Multimodal MRI Template Creation in the Ring-Tailed Lemur and Rhesus Macaque

We present a multimodal registration algorithm for simultaneous alignment of datasets with both scalar and tensor MRI images. We employ a volumetric, cubic B-spline parametrised transformation model. Regularisation is based on the logarithm of the singular values of the local Jacobian and ensures diffeomorphic warps. Tensor registration takes reorientation into account during optimisation, through a finite-strain approximation of rotation due to the warp. The combination of scalar, tensor and regularisation cost functions allows us to optimise the deformations in terms of tissue matching, orientation matching and distortion minimisation simultaneously. We apply our method to creating multimodal T2 and DTI MRI brain templates of two small primates (the ring-tailed lemur and rhesus macaque) from high-quality, ex vivo, 0.5/0.6 mm isotropic data. The resulting templates are of very high quality across both modalities and species. Tissue contrast in the T2 channel is high indicating excellent tissue-boundary alignment. The DTI channel displays strong anisotropy in white matter, as well as consistent left/right orientation information even in relatively isotropic grey matter regions. Finally, we demonstrate where the multimodal templating approach overcomes anatomical inconsistencies introduced by unimodal only methods.

Strong G-Protein-Mediated Inhibition of Sodium Channels

Voltage-gated sodium channels (VGSCs) are strategically positioned to mediate neuronal plasticity because of their influence on action potential waveform. VGSC function may be strongly inhibited by local anesthetic and antiepileptic drugs and modestly modulated via second messenger pathways. Here, we report that the allosteric modulators of the calcium-sensing receptor (CaSR) cinacalcet, calindol, calhex, and NPS 2143 completely inhibit VGSC current in the vast majority of cultured mouse neocortical neurons. This form of VGSC current block persisted in CaSR-deficient neurons, indicating a CaSR-independent mechanism. Cinacalcet-mediated blockade of VGSCs was prevented by the guanosine diphosphate (GDP) analog GDPbs, indicating that G-proteins mediated this effect. Cinacalcet inhibited VGSCs by increasing channel inactivation, and block was reversed by prolonged hyperpolarization. Strong cinacalcet inhibition of VGSC currents was also present in acutely isolated mouse cortical neurons. These data identify a dynamic signaling pathway by which G-proteins regulate VGSC current to indirectly modulate central neuronal excitability.

Mattheisen et al. demonstrate a G-protein-dependent pathway that strongly inhibits voltage-gated sodium channel currents in the vast majority of cortical neurons. The mechanism involves profound slowing of recovery from inactivation. The strong and widespread effects on voltage-gated sodium channels position this signaling pathway to have substantial influence on neuronal excitability.

Resistive exercise in astronauts on prolonged spaceflights provides partial protection against spaceflight-induced bone loss

Bone loss in astronauts during spaceflight may be a risk factor for osteoporosis, fractures and renal stone formation. We previously reported that the bisphosphonate alendronate, combined with exercise that included an Advanced Resistive Exercise Device (ARED), can prevent or attenuate group mean declines in areal bone mineral density (aBMD) measured soon after ~ 6-month spaceflights aboard the International Space Station (ISS). It is unclear however if the beneficial effects on postflight aBMD were due to individual or combined effects of alendronate and ARED. Hence, 10 additional ISS astronauts were recruited who used the ARED (ARED group) without drug administration using similar measurements in the previous study, i.e., densitometry, biochemical assays and analysis of finite element (FE) models. In addition densitometry data (DXA and QCT only) were compared to published data from crewmembers (n = 14-18) flown prior to in-flight access to the ARED (Pre-ARED). Group mean changes from preflight (± SD %) were used to evaluate effects of countermeasures as sequentially modified on the ISS (i.e., Pre-ARED vs. ARED; ARED vs. Bis+ARED). Spaceflight durations were not significantly different between groups. Postflight bone density measurements were significantly reduced from preflight in the Pre-ARED group. As previously reported, combined Bis+ARED prevented declines in all DXA and QCT hip densitometry and in estimates of FE hip strengths; increased the aBMD of lumbar spine; and prevented elevations in urinary markers for bone resorption during spaceflight. ARED without alendronate partially attenuated declines in bone mass but did not suppress biomarkers for bone resorption or prevent trabecular bone loss. Resistive exercise in the ARED group did not prevent declines in hip trabecular vBMD, but prevented reductions in cortical vBMD of the femoral neck, in FE estimate of hip strength for non-linear stance (NLS) and in aBMD of the femoral neck. We conclude that a bisphosphonate, when combined with resistive exercise, enhances the preservation of bone mass because of the added suppression of bone resorption in trabecular bone compartment not evident with ARED alone.

Effective degrees of freedom of the Pearson's correlation coefficient under autocorrelation

The dependence between pairs of time series is commonly quantified by Pearson's correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher's transformation fails to stabilise the variance. Since fMRI time series are notoriously autocorrelated, the issue of biased standard errors - before or after Fisher's transformation - becomes vital in individual-level analysis of resting-state functional connectivity (rsFC) and must be addressed anytime a standardised Z-score is computed. We find that the severity of autocorrelation is highly dependent on spatial characteristics of brain regions, such as the size of regions of interest and the spatial location of those regions. We further show that the available EDF estimators make restrictive assumptions that are not supported by the data, resulting in biased rsFC inferences that lead to distorted topological descriptions of the connectome on the individual level. We propose a practical "xDF" method that accounts not only for distinct autocorrelation in each time series, but instantaneous and lagged cross-correlation. We find the xDF correction varies substantially over node pairs, indicating the limitations of global EDF corrections used previously. In addition to extensive synthetic and real data validations, we investigate the impact of this correction on rsFC measures in data from the Young Adult Human Connectome Project, showing that accounting for autocorrelation dramatically changes fundamental graph theoretical measures relative to no correction.

P5730Do hypertensive women with coronary artery disease benefit from lowering systolic blood pressure under 130 mmHg? Long-term mortality in the INternational VErapamil SR-trandolapril STudy (INVEST)

Background

Hypertension (HTN) and coronary artery disease (CAD) are a prevalent combination in women, however limited data are available to guide blood pressure (BP) management. We hypothesize older women with HTN and CAD may not derive the same prognostic benefit from systolic BP (SBP) lowering <130 mmHg.

Purpose

To investigate the long-term mortality implications of different achieved SBP levels in hypertensive women with CAD.

Methods

Long-term, all-cause mortality data were analyzed for 9216 women, stratified by risk attributable to clinical severity of CAD (women with prior myocardial infarction or revascularization considered at high, all others at low risk) and by age (50 - <65 or ≥65 yo). The prognostic impact of achieving mean in-trial SBP <130 (referent group) was compared with 130 to <140 and ≥140 mmHg using Cox proportional hazards, adjusting for demographic and clinical characteristics.

Results

During 108,838 person-years of follow-up, 2945 deaths occurred. High risk women (n=3011) had increased long-term mortality in comparison to low risk women (n=6205) (adjusted HR 1.38, CI 1.28–1.5, p<0.001). Within risk groups, crude mortality percentages decreased according to BP values (table). As expected, high risk women were more likely to be ≥65 yo (68.68% vs. 50.51%, p<0.0001) or have SBP ≥140 mmHg (43.08% vs. 31.18%, p<0.0001). In adjusted analyses, an SBP ≥140 mmHg was associated with worse outcomes than SBP <130 mmHg in the entire cohort (HR 1.3, CI 1.2–1.5, p<0.0001) and when stratifying by risk (low risk group, HR = 1.47, CI 1.28–1.7, p<0.0001; high risk group, HR = 1.71, CI 1.01–1.35, p=0.03). In analyses stratified by age and risk, women ≥65 years and at high risk had decreased mortality in the 130 - <140 SBP category vs. <130 mmHg (HR 0.812, 95% CI 0.689–0.957, p=0.0133; figure).

Women and deaths by risk and SBP group Group SBP category Women (n) Mortality (n) Mortality (%) High risk <130 773 338 44 130–<140 941 414 44 ≥140 1297 694 54 Low risk <130 2187 390 18 130–<140 2083 451 22 ≥140 1935 658 34 SBP = systolic blood pressure; n = number; % = percent per each group.

Mortality adjusted HRs

Conclusion

In women ≥65 yo with hypertension and prior myocardial infarction and/or coronary revascularization enrolled in INVEST, a SBP between 130 to <140 mmHg was associated with lower all-cause, long-term mortality versus SBP <130 mmHg.

Acknowledgement/Funding

The main INVEST (International Verapamil [SR]/Trandolapril Study) was funded by grants from BASF Pharma, Ludwigshafen, Germany; Abbott Laboratories, A

Discovering markers of healthy aging: a prospective study in a Danish male birth cohort

There is a pressing need to identify markers of cognitive and neural decline in healthy late-midlife participants. We explored the relationship between cross-sectional structural brain-imaging derived phenotypes (IDPs) and cognitive ability, demographic, health and lifestyle factors (non-IDPs). Participants were recruited from the 1953 Danish Male Birth Cohort (N=193). Applying an extreme group design, members were selected in 2 groups based on cognitive change between IQ at age ~20y (IQ-20) and age ~57y (IQ-57). Subjects showing the highest (n=95) and lowest (n=98) change were selected (at age ~57) for assessments on multiple IDPs and non-IDPs. We investigated the relationship between 453 IDPs and 70 non-IDPs through pairwise correlation and multivariate canonical correlation analysis (CCA) models. Significant pairwise associations included positive associations between IQ-20 and gray-matter volume of the temporal pole. CCA identified a richer pattern - a single "positive-negative" mode of population co-variation coupling individual cross-subject variations in IDPs to an extensive range of non-IDP measures (r = 0.75, P_corrected < 0.01). Specifically, this mode linked higher cognitive performance, positive early-life social factors, and mental health to a larger brain volume of several brain structures, overall volume, and microstructural properties of some white matter tracts. Interestingly, both statistical models identified IQ-20 and gray-matter volume of the temporal pole as important contributors to the inter-individual variation observed. The converging patterns provide novel insight into the importance of early adulthood intelligence as a significant marker of late-midlife neural decline and motivates additional study.

Statistical Challenges in "Big Data" Human Neuroimaging

Smith and Nichols discuss "big data" human neuroimaging studies, with very large subject numbers and amounts of data. These studies provide great opportunities for making new discoveries about the brain but raise many new analytical challenges and interpretational risks.

Towards Algorithmic Analytics for Large-scale Datasets

The traditional goals of quantitative analytics cherish simple, transparent models to generate explainable insights. Large-scale data acquisition, enabled for instance by brain scanning and genomic profiling with microarray-type techniques, has prompted a wave of statistical inventions and innovative applications. Modern analysis approaches 1) tame large variable arrays capitalizing on regularization and dimensionality-reduction strategies, 2) are increasingly backed up by empirical model validations rather than justified by mathematical proofs, 3) will compare against and build on open data and consortium repositories, as well as 4) often embrace more elaborate, less interpretable models in order to maximize prediction accuracy. Here we review these trends in learning from "big data" and illustrate examples from imaging neuroscience.

Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank

Measurement of hippocampal volume has proven useful to diagnose and track progression in several brain disorders, most notably in Alzheimer's disease (AD). For example, an objective evaluation of a patient's hippocampal volume status may provide important information that can assist diagnosis or risk stratification of AD. However, clinicians and researchers require access to age-related normative percentiles to reliably categorise a patient's hippocampal volume as being pathologically small. Here we analysed effects of age, sex, and hemisphere on the hippocampus and neighbouring temporal lobe volumes, in 19,793 generally healthy participants in the UK Biobank. A key finding of the current study is a significant acceleration in the rate of hippocampal volume loss in middle age, more pronounced in females than in males. In this report, we provide normative values for hippocampal and total grey matter volume as a function of age for reference in clinical and research settings. These normative values may be used in combination with our online, automated percentile estimation tool to provide a rapid, objective evaluation of an individual's hippocampal volume status. The data provide a large-scale normative database to facilitate easy age-adjusted determination of where an individual hippocampal and temporal lobe volume lies within the normal distribution.

•

Largest normative database for hippocampal volume across age to date.

•

Hippocampal volume loss accelerates in middle age.

•

Acceleration of hippocampal volume loss is more pronounced in women than in men.

•

With the online tool the database provides a useful resource for research and clinical studies.

Estimation of brain age delta from brain imaging

It is of increasing interest to study “brain age” - the apparent age of a subject, as inferred from brain imaging data. The difference between brain age and actual age (the “delta”) is typically computed, reflecting deviation from the population norm. This therefore may reflect accelerated aging (positive delta) or resilience (negative delta) and has been found to be a useful correlate with factors such as disease and cognitive decline. However, although there has been a range of methods proposed for estimating brain age, there has been little study of the optimal ways of computing the delta. In this technical note we describe problems with the most common current approach, and present potential improvements. We evaluate different estimation methods on simulated and real data. We also find the strongest correlations of corrected brain age delta with 5,792 non-imaging variables (non-brain physical measures, life-factor measures, cognitive test scores, etc.), and also with 2,641 multimodal brain imaging-derived phenotypes, with data from 19,000 participants in UK Biobank.

•

It is of interest to study "brain age'', as inferred from brain imaging data.

•

The delta between brain age and actual age is typically computed.

•

We describe problems with the most common current approach.

•

We present potential improvements.

•

We evaluate methods on simulated data and data from UK Biobank.

The relationship between spatial configuration and functional connectivity of brain regions revisited

Previously we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity (Bijsterbosch et al., 2018). Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.

Classification of temporal ICA components for separating global noise from fMRI data: Reply to Power

We respond to a critique of our temporal Independent Components Analysis (ICA) method for separating global noise from global signal in fMRI data that focuses on the signal versus noise classification of several components. While we agree with several of Power's comments, we provide evidence and analysis to rebut his major criticisms and to reassure readers that temporal ICA remains a powerful and promising denoising approach.

Evidence supporting the best clinical management of patients with multimorbidity and polypharmacy: a systematic guideline review and expert consensus

The complexity and heterogeneity of patients with multimorbidity and polypharmacy renders traditional disease-oriented guidelines often inadequate and complicates clinical decision making. To address this challenge, guidelines have been developed on multimorbidity or polypharmacy. To systematically analyse their recommendations, we conducted a systematic guideline review using the Ariadne principles for managing multimorbidity as analytical framework. The information synthesis included a multistep consensus process involving 18 multidisciplinary experts from seven countries. We included eight guidelines (four each on multimorbidity and polypharmacy) and extracted about 250 recommendations. The guideline addressed (i) the identification of the target population (risk factors); (ii) the assessment of interacting conditions and treatments: medical history, clinical and psychosocial assessment including physiological status and frailty, reviews of medication and encounters with healthcare providers highlighting informational continuity; (iii) the need to incorporate patient preferences and goal setting: eliciting preferences and expectations, the process of shared decision making in relation to treatment options and the level of involvement of patients and carers; (iv) individualized management: guiding principles on optimization of treatment benefits over possible harms, treatment communication and the information content of medication/care plans; (v) monitoring and follow-up: strategies in care planning, self-management and medication-related aspects, communication with patients including safety instructions and adherence, coordination of care regarding referral and discharge management, medication appropriateness and safety concerns. The spectrum of clinical and self-management issues varied from guiding principles to specific recommendations and tools providing actionable support. The limited availability of reliable risk prediction models, feasible interventions of proven effectiveness and decision aids, and limited consensus on appropriate outcomes of care highlight major research deficits. An integrated approach to both multimorbidity and polypharmacy should be considered in future guidelines.

Barriers and facilitators to attendance at Type 2 diabetes structured education programmes: a qualitative study of educators and attendees

AIMS

Attendance at structured diabetes education has been recommended internationally for all people with Type 2 diabetes. However, attendance rates are consistently low. This qualitative study aimed to explore experiences of attending and delivering Type 2 diabetes structured education programmes in Ireland and barriers and facilitators to attendance.

METHODS

People with Type 2 diabetes who had attended one of the three programmes delivered in Ireland and educators from the three programmes took part in semi-structured telephone interviews. Interviews were audio-taped, transcribed and analysed using inductive thematic analysis.

RESULTS

Twelve attendees and 14 educators were interviewed. Two themes were identified in relation to experiences of programme attendance and delivery: 'Structured education: addressing an unmet need' and 'The problem of non-attendance'. The third theme 'Barriers to attendance: can't go, won't go, don't know and poor system flow' outlined how practicalities of attending, lack of knowledge of the existence and benefits, and limited resources and support for education within the diabetes care pathway impacts on attendance. The final theme 'Supporting attendance: healthcare professionals and the diabetes care pathway' describes facilitators to participants' attendance and the strategies educators perceived to be important in increasing attendance.

CONCLUSIONS

Healthcare professionals have an important role in improving attendance at structured diabetes education programmes. Improving attendance may require promotion by healthcare professionals and for education to be better embedded and supported within the diabetes care pathway.

NY-ESO-1: a promising cancer testis antigen for sarcoma immunotherapy and diagnosis

Sarcomas are heterogenous malignant mesenchymal neoplasms. These are often notoriously difficult to treat particularly in the metastatic setting. There is therefore an urgent need for development of better and more efficacious targeted therapies. Cancer testis antigens (CTAs) are a family of proteins in which aberrant gene-activation and subsequent high level mRNA expression, are restricted to testicular germ cells and are seen in certain malignancies. Importantly, the restriction of this class of antigens to testicular germ cells and malignancies and not somatic tissue, makes them an excellent choice for targeted immunotherapy. The NY-ESO-1is the most immunogenic of CTA and has, of late, become well-studied for its diagnosis and potential treatment implications in sarcomas. This paper reviews both the role of NY-ESO-1 in the diagnosis of sarcomas, as well as the implications of this CTA in vaccine development and treatment of sarcomas.

Discovering dynamic brain networks from big data in rest and task

Brain activity is a dynamic combination of the responses to sensory inputs and its own spontaneous processing. Consequently, such brain activity is continuously changing whether or not one is focusing on an externally imposed task. Previously, we have introduced an analysis method that allows us, using Hidden Markov Models (HMM), to model task or rest brain activity as a dynamic sequence of distinct brain networks, overcoming many of the limitations posed by sliding window approaches. Here, we present an advance that enables the HMM to handle very large amounts of data, making possible the inference of very reproducible and interpretable dynamic brain networks in a range of different datasets, including task, rest, MEG and fMRI, with potentially thousands of subjects. We anticipate that the generation of large and publicly available datasets from initiatives such as the Human Connectome Project and UK Biobank, in combination with computational methods that can work at this scale, will bring a breakthrough in our understanding of brain function in both health and disease.

Genome-wide association studies of brain imaging phenotypes in UK Biobank

The genetic architecture of brain structure and function is largely unknown. To investigate this, we carried out genome-wide association studies of 3,144 functional and structural brain imaging phenotypes from UK Biobank (discovery dataset 8,428 subjects). Here we show that many of these phenotypes are heritable. We identify 148 clusters of associations between single nucleotide polymorphisms and imaging phenotypes that replicate at P < 0.05, when we would expect 21 to replicate by chance. Notable significant, interpretable associations include: iron transport and storage genes, related to magnetic susceptibility of subcortical brain tissue; extracellular matrix and epidermal growth factor genes, associated with white matter micro-structure and lesions; genes that regulate mid-line axon development, associated with organization of the pontine crossing tract; and overall 17 genes involved in development, pathway signalling and plasticity. Our results provide insights into the genetic architecture of the brain that are relevant to neurological and psychiatric disorders, brain development and ageing.

Spatial parcellations, spectral filtering, and connectivity measures in fMRI: Optimizing for discrimination

The analysis of Functional Connectivity (FC) is a key technique of fMRI, having been used to distinguish brain states and conditions. While many approaches to calculating FC are available, there have been few assessments of their differences, making it difficult to choose approaches, and compare results. Here, we assess the impact of methodological choices on discriminability, using a fully controlled data set of continuous active states involving basic visual and motor tasks, providing robust localized FC changes. We tested a range of anatomical and functional parcellations, including the AAL atlas, parcellations derived from the Human Connectome Project and Independent Component Analysis (ICA) of many dimensionalities. We measure amplitude, covariance, correlation, and regularized partial correlation under different temporal filtering choices. We evaluate features derived from these methods for discriminating states using MVPA. We find that multidimensional parcellations derived from functional data performed similarly, outperforming an anatomical atlas, with correlation and partial correlation (p < .05, FDR). Partial correlation, with appropriate regularization, outperformed correlation. Amplitude and covariance generally discriminated less well, although gave good results with high‐dimensionality ICA. We found that discriminative FC properties are frequency specific; higher frequencies performed surprisingly well under certain configurations of atlas choices and dependency measures, with ICA‐based parcellations revealing greater discriminability at high frequencies compared to other parcellations. Methodological choices in FC analyses can have a profound impact on results and can be selected to optimize accuracy, interpretability, and sharing of results. This work contributes to a basis for consistent selection of approaches to estimating and analyzing FC.

Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects

The Human Connectome Projects in Development (HCP-D) and Aging (HCP-A) are two large-scale brain imaging studies that will extend the recently completed HCP Young-Adult (HCP-YA) project to nearly the full lifespan, collecting structural, resting-state fMRI, task-fMRI, diffusion, and perfusion MRI in participants from 5 to 100+ years of age. HCP-D is enrolling 1300+ healthy children, adolescents, and young adults (ages 5-21), and HCP-A is enrolling 1200+ healthy adults (ages 36-100+), with each study collecting longitudinal data in a subset of individuals at particular age ranges. The imaging protocols of the HCP-D and HCP-A studies are very similar, differing primarily in the selection of different task-fMRI paradigms. We strove to harmonize the imaging protocol to the greatest extent feasible with the completed HCP-YA (1200+ participants, aged 22-35), but some imaging-related changes were motivated or necessitated by hardware changes, the need to reduce the total amount of scanning per participant, and/or the additional challenges of working with young and elderly populations. Here, we provide an overview of the common HCP-D/A imaging protocol including data and rationales for protocol decisions and changes relative to HCP-YA. The result will be a large, rich, multi-modal, and freely available set of consistently acquired data for use by the scientific community to investigate and define normative developmental and aging related changes in the healthy human brain.

Pretreatment Identification of Head and Neck Cancer Nodal Metastasis and Extranodal Extension Using Deep Learning Neural Networks

Identification of nodal metastasis and tumor extranodal extension (ENE) is crucial for head and neck cancer management, but currently only can be diagnosed via postoperative pathology. Pretreatment, radiographic identification of ENE, in particular, has proven extremely difficult for clinicians, but would be greatly influential in guiding patient management. Here, we show that a deep learning convolutional neural network can be trained to identify nodal metastasis and ENE with excellent performance that surpasses what human clinicians have historically achieved. We trained a 3-dimensional convolutional neural network using a dataset of 2,875 CT-segmented lymph node samples with correlating pathology labels, cross-validated and fine-tuned on 124 samples, and conducted testing on a blinded test set of 131 samples. On the blinded test set, the model predicted ENE and nodal metastasis each with area under the receiver operating characteristic curve (AUC) of 0.91 (95%CI: 0.85-0.97). The model has the potential for use as a clinical decision-making tool to help guide head and neck cancer patient management.

Composite Paraganglioma: Pioneering in the Head and Neck

Composite paragangliomas are rare with less than 20 cases documented in the abdomen, retroperitoneum, and urinary bladder. In this article, we report the first case of composite paraganglioma in the head and neck presenting as a soft tissue mass in the neck adjacent to the carotid artery in a 50-year-old woman. We discuss the clinicopathologic findings and genetic implications, and we review the literature of this rare entity.

The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5-21 year olds

Recent technological and analytical progress in brain imaging has enabled the examination of brain organization and connectivity at unprecedented levels of detail. The Human Connectome Project in Development (HCP-D) is exploiting these tools to chart developmental changes in brain connectivity. When complete, the HCP-D will comprise approximately ∼1750 open access datasets from 1300 + healthy human participants, ages 5-21 years, acquired at four sites across the USA. The participants are from diverse geographical, ethnic, and socioeconomic backgrounds. While most participants are tested once, others take part in a three-wave longitudinal component focused on the pubertal period (ages 9-17 years). Brain imaging sessions are acquired on a 3 T Siemens Prisma platform and include structural, functional (resting state and task-based), diffusion, and perfusion imaging, physiological monitoring, and a battery of cognitive tasks and self-reports. For minors, parents additionally complete a battery of instruments to characterize cognitive and emotional development, and environmental variables relevant to development. Participants provide biological samples of blood, saliva, and hair, enabling assays of pubertal hormones, health markers, and banked DNA samples. This paper outlines the overarching aims of the project, the approach taken to acquire maximally informative data while minimizing participant burden, preliminary analyses, and discussion of the intended uses and limitations of the dataset.

Directed functional connectivity using dynamic graphical models

There are a growing number of neuroimaging methods that model spatio-temporal patterns of brain activity to allow more meaningful characterizations of brain networks. This paper proposes dynamic graphical models (DGMs) for dynamic, directed functional connectivity. DGMs are a multivariate graphical model with time-varying coefficients that describe instantaneous directed relationships between nodes. A further benefit of DGMs is that networks may contain loops and that large networks can be estimated. We use network simulations and human resting-state fMRI (N = 500) to investigate the validity and reliability of the estimated networks. We simulate systematic lags of the hemodynamic response at different brain regions to investigate how these lags potentially bias directionality estimates. In the presence of such lag confounds (0.4-0.8 s offset between connected nodes), our method has a sensitivity of 72%-77% to detect the true direction. Stronger lag confounds have reduced sensitivity, but do not increase false positives (i.e., directionality estimates of the opposite direction). In human resting-state fMRI, the default mode network has consistent influence on the cerebellar, the limbic and the auditory/temporal networks. We also show a consistent reciprocal relationship between the visual medial and visual lateral network. Finally, we apply the method in a small mouse fMRI sample and discover a highly plausible relationship between areas in the hippocampus feeding into the cingulate cortex. We provide a computationally efficient implementation of DGM as a free software package for R.

Supporting prescribing in Irish primary care: protocol for a non-randomised pilot study of a general practice pharmacist (GPP) intervention to optimise prescribing in primary care

Background

Prescribing for patients taking multiple medicines (i.e. polypharmacy) is challenging for general practitioners (GPs). Limited evidence suggests that the integration of pharmacists into the general practice team could improve the management of these patients. The aim of this study is to develop and test an intervention involving pharmacists, working within GP practices, to optimise prescribing in Ireland, which has a mixed public and private primary healthcare system.

Methods

This non-randomised pilot study will use a mixed-methods approach. Four general practices will be purposively sampled and recruited. A pharmacist will join the practice team for 6 months. They will participate in the management of repeat prescribing and undertake medication reviews (which will address high-risk prescribing and potentially inappropriate prescribing, deprescribing and cost-effective and generic prescribing) with adult patients. Pharmacists will also provide prescribing advice regarding the use of preferred drugs, undertake clinical audits, join practice team meetings and facilitate practice-based education. Throughout the 6-month intervention period, anonymised practice-level medication (e.g. medication changes) and cost data will be collected. A nested Patient Reported Outcome Measure (PROM) study will be undertaken during months 4 and 5 of the 6-month intervention period to explore the impact of the intervention in older adults (aged ≥ 65 years). For this, a sub-set of 50 patients aged ≥ 65 years with significant polypharmacy (≥ 10 repeat medicines) will be recruited from each practice and invited to a medication review with the pharmacist. PROMs and healthcare utilisation data will be collected using patient questionnaires, and a 6-week follow-up review conducted. Acceptability of the intervention will be explored using pre- and post-intervention semi-structured interviews with key stakeholders. Quantitative and qualitative data analysis will be undertaken and an economic evaluation conducted.

Discussion

This non-randomised pilot study will provide evidence regarding the feasibility and potential effectiveness of general practice-based pharmacists in Ireland and provide data on whether a randomised controlled trial of this intervention is indicated. It will also provide a deeper understanding as to how a pharmacist working as part of the general practice team will affect organisational processes and professional relationships in a mixed public and private primary healthcare system.

Construction of a neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project

We propose a method for constructing a spatio-temporal cortical surface atlas of neonatal brains aged between 36 and 44 weeks of post-menstrual age (PMA) at the time of scan. The data were acquired as part of the Developing Human Connectome Project (dHCP), and the constructed surface atlases are publicly available. The method is based on a spherical registration approach: Multimodal Surface Matching (MSM), using cortical folding for driving the alignment. Templates have been generated for the anatomical cortical surface and for the cortical feature maps: sulcal depth, curvature, thickness, T1w/T2w myelin maps and cortical regions. To achieve this, cortical surfaces from 270 infants were first projected onto the sphere. Templates were then generated in two stages: first, a reference space was initialised via affine alignment to a group average adult template. Following this, templates were iteratively refined through repeated alignment of individuals to the template space until the variability of the average feature sets converged. Finally, bias towards the adult reference was removed by applying the inverse of the average affine transformations on the template and de-drifting the template. We used temporal adaptive kernel regression to produce age-dependant atlases for 9 weeks (36-44 weeks PMA). The generated templates capture expected patterns of cortical development including an increase in gyrification as well as an increase in thickness and T1w/T2w myelination with increasing age.

Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project

The developing Human Connectome Project is set to create and make available to the scientific community a 4-dimensional map of functional and structural cerebral connectivity from 20 to 44 weeks post-menstrual age, to allow exploration of the genetic and environmental influences on brain development, and the relation between connectivity and neurocognitive function. A large set of multi-modal MRI data from fetuses and newborn infants is currently being acquired, along with genetic, clinical and developmental information. In this overview, we describe the neonatal diffusion MRI (dMRI) image processing pipeline and the structural connectivity aspect of the project. Neonatal dMRI data poses specific challenges, and standard analysis techniques used for adult data are not directly applicable. We have developed a processing pipeline that deals directly with neonatal-specific issues, such as severe motion and motion-related artefacts, small brain sizes, high brain water content and reduced anisotropy. This pipeline allows automated analysis of in-vivo dMRI data, probes tissue microstructure, reconstructs a number of major white matter tracts, and includes an automated quality control framework that identifies processing issues or inconsistencies. We here describe the pipeline and present an exemplar analysis of data from 140 infants imaged at 38–44 weeks post-menstrual age.

•

A comprehensive and automated pipeline to consistently analyse neonatal dMRI data.

•

Optimised motion and distortions correction to address newborn specific challenges.

•

The automated QC framework allows to detect issues and to quantify data quality.

•

Automated white matter segmentation allows to extract tract-specific masks.

•

Preliminary data analysis of 140 infants imaged at 38–44 weeks post-menstrual age.

Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks

A Bayesian model for sparse, hierarchical, inver-covariance estimation is presented, and applied to multi-subject functional connectivity estimation in the human brain. It enables simultaneous inference of the strength of connectivity between brain regions at both subject and population level, and is applicable to fMRI, MEG and EEG data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions. A large evaluation of this model, and thirteen methods that represent the state of the art of inverse covariance modelling, is conducted using both simulated and resting-state functional imaging datasets. Our novel Bayesian approach has similar performance to the best extant alternative, Ng et al.'s Sparse Group Gaussian Graphical Model algorithm, which also is based on a hierarchical structure. Using data from the Human Connectome Project, we show that these hierarchical models are able to reduce the measurement error in MEG beta-band functional networks by 10%, producing concomitant increases in estimates of the genetic influence on functional connectivity.

Calcium dependence of spontaneous neurotransmitter release

Spontaneous release of neurotransmitters is regulated by extracellular [Ca2+ ] and intracellular [Ca2+ ]. Curiously, some of the mechanisms of Ca2+ signaling at central synapses are different at excitatory and inhibitory synapses. While the stochastic activity of voltage-activated Ca2+ channels triggers a majority of spontaneous release at inhibitory synapses, this is not the case at excitatory nerve terminals. Ca2+ release from intracellular stores regulates spontaneous release at excitatory and inhibitory terminals, as do agonists of the Ca2+ -sensing receptor. Molecular machinery triggering spontaneous vesicle fusion may differ from that underlying evoked release and may be one of the sources of heterogeneity in release mechanisms.

Disambiguating brain functional connectivity

Functional connectivity (FC) analyses of correlations of neural activity are used extensively in neuroimaging and electrophysiology to gain insights into neural interactions. However, analyses assessing changes in correlation fail to distinguish effects produced by sources as different as changes in neural signal amplitudes or noise levels. This ambiguity substantially diminishes the value of FC for inferring system properties and clinical states. Network modelling approaches may avoid ambiguities, but require specific assumptions. We present an enhancement to FC analysis with improved specificity of inferences, minimal assumptions and no reduction in flexibility. The Additive Signal Change (ASC) approach characterizes FC changes into certain prevalent classes of signal change that involve the input of additional signal to existing activity. With FMRI data, the approach reveals a rich diversity of signal changes underlying measured changes in FC, suggesting that it could clarify our current understanding of FC changes in many contexts. The ASC method can also be used to disambiguate other measures of dependency, such as regression and coherence, providing a flexible tool for the analysis of neural data.

•

A new approach for interpreting functional connectivity (FC) analyses is presented.

•

The approach identifies scenarios where changes may be attributed to changes in signal amplitude.

•

The method helps to disambiguate FC measures.

•

The general approach is applicable to analyses assessing changes in correlation and regression.

The relationship between spatial configuration and functional connectivity of brain regions

Brain connectivity is often considered in terms of the communication between functionally distinct brain regions. Many studies have investigated the extent to which patterns of coupling strength between multiple neural populations relates to behaviour. For example, studies have used ‘functional connectivity fingerprints’ to characterise individuals' brain activity. Here, we investigate the extent to which the exact spatial arrangement of cortical regions interacts with measures of brain connectivity. We find that the shape and exact location of brain regions interact strongly with the modelling of brain connectivity, and present evidence that the spatial arrangement of functional regions is strongly predictive of non-imaging measures of behaviour and lifestyle. We believe that, in many cases, cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Therefore, a better understanding of these effects is important when interpreting the relationship between functional imaging data and cognitive traits.

People differ a lot from one another in terms of their personality, behaviour and lifestyle. This individuality is attributed to the different regions in the brain, and the strength of communication between them. The connectivity pattern between these areas is thought to be as unique as a fingerprint. If the connections are weak or disrupted it can play a role in conditions such as schizophrenia, depression or Alzheimer’s disease. It is thought that the strength of the connection depends on how strongly the nerve cells in these regions communicate. But are these individual differences solely caused by different strengths of connection, or could other factors contribute to them?

Now, Bijsterbosch et al. found that the size, shape and exact position of the brain regions was also strongly linked to the different behaviours of individuals. The study used brain scans, behavioural tests and questionnaires from a large database about lifestyle choices and demographics, to analyse the relationship between the different brain features of healthy individuals. The results showed that the variations in the brain regions were linked to many behavioural factors including intelligence, life satisfaction, drug use and aggression problems. Moreover, Bijsterbosch et al. showed that the existing methods for estimating the strength of connection between brain regions could reveal more about the spatial layout of these regions than the actual connection strength between them. This suggests that new approaches are needed to properly evaluate the strength of the connections.

Some psychiatric and neurological diseases may be associated with changes in size and position of the different regions in the brain. In future, the findings of this study could be applied to individuals affected by such conditions, to see if the location of a region could be used as a diagnostic indicator.

The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction

The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.

Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank

UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.

The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction

The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.

Mixed adenoneuroendocrine carcinoma: A review of pathologic characteristics

Mixed adenoneuroendocrine carcinoma (MANEC) is a rare pathologic entity defined as a tumor exhibiting both adenocarcinoma and neuroendocrine carcinoma components. Though uncommon, these tumors show aggressive behavior and generally portend a poor prognosis. This study seeks to further define clinicopathological characteristics of MANEC to aid in accurate diagnosis and properly direct clinical management. Thirty-four confirmed MANECs were identified in our 25-year retrospective review of cases arising in the gastrointestinal tract. Various gross and microscopic variables were compared to overall survival. Tumors diagnosed at advanced stage (pT4), had a prominent mucinous component and lacked goblet cell clusters, which were all significantly associated with worse overall survival. This study supports previous findings and further elucidates clinicopathologic characteristics of MANEC.

Brain network dynamics are hierarchically organized in time

The brain recruits neuronal populations in a temporally coordinated manner in task and at rest. However, the extent to which large-scale networks exhibit their own organized temporal dynamics is unclear. We use an approach designed to find repeating network patterns in whole-brain resting fMRI data, where networks are defined as graphs of interacting brain areas. We find that the transitions between networks are nonrandom, with certain networks more likely to occur after others. Further, this nonrandom sequencing is itself hierarchically organized, revealing two distinct sets of networks, or metastates, that the brain has a tendency to cycle within. One metastate is associated with sensory and motor regions, and the other involves areas related to higher order cognition. Moreover, we find that the proportion of time that a subject spends in each brain network and metastate is a consistent subject-specific measure, is heritable, and shows a significant relationship with cognitive traits.

Multimodal surface matching with higher-order smoothness constraints

In brain imaging, accurate alignment of cortical surfaces is fundamental to the statistical sensitivity and spatial localisation of group studies, and cortical surface-based alignment has generally been accepted to be superior to volume-based approaches at aligning cortical areas. However, human subjects have considerable variation in cortical folding, and in the location of functional areas relative to these folds. This makes alignment of cortical areas a challenging problem. The Multimodal Surface Matching (MSM) tool is a flexible, spherical registration approach that enables accurate registration of surfaces based on a variety of different features. Using MSM, we have previously shown that driving cross-subject surface alignment, using areal features, such as resting state-networks and myelin maps, improves group task fMRI statistics and map sharpness. However, the initial implementation of MSM's regularisation function did not penalize all forms of surface distortion evenly. In some cases, this allowed peak distortions to exceed neurobiologically plausible limits, unless regularisation strength was increased to a level which prevented the algorithm from fully maximizing surface alignment. Here we propose and implement a new regularisation penalty, derived from physically relevant equations of strain (deformation) energy, and demonstrate that its use leads to improved and more robust alignment of multimodal imaging data. In addition, since spherical warps incorporate projection distortions that are unavoidable when mapping from a convoluted cortical surface to the sphere, we also propose constraints that enforce smooth deformation of cortical anatomies. We test the impact of this approach for longitudinal modelling of cortical development for neonates (born between 31 and 43 weeks of post-menstrual age) and demonstrate that the proposed method increases the biological interpretability of the distortion fields and improves the statistical significance of population-based analysis relative to other spherical methods.

Studying neuroanatomy using MRI

The study of neuroanatomy using imaging enables key insights into how our brains function, are shaped by genes and environment, and change with development, aging and disease. Developments in MRI acquisition, image processing and data modeling have been key to these advances. However, MRI provides an indirect measurement of the biological signals we aim to investigate. Thus, artifacts and key questions of correct interpretation can confound the readouts provided by anatomical MRI. In this review we provide an overview of the methods for measuring macro- and mesoscopic structure and for inferring microstructural properties; we also describe key artifacts and confounds that can lead to incorrect conclusions. Ultimately, we believe that, although methods need to improve and caution is required in interpretation, structural MRI continues to have great promise in furthering our understanding of how the brain works.

Economic evaluation of chronic disease self-management for people with diabetes: a systematic review

AIMS

To systematically review the evidence on the costs and cost-effectiveness of self-management support interventions for people with diabetes.

BACKGROUND

Self-management support is the provision of education and supportive interventions to increase patients' skills and confidence in managing their health problems, potentially leading to improvements in HbA_1c levels in people with diabetes.

METHODS

Randomized controlled trials, observational studies or economic modelling studies were eligible for inclusion in the review. The target population was adults with diabetes. Interventions had to have a substantial component of self-management support and be compared with routine care. Study quality was evaluated using the Consensus on Health Economic Criteria and International Society of Pharmacoeconomic Outcomes Research questionnaires. A narrative review approach was used.

RESULTS

A total of 16 costing and 21 cost-effectiveness studies of a range of self-management support interventions were identified. There was reasonably consistent evidence across 22 studies evaluating education self-management support programmes suggesting these interventions are cost-effective or superior to usual care. Telemedicine-type interventions were more expensive than usual care and potentially not cost-effective. There was insufficient evidence regarding the other types of self-management interventions, including pharmacist-led and behavioural interventions. The identified studies were predominantly of poor quality, with outcomes based on short-term follow-up data and study designs at high risk of bias.

CONCLUSIONS

Self-management support education programmes may be cost-effective. There was limited evidence regarding other formats of self-management support interventions. The poor quality of many of the studies undermines the evidence base regarding the economic efficiency of self-management support interventions for people with diabetes.

The heritability of multi-modal connectivity in human brain activity

Patterns of intrinsic human brain activity exhibit a profile of functional connectivity that is associated with behaviour and cognitive performance, and deteriorates with disease. This paper investigates the relative importance of genetic factors and the common environment between twins in determining this functional connectivity profile. Using functional magnetic resonance imaging (fMRI) on 820 subjects from the Human Connectome Project, and magnetoencephalographic (MEG) recordings from a subset, the heritability of connectivity among 39 cortical regions was estimated. On average over all connections, genes account for about 15% of the observed variance in fMRI connectivity (and about 10% in alpha-band and 20% in beta-band oscillatory power synchronisation), which substantially exceeds the contribution from the environment shared between twins. Therefore, insofar as twins share a common upbringing, it appears that genes, rather than the developmental environment, have the dominant role in determining the coupling of neuronal activity.

Initial Response by a Native Beetle, Chrysochus auratus (Coleoptera: Chrysomelidae), to a Novel Introduced Host-Plant, Vincetoxicum rossicum (Gentianales: Apocynaceae)

Native insects can form novel associations with introduced invasive plants and use them as a food source. The recent introduction into eastern North America of a nonnative European vine, Vincetoxicum rossicum (Kleopow) Barbar., allows us to examine the initial response of a native chrysomelid beetle, Chrysochus auratus F., that feeds on native plants in the same family as V. rossicum (Apocynaceae). We tested C. auratus on V. rossicum and closely related or co-occurring native plants (Apocynum spp., Asclepias spp., and Solidago canadensis L.) using all life stages of the beetle in lab, garden, and field experiments. Experiments measured feeding (presence or absence and amount), survival, oviposition, and whether previous exposure to V. rossicum in the lab or field affected adult beetle feeding. Beetles fed significantly less on V. rossicum than on native Apocynum hosts. Adult beetles engaged in exploratory feeding on leaves of V. rossicum and survived up to 10 d. Females oviposited on V. rossicum, eggs hatched, and larvae fed initially on the roots; however, no larvae survived beyond second instar. Beetles collected from Apocynum cannabinum L. field sites intermixed with V. rossicum were less likely to feed on this novel nonnative host than those collected from colonies further from and less likely to be exposed to V. rossicum (>5 km). Our experimental work indicates that V. rossicum may act as an oviposition sink for C. auratus and that this native beetle has not adapted to survive on this recently introduced novel host plant.

DEK associates with tumor stage and outcome in HPV16 positive oropharyngeal squamous cell carcinoma

Oropharyngeal squamous cell carcinomas (OPSCC) are common, have poor outcomes, and comprise two biologically and clinically distinct diseases. While OPSCC that arise from human papillomavirus infections (HPV+) have better overall survival than their HPV- counterparts, the incidence of HPV+ OPSCC is increasing dramatically, affecting younger individuals which are often left with life-long co-morbidities from aggressive treatment. To identify patients which do poorly versus those who might benefit from milder regimens, risk-stratifying biomarkers are now needed within this population. One potential marker is the DEK oncoprotein, whose transcriptional upregulation in most malignancies is associated with chemotherapy resistance, advanced tumor stage, and worse outcomes. Herein, a retrospective case study was performed on DEK protein expression in therapy-naïve surgical resections from 194 OPSCC patients. We found that DEK was associated with advanced tumor stage, increased hazard of death, and interleukin IL6 expression in HPV16+ disease. Surprisingly, DEK levels in HPV16- OPSCC were not associated with advanced tumor stage or increased hazard of death. Overall, these findings mark HPV16- OPSCC as an exceptional malignancy were DEK expression does not correlate with outcome, and support the potential prognostic utility of DEK to identify aggressive HPV16+ disease.

Heterogeneous fractionation profiles of meta-analytic coactivation networks

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d=20-300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how "parent" functional brain systems decompose into constituent "child" sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication.

Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses

Independent Component Analysis (ICA) is one of the most popular techniques for the analysis of resting state FMRI data because it has several advantageous properties when compared with other techniques. Most notably, in contrast to a conventional seed-based correlation analysis, it is model-free and multivariate, thus switching the focus from evaluating the functional connectivity of single brain regions identified a priori to evaluating brain connectivity in terms of all brain resting state networks (RSNs) that simultaneously engage in oscillatory activity. Furthermore, typical seed-based analysis characterizes RSNs in terms of spatially distributed patterns of correlation (typically by means of simple Pearson's coefficients) and thereby confounds together amplitude information of oscillatory activity and noise. ICA and other regression techniques, on the other hand, retain magnitude information and therefore can be sensitive to both changes in the spatially distributed nature of correlations (differences in the spatial pattern or "shape") as well as the amplitude of the network activity. Furthermore, motion can mimic amplitude effects so it is crucial to use a technique that retains such information to ensure that connectivity differences are accurately localized. In this work, we investigate the dual regression approach that is frequently applied with group ICA to assess group differences in resting state functional connectivity of brain networks. We show how ignoring amplitude effects and how excessive motion corrupts connectivity maps and results in spurious connectivity differences. We also show how to implement the dual regression to retain amplitude information and how to use dual regression outputs to identify potential motion effects. Two key findings are that using a technique that retains magnitude information, e.g., dual regression, and using strict motion criteria are crucial for controlling both network amplitude and motion-related amplitude effects, respectively, in resting state connectivity analyses. We illustrate these concepts using realistic simulated resting state FMRI data and in vivo data acquired in healthy subjects and patients with bipolar disorder and schizophrenia.

Accelerating functional MRI using fixed-rank approximations and radial-cartesian sampling

PURPOSE

Recently, k-t FASTER (fMRI Accelerated in Space-time by means of Truncation of Effective Rank) was introduced for rank-constrained acceleration of fMRI data acquisition. Here we demonstrate improvements achieved through a hybrid three-dimensional radial-Cartesian sampling approach that allows posthoc selection of acceleration factors, as well as incorporation of coil sensitivity encoding in the reconstruction.

METHODS

The multicoil rank-constrained reconstruction used hard thresholding and shrinkage on matrix singular values of the space-time data matrix, using sensitivity encoding and the nonuniform Fast Fourier Transform to enforce data consistency in the multicoil non-Cartesian k-t domain. Variable acceleration factors were made possible using a radial increment based on the golden ratio. Both retrospective and prospectively under-sampled data were used to assess the fidelity of the enhancements to the k-t FASTER technique in resting and task-fMRI data.

RESULTS

The improved k-t FASTER is capable of tailoring acceleration factors for recovery of different signal components, achieving up to R = 12.5 acceleration in visual-motor task data. The enhancements reduce data matrix reconstruction errors even at much higher acceleration factors when compared directly with the original k-t FASTER approach.

CONCLUSION

We have shown that k-t FASTER can be used to significantly accelerate fMRI data acquisition with little penalty to data quality. Magn Reson Med 76:1825-1836, 2016. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Tradeoffs in pushing the spatial resolution of fMRI for the 7T Human Connectome Project

Whole-brain functional magnetic resonance imaging (fMRI), in conjunction with multiband acceleration, has played an important role in mapping the functional connectivity throughout the entire brain with both high temporal and spatial resolution. Ultrahigh magnetic field strengths (7T and above) allow functional imaging with even higher functional contrast-to-noise ratios for improved spatial resolution and specificity compared to traditional field strengths (1.5T and 3T). High-resolution 7T fMRI, however, has primarily been constrained to smaller brain regions given the amount of time it takes to acquire the number of slices necessary for high resolution whole brain imaging. Here we evaluate a range of whole-brain high-resolution resting state fMRI protocols (0.9, 1.25, 1.5, 1.6 and 2mm isotropic voxels) at 7T, obtained with both in-plane and slice acceleration parallel imaging techniques to maintain the temporal resolution and brain coverage typically acquired at 3T. Using the processing pipeline developed by the Human Connectome Project, we demonstrate that high resolution images acquired at 7T provide increased functional contrast to noise ratios with significantly less partial volume effects and more distinct spatial features, potentially allowing for robust individual subject parcellations and descriptions of fine-scaled patterns, such as visuotopic organization.

Using Public Relations Strategies to Prompt Populations at Risk to Seek Health Information: The Hanford Community Health Project

The Hanford Community Health Project (HCHP) addressed health concerns among “downwinders” exposed to releases of radioactive iodine (I-131) from the Hanford Nuclear Reservation in the 1940s and 1950s. After developing educational materials and conducting initial outreach, HCHP had to decide whether to apply its limited resources to an advertising or public relations approach. The decision to apply public relations strategies was effective in driving awareness of the risk communication message at the community level, reinvigorating the affected community, and ultimately increasing the number of people who sought information about their risk of exposure and related health issues. HCHP used a series of communication tools to reach out to local and regional media, medical and health professionals, and community organizations. The campaign was successful in increasing the number of unique visitors to HCHP Web site and educating and activating the medical community around the releases of I-131 and patient care choices.