Resting-state functional magnetic resonance imaging (rfMRI) allows one to study functional connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI scanner, and taking ...advantage of the fact that functionally related brain regions spontaneously co-activate. rfMRI is one of the two primary data modalities being acquired for the Human Connectome Project (the other being diffusion MRI). A key objective is to generate a detailed in vivo mapping of functional connectivity in a large cohort of healthy adults (over 1000 subjects), and to make these datasets freely available for use by the neuroimaging community. In each subject we acquire a total of 1h of whole-brain rfMRI data at 3T, with a spatial resolution of 2×2×2mm and a temporal resolution of 0.7s, capitalizing on recent developments in slice-accelerated echo-planar imaging. We will also scan a subset of the cohort at higher field strength and resolution. In this paper we outline the work behind, and rationale for, decisions taken regarding the rfMRI data acquisition protocol and pre-processing pipelines, and present some initial results showing data quality and example functional connectivity analyses.
•The Human Connectome Project is mapping brain connectivity in vivo in detail.•Resting-state fMRI (rfMRI) is a major modality in the Human Connectome Project.•We describe rfMRI acquisition and analysis protocols for the HCP.
Background
The co‐occurrence of multiple long term conditions (LTCs), termed multimorbidity, is a risk factor for dementia. However, we lack an understanding of how and when specific LTCs accumulate ...over time to impact dementia risk.
Method
338,270 individuals from the UK Biobank with linked electronic health records up to January 2022 were studied, excluding those with either a dementia diagnosis or age of latest follow up < 65 years. LTC diagnoses were derived using hospital episode statistics and GP records (age of diagnosis computed using month/year of birth and month/year of diagnosis). We performed an age‐stratified analysis in which we a) cluster individuals based on 43 non‐dementia LTCs diagnosed within age bins (0‐55, 55‐65, 65‐70 years), and b) compute risk of incident dementia using logistic regression with multimorbidity cluster as predictor and incident dementia as outcome. Diagnosis data was submitted to PCA, and the resulting factor scores were kMeans‐clustered. 6 clusters were derived per age bin.
Result
We named clusters based on the most prevalent LTCs within that cluster (Figure 1). Cardiometabolic, arthritis, gastrointestinal, and mixed clusters existed at all ages. Mental health and respiratory clusters were present exclusively in the younger age bin (0‐55) whereas hypertension and eye disorder clusters were exclusive to older ages (55‐65 and 65‐70). Across the lifespan, compared to being disease free, multimorbidity was significantly associated with incident dementia (OR>1). We also observed age‐specific multimorbidity preceding dementia onset. From 0‐55 years, being in the mixed cluster presented the highest dementia risk (OR = 5.13, 95% CI = 4.61, 5.73). Being in the cardiometabolic cluster was associated with highest risk in 55‐65 (OR = 6.24 5.48, 7.1) and 65‐70 (OR = 6.99 5.91, 8.27) ranges. The most predictive trajectory of incident dementia involved the mixed cluster at 55, followed by the cardiometabolic clusters from 55‐65 and 65‐70. Table 1 displays the top 10 trajectories associated with dementia. All p‐values are bonferroni‐holmes corrected and < 0.01.
Conclusion
We present multimorbidity pathways linked to increased risk of dementia. We describe how and when diseases cluster together prior to dementia diagnosis.
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 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.
•We aim to improve BIANCA to handle population- and subject-wise lesion variability.•Age-based population lesion probability maps did not improve BIANCA performance.•Among 5 classifiers tested, K-Nearest Neighbour still gives the best performance.•LOCally Adaptive Threshold Estimation (LOCATE) proposed for lesion probability maps.•LOCATE improves lesion segmentation and adapts well to lesion load and distribution.
•We explored various domain adaptation methods for robust WM lesion segmentation.•We used a triplanar U-net ensemble network (TrUE-Net) as our baseline model.•Transfer learning: fine-tuning from the ...coarsest encoder layer gave good results.•Semi-supervised domain adversarial training of NNs (DANN) performed the best.•Among unsupervised methods, DANN performed better than domain unlearning.
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Robust automated segmentation of white matter hyperintensities (WMHs) in different datasets (domains) is highly challenging due to differences in acquisition (scanner, sequence), population (WMH amount and location) and limited availability of manual segmentations to train supervised algorithms. In this work we explore various domain adaptation techniques such as transfer learning and domain adversarial learning methods, including domain adversarial neural networks and domain unlearning, to improve the generalisability of our recently proposed triplanar ensemble network, which is our baseline model. We used datasets with variations in intensity profile, lesion characteristics and acquired using different scanners. For the source domain, we considered a dataset consisting of data acquired from 3 different scanners, while the target domain consisted of 2 datasets. We evaluated the domain adaptation techniques on the target domain datasets, and additionally evaluated the performance on the source domain test dataset for the adversarial techniques. For transfer learning, we also studied various training options such as minimal number of unfrozen layers and subjects required for fine-tuning in the target domain. On comparing the performance of different techniques on the target dataset, domain adversarial training of neural network gave the best performance, making the technique promising for robust WMH segmentation.
OBJECTIVE:To determine whether changes in cerebral structure are present after preeclampsia that may explain increased cerebrovascular risk in these women.
METHODS:We conducted a case control study ...in women between 5 and 15 years after either a preeclamptic or normotensive pregnancy. Brain MRI was performed. Analysis of white matter structure was undertaken using voxel-based segmentation of fluid-attenuation inversion recovery sequences to assess white matter lesion volume and diffusion tensor imaging to measure microstructural integrity. Voxel-based analysis of gray matter volumes was performed with adjustment for skull size.
RESULTS:Thirty-four previously preeclamptic women (aged 42.8 ± 5.1 years) and 49 controls were included. Previously preeclamptic women had reduced cortical gray matter volume (523.2 ± 30.1 vs 544.4 ± 44.7 mL, p < 0.05) and, although both groups displayed white matter lesions, changes were more extensive in previously preeclamptic women. They displayed increased temporal lobe white matter disease (lesion volume23.2 ± 24.9 vs 10.9 ± 15.0 μL, p < 0.05) and altered microstructural integrity (radial diffusivity538 ± 19 vs 526 ± 18 × 10 mm/s, p < 0.01), which also extended to occipital and parietal lobes. The degree of temporal lobe white matter change in previously preeclamptic women was independent of their current cardiovascular risk profile (p < 0.05) and increased with time from index pregnancy (p < 0.05).
CONCLUSION:A history of preeclampsia is associated with temporal lobe white matter changes and reduced cortical volume in young women, which is out of proportion to their classic cardiovascular risk profile. The severity of changes is proportional to time since pregnancy, which would be consistent with continued accumulation of damage after pregnancy.
Le Heron et al. demonstrate, using behavioural, physiological and imaging techniques, converging evidence that reduced reward sensitivity underlies apathy in patients with a monogenic form of ...cerebral small vessel disease. This specific change in effort-based decision making points to potential treatment avenues for apathy.
Abstract
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.
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.
BIANCA‐MS is a new tool for multiple sclerosis automated lesion segmentation which harmonizes the optimization procedure across different scanning protocols and implements a cleaning step to reduce the impact of inter‐rater variability and to further refine lesion segmentation. BIANCA‐MS was validated on several datasets with different image characteristics demonstrating its robustness, accuracy and flexibility.
Background
Since August 2020, the Oxford Brain Health Clinic (BHC) has seen over 200 NHS memory clinic patients (O’Donoghue et al., 2022). In addition to high‐quality cognitive and lifestyle ...assessments and opportunities for research participation, patients receive a clinical MRI scan (T1‐weighted, T2‐FLAIR, and SWI) and can consent to additional research scans (diffusion MRI – dMRI, resting‐state functional MRI – rfMRI, and arterial spin labelling ‐ ASL) aligned to the UK Biobank (Griffanti et al., 2022). In this project, we aimed to automatically extract imaging‐derived phenotypes (IDPs) from the different imaging modalities acquired at the BHC and perform unimodal group‐level analyses to explore associations with cognition and diagnoses in this real‐world memory clinic population.
Method
As of January 2023, scans were analysed from 176 BHC patients, 101 of whom completed the additional research MRI scans. Cognitive scores (ACE‐III total score, N = 166) were available from the BHC appointment, and subsequent diagnoses (N = 118) were extracted from electronic healthcare records. Scans were processed using the UK Biobank pipeline (Alfaro‐Almagro et al., 2018), adapted to include previously described modifications (Griffanti et al., 2022). All IDPs were deconfounded for age, sex, and head size. We calculated Spearman correlations with ACE‐III cognitive scores and group comparisons (Kruskal‐Wallis tests) between 3 diagnostic groups: dementia, MCI, and no dementia‐related diagnosis. Results were corrected for multiple comparisons (false discovery rate with Benjamini‐Hochberg procedure; FDR‐adjusted p = 0.000016).
Result
11 IDPs significantly correlated with ACE‐III (Figure 1). Four voxel‐based morphometry temporal lobe measures, 3 FreeSurfer temporal lobe measures, FIRST left hippocampal volume, and SIENAX peripheral grey matter volume positively correlated with ACE‐III, in line with known dementia‐related atrophy patterns (Figure 2). dMRI mean diffusivity in the bilateral parahippocampal part of the cingulum negatively correlated with ACE‐III, highlighting white matter disruption (Figure 3). No FDR‐corrected differences were found between diagnostic groups.
Conclusion
Using an unselected patient population, all of whom have a degree of memory problems, this work provides real‐world validation of associations that are well‐established in the research context. This represents a key step towards integrating research‐quality imaging in the memory clinic. Imaging and clinical variables are planned to be available through DPUK.
Background
Personalised risk reduction is a key driver for the nascent UK Brain Health Clinic (BHC) network, with increasing evidence that modifying dementia risk could delay or prevent disease ...progression. Livingston et al. (2020) demonstrated a number of risk factors for dementia, including less formal education (identified as no secondary education), excessive alcohol consumption (>21 units/week), obesity (BMI >30), depression and presence of an ApoE Ɛ4 allele; whilst increased physical activity has been associated with reduced risk and sleep duration is suggested to have a U‐shaped association with dementia risk. The aim of this study was to understand the prevalence of these risk factors within a typical UK memory clinic population.
Method
The Oxford BHC is a joint clinical and research assessment service for Oxford Health NHS Foundation Trust memory clinic patients (O’Donoghue et al., 2022). It aims to provide detailed clinical assessment and equal access to research opportunities. Between August 2020 and May 2022, 152 BHC patients (93.2%) consented to their clinical data, including subsequent diagnosis, being used for research purposes (mean age = 78.2 years, range 65‐101 years; 52.6% female). Self‐reported questionnaires collected education (N = 124), alcohol consumption (M SASQ, N = 150), depressive symptoms (PHQ9, N = 148), physical activity (IPAQ, N = 152) and sleep (PSQI, N = 152). BMI was calculated from height and weight measured at BHC appointment (N = 146) and ApoE Ɛ genotyping from saliva sampling (N = 76).
Result
Preliminary results show 7% of patients reported no formal qualifications, 1% consumed 6 or more units of alcohol daily/almost daily, 15.7% had a BMI >30 and 45% showed mild‐severe symptoms of depression. Inappropriate sleep was reported by 13%, 71% engaged in less than 150 minutes of physical activity a week and 41% had an ApoE Ɛ4 allele (see Figure 1).
Conclusion
In this population, we found a relatively small prevalence of low education and alcohol consumption, but a large proportion of patients could benefit from interventions focused on improving mood and physical activity. Ongoing analysis aims to create a weighted composite risk score for this population and explore relationships with primary diagnosis, cognition and measures of brain health including hippocampal volume and white matter hyperintensities.
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