BACKGROUND:
Cerebral hypoperfusion is associated with vascular brain injury and neurodegeneration, but their longitudinal relationship is largely unknown, especially in healthy older adults.

METHODS:
We investigated the longitudinal relationship between cerebral hemodynamics and subclinical vascular brain disease in community-dwelling older adults without stroke or dementia at baseline. Participants underwent brain magnetic resonance imaging scans every 3 to 4 years between 2005 and 2016. Cerebral blood flow (CBF) was measured through 2-dimensional phase-contrast magnetic resonance imaging; the cerebrovascular resistance index (CVRi) was defined as the ratio of mean arterial blood pressure to total CBF. Simultaneous progression in subclinical brain disease was evaluated through repeated magnetic resonance imaging assessment of white matter hyperintensities (WMH), cerebral microbleeds, lacune, and brain atrophy. The longitudinal relationship was estimated using generalized estimating equations, with adjustment for age, sex, smoking habits, body mass index, systolic blood pressure (for CBF measures), lipid level, history of diabetes and cardiovascular disease, and the baseline burden of magnetic resonance imaging markers.

RESULTS:
Among 3623 older adults (mean age, 61.4±9.3 years; 54.6% women), large decreases and increases in CBF and increases in CVRi over time were associated with white matter hyperintensity progression. The risk ratios for white matter hyperintensity progression were 1.36 (95% CI, 1.19–1.55) for large decreases in total CBF (lowest quartile), 1.02 (95% CI, 0.91–1.14) for moderate decreases (second quartile), and 1.28 (95% CI, 1.14–1.45) for large increases (highest quartile), compared with stable CBF (third quartile). The corresponding risk ratios for changes in CVRi were 1.13 (95% CI, 1.00–1.30), 1.25 (95% CI, 1.09–1.43), and 1.33 (95% CI, 1.16–1.52) for the second to fourth (versus lowest) quartiles, respectively, showing a dose-response relationship. The changes in CBF also demonstrate a similar U-shaped association with the progression of brain atrophy and incident microbleeds, whereas increases in CVRi were associated with lower microbleed risk.

CONCLUSIONS:
Longitudinal changes in CBF and CVRi may capture distinct pathophysiologies linking cerebral hemodynamics to subclinical brain disease, extending beyond single–time point measurements.@en

Background & Aims: Impaired liver function affects brain health and therefore understanding potential mechanisms for subclinical liver disease is essential. We assessed the liver–brain associations using liver measures with brain imaging markers, and cognitive measures in the general population. Methods: Within the population-based Rotterdam Study, liver serum and imaging measures (ultrasound and transient elastography), metabolic dysfunction-associated fatty liver disease (MAFLD), non-alcoholic fatty liver disease (NAFLD) and fibrosis phenotypes, and brain structure were determined in 3493 non-demented and stroke-free participants in 2009–2014. This resulted in subgroups of n = 3493 for MAFLD (mean age 69 ± 9 years, 56% ♀), n = 2938 for NAFLD (mean age 70 ± 9 years, 56% ♀) and n = 2252 for fibrosis (mean age 65 ± 7 years, 54% ♀). Imaging markers of small vessel disease and neurodegeneration, cerebral blood flow (CBF) and brain perfusion (BP) were acquired from brain MRI (1.5-tesla). General cognitive function was assessed by Mini-Mental State Examination and the g-factor. Multiple linear and logistic regression models were used for liver-brain associations and adjusted for age, sex, intracranial volume, cardiovascular risk factors and alcohol use. Results: Higher gamma-glutamyltransferase (GGT) levels were significantly associated with smaller total brain volume (TBV, standardized mean difference (SMD), −0.02, 95% confidence interval (CI) (−0.03 to −0.01); p = 8.4·10⁻⁴), grey matter volumes, and lower CBF and BP. Liver serum measures were not related to small vessel disease markers, nor to white matter microstructural integrity or general cognition. Participants with ultrasound-based liver steatosis had a higher fractional anisotropy (FA, SMD 0.11, 95% CI (0.04 to 0.17), p = 1.5·10⁻³) and lower CBF and BP. MAFLD and NAFLD phenotypes were associated with alterations in white matter microstructural integrity (NAFLD ~ FA, SMD 0.14, 95% CI (0.07 to 0.22), p = 1.6·10⁻⁴; NAFLD ~ mean diffusivity, SMD −0.12, 95% CI (−0.18 to −0.05), p = 4.7·10⁻⁴) and also with lower CBF and BP (MAFLD ~ CBF, SMD −0.13, 95% CI (−0.20 to −0.06), p = 3.1·10⁻⁴; MAFLD ~ BP, SMD −0.12, 95% CI (−0.20 to −0.05), p = 1.6·10⁻³). Furthermore, fibrosis phenotypes were related to TBV, grey and white matter volumes. Conclusions: Presence of liver steatosis, fibrosis and elevated serum GGT are associated with structural and hemodynamic brain markers in a population-based cross-sectional setting. Understanding the hepatic role in brain changes can target modifiable factors and prevent brain dysfunction.

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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 compared 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.

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Analysis of longitudinal changes in imaging studies often involves both segmentation of structures of interest and registration of multiple timeframes. The accuracy of such analysis could benefit from a tailored framework that jointly optimizes both tasks to fully exploit the information available in the longitudinal data. Most learning- based registration algorithms, including joint optimization approaches, currently suffer from bias due to selection of a fixed reference frame and only support pairwise transformations. We here propose an analytical framework based on an unbiased learning strategy for group-wise registration that simultaneously registers images to the mean space of a group to obtain consistent segmentations. We evaluate the proposed method on longitudinal analysis of a white matter tract in a brain MRI dataset with 2-3 time-points for 3249 individuals, i.e., 8045 images in total. The reproducibility of the method is evaluated on test-retest data from 97 individuals. The results confirm that the implicit reference image is an average of the input image. In addition, the proposed framework leads to consistent segmentations and significantly lower processing bias than that of a pair-wise fixed-reference approach. This processing bias is even smaller than those obtained when translating segmentations by only one voxel, which can be attributed to subtle numerical instabilities and interpolation. Therefore, we postulate that the proposed mean-space learning strategy could be widely applied to learning-based registration tasks. In addition, this group-wise framework introduces a novel way for learning-based longitudinal studies by direct construction of an unbiased within-subject template and allowing reliable and efficient analysis of spatio-temporal imaging biomarkers.

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Background: Identifying persons at risk for cognitive decline may aid in early detection of persons at risk of dementia and to select those that would benefit most from therapeutic or preventive measures for dementia. Objective: In this study we aimed to validate whether cognitive decline in the general population can be predicted with multivariate data using a previously proposed supervised classification method: Disease State Index (DSI). Methods: We included 2,542 participants, non-demented and without mild cognitive impairment at baseline, from the population-based Rotterdam Study (mean age 60.9 ± 9.1 years). Participants with significant global cognitive decline were defined as the 5% of participants with the largest cognitive decline per year. We trained DSI to predict occurrence of significant global cognitive decline using a large variety of baseline features, including magnetic resonance imaging (MRI) features, cardiovascular risk factors, APOE-ε4 allele carriership, gait features, education, and baseline cognitive function as predictors. The prediction performance was assessed as area under the receiver operating characteristic curve (AUC), using 500 repetitions of 2-fold cross-validation experiments, in which (a randomly selected) half of the data was used for training and the other half for testing. Results: A mean AUC (95% confidence interval) for DSI prediction was 0.78 (0.77–0.79) using only age as input feature. When using all available features, a mean AUC of 0.77 (0.75–0.78) was obtained. Without age, and with age-corrected features and feature selection on MRI features, a mean AUC of 0.70 (0.63–0.76) was obtained, showing the potential of other features besides age. Conclusion: The best performance in the prediction of global cognitive decline in the general population by DSI was obtained using only age as input feature. Other features showed potential, but did not improve prediction. Future studies should evaluate whether the performance could be improved by new features, e.g., longitudinal features, and other prediction methods.

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OBJECTIVE: The disconnectivity hypothesis postulates that partial loss of connecting white matter fibers between brain regions contributes to the development of dementia. Using diffusion MRI to quantify global and tract-specific white matter microstructural integrity, we tested this hypothesis in a longitudinal population-based study. METHODS: Global and tract-specific fractional anisotropy (FA) and mean diffusivity (MD) were obtained in 4,415 people without dementia (mean age 63.9 years, 55.0% women) from the prospective population-based Rotterdam Study with brain MRI between 2005 and 2011. We modeled the association of these diffusion measures with risk of dementia (follow-up until 2016) and with changes on repeated cognitive assessment after on average 5.4 years, adjusting for age, sex, education, macrostructural MRI markers, depressive symptoms, cardiovascular risk factors, and APOE genotype. RESULTS: During a median follow-up of 6.8 years, 101 participants had incident dementia, of whom 83 had clinical Alzheimer disease (AD). Lower global values of FA and higher values of MD were associated with an increased risk of dementia (adjusted hazard ratio [95% confidence interval (CI)] per SD increase for MD 1.79 [1.44-2.23] and FA 0.65 [0.52-0.80]). Similarly, lower global values of FA and higher values of MD related to more cognitive decline in people without dementia (difference in global cognition per SD increase in MD [95% CI] was -0.04 [-0.07 to -0.01]). Associations were most profound in the projection, association, and limbic system tracts. CONCLUSIONS: Structural disconnectivity is associated with an increased risk of dementia and more pronounced cognitive decline in the general population.

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Finding automatically multiple lesions in large images is a common problem in medical image analysis. Solving this problem can be challenging if, during optimization, the automated method cannot access information about the location of the lesions nor is given single examples of the lesions. We propose a new weakly supervised detection method using neural networks, that computes attention maps revealing the locations of brain lesions. These attention maps are computed using the last feature maps of a segmentation network optimized only with global image-level labels. The proposed method can generate attention maps at full input resolution without need for interpolation during preprocessing, which allows small lesions to appear in attention maps. For comparison, we modify state-of-the-art methods to compute attention maps for weakly supervised object detection, by using a global regression objective instead of the more conventional classification objective. This regression objective optimizes the number of occurrences of the target object in an image, e.g. the number of brain lesions in a scan, or the number of digits in an image. We study the behavior of the proposed method in MNIST-based detection datasets, and evaluate it for the challenging detection of enlarged perivascular spaces – a type of brain lesion – in a dataset of 2202 3D scans with point-wise annotations in the center of all lesions in four brain regions. In MNIST-based datasets, the proposed method outperforms the other methods. In the brain dataset, the weakly supervised detection methods come close to the human intrarater agreement in each region. The proposed method reaches the best area under the curve in two out of four regions, and has the lowest number of false positive detections in all regions, while its average sensitivity over all regions is similar to that of the other best methods. The proposed method can facilitate epidemiological and clinical studies of enlarged perivascular spaces and help advance research in the etiology of enlarged perivascular spaces and in their relationship with cerebrovascular diseases.

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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, 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 ​= ​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.

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The gap between predicted brain age using magnetic resonance imaging (MRI) and chronological age may serve as a biomarker for early-stage neurodegeneration. However, owing to the lack of large longitudinal studies, it has been challenging to validate this link. We aimed to investigate the utility of such a gap as a risk biomarker for incident dementia using a deep learning approach for predicting brain age based on MRI-derived gray matter (GM). We built a convolutional neural network (CNN) model to predict brain age trained on 3,688 dementia-free participants of the Rotterdam Study (mean age 66 ± 11 y, 55% women). Logistic regressions and Cox proportional hazards were used to assess the association of the age gap with incident dementia, adjusted for age, sex, intracranial volume, GM volume, hippocampal volume, white matter hyperintensities, years of education, and APOE ε4 allele carriership. Additionally, we computed the attention maps, which shows which regions are important for age prediction. Logistic regression and Cox proportional hazard models showed that the age gap was significantly related to incident dementia (odds ratio [OR] = 1.11 and 95% confidence intervals [CI] = 1.05-1.16; hazard ratio [HR] = 1.11, and 95% CI = 1.06-1.15, respectively). Attention maps indicated that GM density around the amygdala and hippocampi primarily drove the age estimation. We showed that the gap between predicted and chronological brain age is a biomarker, complimentary to those that are known, associated with risk of dementia, and could possibly be used for early-stage dementia risk screening.

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Multivariate methods have the potential to better capture complex relationships that may exist between different biological levels. Multiple Factor Analysis (MFA) is one of the most popular methods to obtain factor scores and measures of discrepancy between data sets. However, singular value decomposition in MFA is based on PCA, which is adequate only if the data is normally distributed, linear or stationary. In addition, including strongly correlated variables can overemphasize the contribution of the estimated components. In this work, we introduced a novel method referred as Independent Multifactorial Analysis (ICA-MFA) to derive relevant features from multiscale data. This method is an extended implementation of MFA, where the component value decomposition is based on Independent Component Analysis. In addition, ICA-MFA incorporates a predictive step based on an Independent Component Regression. We evaluated and compared the performance of ICA-MFA with both, the MFA method and traditional univariate analyses, in a simulation study. We showed how ICA-MFA explained up to 10-fold more variance than MFA and univariate methods. We applied the proposed algorithm in a study of 4057 individuals belonging to the population-based Rotterdam Study with available genetic and neuroimaging data, as well as information about executive cognitive functioning. Specifically, we used ICA-MFA to detect relevant genetic features related to structural brain regions, which in turn were involved, in the mechanisms of executive cognitive function. The proposed strategy makes it possible to determine the degree to which the whole set of genetic and/or neuroimaging markers contribute to the variability of the symptomatology jointly, rather than individually. While univariate results and MFA combinations only explained a limited proportion of variance (less than 2%), our method increased the explained variance (10%) and allowed the identification of significant components that maximize the variance explained in the model. The potential application of the ICA-MFA algorithm constitutes an important aspect of integrating multivariate multiscale data, specifically in the field of Neurogenetics.

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Background: It is increasingly recognized that the complex functions of human cognition are not accurately represented by arbitrarily-defined anatomical brain regions. Given the considerable functional specialization within such regions, more fine-grained studies of brain structure could capture such localized associations. However, such analyses/studies in a large community-dwelling population are lacking. Objective: To perform a fine-mapping of cognitive ability to cortical and subcortical grey matter on magnetic resonance imaging (MRI). Methods: In 3,813 stroke-free and non-demented persons from the Rotterdam Study (mean age 69.1 (±8.8) years; 55.8% women) with cognitive assessments and brain MRI, we performed voxel-based morphometry and subcortical shape analysis on global cognition and separate tests that tapped into memory, information processing speed, fine motor speed, and executive function domains. Results: We found that the different cognitive tests significantly associated with grey matter density in differential but also overlapping brain regions, primarily in the left hemisphere. Clusters of significantly associated voxels with global cognition were located within multiple anatomic regions: left amygdala, hippocampus, parietal lobule, superior temporal gyrus, insula and posterior temporal lobe. Subcortical shape analysis revealed associations primarily within the head and tail of the caudate nucleus, putamen, ventral part of the thalamus, and nucleus accumbens, more equally distributed among the left and right hemisphere. Within the caudate nucleus both positive (head) as well as negative (tail) associations were observed with global cognition. Conclusions: In a large population-based sample, we mapped cognitive performance to cortical and subcortical grey matter density using a hypothesis-free approach with high-dimensional neuroimaging. Leveraging the power of our large sample size, we confirmed well-known associations as well as identified novel brain regions related to cognition.

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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.

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Cognition in adults shows variation due to developmental and degenerative components. A recent genomewide association study identified genetic variants for general cognitive function in 148 independent loci. Here, we aimed to elucidate possible developmental and neurodegenerative pathways underlying these genetic variants by relating them to functional, clinical and neuroimaging outcomes. This study was conducted within the population-based Rotterdam Study (N=11,496, mean age 65.3±9.9 years, 58.0% female). We used lead variants for general cognitive function to construct a polygenic score (PGS), and additionally excluded developmental variants at multiple significance thresholds. A higher PGS was related to more years of education (β=0.29, p=4.3×10 ^-7 ) and a larger intracranial volume (β=0.05, p=7.5×10 ^-4 ). To a smaller extent, the PGS was associated with less cognitive decline (βΔG-factor=0.03, p=1.3×10 ^-3 ), which became non-significant after adjusting for education (p=1.6×10 ^-2 ). No associations were found with daily functioning, dementia, parkinsonism, stroke or microstructural white matter integrity. Excluding developmental variants attenuated nearly all associations. In conclusion, this study suggests that the genetic variants identified for general cognitive function are acting mainly through the developmental pathway of cognition. Therefore, cognition, assessed cross-sectionally, seems to have limited value as a biomarker for neurodegeneration.

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Deep learning techniques are often criticized to heavily depend on a large quantity of labeled data. This problem is even more challenging in medical image analysis where the annotator expertise is often scarce. We propose a novel data-augmentation method to regularize neural network regressors that learn from a single global label per image. The principle of the method is to create new samples by recombining existing ones. We demonstrate the performance of our algorithm on two tasks: estimation of the number of enlarged perivascular spaces in the basal ganglia, and estimation of white matter hyperintensities volume. We show that the proposed method improves the performance over more basic data augmentation. The proposed method reached an intraclass correlation coefficient between ground truth and network predictions of 0.73 on the first task and 0.84 on the second task, only using between 25 and 30 scans with a single global label per scan for training. With the same number of training scans, more conventional data augmentation methods could only reach intraclass correlation coefficients of 0.68 on the first task, and 0.79 on the second task.

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Localization of focal vascular lesions on brain MRI is an important component of research on the etiology of neurological disorders. However, manual annotation of lesions can be challenging, time-consuming and subject to observer bias. Automated detection methods often need voxel-wise annotations for training. We propose a novel approach for automated lesion detection that can be trained on scans only annotated with a dot per lesion instead of a full segmentation. From the dot annotations and their corresponding intensity images we compute various distance maps (DMs), indicating the distance to a lesion based on spatial distance, intensity distance, or both. We train a fully convolutional neural network (FCN) to predict these DMs for unseen intensity images. The local optima in the predicted DMs are expected to correspond to lesion locations. We show the potential of this approach to detect enlarged perivascular spaces in white matter on a large brain MRI dataset with an independent test set of 1000 scans. Our method matches the intra-rater performance of the expert rater that was computed on an independent set. We compare the different types of distance maps, showing that incorporating intensity information in the distance maps used to train an FCN greatly improves performance. @en

Hippocampal volume and shape are known magnetic resonance imaging biomarkers of neurodegeneration. Recently, hippocampal texture has been shown to improve prediction of dementia in patients with mild cognitive impairment, but it is unknown whether texture adds prognostic information beyond volume and shape and whether the predictive value extends to cognitively healthy individuals. Using 510 subjects from the Rotterdam Study, a prospective, population-based cohort study, we investigated if hippocampal volume, shape, texture, and their combination were predictive of dementia and determined how predictive performance varied with time to diagnosis and presence of early clinical symptoms of dementia. All features showed significant predictive performance with the area under the receiver operating characteristic curve ranging from 0.700 for texture alone to 0.788 for the combination of volume and texture. Although predictive performance extended to those without objective cognitive complaints or mild cognitive impairment, performance decreased with increasing follow-up time. We conclude that a combination of multiple hippocampal features on magnetic resonance imaging performs better in predicting dementia in the general population than any feature by itself.

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White matter lesions play a role in cognitive decline and dementia. One presumed pathway is through disconnection of functional networks. Little is known about location-specific effects of lesions on functional connectivity. This study examined location-specific effects within anatomically-defined white matter tracts in 1584 participants of the Rotterdam Study, aged 50–95. Tracts were delineated from diffusion magnetic resonance images using probabilistic tractography. Lesions were segmented on fluid-attenuated inversion recovery images. Functional connectivity was defined across each tract on resting-state functional magnetic resonance images by using gray matter parcellations corresponding to the tract ends and calculating the correlation of the mean functional activity between the gray matter regions. A significant relationship between both local and brain-wide lesion load and tract-specific functional connectivity was found in several tracts using linear regressions, also after Bonferroni correction. Indirect connectivity analyses revealed that tract-specific functional connectivity is affected by lesions in several tracts simultaneously. These results suggest that local white matter lesions can decrease tract-specific functional connectivity, both in direct and indirect connections.

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Background: Multiple sclerosis (MS) affects brain structure and cognitive function and has a heritable component. Over a 100 common genetic risk variants have been identified, but most carriers do not develop MS. For other neurodegenerative diseases, risk variants have effects outside patient populations, but this remains uninvestigated for MS. Objectives: To study the effect of MS-associated genetic variants on brain structure and cognitive function in the general population. Methods: We studied middle-aged and elderly individuals (mean age = 65.7 years) from the population-based Rotterdam Study. We determined 107 MS variants and additionally created a risk score combining all variants. Magnetic resonance imaging (N = 4710) was performed to obtain measures of brain macrostructure, white matter microstructure, and gray matter voxel-based morphometry. A cognitive test battery (N = 7556) was used to test a variety of cognitive domains. Results: The MS risk score was associated with smaller gray matter volume over the whole brain (β_standardized = −0.016; p = 0.044), but region-specific analyses did not survive multiple testing correction. Similarly, no significant associations with brain structure were observed for individual variants. For cognition, rs2283792 was significantly associated with poorer memory (β = −0.064; p = 3.4 × 10⁻⁵). Conclusion: Increased genetic susceptibility to MS may affect brain structure and cognition in persons without disease, pointing to a “hidden burden” of MS.

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