SARS-CoV-2 is associated with changes in brain structure in UK Biobank.

There is strong evidence of brain-related abnormalities in COVID-191-13. However, it remains unknown whether the impact of SARS-CoV-2 infection can be detected in milder cases, and whether this can reveal possible mechanisms contributing to brain pathology. Here we investigated brain changes in 785 participants of UK Biobank (aged 51-81 years) who were imaged twice using magnetic resonance imaging, including 401 cases who tested positive for infection with SARS-CoV-2 between their two scans-with 141 days on average separating their diagnosis and the second scan-as well as 384 controls. The availability of pre-infection imaging data reduces the likelihood of pre-existing risk factors being misinterpreted as disease effects. We identified significant longitudinal effects when comparing the two groups, including (1) a greater reduction in grey matter thickness and tissue contrast in the orbitofrontal cortex and parahippocampal gyrus; (2) greater changes in markers of tissue damage in regions that are functionally connected to the primary olfactory cortex; and (3) a greater reduction in global brain size in the SARS-CoV-2 cases. The participants who were infected with SARS-CoV-2 also showed on average a greater cognitive decline between the two time points. Importantly, these imaging and cognitive longitudinal effects were still observed after excluding the 15 patients who had been hospitalised. These mainly limbic brain imaging results may be the in vivo hallmarks of a degenerative spread of the disease through olfactory pathways, of neuroinflammatory events, or of the loss of sensory input due to anosmia. Whether this deleterious effect can be partially reversed, or whether these effects will persist in the long term, remains to be investigated with additional follow-up.

Normative models for neuroimaging markers: Impact of model selection, sample size and evaluation criteria.

Modelling population reference curves or normative modelling is increasingly used with the advent of large neuroimaging studies. In this paper we assess the performance of fitting methods from the perspective of clinical applications and investigate the influence of the sample size. Further, we evaluate linear and non-linear models for percentile curve estimation and highlight how the bias-variance trade-off manifests in typical neuroimaging data. We created plausible ground truth distributions of hippocampal volumes in the age range of 45 to 80 years, as an example application. Based on these distributions we repeatedly simulated samples for sizes between 50 and 50,000 data points, and for each simulated sample we fitted a range of normative models. We compared the fitted models and their variability across repetitions to the ground truth, with specific focus on the outer percentiles (1st, 5th, 10th) as these are the most clinically relevant. Our results quantify the expected decreasing trend in variance of the volume estimates with increasing sample size. However, bias in the volume estimates only decreases a modest amount, without much improvement at large sample sizes. The uncertainty of model performance is substantial for what would often be considered large samples in a neuroimaging context and rises dramatically at the ends of the age range, where fewer data points exist. Flexible models perform better across sample sizes, especially for non-linear ground truth. Surprisingly large samples of several thousand data points are needed to accurately capture outlying percentiles across the age range for applications in research and clinical settings. Performance evaluation methods should assess both bias and variance. Furthermore, caution is needed when attempting to go near the ends of the age range captured by the source data set and, as is a well known general principle, extrapolation beyond the age range should always be avoided. To help with such evaluations of normative models we have made our code available to guide researchers developing or utilising normative models.

BIANCA-MS: An optimized tool for automated multiple sclerosis lesion segmentation.

In this work we present BIANCA-MS, a novel tool for brain white matter lesion segmentation in multiple sclerosis (MS), able to generalize across both the wide spectrum of MRI acquisition protocols and the heterogeneity of manually labeled data. BIANCA-MS is based on the original version of BIANCA and implements two innovative elements: a harmonized setting, tested under different MRI protocols, which avoids the need to further tune algorithm parameters to each dataset; and a cleaning step developed to improve consistency in automated and manual segmentations, thus reducing unwanted variability in output segmentations and validation data. BIANCA-MS was tested on three datasets, acquired with different MRI protocols. First, we compared BIANCA-MS to other widely used tools. Second, we tested how BIANCA-MS performs in separate datasets. Finally, we evaluated BIANCA-MS performance on a pooled dataset where all MRI data were merged. We calculated the overlap using the DICE spatial similarity index (SI) as well as the number of false positive/negative clusters (nFPC/nFNC) in comparison to the manual masks processed with the cleaning step. BIANCA-MS clearly outperformed other available tools in both high- and low-resolution images and provided comparable performance across different scanning protocols, sets of modalities and image resolutions. BIANCA-MS performance on the pooled dataset (SI: 0.72 ± 0.25, nFPC: 13 ± 11, nFNC: 4 ± 8) were comparable to those achieved on each individual dataset (median across datasets SI: 0.72 ± 0.28, nFPC: 14 ± 11, nFNC: 4 ± 8). Our findings suggest that BIANCA-MS is a robust and accurate approach for automated MS lesion segmentation.

Identifying microstructural changes in diffusion MRI; How to circumvent parameter degeneracy.

Biophysical models that attempt to infer real-world quantities from data usually have many free parameters. This over-parameterisation can result in degeneracies in model inversion and render parameter estimation ill-posed. However, in many applications, we are not interested in quantifying the parameters per se, but rather in identifying changes in parameters between experimental conditions (e.g. patients vs controls). Here we present a Bayesian framework to make inference on changes in the parameters of biophysical models even when model inversion is degenerate, which we refer to as Bayesian EstimatioN of CHange (BENCH). We infer the parameter changes in two steps; First, we train models that can estimate the pattern of change in the measurements given any hypothetical direction of change in the parameters using simulations. Next, for any pair of real data sets, we use these pre-trained models to estimate the probability that an observed difference in the data can be explained by each model of change. BENCH is applicable to any type of data and models and particularly useful for biophysical models with parameter degeneracies, where we can assume the change is sparse. In this paper, we apply the approach in the context of microstructural modelling of diffusion MRI data, where the models are usually over-parameterised and not invertible without injecting strong assumptions. Using simulations, we show that in the context of the standard model of white matter our approach is able to identify changes in microstructural parameters from conventional multi-shell diffusion MRI data. We also apply our approach to a subset of subjects from the UK-Biobank Imaging to identify the dominant standard model parameter change in areas of white matter hyperintensities under the assumption that the standard model holds in white matter hyperintensities.

Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets.

Large scale neuroimaging datasets present the possibility of providing normative distributions for a wide variety of neuroimaging markers, which would vastly improve the clinical utility of these measures. However, a major challenge is our current poor ability to integrate measures across different large-scale datasets, due to inconsistencies in imaging and non-imaging measures across the different protocols and populations. Here we explore the harmonisation of white matter hyperintensity (WMH) measures across two major studies of healthy elderly populations, the Whitehall II imaging sub-study and the UK Biobank. We identify pre-processing strategies that maximise the consistency across datasets and utilise multivariate regression to characterise study sample differences contributing to differences in WMH variations across studies. We also present a parser to harmonise WMH-relevant non-imaging variables across the two datasets. We show that we can provide highly calibrated WMH measures from these datasets with: (1) the inclusion of a number of specific standardised processing steps; and (2) appropriate modelling of sample differences through the alignment of demographic, cognitive and physiological variables. These results open up a wide range of applications for the study of WMHs and other neuroimaging markers across extensive databases of clinical data.

Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging.

The concept of brain maintenance refers to the preservation of brain integrity in older age, while cognitive reserve refers to the capacity to maintain cognition in the presence of neurodegeneration or aging-related brain changes. While both mechanisms are thought to contribute to individual differences in cognitive function among older adults, there is currently no "gold standard" for measuring these constructs. Using machine-learning methods, we estimated brain and cognitive age based on deviations from normative aging patterns in the Whitehall II MRI substudy cohort (N = 537, age range = 60.34-82.76), and tested the degree of correspondence between these constructs, as well as their associations with premorbid IQ, education, and lifestyle trajectories. In line with established literature highlighting IQ as a proxy for cognitive reserve, higher premorbid IQ was linked to lower cognitive age independent of brain age. No strong evidence was found for associations between brain or cognitive age and lifestyle trajectories from midlife to late life based on latent class growth analyses. However, post hoc analyses revealed a relationship between cumulative lifestyle measures and brain age independent of cognitive age. In conclusion, we present a novel approach to characterizing brain and cognitive maintenance in aging, which may be useful for future studies seeking to identify factors that contribute to brain preservation and cognitive reserve mechanisms in older age.

White Matter Hyperintensities Quantification in Healthy Adults: A Systematic Review and Meta-Analysis.

BACKGROUND: Although white matter hyperintensities (WMH) volumetric assessment is now customary in research studies, inconsistent WMH measures among homogenous populations may prevent the clinical usability of this biomarker. PURPOSE: To determine whether a point estimate and reference standard for WMH volume in the healthy aging population could be determined. STUDY TYPE: Systematic review and meta-analysis. POPULATION: In all, 9716 adult subjects from 38 studies reporting WMH volume were retrieved following a systematic search on EMBASE. FIELD STRENGTH/SEQUENCE: 1.0T, 1.5T, or 3.0T/fluid-attenuated inversion recovery (FLAIR) and/or proton density/T2 -weighted fast spin echo sequences or gradient echo T1 -weighted sequences. ASSESSMENT: After a literature search, sample size, demographics, magnetic field strength, MRI sequences, level of automation in WMH assessment, study population, and WMH volume were extracted. STATISTICAL TESTS: The pooled WMH volume with 95% confidence interval (CI) was calculated using the random-effect model. The I2 statistic was calculated as a measure of heterogeneity across studies. Meta-regression analysis of WMH volume on age was performed. RESULTS: Of the 38 studies analyzed, 17 reported WMH volume as the mean and standard deviation (SD) and were included in the meta-analysis. Mean and SD of age was 66.11 ± 10.92 years (percentage of men 50.45% ± 21.48%). Heterogeneity was very high (I2  = 99%). The pooled WMH volume was 4.70 cm3 (95% CI: 3.88-5.53 cm3 ). At meta-regression analysis, WMH volume was positively associated with subjects' age (ß = 0.358 cm3 per year, P < 0.05, R2  = 0.27). DATA CONCLUSION: The lack of standardization in the definition of WMH together with the high technical variability in assessment may explain a large component of the observed heterogeneity. Currently, volumes of WMH in healthy subjects are not comparable between studies and an estimate and reference interval could not be determined. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 1.

Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities.

BACKGROUND AND PURPOSE: Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings. METHODS: Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC. RESULTS: In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 (NBEAL), 10q23.1 (TSPAN14/FAM231A), and 10q24.33 (SH3PXD2A). In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 (NOS3) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype. CONCLUSIONS: Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.

Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study.

Brain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II (WHII) MRI cohort using machine learning and imaging-derived measures of gray matter (GM) morphology, white matter microstructure (WM), and resting state functional connectivity (FC). The results showed that the prediction accuracy improved when multiple imaging modalities were included in the model (R2 = 0.30, 95% CI [0.24, 0.36]). The modality-specific GM and WM models showed similar performance (R2 = 0.22 [0.16, 0.27] and R2 = 0.24 [0.18, 0.30], respectively), while the FC model showed the lowest prediction accuracy (R2 = 0.002 [-0.005, 0.008]), indicating that the FC features were less related to chronological age compared to structural measures. Follow-up analyses showed that FC predictions were similarly low in a matched sub-sample from UK Biobank, and although FC predictions were consistently lower than GM predictions, the accuracy improved with increasing sample size and age range. Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with brain aging in the WHII cohort. Blood pressure showed a stronger association with white matter compared to gray matter, while no differences in the associations of alcohol intake and stroke risk with these modalities were observed. In conclusion, machine-learning based brain age prediction can reduce the dimensionality of neuroimaging data to provide meaningful biomarkers of individual brain aging. However, model performance depends on study-specific characteristics including sample size and age range, which may cause discrepancies in findings across studies.

Associations between arterial stiffening and brain structure, perfusion, and cognition in the Whitehall II Imaging Sub-study: A retrospective cohort study.

BACKGROUND: Aortic stiffness is closely linked with cardiovascular diseases (CVDs), but recent studies suggest that it is also a risk factor for cognitive decline and dementia. However, the brain changes underlying this risk are unclear. We examined whether aortic stiffening during a 4-year follow-up in mid-to-late life was associated with brain structure and cognition in the Whitehall II Imaging Sub-study. METHODS AND FINDINGS: The Whitehall II Imaging cohort is a randomly selected subset of the ongoing Whitehall II Study, for which participants have received clinical follow-ups for 30 years, across 12 phases. Aortic pulse wave velocity (PWV) was measured in 2007-2009 (Phase 9) and at a 4-year follow-up in 2012-2013 (Phase 11). Between 2012 and 2016 (Imaging Phase), participants received a multimodal 3T brain magnetic resonance imaging (MRI) scan and cognitive tests. Participants were selected if they had no clinical diagnosis of dementia and no gross brain structural abnormalities. Voxel-based analyses were used to assess grey matter (GM) volume, white matter (WM) microstructure (fractional anisotropy (FA) and diffusivity), white matter lesions (WMLs), and cerebral blood flow (CBF). Cognitive outcomes were performance on verbal memory, semantic fluency, working memory, and executive function tests. Of 542 participants, 444 (81.9%) were men. The mean (SD) age was 63.9 (5.2) years at the baseline Phase 9 examination, 68.0 (5.2) at Phase 11, and 69.8 (5.2) at the Imaging Phase. Voxel-based analysis revealed that faster rates of aortic stiffening in mid-to-late life were associated with poor WM microstructure, viz. lower FA, higher mean, and radial diffusivity (RD) in 23.9%, 11.8%, and 22.2% of WM tracts, respectively, including the corpus callosum, corona radiata, superior longitudinal fasciculus, and corticospinal tracts. Similar voxel-wise associations were also observed with follow-up aortic stiffness. Moreover, lower mean global FA was associated with faster rates of aortic stiffening (B = -5.65, 95% CI -9.75, -1.54, Bonferroni-corrected p < 0.0125) and higher follow-up aortic stiffness (B = -1.12, 95% CI -1.95, -0.29, Bonferroni-corrected p < 0.0125). In a subset of 112 participants who received arterial spin labelling scans, faster aortic stiffening was also related to lower cerebral perfusion in 18.4% of GM, with associations surviving Bonferroni corrections in the frontal (B = -10.85, 95% CI -17.91, -3.79, p < 0.0125) and parietal lobes (B = -12.75, 95% CI -21.58, -3.91, p < 0.0125). No associations with GM volume or WMLs were observed. Further, higher baseline aortic stiffness was associated with poor semantic fluency (B = -0.47, 95% CI -0.76 to -0.18, Bonferroni-corrected p < 0.007) and verbal learning outcomes (B = -0.36, 95% CI -0.60 to -0.12, Bonferroni-corrected p < 0.007). As with all observational studies, it was not possible to infer causal associations. The generalisability of the findings may be limited by the gender imbalance, high educational attainment, survival bias, and lack of ethnic and socioeconomic diversity in this cohort. CONCLUSIONS: Our findings indicate that faster rates of aortic stiffening in mid-to-late life were associated with poor brain WM microstructural integrity and reduced cerebral perfusion, likely due to increased transmission of pulsatile energy to the delicate cerebral microvasculature. Strategies to prevent arterial stiffening prior to this point may be required to offer cognitive benefit in older age. TRIAL REGISTRATION: ClinicalTrials.gov NCT03335696.

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.

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 7.27×10-5, 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.

Dysfunctional effort-based decision-making underlies apathy in genetic cerebral small vessel disease.

Apathy is a syndrome of reduced motivation that commonly occurs in patients with cerebral small vessel disease, including those with the early onset form, CADASIL (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy). The cognitive mechanisms underlying apathy are poorly understood and treatment options are limited. We hypothesized that disrupted effort-based decision-making, the cognitive process by which potential rewards and the effort cost required to obtain them is integrated to drive behaviour, might underlie the apathetic syndrome. Nineteen patients with a genetic diagnosis of CADASIL, as a model of 'pure' vascular cognitive impairment, and 19 matched controls were assessed using two different behavioural paradigms and MRI. On a decision-making task, participants decided whether to accept or reject sequential offers of monetary reward in return for exerting physical effort via handheld dynamometers. Six levels of reward and six levels of effort were manipulated independently so offers spanned the full range of possible combinations. Choice, decision time and force metrics were recorded. Each participant's effort and reward sensitivity was estimated using a computational model of choice. On a separate eye movement paradigm, physiological reward sensitivity was indexed by measuring pupillary dilatation to increasing monetary incentives. This metric was related to apathy status and compared to the behavioural metric of reward sensitivity on the decision-making task. Finally, high quality diffusion imaging and tract-based spatial statistics were used to determine whether tracts linking brain regions implicated in effort-based decision-making were disrupted in apathetic patients. Overall, apathetic patients with CADASIL rejected significantly more offers on the decision-making task, due to reduced reward sensitivity rather than effort hypersensitivity. Apathy was also associated with blunted pupillary responses to incentives. Furthermore, these independent behavioural and physiological markers of reward sensitivity were significantly correlated. Non-apathetic patients with CADASIL did not differ from controls on either task, whilst actual motor performance of apathetic patients in both tasks was also normal. Apathy was specifically associated with reduced fractional anisotropy within tracts connecting regions previously associated with effort-based decision-making. These findings demonstrate behavioural, physiological and anatomical evidence that dysfunctional effort-based decision-making underlies apathy in patients with CADASIL, a model disorder for sporadic small vessel disease. Reduced incentivization by rewards rather than hypersensitivity to effort costs drives this altered pattern of behaviour. The study provides empirical evidence of a cognitive mechanism for apathy in cerebral small vessel disease, and identifies a promising therapeutic target for interventions to improve this debilitating condition.

Apathy in rapid eye movement sleep behaviour disorder is associated with serotonin depletion in the dorsal raphe nucleus.

Apathy is a common and under-recognized disorder that often emerges in the prodromal phase of Parkinsonian diseases. The mechanism by which this occurs is not known, but recent evidence from patients with established Parkinson's disease suggests that serotonergic dysfunction may play a role. The integrity of the raphe serotonergic system can be assessed alongside dopaminergic basal ganglia imaging using the radioligand 123I-ioflupane, which binds both serotonin and dopamine transporters. To investigate the relative roles of these neurotransmitters in prodromal parkinsonism, we imaged patients with idiopathic rapid eye movement sleep behaviour disorder, the majority of whom will develop a parkinsonian disorder in future. Forty-three patients underwent brain imaging with 123I-ioflupane single photon emission computed tomography and structural MRI. Apathy was quantified using the Lille Apathy Rating Scale. Other clinical parkinsonian features were assessed using standard measures. A negative correlation was observed between apathy severity and serotonergic 123I-ioflupane signal in the dorsal raphe nucleus (r = -0.55, P < 0.001). There was no significant correlation between apathy severity and basal ganglia dopaminergic signal, nor between dorsal raphe signal and other neuropsychiatric scores. This specific association between apathy and raphe 123I-ioflupane signal suggests that the serotonergic system might represent a target for the treatment of apathy.

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.

Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank.

UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.

Association of Cardiovascular Risk Factors With MRI Indices of Cerebrovascular Structure and Function and White Matter Hyperintensities in Young Adults.

Importance: Risk of stroke and brain atrophy in later life relate to levels of cardiovascular risk in early adulthood. However, it is unknown whether cerebrovascular changes are present in young adults. Objective: To examine relationships between modifiable cardiovascular risk factors and cerebrovascular structure, function, and white matter integrity in young adults. Design, Setting, and Participants: A cross-sectional observational study of 125 young adults (aged 18-40 years) without clinical evidence of cerebrovascular disease. Data collection was completed between August 2014 and May 2016 at the University of Oxford, United Kingdom. Final data collection was completed on May 31, 2016. Exposures: The number of modifiable cardiovascular risk factors at recommended levels, based on the following criteria: body mass index (BMI) <25; highest tertile of cardiovascular fitness and/or physical activity; alcohol consumption <8 drinks/week; nonsmoker for >6 months; blood pressure on awake ambulatory monitoring <130/80 mm Hg; a nonhypertensive diastolic response to exercise (peak diastolic blood pressure <90 mm Hg); total cholesterol <200 mg/dL; and fasting glucose <100mg/dL. Each risk factor at the recommended level was assigned a value of 1, and participants were categorized from 0-8, according to the number of risk factors at recommended levels, with higher numbers indicating healthier risk categories. Main Outcomes and Measures: Cerebral vessel density, caliber and tortuosity, brain white matter hyperintensity lesion count. In a subgroup (n = 52), brain blood arrival time and cerebral blood flow assessed by brain magnetic resonance imaging (MRI). Results: A total of 125 participants, mean (SD) age 25 (5) years, 49% women, with a mean (SD) score of 6.0 (1.4) modifiable cardiovascular risk factors at recommended levels, completed the cardiovascular risk assessment and brain MRI protocol. Cardiovascular risk factors were correlated with cerebrovascular morphology and white matter hyperintensity count in multivariable models. For each additional modifiable risk factor categorized as healthy, vessel density was greater by 0.3 vessels/cm3 (95% CI, 0.1-0.5; P = .003), vessel caliber was greater by 8 µm (95% CI, 3-13; P = .01), and white matter hyperintensity lesions were fewer by 1.6 lesions (95% CI, -3.0 to -0.5; P = .006). Among the 52 participants with available data, cerebral blood flow varied with vessel density and was 2.5 mL/100 g/min higher for each healthier category of a modifiable risk factor (95% CI, 0.16-4.89; P = .03). Conclusions and Relevance: In this preliminary study involving young adults without clinical evidence of cerebrovascular disease, a greater number of modifiable cardiovascular risk factors at recommended levels was associated with higher cerebral vessel density and caliber, higher cerebral blood flow, and fewer white matter hyperintensities. Further research is needed to verify these findings and determine their clinical importance.

White Matter Imaging Correlates of Early Cognitive Impairment Detected by the Montreal Cognitive Assessment After Transient Ischemic Attack and Minor Stroke.

BACKGROUND AND PURPOSE: Among screening tools for cognitive impairment in large cohorts, the Montreal Cognitive Assessment (MoCA) seems to be more sensitive to early cognitive impairment than the Mini-Mental State Examination (MMSE), particularly after transient ischemic attack or minor stroke. We reasoned that if MoCA-detected early cognitive impairment is pathologically significant, then it should be specifically associated with the presence of white matter hyperintensities (WMHs) and reduced fractional anisotropy (FA) on magnetic resonance imaging. METHODS: Consecutive eligible patients with transient ischemic attack or minor stroke (Oxford Vascular Study) underwent magnetic resonance imaging and cognitive assessment. We correlated MoCA and MMSE scores with WMH and FA, then specifically studied patients with low MoCA and normal MMSE. RESULTS: Among 400 patients, MoCA and MMSE scores were significantly correlated (all P<0.001) with WMH volumes (rMoCA=-0.336; rMMSE=-0.297) and FA (rMoCA=0.409; rMMSE=0.369) and-on voxel-wise analyses-with WMH in frontal white matter and reduced FA in almost all white matter tracts. However, only the MoCA was independently correlated with WMH volumes (r=-0.183; P<0.001), average FA values (r=0.218; P<0.001), and voxel-wise reduced FA in anterior tracts after controlling for the MMSE. In addition, patients with low MoCA but normal MMSE (n=57) had higher WMH volumes (t=3.1; P=0.002), lower average FA (t=-4.0; P<0.001), and lower voxel-wise FA in almost all white matter tracts than those with normal MoCA and MMSE (n=238). CONCLUSIONS: In patients with transient ischemic attack or minor stroke, early cognitive impairment detected with the MoCA but not with the MMSE was independently associated with white matter damage on magnetic resonance imaging, particularly reduced FA.

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.

Associations between self-reported sleep quality and white matter in community-dwelling older adults: A prospective cohort study.

Both sleep disturbances and decline in white matter microstructure are commonly observed in ageing populations, as well as in age-related psychiatric and neurological illnesses. A relationship between sleep and white matter microstructure may underlie such relationships, but few imaging studies have directly examined this hypothesis. In a study of 448 community-dwelling members of the Whitehall II Imaging Sub-Study aged between 60 and 82 years (90 female, mean age 69.2 ± 5.1 years), we used the magnetic resonance imaging technique diffusion tensor imaging to examine the relationship between self-reported sleep quality and white matter microstructure. Poor sleep quality at the time of the diffusion tensor imaging scan was associated with reduced global fractional anisotropy and increased global axial diffusivity and radial diffusivity values, with small effect sizes. Voxel-wise analysis showed that widespread frontal-subcortical tracts, encompassing regions previously reported as altered in insomnia, were affected. Radial diffusivity findings remained significant after additional correction for demographics, general cognition, health, and lifestyle measures. No significant differences in general cognitive function, executive function, memory, or processing speed were detected between good and poor sleep quality groups. The number of times participants reported poor sleep quality over five time-points spanning a 16-year period was not associated with white matter measures. In conclusion, these data demonstrate that current sleep quality is linked to white matter microstructure. Small effect sizes may limit the extent to which poor sleep is a promising modifiable factor that may maintain, or even improve, white matter microstructure in ageing. Hum Brain Mapp 38:5465-5473, 2017. © 2017 Wiley Periodicals, Inc.