential for use in clinical practice and clinical trials, seven grand challenges have been organized in the last decade. The seven grand challenges addressed questions related to screening, clinical status estimation, prediction and monitoring in (pre-clinical) dementia. There was little overlap in clinical questions, tasks and performance metrics. Whereas this aids providing insight on a broad range of questions, it also limits the validation of results across challenges. The validation process itself was mostly comparable between challenges, using similar methods for ensuring objective comparison, uncertainty estimation and statistical testing. In general, winning algorithms performed rigorous data preprocessing and combined a wide range of input features. Despite high state-of-the-art performances, most of the methods evaluated by the challenges are not clinically used. To increase impact, future challenges could pay more attention to statistical analysis of which factors relate to higher performance, to clinical questions beyond Alzheimer's disease, and to using testing data beyond the Alzheimer's Disease Neuroimaging Initiative. Grand challenges would be an ideal venue for assessing the generalizability of algorithm performance to unseen data of other cohorts. Key for increasing impact in this way are larger testing data sizes, which could be reached by sharing algorithms rather than data to exploit data that cannot be shared., Comment: 12 pages, 4 tables

This work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI). We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive preprocessing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross validation in the Alzheimer's Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia. AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924-0.955) and CNN (0.933; 95%CI: 0.918-0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p < 0.01 for McNemar's test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855-0.932) and CNN (0.876; 95%CI: 0.836-0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p = 0.01). Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice.

Background Cognitive impairment in heart failure (HF) interferes with the capacity to self-care and is associated with adverse health outcomes. This underlines the importance of detecting the extent and nature of cognitive impairment in HF. We report on the presence and mutual association of neuroimaging markers and cognitive impairment in patients with HF. Method We included 147 patients with HF (69±10yrs; 32%F; MMSE 29±1) and 121 reference participants (66±8yrs; 46%F; MMSE 29±1) from the Dutch multicenter Heart-Brain study. Brain MRI scans were rated for (lacunar) infarcts and microbleeds. Total grey and white matter volume, hippocampal volume and white matter hyperintensity (WMH) volume were calculated. We used a standardized neuropsychological test battery to measure cognition and calculated compound z-scores for each cognitive domain. Associations between neuroimaging markers and cognitive functioning were investigated using linear regression analyses, with separate models for each cognitive domain. We adjusted for participant group, age, sex and education. To investigate whether associations differed according to participant group, interaction terms were included in our analyses. Result Patients with HF had lower total grey matter volume and more vascular brain injury compared to the reference group, including WMH (median (interquartile range) 1.7 (40) versus 0.6 (1.8), p Conclusion Patients with HF exhibit cognitive deficits more pronounced in the domains of memory and attention/psychomotor speed. Grey matter atrophy, but not vascular brain injury seems to be related to cognitive impairment in HF. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Cardiovasculair Onderzoek Nederland (CVON)

Alzheimer's disease (AD) is the most common form of dementia and is phenotypically heterogeneous. APOE is a triallelic gene which correlates with phenotypic heterogeneity in AD. In this work, we determined the effect of APOE alleles on the disease progression timeline of AD using a discriminative event-based model (DEBM). Since DEBM is a data-driven model, stratification into smaller disease subgroups would lead to more inaccurate models as compared to fitting the model on the entire dataset. Hence our secondary aim is to propose and evaluate novel approaches in which we split the different steps of DEBM into group-aspecific and group-specific parts, where the entire dataset is used to train the group-aspecific parts and only the data from a specific group is used to train the group-specific parts of the DEBM. We performed simulation experiments to benchmark the accuracy of the proposed approaches and to select the optimal approach. Subsequently, the chosen approach was applied to the baseline data of 417 cognitively normal, 235 mild cognitively impaired who convert to AD within 3 years, and 342 AD patients from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset to gain new insights into the effect of APOE carriership on the disease progression timeline of AD. In the ε4 carrier group, the model predicted with high confidence that CSF Amyloidβ42 and the cognitive score of Alzheimer's Disease Assessment Scale (ADAS) are early biomarkers. Hippocampus was the earliest volumetric biomarker to become abnormal, closely followed by the CSF Phosphorylated Tau181 (PTAU) biomarker. In the homozygous ε3 carrier group, the model predicted a similar ordering among CSF biomarkers. However, the volume of the fusiform gyrus was identified as one of the earliest volumetric biomarker. While the findings in the ε4 carrier and the homozygous ε3 carrier groups fit the current understanding of progression of AD, the finding in the ε2 carrier group did not. The model predicted, with relatively low confidence, CSF Neurogranin as one of the earliest biomarkers along with cognitive score of Mini-Mental State Examination (MMSE). Amyloid β42 was found to become abnormal after PTAU. The presented models could aid understanding of the disease, and in selecting homogeneous group of presymptomatic subjects at-risk of developing symptoms for clinical trials.

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.

Introduction: We examined the role of hemodynamic dysfunction in cognition by relating cerebral blood flow (CBF), measured with arterial spin labeling (ASL), to cognitive functioning, in patients with heart failure (HF), carotid occlusive disease (COD), and patients with cognitive complaints and vascular brain injury on magnetic resonance imaging (MRI; ie, possible vascular cognitive impairment [VCI]).Methods: We included 439 participants (124 HF; 75 COD; 127 possible VCI; 113 reference participants) from the Dutch multi-center Heart-Brain Study. We used pseudo-continuous ASL to estimate whole-brain and regional partial volume-corrected CBF. Neuropsychological tests covered global cognition and four cognitive domains.Results: CBF values were lowest in COD, followed by VCI and HF, compared to reference participants. This did not explain cognitive impairment, as we did not find an association between CBF and cognitive functioning.Discussion: We found that reduced CBF is not the major explanatory factor underlying cognitive impairment in patients with hemodynamic dysfunction along the heart-brain axis.

Data-driven disease progression models have provided important insight into the timeline of brain changes inAD phenotypes. However, their utility in predicting the progression of pre-symptomatic AD in a populationbased setting has not yet been investigated. In this study, we investigated if the disease timelines constructedin a case-controlled setting, with subjects stratified according to APOE status, are generalizable to a populationbased cohort, and if progression along these disease timelines is predictive of AD. Seven volumetric biomarkersderived from structural MRI were considered. We estimated APOE-specific disease timelines of changes in thesebiomarkers using a recently proposed method called co-initialized discriminative event-based modeling (co-initDEBM). This method can also estimate a disease stage for new subjects by calculating their position along thedisease timelines. The model was trained and cross-validated on the Alzheimer’s Disease Neuroimaging Initiative(ADNI) dataset, and tested on the population-based Rotterdam Study (RS) cohort. We compared the diagnosticand prognostic value of the disease stage in the two cohorts. Furthermore, we investigated if the rate of change ofdisease stage in RS participants with longitudinal MRI data was predictive of AD. In ADNI, the estimated diseasetimeslines for 𝜖4 non-carriers and carriers were found to be significantly different from one another (𝑝 < 0.001).The estimate disease stage along the respective timelines distinguished AD subjects from controls with an AUCof 0.83 in both APOE 𝜖4 non-carriers and carriers. In the RS cohort, we obtained an AUC of 0.83 and 0.85 in 𝜖4non-carriers and carriers, respectively. Progression along the disease timelines as estimated by the rate of changeof disease stage showed a significant difference (𝑝 < 0.005) for subjects with pre-symptomatic AD as compared tothe general aging population in RS. It distinguished pre-symptomatic AD subjects with an AUC of 0.81 in APOE𝜖4 non-carriers and 0.88 in carriers, which was better than any individual volumetric biomarker, or its rate ofchange, could achieve. Our results suggest that co-init DEBM trained on case-controlled data is generalizable to apopulation-based cohort setting and that progression along the disease timelines is predictive of the developmentof AD in the general population. We expect that this approach can help to identify at-risk individuals from thegeneral population for targeted clinical trials as well as to provide biomarker based objective assessment in suchtrials.

Background Recent studies suggest that cardiovascular disease and dementia are closely related, which led to the concept of a “heart-brain axis”. Dysfunction in any component of the heart-brain axis could be a risk factor for the development of brain damage and consequently to the development of cognitive impairment. In the Heart-Brain study, we focus on vascular cognitive impairment (VCI), symptomatic carotid occlusive disease (COD) and heart failure (HF) as three extreme phenotypes of haemodynamic dysfunction in different components of the heart-brain axis (i.e. heart – carotids – brain). We compared values of cerebral blood flow (CBF), measured with arterial spin labeling (ASL) between patients with HF, COD and VCI and investigated the association between CBF and cognitive functioning. Methods We included 442 participants (129 VCI; 75 COD; 124 HF; and 114 controls) from the Heart-Brain Study (67±9 yrs; 38% F; MMSE 28±2). We used 3T pseudo-continuous ASL to estimate whole-brain and regional partial volume-corrected CBF. Using a standardized neuropsychological assessment, we measured global cognitive functioning and four cognitive domains. Compound z-scores were constructed for each cognitive domain. We investigated associations using linear regression analyses, adjusted for age, sex, education, center and diagnosis. Subsequently, we stratified for diagnosis. Results Whole-brain and regional CBF values were lowest in patients with COD, followed by VCI and HF, compared to controls. Global cognitive functioning was lowest in patients with VCI, followed by COD and HF, compared to controls. Overall, we found hardly any association between whole-brain or regional CBF values and cognitive functioning (standardized beta [stb] = 0.00–0.10, p>0.05). Subsequent stratification for diagnosis showed no association between whole-brain or regional CBF and cognitive functioning in any participant group. Conclusions Our results suggest that reduced CBF is not the major explanatory factor underlying impaired cognitive functioning in patients with disorders along the heart-brain axis. The predisposition of cognitive impairment in these patients is likely to be driven by other (haemodynamic) mechanisms than CBF. Acknowledgement/Funding We acknowledge the support of the Netherlands CardioVascular Research Initiative: the Dutch Heart Foundation (CVON 2012-06 Heart Brain Connection), Du

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=9752, 1.5T MRI). The proposed method showed good segmentation performance and high reproducibility, i.e., a high spatial agreement (Cohen's kappa, k = 0.72 ~ 0.83) and a low scan-rescan error in tract-specific diffusion measures (e.g., fractional anisotropy: error = 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=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., Comment: Preprint to be published in NeuroImage