Multi-dynamic Modelling Reveals Strongly Time-varying Resting fMRI Correlations

•

MAGE is multi-dynamic in that it models temporal fluctuations in FC independently from fluctuations in the mean of the activity.

•

MAGE reveals stronger changes in FC over time than single-dynamic approaches, such as sliding window correlations.

•

Multi-dynamic modelling provides an explanation and a solution as to why resting fMRI FC has previously looked so stable.

•

MAGE models fMRI data as a set of reoccurring brain states, and importantly, these states do not have to be binary and mutually exclusive (e.g., multiple states can be active at one time-point).

•

MAGE estimated time-varying FC is a better predictor of behavioural variability in the resting-state fMRI data than established methods.

The activity of functional brain networks is responsible for the emergence of time-varying cognition and behaviour. Accordingly, time-varying correlations (Functional Connectivity) in resting fMRI have been shown to be predictive of behavioural traits, and psychiatric and neurological conditions. Typically, methods that measure time varying Functional Connectivity (FC), such as sliding windows approaches, do not separately model when changes occur in the mean activity levels from when changes occur in the FC, therefore conflating these two distinct types of modulation. We show that this can bias the estimation of time-varying FC to appear more stable over time than it actually is. Here, we propose an alternative approach that models changes in the mean brain activity and in the FC as being able to occur at different times to each other. We refer to this method as the Multi-dynamic Adversarial Generator Encoder (MAGE) model, which includes a model of the network dynamics that captures long-range time dependencies, and is estimated on fMRI data using principles of Generative Adversarial Networks. We evaluated the approach across several simulation studies and resting fMRI data from the Human Connectome Project (1003 subjects), as well as from UK Biobank (13301 subjects). Importantly, we find that separating fluctuations in the mean activity levels from those in the FC reveals much stronger changes in FC over time, and is a better predictor of individual behavioural variability.

The Human Connectome Project: A retrospective

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium.

Leisure Activities and Their Relationship With MRI Measures of Brain Structure, Functional Connectivity, and Cognition in the UK Biobank Cohort

Introduction: This study aimed to evaluate whether engagement in leisure activities is linked to measures of brain structure, functional connectivity, and cognition in early old age. Methods: We examined data collected from 7,152 participants of the United Kingdom Biobank (UK Biobank) study. Weekly participation in six leisure activities was assessed twice and a cognitive battery and 3T MRI brain scan were administered at the second visit. Based on responses collected at two time points, individuals were split into one of four trajectory groups: (1) stable low engagement, (2) stable weekly engagement, (3) low to weekly engagement, and (4) weekly to low engagement. Results: Consistent weekly attendance at a sports club or gym was associated with connectivity of the sensorimotor functional network with the lateral visual (β = 0.12, 95%CI = [0.07, 0.18], FDR q = 2.48 × 10-3) and cerebellar (β = 0.12, 95%CI = [0.07, 0.18], FDR q = 1.23 × 10-4) networks. Visiting friends and family across the two timepoints was also associated with larger volumes of the occipital lobe (β = 0.15, 95%CI = [0.08, 0.21], FDR q = 0.03). Additionally, stable and weekly computer use was associated with global cognition (β = 0.62, 95%CI = [0.35, 0.89], FDR q = 1.16 × 10-4). No other associations were significant (FDR q > 0.05). Discussion: This study demonstrates that not all leisure activities contribute to cognitive health equally, nor is there one unifying neural signature across diverse leisure activities.

Individual variations in 'brain age' relate to early-life factors more than to longitudinal brain change

Brain age is a widely used index for quantifying individuals’ brain health as deviation from a normative brain aging trajectory. Higher-than-expected brain age is thought partially to reflect above-average rate of brain aging. Here, we explicitly tested this assumption in two independent large test datasets (UK Biobank [main] and Lifebrain [replication]; longitudinal observations ≈ 2750 and 4200) by assessing the relationship between cross-sectional and longitudinal estimates of brain age. Brain age models were estimated in two different training datasets (n ≈ 38,000 [main] and 1800 individuals [replication]) based on brain structural features. The results showed no association between cross-sectional brain age and the rate of brain change measured longitudinally. Rather, brain age in adulthood was associated with the congenital factors of birth weight and polygenic scores of brain age, assumed to reflect a constant, lifelong influence on brain structure from early life. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging.

Scientists who study the brain and aging are keen to find an effective way to measure brain health, which could help identify people at risk for dementia or memory problems. One popular marker is ‘brain age’. This measurement uses a brain scan to estimate a person’s chronological age, then compares the estimated brain age to the person’s actual age to determine whether their brain is aging faster or slower than expected for their age.

However, since brain age relies on one brain scan taken at one point in time, it is not clear whether it really measures brain aging or if it might capture brain differences that have been present throughout the individual’s life. Studies comparing individual brain scans over several years would be necessary to know for sure.

Now, Vidal-Piñeiro et al. show that the brain-age measurement does not reflect faster brain aging. In the experiments, the researchers compared repeated brain scans of thousands of individuals over 40 years of age. The experiments showed that deviations from normative brain age detected in a single scan reflected early life differences more than changes in the brain over time. For example, people with older-looking brains were more likely to have had a low birth weight or to have a combination of genes associated with having an older looking brain.

Vidal-Piñeiro et al. show that brain age mostly reflects a pre-existing brain condition rather than brain aging. The experiments also suggest that genetics and early brain development likely have a strong impact on brain health throughout life. Future studies trying to test or develop brain-aging measurements should use serial measurements to track brain changes over time.

Adapting the UK Biobank Brain Imaging Protocol and Analysis Pipeline for the C-MORE Multi-Organ Study of COVID-19 Survivors

SARS-CoV-2 infection has been shown to damage multiple organs, including the brain. Multiorgan MRI can provide further insight on the repercussions of COVID-19 on organ health but requires a balance between richness and quality of data acquisition and total scan duration. We adapted the UK Biobank brain MRI protocol to produce high-quality images while being suitable as part of a post-COVID-19 multiorgan MRI exam. The analysis pipeline, also adapted from UK Biobank, includes new imaging-derived phenotypes (IDPs) designed to assess the possible effects of COVID-19. A first application of the protocol and pipeline was performed in 51 COVID-19 patients post-hospital discharge and 25 controls participating in the Oxford C-MORE study. The protocol acquires high resolution T1, T2-FLAIR, diffusion weighted images, susceptibility weighted images, and arterial spin labelling data in 17 min. The automated imaging pipeline derives 1,575 IDPs, assessing brain anatomy (including olfactory bulb volume and intensity) and tissue perfusion, hyperintensities, diffusivity, and susceptibility. In the C-MORE data, IDPs related to atrophy, small vessel disease and olfactory bulbs were consistent with clinical radiology reports. Our exploratory analysis tentatively revealed some group differences between recovered COVID-19 patients and controls, across severity groups, but not across anosmia groups. Follow-up imaging in the C-MORE study is currently ongoing, and this protocol is now being used in other large-scale studies. The protocol, pipeline code and data are openly available and will further contribute to the understanding of the medium to long-term effects of COVID-19.

Viscoelastic Testing and Coagulopathy of Traumatic Brain Injury

A unique coagulopathy often manifests following traumatic brain injury, leading the clinician down a difficult decision path on appropriate prophylaxis and therapy. Conventional coagulation assays—such as prothrombin time, partial thromboplastin time, and international normalized ratio—have historically been utilized to assess hemostasis and guide treatment following traumatic brain injury. However, these plasma-based assays alone often lack the sensitivity to diagnose and adequately treat coagulopathy associated with traumatic brain injury. Here, we review the whole blood coagulation assays termed viscoelastic tests and their use in traumatic brain injury. Modified viscoelastic tests with platelet function assays have helped elucidate the underlying pathophysiology and guide clinical decisions in a goal-directed fashion. Platelet dysfunction appears to underlie most coagulopathies in this patient population, particularly at the adenosine diphosphate and/or arachidonic acid receptors. Future research will focus not only on the utility of viscoelastic tests in diagnosing coagulopathy in traumatic brain injury, but also on better defining the use of these tests as evidence-based and/or precision-based tools to improve patient outcomes.

Media coverage of evidence outputs during the COVID-19 pandemic: findings from one national agency

Background

The COVID-19 Evidence Synthesis Team within the Health Information and Quality Authority (HIQA) in Ireland produced a range of evidence-based reports on a broad range of public health topics related to COVID-19. These evidence outputs (EO) arose directly from questions posed by policy makers and clinicians supporting Ireland's National Public Health Emergency Team (NPHET). Findings from these EOs informed the national response to the COVID-19 pandemic in Ireland and influenced international public health guidance. How research findings are presented through domestic news can influence behaviour and risk perceptions.

Methods

We investigated traditional media coverage of nine COVID-19 EOs and associated press releases, published (April to July 2020) by HIQA. NVivo was used for conceptual content analysis of manifest content. ‘Core messages' from each evidence output were proposed and 488 sources from national and regional broadcast, print, and online media were coded at the phrase level. The presence of political and public health actors in coverage were also coded.

Results

Coverage largely did not distort or misrepresent the results of the EOs, however, there was variability in terms of what content was reported on and to what extent different stakeholders were involved in the contextualization of the findings of the EOs. Coverage appeared to focus more on ‘human-interest' stories as opposed to more technical reports (e.g. focusing on viral load, antibodies, testing, etc.). Selective reporting and the variability in the use of quotes from governmental and public health stakeholders changed and contextualized results in different manners than perhaps originally intended in the press release.

Conclusions

Our findings provide a case-study of European media coverage of evidence reports produced by a national agency. Results highlighted several strengths and weaknesses of current communication efforts.

Impact of Transoral Endoscopic Vestibular Approach Thyroidectomy on Pathologic Assessment

CONTEXT.—

Since 2016, transoral endoscopic thyroid resection with vestibular approach (TOETVA) has become increasingly performed in the United States. Although guidelines for the procedure are evolving, indeterminate and malignant preoperative cytopathologic diagnoses are not a contraindication. There are limited data related to the pathologic examination of these specimens.

OBJECTIVE.—

To examine the clinicopathologic features of TOETVA specimens with particular attention to limitations of interpretation of pathologic parameters and final diagnosis.

DESIGN.—

We reviewed age, sex, preoperative imaging and cytologic diagnoses, surgical pathology, and clinical follow-up data in TOETVA resections at our institution between March 2016 and December 2019.

RESULTS.—

Fifty cases of TOETVA were identified, comprising 48 women and 2 men with a mean age of 47 years. Preoperative cytologic diagnoses were available in 47 cases and included 19 nondiagnostic/benign (Bethesda I/II), 24 follicular lesion of undetermined significance/suspicious for follicular neoplasm (Bethesda III/IV), and 4 suspicious/malignant diagnoses (Bethesda V/VI). Thirty-four cases (68%) among the surgical resection specimens showed disruption and/or fragmentation. Thirty-nine cases were negative for carcinoma, including hyperplasias and benign/indolent neoplasms. Eleven cases exhibited papillary thyroid carcinoma. Final diagnoses were reached in all disrupted/fragmented cases. In 2 cases of papillary thyroid carcinoma, tumor size, microscopic extrathyroidal extension, and margin status could not be determined.

CONCLUSIONS.—

A significant proportion of TOETVA specimens are disrupted/fragmented, which can compromise information about tumors, including size, number, margin status, and microscopic extrathyroidal extension. Given that these parameters inform treatment and follow-up, this should be considered when selecting patients for TOETVA.

Evaluating rapid review provision to inform policy during the COVID-19 pandemic

Background

Evidence based decision making is central to population health, particularly during a pandemic. Few studies examine the production and use of evidence in decision-making during crisis management. This study describes and evaluates the provision of rapid evidence products by a single agency to support national decision making.

Methods

Semi-structured interviews were conducted with a convenience sample of evidence providers (who gathered and disseminated the required evidence) and service users (policy makers). Interviews were transcribed verbatim and analysed using thematic analysis. Quantitative data of work activity were summarised descriptively.

Results

Three themes were generated from the data: the work, the use and the future, with a fourth theme cross-cutting across these: the team. The work followed clear protocols and was centrally managed. The scope and changing nature of the evidence were highlighted as issues. The service providers reported a strong sense of team work and ‘being in this together', however, the majority of the participants felt that the workload was huge and exhausting and not sustainable long-term. Overall the service users thought the rapid evidence synthesis was indispensable to the decision-making process and had trust and confidence in the work, largely based on existing working relationships with the team. While they recognised that the evidence synthesis support would be an essential component of the continued pandemic response, they did query the sustainability of the process and reflected on the amount of work the team performed.

Conclusions

This evaluation, drawing on qualitative data, has highlighted that, across the services users and evidence providers, the support provided by HIQA was generally perceived as positive. From the service users' perspective, having access to the team was indispensable to the decision making process. However, the sustainability of the work load was identified as a major challenge.

COVID-19 - Interventions and lifestyle factors that prevent infection or minimise progression to severe disease

Background

This evidence summary synthesised the evidence relating to pharmacological and non-pharmacological interventions in the community to prevent COVID-19/progression to severe disease. An additional aim was to identify potentially modifiable lifestyle factors associated with reduced risk of infection/progression to severe disease.

Methods

A systematic search of published peer-reviewed articles and non-peer-reviewed pre-prints was undertaken from 1 January 2020 to 19 April 2021; no language restrictions were applied. All potentially eligible papers were exported to Covidence. Titles/abstracts and full texts were single screened for relevance. Data extraction and quality appraisal of included studies was completed by a single reviewer and checked by a second.

Results

In total, 50 studies, three randomised controlled trials (RCTs), one non-RCT and 46 cohort studies were included. The four included controlled trials tested variations of the pharmacological intervention, ivermectin. While these controlled trials reported a protective effect for ivermectin use, these trials were of poor quality and had serious risk of bias. Across 46 cohort studies, the modifiable lifestyle risk factors identified were obesity, smoking, vitamin D status, physical activity, alcohol consumption and processed meat consumption. These studies reported mixed results in terms of the association between modifiable lifestyle risk factors and poor COVID-19 outcomes.

Conclusions

At the time of writing there is no high quality evidence of benefit to support pharmacological interventions to prevent COVID-19. Although there were mixed results for the risk factors identified, maintenance of healthy weight, smoking cessation, engaging in physical activity and moderation of alcohol and processed meat consumption are likely to be beneficial to health and should continue to be encouraged.

Hierarchical modelling of functional brain networks in population and individuals from big fMRI data

•

We introduce stochastic PROFUMO (sPROFUMO) for inferring functional brain networks from big data.

•

sPROFUMO hierarchically estimates fMRI networks for the population and every individual.

•

We characterised high dimensional resting state fMRI networks from UK Biobank.

•

Model outperforms ICA and dual regression for estimation of individual-specific network topography.

•

We demonstrate the model's utility for predicting cognitive traits, and capturing subject variability in network topographies versus connectivity.

A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data.

Construction and validation of an ultraviolet germicidal irradiation system using locally available components

Coronavirus disease (COVID-19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, is responsible for a global pandemic characterized by high transmissibility and morbidity. Healthcare workers (HCWs) are at risk of contracting COVID-19, but this risk has been mitigated through the use of personal protective equipment such as N95 Filtering Facepiece Respirators (FFRs). At times the high demand for FFRs has exceeded supply, placing HCWs at increased exposure risk. Effective FFR decontamination of many FFR models using ultraviolet-C germicidal irradiation (UVGI) has been well-described, and could maintain respiratory protection for HCWs in the face of supply line shortages. Here, we detail the construction of an ultraviolet-C germicidal irradiation (UVGI) device using previously existing components available at our institution. We provide data on UV-C dosage delivered with our version of this device, provide information on how users can validate the UV-C dose delivered in similarly constructed systems, and describe a simple, novel methodology to test its germicidal effectiveness using in-house reagents and equipment. As similar components are readily available in many hospitals and industrial facilities, we provide recommendations on the local construction of these systems, as well as guidance and strategies towards successful institutional implementation of FFR decontamination.

Phenotyping the Preterm Brain: Characterizing Individual Deviations From Normative Volumetric Development in Two Large Infant Cohorts

The diverse cerebral consequences of preterm birth create significant challenges for understanding pathogenesis or predicting later outcome. Instead of focusing on describing effects common to the group, comparing individual infants against robust normative data offers a powerful alternative to study brain maturation. Here we used Gaussian process regression to create normative curves characterizing brain volumetric development in 274 term-born infants, modeling for age at scan and sex. We then compared 89 preterm infants scanned at term-equivalent age with these normative charts, relating individual deviations from typical volumetric development to perinatal risk factors and later neurocognitive scores. To test generalizability, we used a second independent dataset comprising of 253 preterm infants scanned using different acquisition parameters and scanner. We describe rapid, nonuniform brain growth during the neonatal period. In both preterm cohorts, cerebral atypicalities were widespread, often multiple, and varied highly between individuals. Deviations from normative development were associated with respiratory support, nutrition, birth weight, and later neurocognition, demonstrating their clinical relevance. Group-level understanding of the preterm brain disguises a large degree of individual differences. We provide a method and normative dataset that offer a more precise characterization of the cerebral consequences of preterm birth by profiling the individual neonatal brain.

Dopaminergic organization of striatum is linked to cortical activity and brain expression of genes associated with psychiatric illness

Dopamine signaling is constrained to discrete tracts yet has brain-wide effects on neural activity. The nature of this relationship between local dopamine signaling and brain-wide neuronal activity is not clearly defined and has relevance for neuropsychiatric illnesses where abnormalities of cortical activity and dopamine signaling coexist. Using simultaneous PET-MRI in healthy volunteers, we find strong evidence that patterns of striatal dopamine signaling and cortical blood flow (an index of local neural activity) contain shared information. This shared information links amphetamine-induced changes in gradients of striatal dopamine receptor availability to changes in brain-wide blood flow and is informed by spatial patterns of gene expression enriched for genes implicated in schizophrenia, bipolar disorder, and autism spectrum disorder. These results advance our knowledge of the relationship between cortical function and striatal dopamine, with relevance for understanding pathophysiology and treatment of diseases in which simultaneous aberrations of these systems exist.

A structural brain network of genetic vulnerability to psychiatric illness

Psychiatry is undergoing a paradigm shift from the acceptance of distinct diagnoses to a representation of psychiatric illness that crosses diagnostic boundaries. How this transition is supported by a shared neurobiology remains largely unknown. In this study, we first identify single nucleotide polymorphisms (SNPs) associated with psychiatric disorders based on 136 genome-wide association studies. We then conduct a joint analysis of these SNPs and brain structural connectomes in 678 healthy children in the PING study. We discovered a strong, robust, and transdiagnostic mode of genome-connectome covariation which is positively and specifically correlated with genetic risk for psychiatric illness at the level of individual SNPs. Similarly, this mode is also significantly positively correlated with polygenic risk scores for schizophrenia, alcohol use disorder, major depressive disorder, a combined bipolar disorder-schizophrenia phenotype, and a broader cross-disorder phenotype, and significantly negatively correlated with a polygenic risk score for educational attainment. The resulting "vulnerability network" is shown to mediate the influence of genetic risks onto behaviors related to psychiatric vulnerability (e.g., marijuana, alcohol, and caffeine misuse, perceived stress, and impulsive behavior). Its anatomy overlaps with the default-mode network, with a network of cognitive control, and with the occipital cortex. These findings suggest that the brain vulnerability network represents an endophenotype funneling genetic risks for various psychiatric illnesses through a common neurobiological root. It may form part of the neural underpinning of the well-recognized but poorly explained overlap and comorbidity between psychiatric disorders.

Enhanced excitability of cortical neurons in low-divalent solutions is primarily mediated by altered voltage-dependence of voltage-gated sodium channels

Increasing extracellular [Ca2+] ([Ca2+]o) strongly decreases intrinsic excitability in neurons but the mechanism is unclear. By one hypothesis, [Ca2+]o screens surface charge, reducing voltage-gated sodium channel (VGSC) activation and by another [Ca2+]o activates Calcium-sensing receptor (CaSR) closing the sodium-leak channel (NALCN). Here we report that neocortical neurons from CaSR-deficient (Casr-/-) mice had more negative resting potentials and did not fire spontaneously in reduced divalent-containing solution (T0.2) in contrast with wild-type (WT). However, after setting membrane potential to −70 mV, T0.2 application similarly depolarized and increased action potential firing in Casr-/- and WT neurons. Enhanced activation of VGSCs was the dominant contributor to the depolarization and increase in excitability by T0.2 and occurred due to hyperpolarizing shifts in VGSC window currents. CaSR deletion depolarized VGSC window currents but did not affect NALCN activation. Regulation of VGSC gating by external divalents is the key mechanism mediating divalent-dependent changes in neocortical neuron excitability.

Restoring CD8+ T cell anti-HIV activity using combinatorial immune checkpoint blockade

Antiretroviral therapy (ART) effectively reduces viral load and improves life expectancy but does not fully restore host immune function. During chronic infection, T cells upregulate multiple inhibitory immune checkpoint receptors (ICR) which results in the loss of T cell functionality; a state called T cell exhaustion. In this study, we utilized HIV-1 controllers (HC) who have an inherent ability to control viral replication in the absence of ART but eventually gain viral susceptibility due to unknown mechanisms. We hypothesize that efficient control of viral replication becomes compromised by CD8+ T cell exhaustion. Using our cohort, we demonstrate that CD8+ T cells of ART naïve HCs are both necessary and sufficient to suppress viral replication. We also demonstrate that during times of clinical viral control, ART naïve HCs have low expression of exhaustion markers and sustain in vitro viral control. Upon loss of control, our HCs upregulate exhaustion markers and are susceptible to in vitro infection. This study evaluates the synergistic effects of immune checkpoint blockade (ICB) to determine if blockade of ICRs can restore anti-viral functionality of exhausted CD8+ T cells. Using our in vitro spreading infection assay, we demonstrate that treatment with single ICB results in a modest improvement of anti-viral functionality in CD8+ T cells from HCs who have lost viral control. However, former HCs who support in vitro viral replication demonstrate restored suppression of viral replication in tissue culture in the presence of combinatorial anti-PD1 and anti-TIM3 ICB. Our data suggests that combinatorial ICB can restore CD8+ T cells anti-viral functionality and has the potential to be a novel HIV therapeutic intervention.

An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank

UK Biobank is a major prospective epidemiological study, including multimodal brain imaging, genetics and ongoing health outcomes. Previously, we published genome-wide associations of 3,144 brain imaging-derived phenotypes, with a discovery sample of 8,428 individuals. Here we present a new open resource of genome-wide association study summary statistics, using the 2020 data release, almost tripling the discovery sample size. We now include the X chromosome and new classes of imaging-derived phenotypes (subcortical volumes and tissue contrast). Previously, we found 148 replicated clusters of associations between genetic variants and imaging phenotypes; in this study, we found 692, including 12 on the X chromosome. We describe some of the newly found associations, focusing on the X chromosome and autosomal associations involving the new classes of imaging-derived phenotypes. Our novel associations implicate, for example, pathways involved in the rare X-linked STAR (syndactyly, telecanthus and anogenital and renal malformations) syndrome, Alzheimer's disease and mitochondrial disorders.

Imaging of the Primary Visual Pathway based on Visual Deficits

Vision loss can occur due to a variety of etiologies along the primary visual pathway. Understanding the anatomic organization of the visual pathway, which spans the globe to the occipital cortex, can help tailor neuroimaging to identify the cause of visual dysfunction. In this review, relevant anatomy and optimization of computed tomography and magnetic resonance imaging techniques will be described. This will be followed by a discussion of imaging findings related to pathologies at each functional anatomic level.

Behavioural relevance of spontaneous, transient brain network interactions in fMRI

How spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods to Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture.

Phenotype discovery from population brain imaging

•

A multimodal independent component analysis approach is presented for performing data fusion in UK biobank scale dataset.

•

This approach can estimate modes of population variability that enhance the ability to predict thousands of non-imaging phenotypes.

•

This approach improves predictive power compared with widely-used analysis strategies, single-modality decompositions and existing IDPs.

•

In UKB data, many interpretable associations with non-imaging phenotypes were identified.

Neuroimaging allows for the non-invasive study of the brain in rich detail. Data-driven discovery of patterns of population variability in the brain has the potential to be extremely valuable for early disease diagnosis and understanding the brain. The resulting patterns can be used as imaging-derived phenotypes (IDPs), and may complement existing expert-curated IDPs. However, population datasets, comprising many different structural and functional imaging modalities from thousands of subjects, provide a computational challenge not previously addressed. Here, for the first time, a multimodal independent component analysis approach is presented that is scalable for data fusion of voxel-level neuroimaging data in the full UK Biobank (UKB) dataset, that will soon reach 100,000 imaged subjects. This new computational approach can estimate modes of population variability that enhance the ability to predict thousands of phenotypic and behavioural variables using data from UKB and the Human Connectome Project. A high-dimensional decomposition achieved improved predictive power compared with widely-used analysis strategies, single-modality decompositions and existing IDPs. In UKB data (14,503 subjects with 47 different data modalities), many interpretable associations with non-imaging phenotypes were identified, including multimodal spatial maps related to fluid intelligence, handedness and disease, in some cases where IDP-based approaches failed.

The Developing Human Connectome Project: typical and disrupted perinatal functional connectivity

The Developing Human Connectome Project is an Open Science project that provides the first large sample of neonatal functional MRI data with high temporal and spatial resolution. These data enable mapping of intrinsic functional connectivity between spatially distributed brain regions under normal and adverse perinatal circumstances, offering a framework to study the ontogeny of large-scale brain organization in humans. Here, we characterize in unprecedented detail the maturation and integrity of resting state networks (RSNs) at term-equivalent age in 337 infants (including 65 born preterm). First, we applied group independent component analysis to define 11 RSNs in term-born infants scanned at 43.5–44.5 weeks postmenstrual age (PMA). Adult-like topography was observed in RSNs encompassing primary sensorimotor, visual and auditory cortices. Among six higher-order, association RSNs, analogues of the adult networks for language and ocular control were identified, but a complete default mode network precursor was not. Next, we regressed the subject-level datasets from an independent cohort of infants scanned at 37–43.5 weeks PMA against the group-level RSNs to test for the effects of age, sex and preterm birth. Brain mapping in term-born infants revealed areas of positive association with age across four of six association RSNs, indicating active maturation in functional connectivity from 37 to 43.5 weeks PMA. Female infants showed increased connectivity in inferotemporal regions of the visual association network. Preterm birth was associated with striking impairments of functional connectivity across all RSNs in a dose-dependent manner; conversely, connectivity of the superior parietal lobules within the lateral motor network was abnormally increased in preterm infants, suggesting a possible mechanism for specific difficulties such as developmental coordination disorder, which occur frequently in preterm children. Overall, we found a robust, modular, symmetrical functional brain organization at normal term age. A complete set of adult-equivalent primary RSNs is already instated, alongside emerging connectivity in immature association RSNs, consistent with a primary-to-higher order ontogenetic sequence of brain development. The early developmental disruption imposed by preterm birth is associated with extensive alterations in functional connectivity.

Shared and Anxiety-Specific Pediatric Psychopathology Dimensions Manifest Distributed Neural Correlates

BACKGROUND

Imaging research has not yet delivered reliable psychiatric biomarkers. One challenge, particularly among youth, is high comorbidity. This challenge might be met through canonical correlation analysis designed to model mutual dependencies between symptom dimensions and neural measures. We mapped the multivariate associations that intrinsic functional connectivity manifests with pediatric symptoms of anxiety, irritability, and attention-deficit/hyperactivity disorder (ADHD) as common, impactful, co-occurring problems. We evaluate the replicability of such latent dimensions in an independent sample.

METHODS

We obtained ratings of anxiety, irritability, and ADHD, and 10 minutes of resting-state functional magnetic resonance imaging data, from two independent cohorts. Both cohorts (discovery: n = 182; replication: n = 326) included treatment-seeking youth with anxiety disorders, with disruptive mood dysregulation disorder, with ADHD, or without psychopathology. Functional connectivity was modeled as partial correlations among 216 brain areas. Using canonical correlation analysis and independent component analysis jointly we sought maximally correlated, maximally interpretable latent dimensions of brain connectivity and clinical symptoms.

RESULTS

We identified seven canonical variates in the discovery and five in the replication cohort. Of these canonical variates, three exhibited similarities across datasets: two variates consistently captured shared aspects of irritability, ADHD, and anxiety, while the third was specific to anxiety. Across cohorts, canonical variates did not relate to specific resting-state networks but comprised edges interconnecting established networks within and across both hemispheres.

CONCLUSIONS

Findings revealed two replicable types of clinical variates, one related to multiple symptom dimensions and a second relatively specific to anxiety. Both types involved a multitude of broadly distributed, weak brain connections as opposed to strong connections encompassing known resting-state networks.

Brain pathology recapitulates physiology: A network meta-analysis

Network architecture is a brain-organizational motif present across spatial scales from cell assemblies to distributed systems. Structural pathology in some neurodegenerative disorders selectively afflicts a subset of functional networks, motivating the network degeneration hypothesis (NDH). Recent evidence suggests that structural pathology recapitulating physiology may be a general property of neuropsychiatric disorders. To test this possibility, we compared functional and structural network meta-analyses drawing upon the BrainMap database. The functional meta-analysis included results from >7,000 experiments of subjects performing >100 task paradigms; the structural meta-analysis included >2,000 experiments of patients with >40 brain disorders. Structure-function network concordance was high: 68% of networks matched (pFWE < 0.01), confirming the broader scope of NDH. This correspondence persisted across higher model orders. A positive linear association between disease and behavioral entropy (p = 0.0006;R2 = 0.53) suggests nodal stress as a common mechanism. Corroborating this interpretation with independent data, we show that metabolic 'cost' significantly differs along this transdiagnostic/multimodal gradient.

Accurate brain age prediction with lightweight deep neural networks

Deep learning has huge potential for accurate disease prediction with neuroimaging data, but the prediction performance is often limited by training-dataset size and computing memory requirements. To address this, we propose a deep convolutional neural network model, Simple Fully Convolutional Network (SFCN), for accurate prediction of brain age using T1-weighted structural MRI data. Compared with other popular deep network architectures, SFCN has fewer parameters, so is more compatible with small dataset size and 3D volume data. The network architecture was combined with several techniques for boosting performance, including data augmentation, pre-training, model regularization, model ensemble and prediction bias correction. We compared our overall SFCN approach with several widely-used machine learning models. It achieved state-of-the-art performance in UK Biobank data (N = 14,503), with mean absolute error (MAE) = 2.14y in brain age prediction and 99.5% in sex classification. SFCN also won (both parts of) the 2019 Predictive Analysis Challenge for brain age prediction, involving 79 competing teams (N = 2,638, MAE = 2.90y). We describe here the details of our approach, and its optimisation and validation. Our approach can easily be generalised to other tasks using different image modalities, and is released on GitHub.

Why do healthcare professionals fail to escalate as per the early warning system (EWS) protocol? A qualitative evidence synthesis of the barriers and facilitators of escalation

Background

Early warning systems (EWSs) are used to assist clinical judgment in the detection of acute deterioration to avoid or reduce adverse events including unanticipated cardiopulmonary arrest, admission to the intensive care unit and death. Sometimes healthcare professionals (HCPs) do not trigger the alarm and escalate for help according to the EWS protocol and it is unclear why this is the case. The aim of this qualitative evidence synthesis was to answer the question ‘why do HCPs fail to escalate care according to EWS protocols?’ The findings will inform the update of the National Clinical Effectiveness Committee (NCEC) National Clinical Guideline No. 1 Irish National Early Warning System (INEWS).

Methods

A systematic search of the published and grey literature was conducted (until February 2018). Data extraction and quality appraisal were conducted by two reviewers independently using standardised data extraction forms and quality appraisal tools. A thematic synthesis was conducted by two reviewers of the qualitative studies included and categorised into the barriers and facilitators of escalation. GRADE CERQual was used to assess the certainty of the evidence.

Results

Eighteen studies incorporating a variety of HCPs across seven countries were included. The barriers and facilitators to the escalation of care according to EWS protocols were developed into five overarching themes: Governance, Rapid Response Team (RRT) Response, Professional Boundaries, Clinical Experience, and EWS parameters. Barriers to escalation included: Lack of Standardisation, Resources, Lack of accountability, RRT behaviours, Fear, Hierarchy, Increased Conflict, Over confidence, Lack of confidence, and Patient variability. Facilitators included: Accountability, Standardisation, Resources, RRT behaviours, Expertise, Additional support, License to escalate, Bridge across boundaries, Clinical confidence, empowerment, Clinical judgment, and a tool for detecting deterioration. These are all individual yet inter-related barriers and facilitators to escalation.

Conclusions

The findings of this qualitative evidence synthesis provide insight into the real world experience of HCPs when using EWSs. This in turn has the potential to inform policy-makers and HCPs as well as hospital management about emergency response system-related issues in practice and the changes needed to address barriers and facilitators and improve patient safety and quality of care.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12873-021-00403-9.

Relationships between retinal layer thickness and brain volumes in the UK Biobank cohort

Background and purpose

Current methods to diagnose neurodegenerative diseases are costly and invasive. Retinal neuroanatomy may be a biomarker for more neurodegenerative processes and can be quantified in vivo using optical coherence tomography (OCT), which is inexpensive and noninvasive. We examined the association of neuroretinal morphology with brain MRI image‐derived phenotypes (IDPs) in a large cohort of healthy older people.

Methods

UK Biobank participants aged 40 to 69 years old underwent comprehensive examinations including ophthalmic and brain imaging assessments. Macular retinal nerve fibre layer (mRNFL), macular ganglion cell‐inner plexiform layer (mGCIPL), macular ganglion cell complex (mGCC) and total macular thicknesses were obtained from OCT. Magnetic resonance imaging (MRI) IDPs assessed included total brain, grey matter, white matter and hippocampal volume. Multivariable linear regression models were used to evaluate associations between retinal layers thickness and brain MRI IDPs, adjusting for demographic factors and vascular risk factors.

Results

A total of 2131 participants (mean age 55 years; 51% women) with both gradable OCT images and brain imaging assessments were included. In multivariable regression analysis, thinner mGCIPL, mGCC and total macular thickness were all significantly associated with smaller total brain (p < 0.001), grey matter and white matter volume (p < 0.01), and grey matter volume in the occipital pole (p < 0.05). Thinner mGCC and total macular thicknesses were associated with smaller hippocampal volume (p < 0.02). No association was found between mRNFL and the MRI IDPs.

Conclusions

Markers of retinal neurodegeneration are associated with smaller brain volumes. Our findings suggest that retinal structure may be a biomarker providing information about important brain structure in healthy older adults.

Large, Stable Spikes Exhibit Differential Broadening in Excitatory and Inhibitory Neocortical Boutons

Presynaptic action potential spikes control neurotransmitter release and thus interneuronal communication. However, the properties and the dynamics of presynaptic spikes in the neocortex remain enigmatic because boutons in the neocortex are small and direct patch-clamp recordings have not been performed. Here, we report direct recordings from boutons of neocortical pyramidal neurons and interneurons. Our data reveal rapid and large presynaptic action potentials in layer 5 neurons and fast-spiking interneurons reliably propagating into axon collaterals. For in-depth analyses, we establish boutons of mature cultured neurons as models for excitatory neocortical boutons, demonstrating that the presynaptic spike amplitude is unaffected by potassium channels, homeostatic long-term plasticity, and high-frequency firing. In contrast to the stable amplitude, presynaptic spikes profoundly broaden during high-frequency firing in layer 5 pyramidal neurons, but not in fast-spiking interneurons. Thus, our data demonstrate large presynaptic spikes and fundamental differences between excitatory and inhibitory boutons in the neocortex.

Optimising a Simple Fully Convolutional Network for Accurate Brain Age Prediction in the PAC 2019 Challenge

Brain age prediction from brain MRI scans not only helps improve brain ageing modelling generally, but also provides benchmarks for predictive analysis methods. Brain-age delta, which is the difference between a subject's predicted age and true age, has become a meaningful biomarker for the health of the brain. Here, we report the details of our brain age prediction models and results in the Predictive Analysis Challenge 2019. The aim of the challenge was to use T1-weighted brain MRIs to predict a subject's age in multicentre datasets. We apply a lightweight deep convolutional neural network architecture, Simple Fully Convolutional Neural Network (SFCN), and combined several techniques including data augmentation, transfer learning, model ensemble, and bias correction for brain age prediction. The model achieved first place in both of the two objectives in the PAC 2019 brain age prediction challenge: Mean absolute error (MAE) = 2.90 years without bias removal (Second Place = 3.09 yrs; Third Place = 3.33 yrs), and MAE = 2.95 years with bias removal, leading by a large margin (Second Place = 3.80 yrs; Third Place = 3.92 yrs).

Medium-term effects of SARS-CoV-2 infection on multiple vital organs, exercise capacity, cognition, quality of life and mental health, post-hospital discharge

BACKGROUND

The medium-term effects of Coronavirus disease (COVID-19) on organ health, exercise capacity, cognition, quality of life and mental health are poorly understood.

METHODS

Fifty-eight COVID-19 patients post-hospital discharge and 30 age, sex, body mass index comorbidity-matched controls were enrolled for multiorgan (brain, lungs, heart, liver and kidneys) magnetic resonance imaging (MRI), spirometry, six-minute walk test, cardiopulmonary exercise test (CPET), quality of life, cognitive and mental health assessments.

FINDINGS

At 2-3 months from disease-onset, 64% of patients experienced breathlessness and 55% reported fatigue. On MRI, abnormalities were seen in lungs (60%), heart (26%), liver (10%) and kidneys (29%). Patients exhibited changes in the thalamus, posterior thalamic radiations and sagittal stratum on brain MRI and demonstrated impaired cognitive performance, specifically in the executive and visuospatial domains. Exercise tolerance (maximal oxygen consumption and ventilatory efficiency on CPET) and six-minute walk distance were significantly reduced. The extent of extra-pulmonary MRI abnormalities and exercise intolerance correlated with serum markers of inflammation and acute illness severity. Patients had a higher burden of self-reported symptoms of depression and experienced significant impairment in all domains of quality of life compared to controls (p<0.0001 to 0.044).

INTERPRETATION

A significant proportion of patients discharged from hospital reported symptoms of breathlessness, fatigue, depression and had limited exercise capacity. Persistent lung and extra-pulmonary organ MRI findings are common in patients and linked to inflammation and severity of acute illness.

FUNDING

NIHR Oxford and Oxford Health Biomedical Research Centres, British Heart Foundation Centre for Research Excellence, UKRI, Wellcome Trust, British Heart Foundation.

A Symmetric Prior for the Regularisation of Elastic Deformations: Improved anatomical plausibility in nonlinear image registration

Nonlinear registration is critical to many aspects of Neuroimaging research. It facilitates averaging and comparisons across multiple subjects, as well as reporting of data in a common anatomical frame of reference. It is, however, a fundamentally ill-posed problem, with many possible solutions which minimise a given dissimilarity metric equally well. We present a regularisation method capable of selectively driving solutions towards those which would be considered anatomically plausible by penalising unlikely lineal, areal and volumetric deformations. This penalty is symmetric in the sense that geometric expansions and contractions are penalised equally, which encourages inverse-consistency. We demonstrate that this method is able to significantly reduce local volume changes and shape distortions compared to state-of-the-art elastic (FNIRT) and plastic (ANTs) registration frameworks. Crucially, this is achieved whilst simultaneously matching or exceeding the registration quality of these methods, as measured by overlap scores of labelled cortical regions. Extensive leveraging of GPU parallelisation has allowed us to solve this highly computationally intensive optimisation problem while maintaining reasonable run times of under half an hour.

Learning patterns of the ageing brain in MRI using deep convolutional networks

Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning models are particularly suited to capture these patterns and can produce a model that is sensitive to changes of interest, despite the large variety in healthy brain appearance. In this paper, the power of convolutional neural networks (CNNs) and the rich UK Biobank dataset, the largest database currently available, are harnessed to address the problem of predicting brain age. We developed a 3D CNN architecture to predict chronological age, using a training dataset of 12,802 T1-weighted MRI images and a further 6,885 images for testing. The proposed method shows competitive performance on age prediction, but, most importantly, the CNN prediction errors Δ_BrainAge=Age_Predicted-Age_True correlated significantly with many clinical measurements from the UK Biobank in the female and male groups. In addition, having used images from only one imaging modality in this experiment, we examined the relationship between Δ_BrainAge and the image-derived phenotypes (IDPs) from all other imaging modalities in the UK Biobank, showing correlations consistent with known patterns of ageing. Furthermore, we show that the use of nonlinearly registered images to train CNNs can lead to the network being driven by artefacts of the registration process and missing subtle indicators of ageing, limiting the clinical relevance. Due to the longitudinal aspect of the UK Biobank study, in the future it will be possible to explore whether the Δ_BrainAge from models such as this network were predictive of any health outcomes.

Discovering correlates of age-related decline in a healthy late-midlife male birth cohort

Studies exploring age-related brain and cognitive change have identified substantial heterogeneity among individuals, but the underlying reasons for the differential trajectories remain largely unknown. We investigated cross-sectional and longitudinal associations between brain-imaging phenotypes (IDPs) and cognitive ability, and how these relations may be modified by common risk and protective factors. Participants were recruited from the 1953 Danish Male Birth Cohort (N=123), a longitudinal study of cognitive and brain ageing. Childhood IQ and socio-demographic factors are available for these participants who have been assessed regularly on multiple IDPs and behavioural factors in midlife. Using Pearson correlations and canonical correlation analysis (CCA), we explored the relation between 454 IDPs and 114 behavioural variables. CCA identified a single mode of population covariation coupling cross-subject longitudinal changes in brain structure to changes in cognitive performance and to a range of age-related covariates (r=0.92, Pcorrected < 0.001). Specifically, this CCA-mode indicated that; decreases in IQ and speed assessed tasks, higher rates of familial myocardial infarct, less physical activity, and poorer mental health are associated with larger decreases in whole brain grey matter and white matter. We found no evidence supporting the role of baseline scores as predictors of impending brain and behavioural change in late-midlife.

A data-driven approach to optimising the encoding for multi-shell diffusion MRI with application to neonatal imaging

Diffusion MRI has the potential to provide important information about the connectivity and microstructure of the human brain during normal and abnormal development, noninvasively and in vivo. Recent developments in MRI hardware and reconstruction methods now permit the acquisition of large amounts of data within relatively short scan times. This makes it possible to acquire more informative multi-shell data, with diffusion sensitisation applied along many directions over multiple b-value shells. Such schemes are characterised by the number of shells acquired, and the specific b-value and number of directions sampled for each shell. However, there is currently no clear consensus as to how to optimise these parameters. In this work, we propose a means of optimising multi-shell acquisition schemes by estimating the information content of the diffusion MRI signal, and optimising the acquisition parameters for sensitivity to the observed effects, in a manner agnostic to any particular diffusion analysis method that might subsequently be applied to the data. This method was used to design the acquisition scheme for the neonatal diffusion MRI sequence used in the developing Human Connectome Project (dHCP), which aims to acquire high quality data and make it freely available to the research community. The final protocol selected by the algorithm, and currently in use within the dHCP, consists of 20 b=0 images and diffusion-weighted images at b = 400, 1000 and 2600 s/mm² with 64, 88 and 128 directions per shell, respectively.

The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants

•

An automated and robust pipeline to minimally pre-process highly confounded neonatal fMRI data.

•

Includes integrated dynamic distortion and slice-to-volume motion correction.

•

A robust multimodal registration approach which includes custom neonatal templates.

•

Incorporates an automated and self-reporting QC framework to quantify data quality and identify issues for further inspection.

•

Data analysis of 538 infants imaged at 26–45 weeks post-menstrual age.

The developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20–45 weeks post-menstrual age. This is being achieved through the acquisition of multi-modal MRI data from over 1000 in- and ex-utero subjects combined with the development of optimised pre-processing pipelines. In this paper we present an automated and robust pipeline to minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, with low failure rates and high quality-assurance. The pipeline has been designed to specifically address the challenges that neonatal data presents including low and variable contrast and high levels of head motion. We provide a detailed description and evaluation of the pipeline which includes integrated slice-to-volume motion correction and dynamic susceptibility distortion correction, a robust multimodal registration approach, bespoke ICA-based denoising, and an automated QC framework. We assess these components on a large cohort of dHCP subjects and demonstrate that processing refinements integrated into the pipeline provide substantial reduction in movement related distortions, resulting in significant improvements in SNR, and detection of high quality RSNs from neonates.

Modelling subject variability in the spatial and temporal characteristics of functional modes

Recent work has highlighted the scale and ubiquity of subject variability in observations from functional MRI data (fMRI). Furthermore, it is highly likely that errors in the estimation of either the spatial presentation of, or the coupling between, functional regions can confound cross-subject analyses, making accurate and unbiased representations of functional data essential for interpreting any downstream analyses. Here, we extend the framework of probabilistic functional modes (PFMs) (Harrison et al., 2015) to capture cross-subject variability not only in the mode spatial maps, but also in the functional coupling between modes and in mode amplitudes. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git.fmrib.ox.ac.uk/samh/profumo. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets. Using simulated data, resting-state data from 1000 subjects collected as part of the Human Connectome Project (Van Essen et al., 2013), and an analysis of 14 subjects in a variety of continuous task-states (Kieliba et al., 2019), we demonstrate how PFMs are able to capture, within a single model, a rich description of how the spatio-temporal structure of resting-state fMRI activity varies across subjects. We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assumptions, much more of the (behaviorally relevant) cross-subject variability in fMRI activity should be attributed to the variability in spatial maps, and that, after accounting for this, functional coupling between modes primarily reflects current cognitive state. This has fundamental implications for the interpretation of cross-sectional studies of functional connectivity that do not capture cross-subject variability to the same extent as PFMs.

Impaired glucose metabolism in patients with diabetes, prediabetes, and obesity is associated with severe COVID-19

Background

Identification of risk factors of severe coronavirus disease 2019 (COVID‐19) is critical for improving therapies and understanding severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) pathogenesis. We analyzed 184 patients hospitalized for COVID‐19 in Livingston, New Jersey for clinical characteristics associated with severe disease. The majority of patients with COVID‐19 had diabetes mellitus (DM) (62.0%), Pre‐DM (23.9%) with elevated fasting blood glucose (FBG), or a body mass index >30 with normal hemoglobin A1c (HbA1C) (4.3%). SARS‐CoV‐2 infection was associated with new and persistent hyperglycemia in 29 patients, including several with normal HbA1C levels. Forty‐four patients required intubation, which occurred significantly more often in patients with DM as compared with non‐diabetics. Severe COVID‐19 occurs in the presence of impaired glucose metabolism in patients, including those with DM, preDM, and obesity. COVID‐19 is associated with elevated FBG and several patients presented with new onset DM or in DKA. The association of dysregulated glucose metabolism and severe COVID‐19 suggests that SARS‐CoV‐2 pathogenesis involves a novel interplay with glucose metabolism. Exploration of pathways by which SARS‐CoV‐2 interacts glucose metabolism is critical for understanding disease pathogenesis and developing therapies.

The vast majority of hospitalized COVID‐19 patients had diabetes mellitus (60.2%), prediabetes (23.9%) or obesity alone (4.3%).

All COVID‐19 patients requiring intubation were diabetic, obese or both.

SARS‐CoV‐2 infection is associated with new and persistent hyperglycemia in non‐diabetics, consistent with new‐onset diabetes, and with diabetic‐ketoacidosis in diabetics.

At presentation, a patient s diabetes status, BMI, A1C level and initial blood glucose level, along with age, can be used to identify patients at‐risk for severe COVID‐19.

Through a novel pathogenic interaction with glucose metabolism, SARS‐CoV‐2 worsens hyperglycemia, which, in turn, worsens severity of COVID‐19 pneumonia.

Elucidation and understanding this unique interaction between virus and host could lead to COVID‐19 therapeutic strategies, which target glucose metabolism alone.

Beyond Description: The Many Facets of Dental Biomechanics

Teeth lie at the interface between an animal and its environment and, with some exceptions, act as a major component of resource procurement through food acquisition and processing. Therefore, the shape of a tooth is closely tied to the type of food being eaten. This tight relationship is of use to biologists describing the natural history of species and given the high instance of tooth preservation in the fossil record, is especially useful for paleontologists. However, correlating gross tooth morphology to diet is only part of the story, and much more can be learned through the study of dental biomechanics. We can explore the mechanics of how teeth work, how different shapes evolved, and the underlying forces that constrain tooth shape. This review aims to provide an overview of the research on dental biomechanics, in both mammalian and non-mammalian teeth, and to synthesize two main approaches to dental biomechanics to develop an integrative framework for classifying and evaluating dental functional morphology. This framework relates food material properties to the dynamics of food processing, in particular how teeth transfer energy to food items, and how these mechanical considerations may have shaped the evolution of tooth morphology. We also review advances in technology and new techniques that have allowed more in-depth studies of tooth form and function.

Facial swelling for the emergency radiologist-typical and atypical causes

There are a wide variety of inflammatory, infectious, and cystic lesions which may lead patients to seek acute care for facial swelling. Computed tomography (CT) has become the mainstay for imaging in the urgent/emergent setting. However, magnetic resonance imaging (MRI) can also serve as a powerful problem solving tool in the modern era. As volume continues to increase, a wide variety of facial pathology will be encountered by the emergency radiologist. Recognition of both common and uncommon pathology will assist in diagnosis and value-based care. This article serves as an image-rich review of the many causes of facial swelling with an emphasis on key imaging findings and possible complications.

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.

Confound modelling in UK Biobank brain imaging

Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including nonlinear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds.

The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions

UK Biobank is a population-based cohort of half a million participants aged 40-69 years recruited between 2006 and 2010. In 2014, UK Biobank started the world's largest multi-modal imaging study, with the aim of re-inviting 100,000 participants to undergo brain, cardiac and abdominal magnetic resonance imaging, dual-energy X-ray absorptiometry and carotid ultrasound. The combination of large-scale multi-modal imaging with extensive phenotypic and genetic data offers an unprecedented resource for scientists to conduct health-related research. This article provides an in-depth overview of the imaging enhancement, including the data collected, how it is managed and processed, and future directions.

Homegrown Ultraviolet Germicidal Irradiation for Hospital-Based N95 Decontamination during the COVID-19 Pandemic

Coronavirus disease (COVID-19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, is responsible for the 2020 global pandemic and characterized by high transmissibility and morbidity. Healthcare workers (HCWs) are at risk of contracting COVID-19, and this risk is mitigated through the use of personal protective equipment such as N95 Filtering Facepiece Respirators (FFRs). The high demand for FFRs is not currently met by global supply chains, potentially placing HCWs at increased exposure risk. Effective FFR decontamination modalities exist, which could maintain respiratory protection for HCWs in the midst of the current pandemic, through the decontamination and re-use of FFRs. Here, we present a locally-implemented ultraviolet-C germicidal irradiation (UVGI)-based FFR decontamination pathway, utilizing a home-built UVGI array assembled entirely with previously existing components available at our institution. We provide recommendations on the construction of similar systems, as well as guidance and strategies towards successful institutional implementation of FFR decontamination.

Optimising network modelling methods for fMRI

A major goal of neuroimaging studies is to develop predictive models to analyze the relationship between whole brain functional connectivity patterns and behavioural traits. However, there is no single widely-accepted standard pipeline for analyzing functional connectivity. The common procedure for designing functional connectivity based predictive models entails three main steps: parcellating the brain, estimating the interaction between defined parcels, and lastly, using these integrated associations between brain parcels as features fed to a classifier for predicting non-imaging variables e.g., behavioural traits, demographics, emotional measures, etc. There are also additional considerations when using correlation-based measures of functional connectivity, resulting in three supplementary steps: utilising Riemannian geometry tangent space parameterization to preserve the geometry of functional connectivity; penalizing the connectivity estimates with shrinkage approaches to handle challenges related to short time-series (and noisy) data; and removing confounding variables from brain-behaviour data. These six steps are contingent on each-other, and to optimise a general framework one should ideally examine these various methods simultaneously. In this paper, we investigated strengths and short-comings, both independently and jointly, of the following measures: parcellation techniques of four kinds (categorized further depending upon number of parcels), five measures of functional connectivity, the decision of staying in the ambient space of connectivity matrices or in tangent space, the choice of applying shrinkage estimators, six alternative techniques for handling confounds and finally four novel classifiers/predictors. For performance evaluation, we have selected two of the largest datasets, UK Biobank and the Human Connectome Project resting state fMRI data, and have run more than 9000 different pipeline variants on a total of ∼14000 individuals to determine the optimum pipeline. For independent performance validation, we have run some best-performing pipeline variants on ABIDE and ACPI datasets (∼1000 subjects) to evaluate the generalisability of proposed network modelling methods.

Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

Brain imaging can be used to study how individuals' brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single 'brain age' is estimated per subject, whereas here we identified 62 modes of subject variability, from 21,407 subjects' multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease.

Expanding the differential of superficial tumors with round-cell morphology: Report of three cases of CIC-rearranged sarcoma, a potentially under-recognized entity

Among sarcomas with a round-cell morphology that lack rearrangement of the EWSR1 gene, rearrangements involving the CIC gene are the most common. In comparison with Ewing Sarcoma, CIC-rearranged sarcomas present at an older average age, arise almost exclusively in soft tissues, are clinically more aggressive, and are more likely to be resistant to the chemotherapy regimens used for Ewing sarcoma. CIC-rearranged sarcomas present more commonly in a deep location, and we suspect that superficial presentations may be under-recognized. In this case series, we report three of such cases. Overall, the morphology is similar to CIC-rearranged sarcomas of deeper locations. We hope to raise awareness among the dermatopathology community by expanding the differential of superficial tumors with round cell morphology.