The scans of people with Alzheimer's disease showed the greatest overall difference from the scans of individuals of the same age without neurological diseases, closely followed by scans from ...patients with mild cognitive impairment or schizophrenia. The large sample size and the virtually complete age range of Bethlehem and colleagues’ study could ultimately inform clinical practice for patients outside of the memory clinic setting with more accurate estimations of brain volumetrics against normative reference data. ...the work by Bethlehem and colleagues has brought us one important step closer to understanding what is healthy in structural brain development and brain ageing, which in time will help us to better recognise what is unhealthy.
The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially ...hampers the application of otherwise successful supervised-learning techniques which, in order to perform well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two magnetic resonance imaging brain-segmentation tasks with multi-site data: white matter, gray matter, and cerebrospinal fluid segmentation; and white-matter-/MS-lesion segmentation. The experiments showed that when there is only a small amount of representative training data available, transfer learning can greatly outperform common supervised-learning approaches, minimizing classification errors by up to 60%.
Background and purpose
This study was undertaken to compare risk factors, neuroimaging characteristics and prognosis between two clinical prodromes of dementia, namely, the motoric cognitive risk ...syndrome (MCRS) and mild cognitive impairment (MCI).
Methods
Between 2009 and 2015, dementia‐free participants of the population‐based Rotterdam Study were classified with a dementia prodrome if they had subjective cognitive complaints and scored >1 SD below the population mean of gait speed (MCRS) or >1.5 SD below the population mean of cognitive test scores (MCI). Using multinomial logistic regression models, we determined cross‐sectional associations of risk factors and structural neuroimaging markers with MCRS and MCI, followed by subdistribution hazard models, to determine risk of incident dementia until 2016.
Results
Of 3025 included participants (mean age = 70.4 years, 54.7% women), 231 had MCRS (7.6%), 132 had MCI (4.4%), and 62 (2.0%) fulfilled criteria for both. Although many risk factors were shared, a higher body mass index predisposed to MCRS, whereas male sex and hypercholesterolemia were associated with MCI only. Gray matter volumes, hippocampal volumes, white matter hyperintensities, and structural white matter integrity were worse in both MCRS and MCI. During a mean follow‐up of 3.9 years, 71 individuals developed dementia and 200 died. Five‐year cumulative risk of dementia was 7.0% (2.5%–11.5%) for individuals with MCRS, versus 13.3% (5.8%–20.8%) with MCI and only 2.3% (1.5%–3.1%) in unaffected individuals.
Conclusions
MCRS is associated with imaging markers of neurodegeneration and risk of dementia, even in the absence of MCI, highlighting the potential of motor function assessment in early risk stratification for dementia.
Individuals with motoric cognitive risk syndrome are at increased risk of dementia and mortality in the absence of mild cognitive impairment. Risk factors and neuroimaging markers were similar between the two dementia prodromes.
Background and Purpose- Blood pressure (BP) variability may increase the risk of stroke and dementia. It remains inconclusive whether BP variability is associated with cerebral small vessel disease, ...a common and potentially devastating subclinical disease that contributes significantly to both stroke and dementia. Methods- A systematic review and meta-analysis of prospective cohort studies that examined the association between BP variability and the presence or progression of established markers of cerebral small vessel disease, including white matter hyperintensities, lacunes, and microbleeds on magnetic resonance imaging. We searched MEDLINE, EMBASE, and Web of Science. Ten studies met the criteria for qualitative synthesis and 7 could be included in the meta-analysis. Data were synthetized using random-effect models. Results- These studies included a total of 2796 individuals aged 74 (mean) ±4 (SD) years, with a median follow-up of 4.0 years. A one SD increase in systolic BP variability was associated with increased odds of the presence or progression of white matter hyperintensities (odds ratio, 1.26 95% CI, 1.06-1.50). The association of systolic BP variability with the presence of lacunes (odds ratio, 0.93 95% CI, 0.74-1.16) and the presence of microbleeds (odds ratio, 1.13 95% CI, 0.89-1.44) were not statistically significant. Conclusions- A larger BP variability may be associated with a higher risk of having a higher burden of white matter hyperintensities. Targeting large BP variability has the potential to prevent cerebral small vessel disease and thereby reducing the risk of stroke and dementia. The potential issue of reverse causation and the heterogeneity in the assessment of cerebral small vessel disease markers should be better addressed in future studies.
Increasing evidence suggests that atherosclerotic plaque composition rather than plaque size is linked to ischemic cardiovascular events, yet largescale population-based data in asymptomatic ...individuals remain scarce.
This study sought to investigate carotid plaque composition in relation to incident stroke and coronary heart disease (CHD) in a population-based setting.
Between 2007 and 2012, 1,349 persons (mean age 72 years, 49.5% women) from the population-based Rotterdam Study who were free from a history of stroke or CHD, in whom carotid ultrasonography showed subclinical atherosclerosis, and who underwent high-resolution magnetic resonance imaging of the carotid arteries to assess plaque characteristics. These included the presence of specific plaque components (intraplaque hemorrhage IPH, lipid-rich necrotic core, and calcification), and measures of plaque size (maximum plaque thickness and presence of stenosis of more than 30%). Individuals were continuously followed for the occurrence of stroke or CHD until January 1, 2015. The authors used Cox regression models to assess the association of the plaque characteristics with the incidence of stroke and CHD, with adjustments for age, sex, and cardiovascular risk factors.
During a median of 5.1 years’ follow-up for stroke and 4.8 years for CHD, 51 individuals had a stroke and 83 developed CHD. Independent of maximum plaque thickness and cardiovascular risk factors, the presence of IPH was associated with incident stroke and CHD (fully adjusted hazard ratio: 2.42 95% confidence interval: 1.30 to 4.50, and 1.95 95% confidence interval: 1.20 to 3.14). Presence of a lipid-rich necrotic core and calcification were not associated with stroke or CHD.
The presence of IPH in the carotid atherosclerotic plaque is an independent risk factor for stroke and CHD. These findings indicate the promise of IPH as a marker of plaque vulnerability in healthy persons with subclinical atherosclerosis.
Display omitted
Cerebral hypoperfusion has previously been associated with mild cognitive impairment and dementia in various cross-sectional studies, but whether hypoperfusion precedes neurodegeneration is unknown. ...We prospectively determined the association of cerebral perfusion with subsequent cognitive decline and development of dementia.
Between 2005 and 2012, we measured cerebral blood flow by 2-dimensional phase-contrast magnetic resonance imaging in participants of the population-based Rotterdam Study without dementia. We determined the association of cerebral perfusion (mL/100mL/min) with risk of dementia (until 2015) using a Cox model, adjusting for age, sex, demographics, cardiovascular risk factors, and apolipoprotein E genotype. We repeated analyses for Alzheimer disease and accounting for stroke. We used linear regression to determine change in cognitive performance during 2 consecutive examination rounds in relation to perfusion. Finally, we investigated whether associations were modified by baseline severity of white matter hyperintensities.
Of 4759 participants (median age 61.3 years, 55.2% women) with a median follow-up of 6.9 years, 123 participants developed dementia (97 Alzheimer disease). Lower cerebral perfusion was associated with higher risk of dementia (adjusted hazard ratio, 1.31; 95% confidence interval per standard deviation decrease, 1.07-1.61), similar for Alzheimer disease only, and unaltered by accounting for stroke. Risk of dementia with hypoperfusion was higher with increasing severity of white matter hyperintensities (with severe white matter hyperintensities; hazard ratio, 1.54; 95% confidence interval, 1.11-2.14). At cognitive reexamination after on average 5.7 years, lower baseline perfusion was associated with accelerated decline in cognition (global cognition: β=-0.029,
=0.003), which was similar after excluding those with incident dementia, and again most profound in individuals with higher volume of white matter hyperintensities (
value for interaction=0.019).
Cerebral hypoperfusion is associated with accelerated cognitive decline and an increased risk of dementia in the general population.
Cortical superficial siderosis describes a distinct pattern of blood-breakdown product deposition limited to cortical sulci over the convexities of the cerebral hemispheres, sparing the brainstem, ...cerebellum and spinal cord. Although cortical superficial siderosis has many possible causes, it is emerging as a key feature of cerebral amyloid angiopathy, a common and important age-related cerebral small vessel disorder leading to intracerebral haemorrhage and dementia. In cerebral amyloid angiopathy cohorts, cortical superficial siderosis is associated with characteristic clinical symptoms, including transient focal neurological episodes; preliminary data also suggest an association with a high risk of future intracerebral haemorrhage, with potential implications for antithrombotic treatment decisions. Thus, cortical superficial siderosis is of relevance to neurologists working in neurovascular, memory and epilepsy clinics, and neurovascular emergency services, emphasizing the need for appropriate blood-sensitive magnetic resonance sequences to be routinely acquired in these clinical settings. In this review we focus on recent developments in neuroimaging and detection, aetiology, prevalence, pathophysiology and clinical significance of cortical superficial siderosis, with a particular emphasis on cerebral amyloid angiopathy. We also highlight important areas for future investigation and propose standards for evaluating cortical superficial siderosis in research studies.
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.
With aging, the brain undergoes several structural changes. These changes reflect the normal aging process and are therefore not necessarily pathologic. In fact, better understanding of these normal ...changes is an important cornerstone to also disentangle pathologic changes. Several studies have investigated normal brain aging, both cross-sectional and longitudinal, and focused on a broad range of magnetic resonance imaging (MRI) markers. This study aims to comprise the different aspects in brain aging, by performing a comprehensive longitudinal assessment of brain aging, providing trajectories of volumetric (global and lobar; subcortical and cortical), microstructural, and focal (presence of microbleeds, lacunar or cortical infarcts) brain imaging markers in aging and the sequence in which these markers change in aging. Trajectories were calculated on 10,755 MRI scans that were acquired between 2005 and 2016 among 5286 persons aged 45 years and older from the population-based Rotterdam Study. The average number of MRI scans per participant was 2 scans (ranging from 1 to 4 scans), with a mean interval between MRI scans of 3.3 years (ranging from 0.2 to 9.5 years) and an average follow-up time of 5.2 years (ranging from 0.3 to 9.8 years). We found that trajectories of the different volumetric, microstructural, and focal markers show nonlinear curves, with accelerating change with advancing age. We found earlier acceleration of change in global and lobar volumetric and microstructural markers in men compared with women. For subcortical and cortical volumes, results show a mix of more linear and nonlinear trajectories, either increasing, decreasing, or stable over age for the subcortical and cortical volume and thickness. Differences between men and women are visible in several parcellations; however, the direction of these differences is mixed. The presence of focal markers show a nonlinear increase with age, with men having a higher probability for cortical or lacunar infarcts. The data presented in this study provide insight into the normal aging process in the brain, and its variability.
PDF
