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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.

Total hippocampal volume has been consistently linked to cognitive function and dementia. Yet, given its complex and parcellated internal structure, the role of subregions of the hippocampus in cognition and risk of dementia remains relatively underexplored. We studied subregions of the hippocampus in a large population-based cohort to further understand their role in cognitive impairment and dementia risk. We studied 5035 dementia- and stroke-free persons from the Rotterdam Study, aged over 45 years. All participants underwent magnetic resonance imaging (1.5 T) between 2005 and 2015. Automatic segmentation of the hippocampus and 12 of its subregions was performed using the FreeSurfer software (version 6.0). A cognitive test battery was performed, and participants were followed up for the development of dementia until 2015. Associations of hippocampal subregion volumes with cognition and incident dementia were examined using linear and Cox regression models, respectively. All analyses were adjusted for age, sex, education, and total hippocampal volume. Mean age was 64.3 years (SD 10.6) with 56% women. Smaller volumes of the hippocampal fimbria, presubiculum and subiculum showed the strongest associations with poor performance on several cognitive domains, including executive function but not memory. During a mean follow-up of 5.5 years, 76 persons developed dementia. Smaller subiculum volume was associated with risk of dementia adjusted for total volume (hazard ratio per SD decrease in volume: 1.75, 95% confidence interval 1.35; 2.26). In a community-dwelling non-demented population, we describe patterns of association between hippocampal subregions with cognition and risk of dementia. Specifically, the subiculum was associated with both poorer cognition and higher risk of dementia. •Hippocampal subregions were related to many cognitive domains.•The subiculum and pre-subiculum displayed strong relations to executive dysfunction.•Smaller subiculum volume was associated to risk of incident dementia.•The subiculum relation to dementia risk remained stable over the follow-up period.

Applying deep learning in population genomics is challenging because of computational issues and lack of interpretable models. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotypes from genetic variants. In this framework, interpretable and memory-efficient neural network architectures are constructed by embedding biologically knowledge from public databases, resulting in neural networks that contain only biologically plausible connections. We applied the framework to seventeen phenotypes and found well-replicated genes such as HERC2 and OCA2 for hair and eye color, and novel genes such as ZNF773 and PCNT for schizophrenia. Additionally, the framework identified ubiquitin mediated proteolysis, endocrine system and viral infectious diseases as most predictive biological pathways for schizophrenia. GenNet is a freely available, end-to-end deep learning framework that allows researchers to develop and use interpretable neural networks to obtain novel insights into the genetic architecture of complex traits and diseases.van Hilten and colleagues present GenNet, a deep-learning framework for predicting phenotype from genetic data. This framework generates interpretable neural networks that provide insight into the genetic basis of complex traits and diseases.

Background Vascular dementia is a common disorder resulting in considerable morbidity and mortality. Determining the extent to which genes play a role in disease susceptibility and their pathophysiological mechanisms could improve our understanding of vascular dementia, leading to a potential translation of this knowledge to clinical practice. Discussion In this review, we discuss what is currently known about the genetics of vascular dementia. The identification of causal genes remains limited to monogenic forms of the disease, with findings for sporadic vascular dementia being less robust. However, progress in genetic research on associated phenotypes, such as cerebral small vessel disease, Alzheimer’s disease, and stroke, have the potential to inform on the genetics of vascular dementia. We conclude by providing an overview of future developments in the field and how such work could impact patients and clinicians. Conclusion The genetic background of vascular dementia is well established for monogenic disorders, but remains relatively obscure for the sporadic form. More work is needed for providing robust findings that might eventually lead to clinical translation.

The authors identified a protective genetic allele associated with lower PU.1 ( SPI1 ) expression in myeloid cells by conducting a genome-wide scan of Alzheimer's disease (AD). PU.1 binds the promoters of AD-associated genes (e.g., CD33 , MS4A4A & MS4A6A , TYROBP ) and modulates their expression, suggesting it may reduce AD risk by regulating myeloid cell gene expression. A genome-wide survival analysis of 14,406 Alzheimer's disease (AD) cases and 25,849 controls identified eight previously reported AD risk loci and 14 novel loci associated with age at onset. Linkage disequilibrium score regression of 220 cell types implicated the regulation of myeloid gene expression in AD risk. The minor allele of rs1057233 (G), within the previously reported CELF1 AD risk locus, showed association with delayed AD onset and lower expression of SPI1 in monocytes and macrophages. SPI1 encodes PU.1, a transcription factor critical for myeloid cell development and function. AD heritability was enriched within the PU.1 cistrome, implicating a myeloid PU.1 target gene network in AD. Finally, experimentally altered PU.1 levels affected the expression of mouse orthologs of many AD risk genes and the phagocytic activity of mouse microglial cells. Our results suggest that lower SPI1 expression reduces AD risk by regulating myeloid gene expression and cell function.

Background Bisulfite conversion (BC) has been the gold standard in DNA methylation profiling for decades. During this chemical process, non-methylated cytosines are converted into uracils, while methylated cytosines remain intact. Despite its popularity, BC has major drawbacks when used for sensitive applications with low-quality and -quantity DNA samples, such as the required large amount of DNA input, the caused DNA fragmentation and loss, and the resulting reduced sequence complexity. Lately, to account for BC-related disadvantages the first commercial enzymatic conversion (EC) kit was launched. While EC follows the same conversion principle as BC it uses two enzymatic steps instead of one chemical step with BC. In this study, we validated and compared the conversion performance of the most widely used BC and EC kits using a multiplex qPCR assay (qBiCo) we recently developed, which provides several indexes: conversion efficiency, converted DNA recovery and fragmentation. Results Firstly, we implemented and standardized both DNA conversion methods. Secondly, using qBiCo, we performed a developmental validation for both conversion approaches, including testing the following parameters: repeatability, reproducibility, sensitivity and robustness. Regarding conversion efficiency, both methods performed similarly, with the limit of reproducible conversion being 5 ng and 10 ng for BC and EC, respectively. The recovery, however, is structurally overestimated for BC: 2.3 ± 0.7 and 0.7 ± 0.2 for EC. In contrast, degraded DNA input resulted in high fragmentation values after BC and low-medium values for EC (14.4 ± 1.2 and 3.3 ± 0.4, respectively). Finally, we converted 10 ng of 22 genomic DNA samples using both methods. We observed an overestimation of the BC DNA recovery (130%) and a low recovery for EC (40%). Conclusions Our findings indicate that both DNA conversion methods have strengths and weaknesses. BC shows a high recovery, whereas EC does not cause extensive fragmentation that is characteristic to BC. EC is, therefore, more robust to the analysis of degraded DNA such as forensic-type or cell-free DNA, at least for the genomic DNA inputs tested here. We believe that the low recovery of EC could be improved by further optimizing and automating the bead-based cleanup steps. Overall, our study provides the first independent benchmarking of bisulfite- and enzyme-based conversion kits.

The volumes of subcortical brain structures are highly heritable, but genetic underpinnings of their shape remain relatively obscure. Here we determine the relative contribution of genetic factors to individual variation in the shape of seven bilateral subcortical structures: the nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen and thalamus. In 3,686 unrelated individuals aged between 45 and 98 years, brain magnetic resonance imaging and genotyping was performed. The maximal heritability of shape varies from 32.7 to 53.3% across the subcortical structures. Genetic contributions to shape extend beyond influences on intracranial volume and the gross volume of the respective structure. The regional variance in heritability was related to the reliability of the measurements, but could not be accounted for by technical factors only. These findings could be replicated in an independent sample of 1,040 twins. Differences in genetic contributions within a single region reveal the value of refined brain maps to appreciate the genetic complexity of brain structures. The volume of subcortical brain structures is known to be heritable. Here, Roshchupkin and colleagues studied seven different subcortical brain structures in the general population and show that the genetic contributions go beyond these volumetric measurements, and also extend to their shapes.

Albuminuria reflects systemic endothelial dysfunction, but its relationships with subclinical brain abnormalities have not been comprehensively catalogued. The Cardiovascular Health Study recruited older adults from four US communities, beginning in 1989–1990. Systematic measurements of albuminuria were performed in 1996–1997; two brain MRIs, in 1992–1994 and 1997–1999; and serum neurofilament light chain (NfL) measurements from 1996–1997 stored samples. We examined the associations of albuminuria with longitudinal progression of white matter hyperintensities (WMH) and ventricular size, incident infarcts, and cross-sectional quantitative brain volumes and circulating biomarkers of neuronal injury (n = 834–1950). Albuminuria was positively associated with ventricular grade worsening (odds ratio per doubling 1.10, 95% confidence interval (CI) 1.01–1.19) and with circulating NfL levels (2% higher per doubling, 95% CI 1–4%), even after adjustment for vascular risk factors. Albuminuria was also associated with worsening of WMH, incident infarcts, and quantitative WMH and hippocampal volumes, but these latter associations appeared to reflect burden of cardiovascular risk factors. Albuminuria is independently associated with worsening ventricular size and circulating NfL, suggesting a specific role of microvascular dysfunction in brain atrophy. It also reflects cardiovascular risk factor burden on markers of vascular brain injury. These results highlight the diverse associations of albuminuria with common brain abnormalities of aging.