Voxel-wise multivariate analysis of brain-psychosocial associations in adolescents reveals six latent dimensions of cognition and psychopathology.

BACKGROUND: Adolescence heralds the onset of much psychopathology, which may be conceptualized as an emergence of altered covariation between symptoms and brain measures. Multivariate methods can detect such modes of covariation or latent dimensions, but none specifically relating to psychopathology have yet been found using population-level structural brain data. Using voxel-wise (instead of parcellated) brain data may strengthen latent dimensions' brain-psychosocial relationships, but this creates computational challenges. METHODS: We obtained voxel-wise grey matter density and psychosocial variables from the baseline (aged 9-10 years) Adolescent Brain and Cognitive Development cohort (n=11288), and employed a state-of-the-art segmentation method, sparse partial least squares, and a rigorous machine learning framework to prevent overfitting. RESULTS: We found six latent dimensions, four pertaining specifically to mental health. The mental health dimensions related to overeating, anorexia/internalizing, oppositional symptoms (all p<0.002) and ADHD symptoms (p=0.03). ADHD related to increased and internalizing related to decreased grey matter density in dopaminergic and serotonergic midbrain areas, whereas oppositional symptoms related to increased grey matter in a noradrenergic nucleus. Internalizing related to increased and oppositional symptoms to reduced grey matter density in insula, cingulate and auditory cortices. Striatal regions featured strongly, with reduced caudate nucleus grey matter in ADHD, and reduced putamen grey matter in oppositional/conduct problems. Voxel-wise grey matter density generated stronger brain-psychosocial correlations than brain parcellations. CONCLUSIONS: Voxel-wise brain data strengthen latent dimensions of brain-psychosocial covariation and sparse multivariate methods increase their psychopathological specificity. Internalizing and externalizing are associated with opposite grey matter changes in similar cortical and subcortical areas.

Voxel-based dysconnectomic brain morphometry with computed tomography in Down syndrome.

OBJECTIVE: Alzheimer's disease (AD) is a major health concern for aging adults with Down syndrome (DS), but conventional diagnostic techniques are less reliable in those with severe baseline disability. Likewise, acquisition of magnetic resonance imaging to evaluate cerebral atrophy is not straightforward, as prolonged scanning times are less tolerated in this population. Computed tomography (CT) scans can be obtained faster, but poor contrast resolution limits its function for morphometric analysis. We implemented an automated analysis of CT scans to characterize differences across dementia stages in a cross-sectional study of an adult DS cohort. METHODS: CT scans of 98 individuals were analyzed using an automatic algorithm. Voxel-based correlations with clinical dementia stages and AD plasma biomarkers (phosphorylated tau-181 and neurofilament light chain) were identified, and their dysconnectomic patterns delineated. RESULTS: Dementia severity was negatively correlated with gray (GM) and white matter (WM) volumes in temporal lobe regions, including parahippocampal gyri. Dysconnectome analysis revealed an association between WM loss and temporal lobe GM volume reduction. AD biomarkers were negatively associated with GM volume in hippocampal and cingulate gyri. INTERPRETATION: Our automated algorithm and novel dysconnectomic analysis of CT scans successfully described brain morphometric differences related to AD in adults with DS, providing a new avenue for neuroimaging analysis in populations for whom magnetic resonance imaging is difficult to obtain.

Predicting mortality in acutely hospitalised older patients: the impact of model dimensionality.

BACKGROUND: The prediction of long-term mortality following acute illness can be unreliable for older patients, inhibiting the delivery of targeted clinical interventions. The difficulty plausibly arises from the complex, multifactorial nature of the underlying biology in this population, which flexible, multimodal models based on machine learning may overcome. Here, we test this hypothesis by quantifying the comparative predictive fidelity of such models in a large consecutive sample of older patients acutely admitted to hospital and characterise their biological support. METHODS: A set of 804 admission episodes involving 616 unique patients with a mean age of 84.5 years consecutively admitted to the Acute Geriatric service at University College Hospital were identified, in whom clinical diagnoses, blood tests, cognitive status, computed tomography of the head, and mortality within 600 days after admission were available. We trained and evaluated out-of-sample an array of extreme gradient boosted trees-based predictive models of incrementally greater numbers of investigational modalities and modelled features. Both linear and non-linear associations with investigational features were quantified. RESULTS: Predictive models of mortality showed progressively increasing fidelity with greater numbers of modelled modalities and dimensions. The area under the receiver operating characteristic curve rose from 0.67 (sd = 0.078) for age and sex to 0.874 (sd = 0.046) for the most comprehensive model. Extracranial bone and soft tissue features contributed more than intracranial features towards long-term mortality prediction. The anterior cingulate and angular gyri, and serum albumin, were the greatest intracranial and biochemical model contributors respectively. CONCLUSIONS: High-dimensional, multimodal predictive models of mortality based on routine clinical data offer higher predictive fidelity than simpler models, facilitating individual level prognostication and interventional targeting. The joint contributions of both extracranial and intracranial features highlight the potential importance of optimising somatic as well as neural functions in healthy ageing. Our findings suggest a promising path towards a high-fidelity, multimodal index of frailty.

Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning.

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.

Correcting inter-scan motion artifacts in quantitative R1 mapping at 7T.

PURPOSE: Inter-scan motion is a substantial source of error in R1 estimation methods based on multiple volumes, for example, variable flip angle (VFA), and can be expected to increase at 7T where B1 fields are more inhomogeneous. The established correction scheme does not translate to 7T since it requires a body coil reference. Here we introduce two alternatives that outperform the established method. Since they compute relative sensitivities they do not require body coil images. THEORY: The proposed methods use coil-combined magnitude images to obtain the relative coil sensitivities. The first method efficiently computes the relative sensitivities via a simple ratio; the second by fitting a more sophisticated generative model. METHODS: R1 maps were computed using the VFA approach. Multiple datasets were acquired at 3T and 7T, with and without motion between the acquisition of the VFA volumes. R1 maps were constructed without correction, with the proposed corrections, and (at 3T) with the previously established correction scheme. The effect of the greater inhomogeneity in the transmit field at 7T was also explored by acquiring B1+ maps at each position. RESULTS: At 3T, the proposed methods outperform the baseline method. Inter-scan motion artifacts were also reduced at 7T. However, at 7T reproducibility only converged on that of the no motion condition if position-specific transmit field effects were also incorporated. CONCLUSION: The proposed methods simplify inter-scan motion correction of R1 maps and are applicable at both 3T and 7T, where a body coil is typically not available. The open-source code for all methods is made publicly available.

Model-based multi-parameter mapping.

Quantitative MR imaging is increasingly favoured for its richer information content and standardised measures. However, computing quantitative parameter maps, such as those encoding longitudinal relaxation rate (R1), apparent transverse relaxation rate (R2*) or magnetisation-transfer saturation (MTsat), involves inverting a highly non-linear function. Many methods for deriving parameter maps assume perfect measurements and do not consider how noise is propagated through the estimation procedure, resulting in needlessly noisy maps. Instead, we propose a probabilistic generative (forward) model of the entire dataset, which is formulated and inverted to jointly recover (log) parameter maps with a well-defined probabilistic interpretation (e.g., maximum likelihood or maximum a posteriori). The second order optimisation we propose for model fitting achieves rapid and stable convergence thanks to a novel approximate Hessian. We demonstrate the utility of our flexible framework in the context of recovering more accurate maps from data acquired using the popular multi-parameter mapping protocol. We also show how to incorporate a joint total variation prior to further decrease the noise in the maps, noting that the probabilistic formulation allows the uncertainty on the recovered parameter maps to be estimated. Our implementation uses a PyTorch backend and benefits from GPU acceleration. It is available at https://github.com/balbasty/nitorch.

Outcome after acute ischemic stroke is linked to sex-specific lesion patterns.

Acute ischemic stroke affects men and women differently. In particular, women are often reported to experience higher acute stroke severity than men. We derived a low-dimensional representation of anatomical stroke lesions and designed a Bayesian hierarchical modeling framework tailored to estimate possible sex differences in lesion patterns linked to acute stroke severity (National Institute of Health Stroke Scale). This framework was developed in 555 patients (38% female). Findings were validated in an independent cohort (n = 503, 41% female). Here, we show brain lesions in regions subserving motor and language functions help explain stroke severity in both men and women, however more widespread lesion patterns are relevant in female patients. Higher stroke severity in women, but not men, is associated with left hemisphere lesions in the vicinity of the posterior circulation. Our results suggest there are sex-specific functional cerebral asymmetries that may be important for future investigations of sex-stratified approaches to management of acute ischemic stroke.

Factorisation-Based Image Labelling.

Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully automated and general purpose brain segmentation algorithm is highly desirable. To this end, we propose a patched-based labell propagation approach based on a generative model with latent variables. Once trained, our Factorisation-based Image Labelling (FIL) model is able to label target images with a variety of image contrasts. We compare the effectiveness of our proposed model against the state-of-the-art using data from the MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labelling. As our approach is intended to be general purpose, we also assess how well it can handle domain shift by labelling images of the same subjects acquired with different MR contrasts.

Machine phenotyping of cluster headache and its response to verapamil.

Cluster headache is characterized by recurrent, unilateral attacks of excruciating pain associated with ipsilateral cranial autonomic symptoms. Although a wide array of clinical, anatomical, physiological, and genetic data have informed multiple theories about the underlying pathophysiology, the lack of a comprehensive mechanistic understanding has inhibited, on the one hand, the development of new treatments and, on the other, the identification of features predictive of response to established ones. The first-line drug, verapamil, is found to be effective in only half of all patients, and after several weeks of dose escalation, rendering therapeutic selection both uncertain and slow. Here we use high-dimensional modelling of routinely acquired phenotypic and MRI data to quantify the predictability of verapamil responsiveness and to illuminate its neural dependants, across a cohort of 708 patients evaluated for cluster headache at the National Hospital for Neurology and Neurosurgery between 2007 and 2017. We derive a succinct latent representation of cluster headache from non-linear dimensionality reduction of structured clinical features, revealing novel phenotypic clusters. In a subset of patients, we show that individually predictive models based on gradient boosting machines can predict verapamil responsiveness from clinical (410 patients) and imaging (194 patients) features. Models combining clinical and imaging data establish the first benchmark for predicting verapamil responsiveness, with an area under the receiver operating characteristic curve of 0.689 on cross-validation (95% confidence interval: 0.651 to 0.710) and 0.621 on held-out data. In the imaged patients, voxel-based morphometry revealed a grey matter cluster in lobule VI of the cerebellum (-4, -66, -20) exhibiting enhanced grey matter concentrations in verapamil non-responders compared with responders (familywise error-corrected P = 0.008, 29 voxels). We propose a mechanism for the therapeutic effect of verapamil that draws on the neuroanatomy and neurochemistry of the identified region. Our results reveal previously unrecognized high-dimensional structure within the phenotypic landscape of cluster headache that enables prediction of treatment response with modest fidelity. An analogous approach applied to larger, globally representative datasets could facilitate data-driven redefinition of diagnostic criteria and stronger, more generalizable predictive models of treatment responsiveness.

An algorithm for learning shape and appearance models without annotations.

This paper presents a framework for automatically learning shape and appearance models for medical (and certain other) images. The algorithm was developed with the aim of eventually enabling distributed privacy-preserving analysis of brain image data, such that shared information (shape and appearance basis functions) may be passed across sites, whereas latent variables that encode individual images remain secure within each site. These latent variables are proposed as features for privacy-preserving data mining applications. The approach is demonstrated qualitatively on the KDEF dataset of 2D face images, showing that it can align images that traditionally require shape and appearance models trained using manually annotated data (manually defined landmarks etc.). It is applied to the MNIST dataset of handwritten digits to show its potential for machine learning applications, particularly when training data is limited. The model is able to handle "missing data", which allows it to be cross-validated according to how well it can predict left-out voxels. The suitability of the derived features for classifying individuals into patient groups was assessed by applying it to a dataset of over 1900 segmented T1-weighted MR images, which included images from the COBRE and ABIDE datasets.

ConoSurf: Open-source 3D scanning system based on a conoscopic holography device for acquiring surgical surfaces.

BACKGROUND: A difficulty in computer-assisted interventions is acquiring the patient's anatomy intraoperatively. Standard modalities have several limitations: low image quality (ultrasound), radiation exposure (computed tomography) or high costs (magnetic resonance imaging). An alternative approach uses a tracked pointer; however, the pointer causes tissue deformation and requires sterilizing. Recent proposals, utilizing a tracked conoscopic holography device, have shown promising results without the previously mentioned drawbacks. METHODS: We have developed an open-source software system that enables real-time surface scanning using a conoscopic holography device and a wide variety of tracking systems, integrated into pre-existing and well-supported software solutions. RESULTS: The mean target registration error of point measurements was 1.46 mm. For a quick guidance scan, surface reconstruction improved the surface registration error compared with point-set registration. CONCLUSIONS: We have presented a system enabling real-time surface scanning using a tracked conoscopic holography device. Results show that it can be useful for acquiring the patient's anatomy during surgery.

Towards real-time, tracker-less 3D ultrasound guidance for spine anaesthesia.

PURPOSE: Epidural needle insertions and facet joint injections play an important role in spine anaesthesia. The main challenge of safe needle insertion is the deep location of the target, resulting in a narrow and small insertion channel close to sensitive anatomy. Recent approaches utilizing ultrasound (US) as a low-cost and widely available guiding modality are promising but have yet to become routinely used in clinical practice due to the difficulty in interpreting US images, their limited view of the internal anatomy of the spine, and/or inclusion of cost-intensive tracking hardware which impacts the clinical workflow. METHODS: We propose a novel guidance system for spine anaesthesia. An efficient implementation allows us to continuously align and overlay a statistical model of the lumbar spine on the live 3D US stream without making use of additional tracking hardware. The system is evaluated in vivo on 12 volunteers. RESULTS: The in vivo study showed that the anatomical features of the epidural space and the facet joints could be continuously located, at a volume rate of 0.5 Hz, within an accuracy of 3 and 7 mm, respectively. CONCLUSIONS: A novel guidance system for spine anaesthesia has been presented which augments a live 3D US stream with detailed anatomical information of the spine. Results from an in vivo study indicate that the proposed system has potential for assisting the physician in quickly finding the target structure and planning a safe insertion trajectory in the spine.