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

High-Dimensional ICA Analysis Detects Within-Network Functional Connectivity Damage of Default-Mode and Sensory-Motor Networks in Alzheimer’s Disease

High-dimensional independent component analysis (ICA), compared to low-dimensional ICA, allows to conduct a detailed parcellation of the resting-state networks. The purpose of this study was to give further insight into functional connectivity (FC) in Alzheimer's disease (AD) using high-dimensional ICA. For this reason, we performed both low- and high-dimensional ICA analyses of resting-state fMRI data of 20 healthy controls and 21 patients with AD, focusing on the primarily altered default-mode network (DMN) and exploring the sensory-motor network. As expected, results obtained at low dimensionality were in line with previous literature. Moreover, high-dimensional results allowed us to observe either the presence of within-network disconnections and FC damage confined to some of the resting-state subnetworks. Due to the higher sensitivity of the high-dimensional ICA analysis, our results suggest that high-dimensional decomposition in subnetworks is very promising to better localize FC alterations in AD and that FC damage is not confined to the DMN

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

ICA-based denoising for ASL perfusion imaging

Arterial Spin Labelling (ASL) imaging derives a perfusion image by tracing the accumulation of magnetically labeled blood water in the brain. As the image generated has an intrinsically low signal to noise ratio (SNR), multiple measurements are routinely acquired and averaged, at a penalty of increased scan duration and opportunity for motion artefact. However, this strategy alone might be ineffective in clinical settings where the time available for acquisition is limited and patient motion are increased. This study investigates the use of an Independent Component Analysis (ICA) approach for denoising ASL data, and its potential for automation.72 ASL datasets (pseudo-continuous ASL; 5 different post-labeling delays: 400, 800, 1200, 1600, 2000 m s; total volumes = 60) were collected from thirty consecutive acute stroke patients. The effects of ICA-based denoising (manual and automated) where compared to two different denoising approaches, aCompCor, a Principal Component-based method, and Enhancement of Automated Blood Flow Estimates (ENABLE), an algorithm based on the removal of corrupted volumes. Multiple metrics were used to assess the changes in the quality of the data following denoising, including changes in cerebral blood flow (CBF) and arterial transit time (ATT), SNR, and repeatability. Additionally, the relationship between SNR and number of repetitions acquired was estimated before and after denoising the data.The use of an ICA-based denoising approach resulted in significantly higher mean CBF and ATT values (p [less than] 0.001), lower CBF and ATT variance (p [less than] 0.001), increased SNR (p [less than] 0.001), and improved repeatability (p [less than] 0.05) when compared to the raw data. The performance of manual and automated ICA-based denoising was comparable. These results went beyond the effects of aCompCor or ENABLE. Following ICA-based denoising, the SNR was higher using only 50% of the ASL-dataset collected than when using the whole raw data.The results show that ICA can be used to separate signal from noise in ASL data, improving the quality of the data collected. In fact, this study suggests that the acquisition time could be reduced by 50% without penalty to data quality, something that merits further study. Independent component classification and regression can be carried out either manually, following simple criteria, or automatically

Effective artifact removal in resting state fMRI data improves detection of DMN functional connectivity alteration in Alzheimer's disease

Artifact removal from resting state fMRI data is an essential step for a better identification of the resting state networks and the evaluation of their functional connectivity (FC), especially in pathological conditions. There is growing interest in the development of cleaning procedures, especially those not requiring external recordings (data-driven), which are able to remove multiple sources of artifacts. It is important that only inter-subject variability due to the artifacts is removed, preserving the between-subject variability of interest\u2014crucial in clinical applications using clinical scanners to discriminate different pathologies and monitor their staging. In Alzheimer's disease (AD) patients, decreased FC is usually observed in the posterior cingulate cortex within the default mode network (DMN), and this is becoming a possible biomarker for AD. The aim of this study was to compare four different data-driven cleaning procedures (regression of motion parameters; regression of motion parameters, mean white matter and cerebrospinal fluid signal; FMRIB's ICA-based Xnoiseifier\u2014FIX\u2014cleanup with soft and aggressive options) on data acquired at 1.5 T. The approaches were compared using data from 20 elderly healthy subjects and 21 AD patients in a mild stage, in terms of their impact on within-group consistency in FC and ability to detect the typical FC alteration of the DMN in AD patients. Despite an increased within-group consistency across subjects after applying any of the cleaning approaches, only after cleaning with FIX the expected DMN FC alteration in AD was detectable. Our study validates the efficacy of artifact removal even in a relatively small clinical population, and supports the importance of cleaning fMRI data for sensitive detection of FC alterations in a clinical environment

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

Intrinsic network activity reflects the ongoing experience of chronic pain

Analyses of intrinsic network activity have been instrumental in revealing cortical processes that are altered in chronic pain patients. In a novel approach, we aimed to elucidate how intrinsic functional networks evolve in regard to the fluctuating intensity of the experience of chronic pain. In a longitudinal study with 156 fMRI sessions, 20 chronic back pain patients and 20 chronic migraine patients were asked to continuously rate the intensity of their endogenous pain. We investigated the relationship between the fluctuation of intrinsic network activity with the time course of subjective pain ratings. For chronic back pain, we found increased cortical network activity for the salience network and a local pontine network, as well as decreased network activity in the anterior and posterior default mode network for higher pain intensities. Higher pain intensities in chronic migraine were accompanied with lower activity in a prefrontal cortical network. By taking the perspective of the individual, we focused on the variability of the subjective perception of pain, which include phases of relatively low pain and phases of relatively high pain. The present design of the assessment of ongoing endogenous pain can be a powerful and promising tool to assess the signature of a patient's endogenous pain encoding

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

Is your style transfer doing anything useful? an investigation into hippocampus segmentation and the role of preprocessing

Brain atrophy assessment in MRI, particularly of the hippocampus, is commonly used to support diagnosis and monitoring of dementia. Consequently, there is a demand for accurate automated hippocampus quantification. Most existing segmentation methods have been developed and validated on research datasets and, therefore, may not be appropriate for clinical MR images and populations, leading to potential gaps between dementia research and clinical practice. In this study, we investigated the performance of segmentation models trained on research data that were style-transferred to resemble clinical scans. Our results highlighted the importance of intensity normalisation methods in MRI segmentation, and their relation to domain shift and style-transfer. We found that whilst normalising intensity based on min and max values, commonly used in generative MR harmonisation methods, may create a need for style transfer, Z-score normalisation effectively maintains style consistency, and optimises performance. Moreover, we show for our datasets spatial augmentations are more beneficial than style harmonisation. Thus, emphasising robust normalisation techniques and spatial augmentation significantly improves MRI hippocampus segmentation