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Journal articleCox DJ, Bai W, Price AN, et al., 2019,
Ventricular remodeling in preterm infants: computational cardiac magnetic resonance atlasing shows significant early remodeling of the left ventricle
, PEDIATRIC RESEARCH, Vol: 85, Pages: 807-815, ISSN: 0031-3998- Author Web Link
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- Citations: 28
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Journal articleScott G, Carhart-Harris R, 2019,
Psychedelics as a treatment for disorders of consciousness
, Neuroscience of Consciousness, Vol: 2019, Pages: 1-8, ISSN: 2057-2107Based on its ability to increase brain complexity, a seemingly reliable index of conscious level, we proposetesting the capacity ofthe classic psychedelic, psilocybin,to increase conscious awarenessin patients with disorders of consciousness.We alsoconfrontthe considerable ethical and practical challengesthis proposal must address, if this hypothesis is to be directly assessed.
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Journal articleSandrone S, Moreno-Zambrano D, Kipnis J, et al., 2019,
A (delayed) history of the brain lymphatic system.
, Nat Med, Vol: 25, Pages: 538-540 -
Journal articleRobinson R, Valindria VV, Bai W, et al., 2019,
Automated quality control in image segmentation: application to the UK Biobank cardiac MR imaging study
, Journal of Cardiovascular Magnetic Resonance, Vol: 21, ISSN: 1097-6647Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools, e.g. image segmentation methods, are employed to derive quantitative measures or biomarkers for later analyses. Manual inspection and visual QC of each segmentation isn't feasible at large scale. However, it's important to be able to automatically detect when a segmentation method fails so as to avoid inclusion of wrong measurements into subsequent analyses which could lead to incorrect conclusions. Methods: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4,800 cardiac magnetic resonance scans. We then apply our method to a large cohort of 7,250 cardiac MRI on which we have performed manual QC. Results: We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4,800 scans for which manual segmentations were available. We mimic real-world application of the method on 7,250 cardiac MRI where we show good agreement between predicted quality metrics and manual visual QC scores. Conclusions: We show that RCA has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.
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Journal articleSoreq E, Leech R, Hampshire A, 2019,
Dynamic network coding of working-memory domains and working-memory processes
, Nature Communications, Vol: 10, ISSN: 2041-1723The classic mapping of distinct aspects of working memory (WM) to mutually exclusive brain areas is at odds with the distributed processing mechanisms proposed by contemporary network science theory. Here, we use machinelearning to determine how aspects of WM are dynamically coded in the human brain. Using cross-validation across independent fMRI studies, we demonstrate that stimulus domains (spatial, number and fractal) and WM processes(encode, maintain, probe) are classifiable with high accuracy from the patterns of network activity and connectivitythat they evoke. This is the case even when focusing on ‘multiple demands’ brain regions, which are active across all WM conditions. Contrary to early neuropsychological perspectives, these aspects of WM do not map exclusively tobrain areas or processing streams; however, the mappings from that literature form salient features within the corresponding multivariate connectivity patterns. Furthermore, connectivity patterns provide the most precise basis for classification and become fine-tuned as maintenance load increases. These results accord with a network-codingmechanism, where the same brain regions support diverse WM demands by adopting different connectivity states.
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Journal articleLi L, Ribeiro Violante I, Leech R, et al., 2019,
Brain state and polarity dependent modulation of brain networks by transcranial direct current stimulation
, Human Brain Mapping, Vol: 40, Pages: 904-915, ISSN: 1065-9471Despite its widespread use in cognitive studies, there is still limited understanding of whether and how transcranial direct current stimulation (tDCS) modulates brain network function. To clarify its physiological effects, we assessed brain network function using functional magnetic resonance imaging (fMRI) simultaneously acquired during tDCS stimulation. Cognitive state was manipulated by having subjects perform a Choice Reaction Task or being at “rest.” A novel factorial design was used to assess the effects of brain state and polarity. Anodal and cathodal tDCS were applied to the right inferior frontal gyrus (rIFG), a region involved in controlling activity large‐scale intrinsic connectivity networks during switches of cognitive state. tDCS produced widespread modulation of brain activity in a polarity and brain state dependent manner. In the absence of task, the main effect of tDCS was to accentuate default mode network (DMN) activation and salience network (SN) deactivation. In contrast, during task performance, tDCS increased SN activation. In the absence of task, the main effect of anodal tDCS was more pronounced, whereas cathodal tDCS had a greater effect during task performance. Cathodal tDCS also accentuated the within‐DMN connectivity associated with task performance. There were minimal main effects of stimulation on network connectivity. These results demonstrate that rIFG tDCS can modulate the activity and functional connectivity of large‐scale brain networks involved in cognitive function, in a brain state and polarity dependent manner. This study provides an important insight into mechanisms by which tDCS may modulate cognitive function, and also has implications for the design of future stimulation studies.
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Conference paperChen C, Bai W, Rueckert D, 2019,
Multi-task learning for left atrial segmentation on GE-MRI
, International Workshop on Statistical Atlases and Computational Models of the Heart, Publisher: Springer Verlag, Pages: 292-301, ISSN: 0302-9743Segmentation of the left atrium (LA) is crucial for assessing its anatomy in both pre-operative atrial fibrillation (AF) ablation planning and post-operative follow-up studies. In this paper, we present a fully automated framework for left atrial segmentation in gadolinium-enhanced magnetic resonance images (GE-MRI) based on deep learning. We propose a fully convolutional neural network and explore the benefits of multi-task learning for performing both atrial segmentation and pre/post ablation classification. Our results show that, by sharing features between related tasks, the network can gain additional anatomical information and achieve more accurate atrial segmentation, leading to a mean Dice score of 0.901 on a test set of 20 3D MRI images. Code of our proposed algorithm is available at https://github.com/cherise215/atria_segmentation_2018/.
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Journal articleThayyil S, Liow N, Montaldo P, et al., 2019,
Pre-emptive morphine during therapeutic hypothermia after neonatal encephalopathy: a secondary analysis
, Therapeutic Hypothermia and Temperature Management, Vol: 10, Pages: 45-52, ISSN: 2153-7658Although therapeutic hypothermia (TH) improves outcomes after neonatal encephalopathy (NE), the safety and efficacy of preemptive opioid sedation during cooling therapy is unclear. We performed a secondary analysis of the data from a large multicountry prospective observational study (Magnetic Resonance Biomarkers in Neonatal Encephalopathy [MARBLE]) to examine the association of preemptive morphine infusion during TH on brain injury and neurodevelopmental outcomes after NE. All recruited infants had 3.0 Tesla magnetic resonance imaging and spectroscopy at 1 week, and neurodevelopmental outcome assessments at 22 months. Of 223 babies recruited to the MARBLE study, the data on sedation were available from 169 babies with moderate (n = 150) or severe NE (n = 19). Although the baseline characteristics and admission status were similar, the babies who received morphine infusion (n = 141) were more hypotensive (49% vs. 25%, p = 0.02) and had a significantly longer hospital stay (12 days vs. 9 days, p = 0.009) than those who did not (n = 28). Basal ganglia/thalamic injury (score ≥1) and cortical injury (score ≥1) was seen in 34/141 (24%) and 37/141 (26%), respectively, of the morphine group and 4/28 (14%) and 3/28 (11%) of the nonmorphine group (p > 0.05). On regression modeling adjusted for potential confounders, preemptive morphine was not associated with mean (standard deviation [SD]) thalamic N-acetylaspartate (NAA) concentration (6.9 ± 0.9 vs. 6.5 ± 1.5; p = 0.97), and median (interquartile range) lactate/NAA peak area ratios (0.16 [0.12–0.21] vs. 0.13 [0.11–0.18]; p = 0.20) at 1 week, and mean (SD) Bayley-III composite motor (92 ± 23 vs. 94 ± 10; p = 0.98), language (89 ± 22 vs. 93 ±
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Journal articleGilbert K, Bai W, Mauger C, et al., 2019,
Independent left ventricular morphometric atlases show consistent relationships with cardiovascular risk factors: A UK Biobank study
, Scientific Reports, Vol: 9, ISSN: 2045-2322Left ventricular (LV) mass and volume are important indicators of clinical and pre-clinical disease processes. However, much of the shape information present in modern imaging examinations is currently ignored. Morphometric atlases enable precise quantification of shape and function, but there has been no objective comparison of different atlases in the same cohort. We compared two independent LV atlases using MRI scans of 4547 UK Biobank participants: (i) a volume atlas derived by automatic non-rigid registration of image volumes to a common template, and (ii) a surface atlas derived from manually drawn epicardial and endocardial surface contours. The strength of associations between atlas principal components and cardiovascular risk factors (smoking, diabetes, high blood pressure, high cholesterol and angina) were quantified with logistic regression models and five-fold cross validation, using area under the ROC curve (AUC) and Akaike Information Criterion (AIC) metrics. Both atlases exhibited similar principal components, showed similar relationships with risk factors, and had stronger associations (higher AUC and lower AIC) than a reference model based on LV mass and volume, for all risk factors (DeLong p < 0.05). Morphometric variations associated with each risk factor could be quantified and visualized and were similar between atlases. UK Biobank LV shape atlases are robust to construction method and show stronger relationships with cardiovascular risk factors than mass and volume.
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Journal articleCreese B, Brooker H, Ismail Z, et al., 2019,
Mild Behavioral Impairment as a Marker of Cognitive Decline in Cognitively Normal Older Adults
, American Journal of Geriatric Psychiatry, ISSN: 1064-7481© 2019 American Association for Geriatric Psychiatry Objective: Mild behavioral impairment (MBI) is a neurobehavioral syndrome characterized by later life emergent neuropsychiatric symptoms (NPS) that represent an at-risk state for incident cognitive decline and dementia in people with mild cognitive impairment (MCI). We undertook a study to determine whether MBI was associated with progressive changes in neuropsychological performance in people without significant cognitive impairment. Methods: A total of 9,931 older adults enrolled in the PROTECT study who did not have MCI or dementia undertook a comprehensive neuropsychological battery measuring attention, reasoning, executive function, and working memory at baseline and 1 year. MBI was ascertained using self-administration of the Mild Behavioral Impairment Checklist at 1 year, and participants were grouped according to MBI status: No Symptoms, Intermediate NPS and MBI. All assessments were completed online, and data analyzed using mixed-effects model repeated measures analysis of covariance. Results: A total of 949 (10%) people had MBI. These individuals had significantly worse cognitive performance at baseline and significantly greater decline over 1 year in the four composite cognitive scores measuring attentional intensity (F [2,8578] = 3.97; p = 0.019), sustained attention (F [2,8578] = 18.63; p <0.0001), attentional fluctuation (F [2,8578] = 10.13; p <0.0001) and working memory (F [2,9895] = 13.1; p <0.0001). Conclusion: Our novel findings show that MBI is associated with faster decline in attention and working memory in this cognitively normal sample. MBI may be an earlier marker of neurodegenerative disease than MCI, captured at the stage of subjective cognitive decline or before, raising the possibility that MBI represents a novel target for dementia clinical trials or prevention strategies.
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