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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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.

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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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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

Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, are common in aging, and are considered a reflection of cerebral small vessel disease. As such, assessing the burden of PVS has promise as a brain imaging marker. Visual and manual scoring of PVS is a tedious and observer-dependent task. Automated methods would advance research into the etiology of PVS, could aid to assess what a “normal” burden is in aging, and could evaluate the potential of PVS as a biomarker of cerebral small vessel disease. In this work, we propose and evaluate an automated method to quantify PVS in the midbrain, hippocampi, basal ganglia and centrum semiovale. We also compare associations between (earlier established) determinants of PVS and visual PVS scores versus the automated PVS scores, to verify whether automated PVS scores could replace visual scoring of PVS in epidemiological and clinical studies. Our approach is a deep learning algorithm based on convolutional neural network regression, and is contingent on successful brain structure segmentation. In our work we used FreeSurfer segmentations. We trained and validated our method on T2-contrast MR images acquired from 2115 subjects participating in a population-based study. These scans were visually scored by an expert rater, who counted the number of PVS in each brain region. Agreement between visual and automated scores was found to be excellent for all four regions, with intraclass correlation coefficients (ICCs) between 0.75 and 0.88. These values were higher than the inter-observer agreement of visual scoring (ICCs between 0.62 and 0.80). Scan-rescan reproducibility was high (ICCs between 0.82 and 0.93). The association between 20 determinants of PVS, including aging, and the automated scores were similar to those between the same 20 determinants of PVS and visual scores. We conclude that this method may replace visual scoring and facilitate large epidemiological and clinical studies of PVS.

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