Age-dependent association of white matter abnormality with cognition after TIA or minor stroke

ObjectiveTo investigate if the association between MRI-detectable white matter hyperintensity (WMH) and cognitive status reported in previous studies persists at older ages (>80 years), when some white matter abnormality is almost universally reported in clinical practice.MethodsConsecutive eligible patients from a population-based cohort of all TIA/nondisabling stroke (Oxford Vascular Study) underwent multimodal MRI, including fluid-Attenuated inversion recovery and diffusion-weighted imaging, allowing automated measurement of WMH volume, mean diffusivity (MD), and fractional anisotropy (FA) in normal-Appearing white matter using FSL tools. These measures were related to cognitive status (Montreal Cognitive Assessment) at age 6480 vs >80 years.ResultsOf 566 patients (mean [range] age 66.7 [20-102] years), 107 were aged >80 years. WMH volumes and MD/FA were strongly associated with cognitive status in patients aged 6480 years (all p < 0.001 for WMH, MD, and FA) but not in patients aged >80 years (not significant for WMH, MD, and FA), with age interactions for WMH volume (pinteraction = 0.016) and MD (pinteraction = 0.037). Voxel-wise analyses also showed that lower Montreal Cognitive Assessment scores were associated with frontal WMH in patients 6480 years, but not >80 years.ConclusionMRI markers of white matter damage are strongly related to cognition in patients with TIA/minor stroke at younger ages, but not at age >80 years. Clinicians and patients should not overinterpret the significance of these abnormalities at older ages

Hand classification of fMRI ICA noise components

We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets

The lifetime accumulation of multimorbidity and its influence on dementia risk: a UK Biobank study

The number of people living with dementia worldwide is projected to reach 150 million by 2050, making prevention a crucial priority for health services. The co-occurrence of two or more chronic health conditions, termed multimorbidity, occurs in up to 80% of dementia patients, raising the potential of multimorbidity as an important risk factor for dementia. However, precise understanding of which specific conditions, as well as their age of onset, drive the link between multimorbidity and dementia is unclear. We defined the patterns of accumulation of 46 chronic conditions over their lifetime in 282,712 individuals from the UK Biobank. By grouping individuals based on their life-history of chronic illness, we show here that risk of incident dementia can be stratified by both the type and timing of their accumulated chronic conditions. We identified several distinct clusters of multimorbidity, and their associated risks varied in an age-specific manner. Compared to low multimorbidity, cardiometabolic and neurovascular conditions acquired before 55 years were most strongly associated with dementia. Acquisition of mental health and neurovascular conditions between the ages of 55 and 70 was associated with an over two-fold increase in dementia risk compared to low multimorbidity. The age-dependent role of multimorbidity in predicting dementia risk could be used for early stratification of individuals into high and low risk groups and inform targeted prevention strategies based on a person’s prior history of chronic disease

Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference

White matter hyperintensities (WMH), also known as white matter lesions, are localised white matter areas that appear hyperintense on MRI scans. WMH commonly occur in the ageing population, and are often associated with several factors such as cognitive disorders, cardiovascular risk factors, cerebrovascular and neurodegenerative diseases. Despite the fact that some links between lesion location and parametric factors such as age have already been established, the relationship between voxel-wise spatial distribution of lesions and these factors is not yet well understood. Hence, it would be of clinical importance to model the distribution of lesions at the population-level and quantitatively analyse the effect of various factors on the lesion distribution model. In this work we compare various methods, including our proposed method, to generate voxel-wise distributions of WMH within a population with respect to various factors. Our proposed Bayesian spline method models the spatio-temporal distribution of WMH with respect to a parametric factor of interest, in this case age, within a population. Our probabilistic model takes as input the lesion segmentation binary maps of subjects belonging to various age groups and provides a population-level parametric lesion probability map as output. We used a spline representation to ensure a degree of smoothness in space and the dimension associated with the parameter, and formulated our model using a Bayesian framework. We tested our algorithm output on simulated data and compared our results with those obtained using various existing methods with different levels of algorithmic and computational complexity. We then compared the better performing methods on a real dataset, consisting of 1000 subjects of the UK Biobank, divided in two groups based on hypertension diagnosis. Finally, we applied our method on a clinical dataset of patients with vascular disease. On simulated dataset, the results from our algorithm showed a mean square error (MSE) value of , which was lower than the MSE value reported in the literature, with the advantage of being robust and computationally efficient. In the UK Biobank data, we found that the lesion probabilities are higher for the hypertension group compared to the non-hypertension group and further verified this finding using a statistical t-test. Finally, when applying our method on patients with vascular disease, we observed that the overall probability of lesions is significantly higher in later age groups, which is in line with the current literature

Classifying white matter hyperintensities according to intensity and spatial localisation reveals specific association with cognition

AbstractBackgroundWhite matter hyperintensities (WMH) on T2‐weighted images are imaging biomarkers of brain small vessel disease. When classified according to location (periventricular/deep), they have shown different associations with cognition. WMH can also appear hypointense on T1‐weighted (T1w) images as a possible sign of irreversible tissue damage. We hypothesise that sub‐classifying WMH combining intensity information and spatial localisation may provide better insight into the association with cognition, not detectable for the total WMH burden.MethodWe analysed data from 684 subjects of the Whitehall II imaging sub‐study. A supervised machine learning method (BIANCA) was used to segment WMH. An automatic method based on cluster localisation and image intensity was then applied to classify WMH into 4 categories according to adjacency to the ventricles (periventricular/deep) and appearance on T1w images (either T1w‐hypointense or not) (Figure 1). Derived volumes were entered into a general linear model as predictors of the participants' cognitive scores on neuropsychological tests.ResultPeriventricular T1w‐hypointense WMH were significantly related to worse performance in the trail‐making test A (p = 0.011), digit‐symbol (p = 0.028), and digit‐coding (p = 0.009) tests. When including only the total WMH burden in the model, we could not find any associations between WMH and cognition. Age, gender, years of education, systolic and diastolic blood pressure were used as covarietes in the statistical models.ConclusionSub‐classifying WMH according to both location and appearance on T1w images provided added value compared to total WMH burden alone. These are promising findings for WMH interpretation in the clinical practice and for the development of methods for analysing imaging biomarkers related to cognition

Automated lesion segmentation with BIANCA: Impact of population-level features, classification algorithm and locally adaptive thresholding

White matter hyperintensities (WMH) or white matter lesions exhibit high variability in their characteristics both at population- and subject-level, making their detection a challenging task. Population-level factors such as age, vascular risk factors and neurodegenerative diseases affect lesion load and spatial distribution. At the individual level, WMH vary in contrast, amount and distribution in different white matter regions. In this work, we aimed to improve BIANCA, the FSL tool for WMH segmentation, in order to better deal with these sources of variability. We worked on two stages of BIANCA by improving the lesion probability map estimation (classification stage) and making the lesion probability map thresholding stage automated and adaptive to local lesion probabilities. Firstly, in order to take into account the effect of population-level factors, we included population-level lesion probabilities, modelled with respect to a parametric factor (e.g. age), in the classification stage. Secondly, we tested BIANCA performance when using four alternative classifiers commonly used in the literature with respect to K-nearest neighbour algorithm (currently used for lesion probability map estimation in BIANCA). Finally, we propose LOCally Adaptive Threshold Estimation (LOCATE), a supervised method for determining optimal local thresholds to apply to the estimated lesion probability map, as an alternative option to global thresholding (i.e. applying the same threshold to the entire lesion probability map). For these experiments we used data from a neurodegenerative cohort, a vascular cohort and the cohorts available publicly as a part of a segmentation challenge. We observed that including population-level parametric lesion probabilities with respect to age and using alternative machine learning techniques provided negligible improvement. However, LOCATE provided a substantial improvement in the lesion segmentation performance, when compared to the global thresholding. It allowed to detect more deep lesions and provided better segmentation of periventricular lesion boundaries, despite the differences in the lesion spatial distribution and load across datasets. We further validated LOCATE on a cohort of CADASIL (Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy) patients, a genetic form of cerebral small vessel disease, and healthy controls, showing that LOCATE adapts well to wide variations in lesion load and spatial distribution

BIANCA (Brain Intensity AbNormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities

Reliable quantification of white matter hyperintensities of presumed vascular origin (WMHs) is increasingly needed, given the presence of these MRI findings in patients with several neurological and vascular disorders, as well as in elderly healthy subjects. We present BIANCA (Brain Intensity AbNormality Classification Algorithm), a fully automated, supervised method for WMH detection, based on the k-nearest neighbour (k-NN) algorithm. Relative to previous k-NN based segmentation methods, BIANCA offers different options for weighting the spatial information, local spatial intensity averaging, and different options for the choice of the number and location of the training points. BIANCA is multimodal and highly flexible so that the user can adapt the tool to their protocol and specific needs. We optimised and validated BIANCA on two datasets with different MRI protocols and patient populations (a “predominantly neurodegenerative” and a “predominantly vascular” cohort). BIANCA was first optimised on a subset of images for each dataset in terms of overlap and volumetric agreement with a manually segmented WMH mask. The correlation between the volumes extracted with BIANCA (using the optimised set of options), the volumes extracted from the manual masks and visual ratings showed that BIANCA is a valid alternative to manual segmentation. The optimised set of options was then applied to the whole cohorts and the resulting WMH volume estimates showed good correlations with visual ratings and with age. Finally, we performed a reproducibility test, to evaluate the robustness of BIANCA, and compared BIANCA performance against existing methods. Our findings suggest that BIANCA, which will be freely available as part of the FSL package, is a reliable method for automated WMH segmentation in large cross-sectional cohort studies. © 2016 The Author

Classification and characterization of periventricular and deep white matter hyperintensities on MRI: A study in older adults.

White matter hyperintensities (WMH) are frequently divided into periventricular (PWMH) and deep (DWMH), and the two classes have been associated with different cognitive, microstructural, and clinical correlates. However, although this distinction is widely used in visual ratings scales, how to best anatomically define the two classes is still disputed. In fact, the methods used to define PWMH and DWMH vary significantly between studies, making results difficult to compare. The purpose of this study was twofold: first, to compare four current criteria used to define PWMH and DWMH in a cohort of healthy older adults (mean age: 69.58 ± 5.33 years) by quantifying possible differences in terms of estimated volumes; second, to explore associations between the two WMH sub-classes with cognition, tissue microstructure and cardiovascular risk factors, analysing the impact of different criteria on the specific associations. Our results suggest that the classification criterion used for the definition of PWMH and DWMH should not be considered a major obstacle for the comparison of different studies. We observed that higher PWMH load is associated with reduced cognitive function, higher mean arterial pressure and age. Higher DWMH load is associated with higher body mass index. PWMH have lower fractional anisotropy than DWMH, which also have more heterogeneous microstructure. These findings support the hypothesis that PWMH and DWMH are different entities and that their distinction can provide useful information about healthy and pathological aging processes

Exploring variability in basal ganglia connectivity with functional MRI in healthy aging.

Changes in functional connectivity (FC) measured using resting state fMRI within the basal ganglia network (BGN) have been observed in pathologies with altered neurotransmitter systems and conditions involving motor control and dopaminergic processes. However, less is known about non-disease factors affecting FC in the BGN. The aim of this study was to examine associations of FC within the BGN with dopaminergic processes in healthy older adults. We explored the relationship between FC in the BGN and variables related to demographics, impulsive behavior, self-paced tasks, mood, and motor correlates in 486 participants in the Whitehall-II imaging sub-study using both region-of-interest- and voxel-based approaches. Age was the only correlate of FC in the BGN that was consistently significant with both analyses. The observed adverse effect of aging on FC may relate to alterations of the dopaminergic system, but no unique dopamine-related function seemed to have a link with FC beyond those detectable in and linearly correlated with healthy aging

Automated Detection of Candidate Subjects With Cerebral Microbleeds Using Machine Learning

Cerebral microbleeds (CMBs) appear as small, circular, well defined hypointense lesions of a few mm in size on T2*-weighted gradient recalled echo (T2*-GRE) images and appear enhanced on susceptibility weighted images (SWI). Due to their small size, contrast variations and other mimics (e.g., blood vessels), CMBs are highly challenging to detect automatically. In large datasets (e.g., the UK Biobank dataset), exhaustively labelling CMBs manually is difficult and time consuming. Hence it would be useful to preselect candidate CMB subjects in order to focus on those for manual labelling, which is essential for training and testing automated CMB detection tools on these datasets. In this work, we aim to detect CMB candidate subjects from a larger dataset, UK Biobank, using a machine learning-based, computationally light pipeline. For our evaluation, we used 3 different datasets, with different intensity characteristics, acquired with different scanners. They include the UK Biobank dataset and two clinical datasets with different pathological conditions. We developed and evaluated our pipelines on different types of images, consisting of SWI or GRE images. We also used the UK Biobank dataset to compare our approach with alternative CMB preselection methods using non-imaging factors and/or imaging data. Finally, we evaluated the pipeline's generalisability across datasets. Our method provided subject-level detection accuracy > 80% on all the datasets (within-dataset results), and showed good generalisability across datasets, providing a consistent accuracy of over 80%, even when evaluated across different modalities