Cognitive dysfunction in patients with cerebral microbleeds on T2*-weighted gradient-echo MRI.

Gradient echo T2*-weighted MRI has high sensitivity in detecting cerebral microbleeds, which appear as small dot-like hypointense lesions. Microbleeds are strongly associated with intracerebral haemorrhage, hypertension, lacunar stroke and ischaemic small vessel disease, and have generated interest as a marker of bleeding-prone microangiopathy. Microbleeds have generally been considered to be clinically silent; however, since they are located in widespread cortical and basal ganglia regions and are histologically characterized by tissue damage, we hypothesized that they would cause cognitive dysfunction. We therefore studied patients with microbleeds (n = 25) and a non-microbleed control group (n = 30) matched for age, gender and intelligence quotient. To avoid the confounding effects of coexisting cerebrovascular disease, the groups were also matched for the extent of MRI-visible white matter changes of presumed ischaemic origin, location of cortical strokes, and for the proportion of patients with different stroke subtypes (including lacunar stroke). A battery of neuropsychological tests was used to assess current intellectual function, verbal and visual memory, naming and perceptual skills, speed and attention and executive function. Microbleeds were most common in the basal ganglia but were also found in frontal, parieto-occipital, temporal and infratentorial regions. There was a striking difference between the groups in the prevalence of executive dysfunction, which was present in 60% of microbleed patients compared with 30% of non-microbleed patients (P = 0.03). Logistic regression confirmed that microbleeds (but not white matter changes) were an independent predictor of executive impairment (adjusted odds ratio = 1.32, 95% confidence interval 1.01-1.70, P = 0.04). Patients with executive dysfunction had more microbleeds in the frontal region (mean count 1.54 versus 0.03; P = 0.002) and in the basal ganglia (mean 1.17 versus 0.32; P = 0.048). There was a modest correlation between the number of microbleeds and the number of cognitive domains impaired (r = 0.44, P = 0.03). This study provides novel evidence that microbleeds are associated with cognitive dysfunction, independent of the extent of white matter changes of presumed ischaemic origin, or the presence of ischaemic stroke. The striking effect of microbleeds on executive dysfunction is likely to result from associated tissue damage in the frontal lobes and basal ganglia. These findings have implications for the diagnosis of stroke patients with cognitive impairment, and for the appropriate use of antihypertensive and antiplatelet treatments in these patients

VTrails: Inferring Vessels with Geodesic Connectivity Trees

The analysis of vessel morphology and connectivity has an impact on a number of cardiovascular and neurovascular applications by providing patient-specific high-level quantitative features such as spatial location, direction and scale. In this paper we present an end-to-end approach to extract an acyclic vascular tree from angiographic data by solving a connectivity-enforcing anisotropic fast marching over a voxel-wise tensor field representing the orientation of the underlying vascular tree. The method is validated using synthetic and real vascular images. We compare VTrails against classical and state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field as proof of concept. VTrails performance is evaluated on images with different levels of degradation: we verify that the extracted vascular network is an acyclic graph (i.e. a tree), and we report the extraction accuracy, precision and recall

High-dimensional therapeutic inference in the focally damaged human brain

Though consistency across the population renders the extraordinarily complex functional anatomy of the human brain surveyable, the inverse inference-from common functional maps to individual behaviour-is constrained by marked individual deviation from the population mean. Such inference is fundamental to the evaluation of therapeutic interventions in focal brain injury, where the impact of an induced structural change in the brain is quantified by its behavioural consequences, inevitably refracted through the lens of lesion-outcome relations. Current therapeutic evaluations do not incorporate inferences to the individual outcome derived from a detailed specification of the lesion anatomy, relying only on reductive parameters such as lesion volume and crudely discretised location. Examining 1172 patients with anatomically registered focal brain lesions, here we show that such low-dimensional models are highly insensitive to therapeutic effects. In contrast, high-dimensional models supported by machine learning dramatically improve sensitivity by leveraging complex individuating patterns in the functional architecture of the brain. The failure to replicate in humans positive interventional effects in experimental animals is thus revealed to have a remediable inferential cause, forcing a radical re-evaluation of therapeutic inference in the human brain

Let's agree to disagree: learning highly debatable multirater labelling

Classification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time. In a radiological setting, objects commonly have high within-class appearance variability whilst sharing certain characteristics across different classes, making their distinction even more difficult. As an example, markers of cerebral small vessel disease, such as enlarged perivascular spaces (EPVS) and lacunes, can be very varied in their appearance while exhibiting high inter-class similarity, making this task highly challenging for human raters. In this work, we investigate joint models of individual rater behaviour and multirater consensus in a deep learning setting, and apply it to a brain lesion object-detection task. Results show that jointly modelling both individual and consensus estimates leads to significant improvements in performance when compared to directly predicting consensus labels, while also allowing the characterization of human-rater consistency.Comment: Accepted at MICCAI 201

Domain-specific neuropsychological investigation of CAA with and without intracerebral haemorrhage

Background: Cerebral amyloid angiopathy (CAA) is associated with cognitive impairment, but the contributions of lobar intracerebral haemorrhage (ICH), underlying diffuse vasculopathy, and neurodegeneration, remain uncertain. We investigated the domain-specific neuropsychological profile of CAA with and without ICH, and their associations with structural neuroimaging features. // Methods: Data were collected from patients with possible or probable CAA attending a specialist outpatient clinic. Patients completed standardised neuropsychological assessment covering seven domains. MRI scans were scored for markers of cerebral small vessel disease and neurodegeneration. Patients were grouped into those with and without a macro-haemorrhage (CAA-ICH and CAA-non-ICH). // Results: We included 77 participants (mean age 72, 65% male). 26/32 (81%) CAA-non-ICH patients and 41/45 (91%) CAA-ICH patients were impaired in at least one cognitive domain. Verbal IQ and non-verbal IQ were the most frequently impaired, followed by executive functions and processing speed. We found no significant differences in the frequency of impairment across domains between the two groups. Medial temporal atrophy was the imaging feature most consistently associated with cognitive impairment (both overall and in individual domains) in both univariable and multivariable analyses. // Discussion: Cognitive impairment is common in CAA, even in the absence of ICH, suggesting a key role for diffuse processes related to small vessel disease and/or neurodegeneration. Our findings indicate that neurodegeneration, possibly due to co-existing Alzheimer’s disease pathology, may be the most important contributor. The observation that general intelligence is the most frequently affected domain suggests that CAA has a generalised rather than focal cognitive impact

Transformer-based out-of-distribution detection for clinically safe segmentation

In a clinical setting it is essential that deployed image processing systems are robust to the full range of inputs they might encounter and, in particular, do not make confidently wrong predictions. The most popular approach to safe processing is to train networks that can provide a measure of their uncertainty, but these tend to fail for inputs that are far outside the training data distribution. Recently, generative modelling approaches have been proposed as an alternative; these can quantify the likelihood of a data sample explicitly, filtering out any out-of-distribution (OOD) samples before further processing is performed. In this work, we focus on image segmentation and evaluate several approaches to network uncertainty in the far-OOD and near-OOD cases for the task of segmenting haemorrhages in head CTs. We find all of these approaches are unsuitable for safe segmentation as they provide confidently wrong predictions when operating OOD. We propose performing full 3D OOD detection using a VQ-GAN to provide a compressed latent representation of the image and a transformer to estimate the data likelihood. Our approach successfully identifies images in both the far- and near-OOD cases. We find a strong relationship between image likelihood and the quality of a model’s segmentation, making this approach viable for filtering images unsuitable for segmentation. To our knowledge, this is the first time transformers have been applied to perform OOD detection on 3D image data.</p

Latent Transformer Models for out-of-distribution detection

Any clinically-deployed image-processing pipeline must be robust to the full range of inputs it may be presented with. One popular approach to this challenge is to develop predictive models that can provide a measure of their uncertainty. Another approach is to use generative modelling to quantify the likelihood of inputs. Inputs with a low enough likelihood are deemed to be out-of-distribution and are not presented to the downstream predictive model. In this work, we evaluate several approaches to segmentation with uncertainty for the task of segmenting bleeds in 3D CT of the head. We show that these models can fail catastrophically when operating in the far out-of-distribution domain, often providing predictions that are both highly confident and wrong. We propose to instead perform out-of-distribution detection using the Latent Transformer Model: a VQ-GAN is used to provide a highly compressed latent representation of the input volume, and a transformer is then used to estimate the likelihood of this compressed representation of the input. We demonstrate this approach can identify images that are both far- and near- out-of-distribution, as well as provide spatial maps that highlight the regions considered to be out-of-distribution. Furthermore, we find a strong relationship between an image's likelihood and the quality of a model's segmentation on it, demonstrating that this approach is viable for filtering out unsuitable images

Cumulative occupational lumbar load and lumbar disc disease – results of a German multi-center case-control study (EPILIFT)

Background The to date evidence for a dose-response relationship between physical workload and the development of lumbar disc diseases is limited. We therefore investigated the possible etiologic relevance of cumulative occupational lumbar load to lumbar disc diseases in a multi-center case-control study. Methods In four study regions in Germany (Frankfurt/Main, Freiburg, Halle/Saale, Regensburg), patients seeking medical care for pain associated with clinically and radiologically verified lumbar disc herniation (286 males, 278 females) or symptomatic lumbar disc narrowing (145 males, 206 females) were prospectively recruited. Population control subjects (453 males and 448 females) were drawn from the regional population registers. Cases and control subjects were between 25 and 70 years of age. In a structured personal interview, a complete occupational history was elicited to identify subjects with certain minimum workloads. On the basis of job task-specific supplementary surveys performed by technical experts, the situational lumbar load represented by the compressive force at the lumbosacral disc was determined via biomechanical model calculations for any working situation with object handling and load-intensive postures during the total working life. For this analysis, all manual handling of objects of about 5 kilograms or more and postures with trunk inclination of 20 degrees or more are included in the calculation of cumulative lumbar load. Confounder selection was based on biologic plausibility and on the change-in-estimate criterion. Odds ratios (OR) and 95% confidence intervals (CI) were calculated separately for men and women using unconditional logistic regression analysis, adjusted for age, region, and unemployment as major life event (in males) or psychosocial strain at work (in females), respectively. To further elucidate the contribution of past physical workload to the development of lumbar disc diseases, we performed lag-time analyses. Results We found a positive dose-response relationship between cumulative occupational lumbar load and lumbar disc herniation as well as lumbar disc narrowing among men and women. Even past lumbar load seems to contribute to the risk of lumbar disc disease. Conclusions According to our study, cumulative physical workload is related to lumbar disc diseases among men and women

Atrial fibrillation detected before or after stroke: role of anticoagulation

BACKGROUND: Atrial fibrillation (AF) known before ischemic stroke (KAF) has been postulated to be an independent category with a recurrence risk higher than that of AF detected after stroke (AFDAS). However, it is unknown whether this risk difference is confounded by pre-existing anticoagulation, which is most common in KAF and also indicates a high ischemic stroke recurrence risk. METHODS: Individual patient data analysis from 5 prospective cohorts of anticoagulated patients following AF-associated ischemic stroke. We compared the primary (ischemic stroke recurrence) and secondary outcome (all-cause death) among patients with AFDAS versus KAF and among anticoagulation-naïve versus previously anticoagulated patients using multivariable Cox, Fine-Gray models and goodness-of-fit statistics to investigate the relative independent prognostic importance of AF-category and pre-existing anticoagulation. RESULTS: Of 4,357 patients, 1,889(43%) had AFDAS and 2,468(57%) had KAF, while 3,105(71%) were anticoagulation-naïve before stroke and 1,252(29%) were previously anticoagulated. During 6,071 patient-years of follow-up we observed 244 recurrent strokes and 661 deaths. Only pre-existing anticoagulation (but not KAF) was independently associated with a higher hazard for stroke recurrence in both Cox and Fine-Gray models. Models incorporating pre-existing anticoagulation showed better fit than those with AF-category; adding AF-category did not result in better model fit. Neither pre-existing anticoagulation nor KAF were independently associated with death. CONCLUSION: Our findings challenge the notion that KAF and AFDAS are clinically relevant and distinct prognostic entities. Instead of attributing an independently high stroke recurrence risk to KAF, future research should focus on the causes of stroke despite anticoagulation to develop improved preventive treatments. This article is protected by copyright. All rights reserved