Preliminary assessment of the potential for, and limitations to, terrestrial negative emission technologies in the UK

The aggregate technical potential for land-based negative emissions technologies (NETs) in the UK is estimated to be 12-49 Mt C eq. per year, representing around 8-32% of current emissions. The proportion of this potential that could be realized is limited by a number of cost, energy and environmental constraints which vary greatly between NETs.

Paritaprevir/ritonavir/ombitasvir+dasabuvir plus ribavirin therapy and inhibition of the anticoagulant effect of warfarin: a case report

WHAT IS KNOWN AND OBJECTIVE

Paritaprevir/ritonavir/ombitasvir+dasabuvir (PrOD) is a direct-acting antiviral (DAA) approved for the treatment of chronic hepatitis C virus. We report on a probable interaction between PrOD with ribavirin and warfarin.

CASE DESCRIPTION

Two weeks after the start of PrOD with ribavirin, the patient's international normalized ratio (INR) became subtherapeutic. Eleven weeks into therapy and following a 125% total increase in the weekly warfarin dose, therapeutic INR was achieved. Thirteen days after DAA therapy was completed and discontinued, the patient's INR became critically supratherapeutic.

WHAT IS NEW AND CONCLUSION

Patients on PrOD plus ribavirin with warfarin should have INR followed closely upon initiation and discontinuation of therapy due to a probable drug interaction.

Multimodal population brain imaging in the UK Biobank prospective epidemiological study

Medical imaging has enormous potential for early disease prediction, but is impeded by the difficulty and expense of acquiring data sets before symptom onset. UK Biobank aims to address this problem directly by acquiring high-quality, consistently acquired imaging data from 100,000 predominantly healthy participants, with health outcomes being tracked over the coming decades. The brain imaging includes structural, diffusion and functional modalities. Along with body and cardiac imaging, genetics, lifestyle measures, biological phenotyping and health records, this imaging is expected to enable discovery of imaging markers of a broad range of diseases at their earliest stages, as well as provide unique insight into disease mechanisms. We describe UK Biobank brain imaging and present results derived from the first 5,000 participants' data release. Although this covers just 5% of the ultimate cohort, it has already yielded a rich range of associations between brain imaging and other measures collected by UK Biobank.

Charge-Disproportionation Symmetry Breaking Creates a Heterodimeric Myoglobin Complex with Enhanced Affinity and Rapid Intracomplex Electron Transfer

We report rapid photoinitiated intracomplex electron transfer (ET) within a "charge-disproportionated" myoglobin (Mb) dimer with greatly enhanced affinity. Two mutually supportive Brownian Dynamics (BD) interface redesign strategies, one a new "heme-filtering" approach, were employed to "break the symmetry" of a Mb homodimer by pairing Mb constructs with complementary highly positive and highly negative net surface charges, introduced through D/E → K and K → E mutations, respectively. BD simulations using a previously developed positive mutant, Mb(+6) = Mb(D44K/D60K/E85K), led to construction of the complementary negative mutant Mb(-6) = Mb(K45E, K63E, K95E). Simulations predict the pair will form a well-defined complex comprising a tight ensemble of conformations with nearly parallel hemes, at a metal-metal distance ∼18-19 Å. Upon expression and X-ray characterization of the partners, BD predictions were verified through ET photocycle measurements enabled by Zn-deuteroporphyrin substitution, forming the [ZnMb(-6), Fe(3+)Mb(+6)] complex. Triplet ET quenching shows charge disproportionation increases the binding constant by no less than ∼5 orders of magnitude relative to wild-type Mb values. All progress curves for charge separation (CS) and charge recombination (CR) are reproduced by a generalized kinetic model for the interprotein ET photocycle. The intracomplex ET rate constants for both CS and CR are increased by over 5 orders of magnitude, and their viscosity independence is indicative of true interprotein ET, rather than dynamic gating as seen in previous studies. The complex displays an unprecedented timecourse for CR of the CS intermediate I. After a laser flash, I forms through photoinduced CS, accumulates to a maximum concentration, then dies away through CR. However, before completely disappearing, I reappears without another flash and reaches a second maximum before disappearing completely.

The Human Connectome Project's neuroimaging approach

Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease.

A multi-modal parcellation of human cerebral cortex

Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal ‘fingerprint’ of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.

Locally limited inhibition of bone resorption and orthodontic relapse by recombinant osteoprotegerin protein

OBJECTIVES

To determine minimal dose levels required for local inhibition of orthodontic relapse by recombinant OPG protein (OPG-Fc), while also determining effects of injected OPG-Fc on alveolar bone and long bone.

SETTING AND SAMPLE POPULATION

The Department of Orthodontics and Pediatric Dentistry at the University of Michigan. Eighteen male Sprague Dawley rats.

MATERIALS & METHODS

Maxillary molars were moved with nickel-titanium springs and then allowed to relapse in Sprague Dawley rats. Upon appliance removal, animals were injected with a single dose of 1.0 mg/kg OPG-Fc, 0.1 mg/kg OPG-Fc, or phosphate-buffered saline (vehicle) just distal to the molar teeth. Tooth movement measurements were made from stone casts, which were scanned and digitally measured. Alveolar tissues were examined by histology. Micro-computed tomography was used to quantify changes in alveolar and femur bone.

RESULTS

Local injection of OPG-Fc inhibited molar but not incisor relapse, when compared to vehicle-injected animals. No significant differences in alveolar or femur bone were seen between the three treatment groups after 24 days of relapse.

CONCLUSIONS

Our results demonstrate that a single local injection of OPG-Fc effectively inhibits orthodontic relapse, with minimal systemic bone metabolic effects. Our results also show that a single injection of OPG-Fc will influence tooth movement only in teeth close to the injection site. These findings indicate that OPG-Fc has potential as a safe and effective pharmacological means to locally control osteoclasts, for uses such as maintaining anchorage during orthodontic tooth movement and preventing orthodontic relapse in humans.

Celerity and Cajolery: Rapid Speech May Promote or Inhibit Persuasion through its Impact on Message Elaboration

The present experiment tests credibility-cue and elaboration likelihood (ELM) hypotheses about the effect of compressed speech on message-based persuasion. Participants heard either a proattitudinal or a counterattitudinal message on an involving topic, delivered at a slow (144 words per minute), an intermediate (182 wpm), or a rapid (214 wpm) rate of speech. Consistent with the ELM predictions, rapid speech suppressed the tendency to rebut the counterattitudinal message and enhanced persuasion, whereas the same rapid speech rate inhibited favorable elaboration of the proattitudinal message while undermining its persuasive impact. Thus, a distinctly faster than normal rate of speech on an involving topic can either promote or inhibit persuasion by its impact on message elaboration. The generality of these speech rate effects and the conditions under which rapid speech might serve as a peripheral (i.e., credibility) cue are discussed.

Message Framing and Persuasion: A Message Processing Analysis

Previous research has indicated that the way in which a message is framed affects the amount of persuasion it elicits. Various processes have been suggested to account for the differential persuasiveness of negatively (or loss-) framed messages versus positively (or gain-) framed messages. The authors hypothesized that differential processing of negatively and positively framed messages is a factor that contributes to the differential persuasiveness that has been observed. Experiment 1 demonstrated that message frames are consequential in determining the extent to which a message is scrutinized. Experiment 2 provided evidence for a mechanism by which this occurs. Specifically, by manipulating the expected framing and the actual framing of messages, the authors found that either negatively or positively framed messages could lead to more extensive processing, depending on which was less expected.

Speed of Speech and Persuasion: Evidence for Multiple Effects

This study examined the possibility that increased speech rate can affect persuasion either by acting as an agreement cue or through its impact on message processing. Participants heard messages that were either moderate or high in personal relevance, consisted of weak or strong arguments, and were presented at either moderate (180 words per minute) or fast (220 wpm) rates of speech. Consistent with hypotheses derived from the elaboration likelihood model, fast speech served to inhibit participants' tendency to differentially agree with strong versus weak message arguments under both moderate and high relevance. However; fast speech was associated with increased persuasion only for moderate involvement subjects, and this influence was mediated by perceptions of source credibility. Polarity of message-based elaborations predicted attitudes of both moderately and highly involved subjects. Thus, central and peripheral route processes appeared to be co-occurring for moderately involved participants.

Vividness Can Undermine or Enhance Message Processing: The Moderating Role of Vividness Congruency

Previous research on the subject of vividness effects in persuasion has yielded conflicting outcomes that are difficult to interpret. The authors outline a theoretical position that anticipates conditions under which vivid message presentations can either enhance or inhibit message processing and persuasion. The key moderator is vividness congruency, which is defined as the extent to which the vivid elements of a message are congruent with the theme of the message itself. Two experiments were conducted that suggest that this previously unexamined variable is an important moderator of vividness effects. Experiment 1 demonstrated that vividness effects on message recall are contingent on the congruency between message content and vivid elements. Experiment 2 showed that message processing (indexed via an argument quality manipulation) can be reduced by adding vivid but incongruent images to a message, relative to pallid messages. Theoretical and applied implications are discussed.

Primary Lingual Colonic-Type Adenocarcinoma: A Rare and Emerging Distinct Entity!

Primary colonic-type adenocarcinoma involving the tongue (CTAT) is exquisitely rare, with only four cases having been reported in the literature. We report the case of a 53-year-old woman with an anterior (oral) tongue mass. A review of literature was performed. Histomorphologic features were evaluated with standard hematoxylin and eosin stained sections. Ancillary testing was performed. The mass consisted of invasive adenocarcinoma associated with "dirty necrosis", akin to the phenotype seen in colorectal adenocarcinoma. By immunohistochemistry, the tumor cells were positive for AE1/3, CDX2, CK20, SATB2 and beta-catenin. This was initially felt to represent a metastatic colorectal adenocarcinoma but subsequent PET/CT and colonoscopy examination were negative for colorectal mass, excluding the possibility of a metastasis and confirming a diagnosis of CTAT. We raise awareness of the existence of this entity and recommend that metastatic disease be excluded before rendering a diagnosis of CTAT.

Faster permutation inference in brain imaging

Permutation tests are increasingly being used as a reliable method for inference in neuroimaging analysis. However, they are computationally intensive. For small, non-imaging datasets, recomputing a model thousands of times is seldom a problem, but for large, complex models this can be prohibitively slow, even with the availability of inexpensive computing power. Here we exploit properties of statistics used with the general linear model (GLM) and their distributions to obtain accelerations irrespective of generic software or hardware improvements. We compare the following approaches: (i) performing a small number of permutations; (ii) estimating the p-value as a parameter of a negative binomial distribution; (iii) fitting a generalised Pareto distribution to the tail of the permutation distribution; (iv) computing p-values based on the expected moments of the permutation distribution, approximated from a gamma distribution; (v) direct fitting of a gamma distribution to the empirical permutation distribution; and (vi) permuting a reduced number of voxels, with completion of the remainder using low rank matrix theory. Using synthetic data we assessed the different methods in terms of their error rates, power, agreement with a reference result, and the risk of taking a different decision regarding the rejection of the null hypotheses (known as the resampling risk). We also conducted a re-analysis of a voxel-based morphometry study as a real-data example. All methods yielded exact error rates. Likewise, power was similar across methods. Resampling risk was higher for methods (i), (iii) and (v). For comparable resampling risks, the method in which no permutations are done (iv) was the absolute fastest. All methods produced visually similar maps for the real data, with stronger effects being detected in the family-wise error rate corrected maps by (iii) and (v), and generally similar to the results seen in the reference set. Overall, for uncorrected p-values, method (iv) was found the best as long as symmetric errors can be assumed. In all other settings, including for familywise error corrected p-values, we recommend the tail approximation (iii). The methods considered are freely available in the tool PALM - Permutation Analysis of Linear Models.

How reliable are MEG resting-state connectivity metrics?

MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network estimation methods for MEG, and little in the way of existing guidance on which ones to employ. In this technical note, we investigate the extent to which many popular measures of stationary connectivity are suitable for use in resting-state MEG, localising magnetic sources with a scalar beamformer. We use as empirical criteria that network measures for individual subjects should be repeatable, and that group-level connectivity estimation shows good reproducibility. Using publically-available data from the Human Connectome Project, we test the reliability of 12 network estimation techniques against these criteria. We find that the impact of magnetic field spread or spatial leakage artefact is profound, creates a major confound for many connectivity measures, and can artificially inflate measures of consistency. Among those robust to this effect, we find poor test-retest reliability in phase- or coherence-based metrics such as the phase lag index or the imaginary part of coherency. The most consistent methods for stationary connectivity estimation over all of our tests are simple amplitude envelope correlation and partial correlation measures.

Task-free MRI predicts individual differences in brain activity during task performance

When asked to perform the same task, different individuals exhibit markedly different patterns of brain activity. This variability is often attributed to volatile factors, such as task strategy or compliance. We propose that individual differences in brain responses are, to a large degree, inherent to the brain and can be predicted from task-independent measurements collected at rest. Using a large set of task conditions, spanning several behavioral domains, we train a simple model that relates task-independent measurements to task activity and evaluate the model by predicting task activation maps for unseen subjects using magnetic resonance imaging. Our model can accurately predict individual differences in brain activity and highlights a coupling between brain connectivity and function that can be captured at the level of individual subjects.

Calcium-Sensing Receptor: A Key Target for Extracellular Calcium Signaling in Neurons

Though both clinicians and scientists have long recognized the influence of extracellular calcium on the function of muscle and nervous tissue, recent insights reveal that the mechanisms allowing changes in extracellular calcium to alter cellular excitability have been incompletely understood. For many years the effects of calcium on neuronal signaling were explained only in terms of calcium entry through voltage-gated calcium channels and biophysical charge screening. More recently however, it has been recognized that the calcium-sensing receptor is prevalent in the nervous system and regulates synaptic transmission and neuronal activity via multiple signaling pathways. Here we review the multiplicity of mechanisms by which changes in extracellular calcium alter neuronal signaling and propose that multiple mechanisms are required to describe the full range of experimental observations.

Non-parametric combination and related permutation tests for neuroimaging

In this work, we show how permutation methods can be applied to combination analyses such as those that include multiple imaging modalities, multiple data acquisitions of the same modality, or simply multiple hypotheses on the same data. Using the well-known definition of union-intersection tests and closed testing procedures, we use synchronized permutations to correct for such multiplicity of tests, allowing flexibility to integrate imaging data with different spatial resolutions, surface and/or volume-based representations of the brain, including non-imaging data. For the problem of joint inference, we propose and evaluate a modification of the recently introduced non-parametric combination (NPC) methodology, such that instead of a two-phase algorithm and large data storage requirements, the inference can be performed in a single phase, with reasonable computational demands. The method compares favorably to classical multivariate tests (such as MANCOVA), even when the latter is assessed using permutations. We also evaluate, in the context of permutation tests, various combining methods that have been proposed in the past decades, and identify those that provide the best control over error rate and power across a range of situations. We show that one of these, the method of Tippett, provides a link between correction for the multiplicity of tests and their combination. Finally, we discuss how the correction can solve certain problems of multiple comparisons in one-way ANOVA designs, and how the combination is distinguished from conjunctions, even though both can be assessed using permutation tests. We also provide a common algorithm that accommodates combination and correction.

Learning to identify CNS drug action and efficacy using multistudy fMRI data

The therapeutic effects of centrally acting pharmaceuticals can manifest gradually and unreliably in patients, making the drug discovery process slow and expensive. Biological markers providing early evidence for clinical efficacy could help prioritize development of the more promising drug candidates. A potential source of such markers is functional magnetic resonance imaging (fMRI), a noninvasive imaging technique that can complement molecular imaging. fMRI has been used to characterize how drugs cause changes in brain activity. However, variation in study protocols and analysis techniques has made it difficult to identify consistent associations between subtle modulations of brain activity and clinical efficacy. We present and validate a general protocol for functional imaging-based assessment of drug activity in the central nervous system. The protocol uses machine learning methods and data from multiple published studies to identify reliable associations between drug-related activity modulations and drug efficacy, which can then be used to assess new data. A proof-of-concept version of this approach was developed and is shown here for analgesics (pain medication), and validated with eight separate studies of analgesic compounds. Our results show that the systematic integration of multistudy data permits the generalized inferences required for drug discovery. Multistudy integrative strategies of this type could help optimize the drug discovery and validation pipeline.

Classification of follicular lymphoma: the effect of computer aid on pathologists grading

Background

Follicular lymphoma (FL) is one of the most common lymphoid malignancies in the western world. FL cases are stratified into three histological grades based on the average centroblast count per high power field (HPF). The centroblast count is performed manually by the pathologist using an optical microscope and hematoxylin and eosin (H&E) stained tissue section. Although this is the current clinical practice, it suffers from high inter- and intra-observer variability and is vulnerable to sampling bias.

Methods

In this paper, we present a system, called Follicular Lymphoma Grading System (FLAGS), to assist the pathologist in grading FL cases. We also assess the effect of FLAGS on accuracy of expert and inexperienced readers. FLAGS automatically identifies possible HPFs for examination by analyzing H&E and CD20 stains, before classifying them into low or high risk categories. The pathologist is first asked to review the slides according to the current routine clinical practice, before being presented with FLAGS classification via color-coded map. The accuracy of the readers with and without FLAGS assistance is measured.

Results

FLAGS was used by four experts (board-certified hematopathologists) and seven pathology residents on 20 FL slides. Access to FLAGS improved overall reader accuracy with the biggest improvement seen among residents. An average AUC value of 0.75 was observed which generally indicates “acceptable” diagnostic performance.

Conclusions

The results of this study show that FLAGS can be useful in increasing the pathologists’ accuracy in grading the tissue. To the best of our knowledge, this study measure, for the first time, the effect of computerized image analysis on pathologists’ grading of follicular lymphoma. When fully developed, such systems have the potential to reduce sampling bias by examining an increased proportion of HPFs within follicle regions, as well as to reduce inter- and intra-reader variability.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-015-0235-6) contains supplementary material, which is available to authorized users.

Six-year follow-up of the SPHERE RCT: secondary prevention of heart disease in general practice

OBJECTIVE

To determine the long-term effectiveness of a complex intervention in primary care aimed at improving outcomes for patients with coronary heart disease.

DESIGN

A 6-year follow-up of a cluster randomised controlled trial, which found after 18 months that both total and cardiovascular hospital admissions were significantly reduced in intervention practices (8% absolute reduction).

SETTING

48 general practices in the Republic of Ireland and Northern Ireland.

PARTICIPANTS

903 patients with established coronary heart disease at baseline in the original trial.

INTERVENTION

The original intervention consisted of tailored practice and patient plans; training sessions for practitioners in medication prescribing and behavioural change; and regular patient recall system. Control practices provided usual care. Following the intervention period, all supports from the research team to intervention practices ceased.

PRIMARY OUTCOME

hospital admissions, all cause and cardiovascular; secondary outcomes: mortality; blood pressure and cholesterol control.

RESULTS

At 6-year follow-up, data were collected from practice records of 696 patients (77%). For those who had died, we censored their data at the point of death and cause of death was established. There were no significant differences between the intervention and control practices in either total (OR 0.83 (95% CI 0.54 to 1.28)) or cardiovascular hospital admissions (OR 0.91 (95% CI 0.49 to 1.65)). We confirmed mortality status of 886 of the original 903 patients (98%). There were no significant differences in mortality (15% in intervention and 16% in control) or in the proportions of patients above target control for systolic blood pressure or total cholesterol.

CONCLUSIONS

Initial significant differences in the numbers of total and cardiovascular hospital admissions were not maintained at 6 years and no differences were found in mortality or blood pressure and cholesterol control. Policymakers need to continue to assess the effectiveness of previously efficacious programmes.

TRIAL REGISTRATION NUMBER

Current Controlled Trials ISRCTN24081411.

Disintegration of Sensorimotor Brain Networks in Schizophrenia

BACKGROUND

Schizophrenia is a severe mental disorder associated with derogated function across various domains, including perception, language, motor, emotional, and social behavior. Due to its complex symptomatology, schizophrenia is often regarded a disorder of cognitive processes. Yet due to the frequent involvement of sensory and perceptual symptoms, it has been hypothesized that functional disintegration between sensory and cognitive processes mediates the heterogeneous and comprehensive schizophrenia symptomatology.

METHODS

Here, using resting-state functional magnetic resonance imaging in 71 patients and 196 healthy controls, we characterized the standard deviation in BOLD (blood-oxygen-level-dependent) signal amplitude and the functional connectivity across a range of functional brain networks. We investigated connectivity on the edge and node level using network modeling based on independent component analysis and utilized the brain network features in cross-validated classification procedures.

RESULTS

Both amplitude and connectivity were significantly altered in patients, largely involving sensory networks. Reduced standard deviation in amplitude was observed in a range of visual, sensorimotor, and auditory nodes in patients. The strongest differences in connectivity implicated within-sensorimotor and sensorimotor-thalamic connections. Furthermore, sensory nodes displayed widespread alterations in the connectivity with higher-order nodes. We demonstrated robustness of effects across subjects by significantly classifying diagnostic group on the individual level based on cross-validated multivariate connectivity features.

CONCLUSION

Taken together, the findings support the hypothesis of disintegrated sensory and cognitive processes in schizophrenia, and the foci of effects emphasize that targeting the sensory and perceptual domains may be key to enhance our understanding of schizophrenia pathophysiology.

A positive-negative mode of population covariation links brain connectivity, demographics and behavior

We investigated the relationship between individual subjects' functional connectomes and 280 behavioral and demographic measures in a single holistic multivariate analysis relating imaging to non-imaging data from 461 subjects in the Human Connectome Project. We identified one strong mode of population co-variation: subjects were predominantly spread along a single 'positive-negative' axis linking lifestyle, demographic and psychometric measures to each other and to a specific pattern of brain connectivity.

ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI

Resting-state fMRI (R-fMRI) has shown considerable promise in providing potential biomarkers for diagnosis, prognosis and drug response across a range of diseases. Incorporating R-fMRI into multi-center studies is becoming increasingly popular, imposing technical challenges on data acquisition and analysis, as fMRI data is particularly sensitive to structured noise resulting from hardware, software, and environmental differences. Here, we investigated whether a novel clean up tool for structured noise was capable of reducing center-related R-fMRI differences between healthy subjects. We analyzed three Tesla R-fMRI data from 72 subjects, half of whom were scanned with eyes closed in a Philips Achieva system in The Netherlands, and half of whom were scanned with eyes open in a Siemens Trio system in the UK. After pre-statistical processing and individual Independent Component Analysis (ICA), FMRIB's ICA-based X-noiseifier (FIX) was used to remove noise components from the data. GICA and dual regression were run and non-parametric statistics were used to compare spatial maps between groups before and after applying FIX. Large significant differences were found in all resting-state networks between study sites before using FIX, most of which were reduced to non-significant after applying FIX. The between-center difference in the medial/primary visual network, presumably reflecting a between-center difference in protocol, remained statistically significant. FIX helps facilitate multi-center R-fMRI research by diminishing structured noise from R-fMRI data. In doing so, it improves combination of existing data from different centers in new settings and comparison of rare diseases and risk genes for which adequate sample size remains a challenge.

Effective artifact removal in resting state fMRI data improves detection of DMN functional connectivity alteration in Alzheimer's disease

Artifact removal from resting state fMRI data is an essential step for a better identification of the resting state networks and the evaluation of their functional connectivity (FC), especially in pathological conditions. There is growing interest in the development of cleaning procedures, especially those not requiring external recordings (data-driven), which are able to remove multiple sources of artifacts. It is important that only inter-subject variability due to the artifacts is removed, preserving the between-subject variability of interest—crucial in clinical applications using clinical scanners to discriminate different pathologies and monitor their staging. In Alzheimer's disease (AD) patients, decreased FC is usually observed in the posterior cingulate cortex within the default mode network (DMN), and this is becoming a possible biomarker for AD. The aim of this study was to compare four different data-driven cleaning procedures (regression of motion parameters; regression of motion parameters, mean white matter and cerebrospinal fluid signal; FMRIB's ICA-based Xnoiseifier—FIX—cleanup with soft and aggressive options) on data acquired at 1.5 T. The approaches were compared using data from 20 elderly healthy subjects and 21 AD patients in a mild stage, in terms of their impact on within-group consistency in FC and ability to detect the typical FC alteration of the DMN in AD patients. Despite an increased within-group consistency across subjects after applying any of the cleaning approaches, only after cleaning with FIX the expected DMN FC alteration in AD was detectable. Our study validates the efficacy of artifact removal even in a relatively small clinical population, and supports the importance of cleaning fMRI data for sensitive detection of FC alterations in a clinical environment.

k-t FASTER: Acceleration of functional MRI data acquisition using low rank constraints

PURPOSE

In functional MRI (fMRI), faster sampling of data can provide richer temporal information and increase temporal degrees of freedom. However, acceleration is generally performed on a volume-by-volume basis, without consideration of the intrinsic spatio-temporal data structure. We present a novel method for accelerating fMRI data acquisition, k-t FASTER (FMRI Accelerated in Space-time via Truncation of Effective Rank), which exploits the low-rank structure of fMRI data.

THEORY AND METHODS

Using matrix completion, 4.27× retrospectively and prospectively under-sampled data were reconstructed (coil-independently) using an iterative nonlinear algorithm, and compared with several different reconstruction strategies. Matrix reconstruction error was evaluated; a dual regression analysis was performed to determine fidelity of recovered fMRI resting state networks (RSNs).

RESULTS

The retrospective sampling data showed that k-t FASTER produced the lowest error, approximately 3-4%, and the highest quality RSNs. These results were validated in prospectively under-sampled experiments, with k-t FASTER producing better identification of RSNs than fully sampled acquisitions of the same duration.

CONCLUSION

With k-t FASTER, incoherently under-sampled fMRI data can be robustly recovered using only rank constraints. This technique can be used to improve the speed of fMRI sampling, particularly for multivariate analyses such as temporal independent component analysis.

Brain atrophy in cognitively impaired elderly: the importance of long-chain ω-3 fatty acids and B vitamin status in a randomized controlled trial

BACKGROUND

Increased brain atrophy rates are common in older people with cognitive impairment, particularly in those who eventually convert to Alzheimer disease. Plasma concentrations of omega-3 (ω-3) fatty acids and homocysteine are associated with the development of brain atrophy and dementia.

OBJECTIVE

We investigated whether plasma ω-3 fatty acid concentrations (eicosapentaenoic acid and docosahexaenoic acid) modify the treatment effect of homocysteine-lowering B vitamins on brain atrophy rates in a placebo-controlled trial (VITACOG).

DESIGN

This retrospective analysis included 168 elderly people (≥70 y) with mild cognitive impairment, randomly assigned either to placebo (n = 83) or to daily high-dose B vitamin supplementation (folic acid, 0.8 mg; vitamin B-6, 20 mg; vitamin B-12, 0.5 mg) (n = 85). The subjects underwent cranial magnetic resonance imaging scans at baseline and 2 y later. The effect of the intervention was analyzed according to tertiles of baseline ω-3 fatty acid concentrations.

RESULTS

There was a significant interaction (P = 0.024) between B vitamin treatment and plasma combined ω-3 fatty acids (eicosapentaenoic acid and docosahexaenoic acid) on brain atrophy rates. In subjects with high baseline ω-3 fatty acids (>590 μmol/L), B vitamin treatment slowed the mean atrophy rate by 40.0% compared with placebo (P = 0.023). B vitamin treatment had no significant effect on the rate of atrophy among subjects with low baseline ω-3 fatty acids (<390 μmol/L). High baseline ω-3 fatty acids were associated with a slower rate of brain atrophy in the B vitamin group but not in the placebo group.

CONCLUSIONS

The beneficial effect of B vitamin treatment on brain atrophy was observed only in subjects with high plasma ω-3 fatty acids. It is also suggested that the beneficial effect of ω-3 fatty acids on brain atrophy may be confined to subjects with good B vitamin status. The results highlight the importance of identifying subgroups likely to benefit in clinical trials. This trial was registered at www.controlled-trials.com as ISRCTN94410159.

Health-related quality of life in persons with apparent treatment-resistant hypertension on at least four antihypertensives

Little is known about the impact of treatment-resistant hypertension (TRH) on health-related quality of life (HrQoL). We aimed to compare HrQoL measures in adults with apparent TRH (aTRH) and non-resistant hypertension among nationally representative US Medical Expenditure Panel Survey data pooled from 2000 to 2011. Cohorts compared were adults with aTRH (⩾2 unique fills from ⩾4 antihypertensive classes during a year) versus non-resistant hypertension (those with hypertension not meeting the aTRH definition). Key outcomes were cohort differences in SF-12v2 physical component summary (PCS) and mental component summary (MCS) scores and disease-state utility using the SF-6D. Of 57 150 adults with hypertension, 2501 (4.4%) met criteria for aTRH. Persons with aTRH, compared with non-resistant hypertension, were older (mean, 68 vs 61 years), had a higher BMI (30.9 vs 29.7 kg m(-)(2)) and were more likely to be Black (20% vs 14%), but less likely to be female (46% vs 54%). Persons with aTRH, compared with non-resistant hypertension, had lower mean PCS scores (35.8 vs 43.2; P<0.0001), and utility (0.68 vs 0.74; P<0.0001), but similar MCS scores (49.1 vs 50.4). In multivariable-adjusted analyses, aTRH was associated with a 2.37 (95% CI 1.71 to 3.02) lower PCS score and 0.02 (95% CI 0.01 to 0.03) lower utility, compared with non-resistant hypertension. In conclusion, aTRH was associated with substantially lower HrQoL in physical functioning and health utility, but not in mental functioning, compared with non-resistant hypertension. The multivariable-adjusted reduction in physical functioning was similar in magnitude to previous observations comparing hypertension with no hypertension.

Multi-level block permutation

Under weak and reasonable assumptions, mainly that data are exchangeable under the null hypothesis, permutation tests can provide exact control of false positives and allow the use of various non-standard statistics. There are, however, various common examples in which global exchangeability can be violated, including paired tests, tests that involve repeated measurements, tests in which subjects are relatives (members of pedigrees) - any dataset with known dependence among observations. In these cases, some permutations, if performed, would create data that would not possess the original dependence structure, and thus, should not be used to construct the reference (null) distribution. To allow permutation inference in such cases, we test the null hypothesis using only a subset of all otherwise possible permutations, i.e., using only the rearrangements of the data that respect exchangeability, thus retaining the original joint distribution unaltered. In a previous study, we defined exchangeability for blocks of data, as opposed to each datum individually, then allowing permutations to happen within block, or the blocks as a whole to be permuted. Here we extend that notion to allow blocks to be nested, in a hierarchical, multi-level definition. We do not explicitly model the degree of dependence between observations, only the lack of independence; the dependence is implicitly accounted for by the hierarchy and by the permutation scheme. The strategy is compatible with heteroscedasticity and variance groups, and can be used with permutations, sign flippings, or both combined. We evaluate the method for various dependence structures, apply it to real data from the Human Connectome Project (HCP) as an example application, show that false positives can be avoided in such cases, and provide a software implementation of the proposed approach.

A symmetric multivariate leakage correction for MEG connectomes

Ambiguities in the source reconstruction of magnetoencephalographic (MEG) measurements can cause spurious correlations between estimated source time-courses. In this paper, we propose a symmetric orthogonalisation method to correct for these artificial correlations between a set of multiple regions of interest (ROIs). This process enables the straightforward application of network modelling methods, including partial correlation or multivariate autoregressive modelling, to infer connectomes, or functional networks, from the corrected ROIs. Here, we apply the correction to simulated MEG recordings of simple networks and to a resting-state dataset collected from eight subjects, before computing the partial correlations between power envelopes of the corrected ROItime-courses. We show accurate reconstruction of our simulated networks, and in the analysis of real MEGresting-state connectivity, we find dense bilateral connections within the motor and visual networks, together with longer-range direct fronto-parietal connections.

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A method for removing source leakage from multivariate network analyses in MEG.

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Network inference performed using regularised partial correlations between ROIs.

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Artificial correlations are removed using a symmetric orthogonalisation step.

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Simulations show accurate false-positive rates for network edge detection.

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Resting-state networks show increased bilateral connectivity after correction.

Molecular diagnostics in soft tissue sarcomas and gastrointestinal stromal tumors

Soft tissue sarcomas are rare malignant heterogenous tumors of mesenchymal origin with over fifty subtypes. The use of hematoxylin and eosin stained sections (and immunohistochemistry) in the morphologic assessment of these tumors has been the bane of clinical diagnosis until recently. The last decade has witnessed considerable progress in the understanding and application of molecular techniques in refining the current understanding of soft tissue sarcomas and gastrointestinal stromal tumors beyond the limits of traditional approaches. Indeed, the identification of reciprocal chromosomal translocations and fusion genes in some subsets of sarcomas with potential implications in the pathogenesis, diagnosis and treatment has been revolutionary. The era of molecular targeted therapy presents a platform that continues to drive biomarker discovery and personalized medicine in soft tissue sarcomas and gastrointestinal stromal tumors. In this review, we highlight how the different molecular techniques have enhanced the diagnosis of these tumors with prognostic and therapeutic implications.

Large-scale probabilistic functional modes from resting state fMRI

It is well established that it is possible to observe spontaneous, highly structured, fluctuations in human brain activity from functional magnetic resonance imaging (fMRI) when the subject is ‘at rest’. However, characterising this activity in an interpretable manner is still a very open problem.

In this paper, we introduce a method for identifying modes of coherent activity from resting state fMRI (rfMRI) data. Our model characterises a mode as the outer product of a spatial map and a time course, constrained by the nature of both the between-subject variation and the effect of the haemodynamic response function. This is presented as a probabilistic generative model within a variational framework that allows Bayesian inference, even on voxelwise rfMRI data. Furthermore, using this approach it becomes possible to infer distinct extended modes that are correlated with each other in space and time, a property which we believe is neuroscientifically desirable.

We assess the performance of our model on both simulated data and high quality rfMRI data from the Human Connectome Project, and contrast its properties with those of both spatial and temporal independent component analysis (ICA). We show that our method is able to stably infer sets of modes with complex spatio-temporal interactions and spatial differences between subjects.

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We introduce a probabilistic model for modes in resting state fMRI.

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Our hierarchical model captures subject variability and haemodynamic effects.

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We illustrate its performance on simulated data and rfMRI data from 200 subjects.

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We demonstrate the ability of our method to infer spatio-temporally interacting modes.

Attentional load modulates large-scale functional brain connectivity beyond the core attention networks

In line with the notion of a continuously active and dynamic brain, functional networks identified during rest correspond with those revealed by task-fMRI. Characterizing the dynamic cross-talk between these network nodes is key to understanding the successful implementation of effortful cognitive processing in healthy individuals and its breakdown in a variety of conditions involving aberrant brain biology and cognitive dysfunction. We employed advanced network modeling on fMRI data collected during a task involving sustained attentive tracking of objects at two load levels and during rest. Using multivariate techniques, we demonstrate that attentional load levels can be significantly discriminated, and from a resting-state condition, the accuracy approaches 100%, by means of estimates of between-node functional connectivity. Several network edges were modulated during task engagement: The dorsal attention network increased connectivity with a visual node, while decreasing connectivity with motor and sensory nodes. Also, we observed a decoupling between left and right hemisphere dorsal visual streams. These results support the notion of dynamic network reconfigurations based on attentional effort. No simple correspondence between node signal amplitude change and node connectivity modulations was found, thus network modeling provides novel information beyond what is revealed by conventional task-fMRI analysis. The current decoding of attentional states confirms that edge connectivity contains highly predictive information about the mental state of the individual, and the approach shows promise for the utilization in clinical contexts.

A comment on 'a unique case of facial burn superinfected with Dermatobia hominis larvae'

A recently reported case of a massive infection of a facial wound by Dermatobia hominis is almost certainly a misidentification of the insect.

Alternative donors extend transplantation for patients with lymphoma who lack an HLA matched donor

Alternative donor transplantation is increasingly used for high risk lymphoma patients. We analyzed 1593 transplant recipients (2000 to 2010) and compared transplant outcomes in recipients of 8/8 allele human leukocyte antigen (HLA)-A, -B, -C, and DRB1 matched unrelated donors (MUD; n=1176), 7/8 allele HLA-matched unrelated donors (MMUD; n=275) and umbilical cord blood donors (1 or 2 units UCB; n=142). Adjusted 3-year non-relapse mortality of MMUD (44%) was higher as compared to MUD (35%; p=0.004), but similar to UCB recipients (37%; p=0.19), although UCB had lower rates of neutrophil and platelet recovery compared to unrelated donor groups. With a median follow-up of 55 months, 3-year adjusted cumulative incidence of relapse was lower after MMUD compared with MUD (25% vs 33%, p=0.003) but similar between UCB and MUD (30% vs 33%; p=0.48). In multivariate analysis UCB recipients had lower risks of acute and chronic graft versus host disease compared with adult donor groups (UCB vs MUD: HR=0.68, p=0.05; HR=0.35; p<0.001). Adjusted 3-year overall survival was comparable (43% MUD, 37% MMUD and 41% UCB). Data highlight that patients with lymphoma have acceptable survival after alternative donor transplantation. MMUD and UCB can expand the curative potential of allotransplant to patients who lack suitable HLA-matched sibling or MUD.

Peripheral T-cell lymphomas in a large US multicenter cohort: prognostication in the modern era including impact of frontline therapy

BACKGROUND

Optimal frontline therapy for peripheral T-cell lymphoma (PTCL) in the modern era remains unclear.

PATIENTS AND METHODS

We examined patient characteristics, treatment, and outcomes among 341 newly diagnosed PTCL patients from 2000 to 2011. Outcome was compared with a matched cohort of diffuse large B-cell lymphoma (DLBCL) patients, and prognostic factors were assessed using univariate and multivariate analyses.

RESULTS

PTCL subtypes included PTCL, not otherwise specified (PTCL-NOS) (31%), anaplastic large T-cell lymphoma (ALCL) (26%), angioimmunoblastic T-cell lymphoma (23%), NK/T-cell lymphoma (7%), acute T-cell leukemia/lymphoma (6%), and other (7%). Median age was 62 years (range 18-95 years), and 74% had stage III-IV disease. Twenty-three (7%) patients received only palliative care whereas 318 received chemotherapy: CHOP-like regimens (70%), hyperCVAD/MA (6%), or other (18%). Thirty-three patients (10%) underwent stem-cell transplantation (SCT) in first remission. The overall response rate was 73% (61% complete); 24% had primary refractory disease. With 39-month median follow-up, 3-year progression-free survival (PFS) and overall survival (OS) were 32% and 52%. PFS and OS for PTCL patients were significantly inferior to matched patients with DLBCL. On multivariate analysis, stage I-II disease was the only significant pretreatment prognostic factor [PFS: hazard ratio (HR) 0.54, 95% confidence interval (CI) 0.34-0.85, P = 0.007; OS: HR 0.42, 95% CI 0.22-0.78, P = 0.006]. ALK positivity in ALCL was prognostic on univariate analysis, but lost significance on multivariate analysis. The most dominant prognostic factor was response to initial therapy (complete response versus other), including adjustment for stage and SCT [PFS: HR 0.19, 95% CI 0.14-0.28, P < 0.0001; OS: HR 0.26, 95% CI 0.17-0.40, P < 0.0001]. No overall survival difference was observed based on choice of upfront regimen or SCT in first remission.

CONCLUSIONS

This analysis identifies early-stage disease and initial treatment response as dominant prognostic factors in PTCL. No clear benefit was observed for patients undergoing consolidative SCT. Novel therapeutic approaches for PTCL are critically needed.

Calcium kinetics during bed rest with artificial gravity and exercise countermeasures

We assessed the potential for countermeasures to lessen the loss of bone calcium during bed rest. Subjects ingested less calcium during bed rest, and with artificial gravity, they also absorbed less calcium. With exercise, they excreted less calcium. To retain bone during bed rest, calcium intake needs to be maintained.

INTRODUCTION

This study aims to assess the potential for artificial gravity (AG) and exercise (EX) to mitigate loss of bone calcium during space flight.

METHODS

We performed two studies: (1) a 21-day bed rest (BR) study with subjects receiving 1 h/day AG (n = 8) or no AG (n = 7) and (2) a 28-day BR study with 1 h/day resistance EX (n = 10) or no EX (n = 3). In both studies, stable isotopes of Ca were administered orally and intravenously, at baseline and after 10 days of BR, and blood, urine, and feces were sampled for up to 14 days post dosing. Tracers were measured using thermal ionization mass spectrometry. Data were analyzed by compartmental modeling.

RESULTS

Less Ca was absorbed during BR, resulting in lower Ca balance in BR+AG (-6.04 ± 3.38 mmol/day, P = 0.023). However, Ca balance did not change with BR+EX, even though absorbed Ca decreased and urinary Ca excretion increased, because endogenous excretion decreased, and there was a trend for increased bone deposition (P = 0.06). Urinary N-telopeptide excretion increased in controls during BR, but not in the EX group. Markers of bone formation were not different between treatment groups for either study. Ca intake decreased during BR (by 5.4 mmol/day in the AG study and 2.8 mmol/day in the EX study), resulting in lower absorbed Ca.

CONCLUSIONS

During BR (or space flight), Ca intake needs to be maintained or even increased with countermeasures such as exercise, to enable maintenance of bone Ca.

First steps in using machine learning on fMRI data to predict intrusive memories of traumatic film footage

After psychological trauma, why do some only some parts of the traumatic event return as intrusive memories while others do not? Intrusive memories are key to cognitive behavioural treatment for post-traumatic stress disorder, and an aetiological understanding is warranted. We present here analyses using multivariate pattern analysis (MVPA) and a machine learning classifier to investigate whether peri-traumatic brain activation was able to predict later intrusive memories (i.e. before they had happened). To provide a methodological basis for understanding the context of the current results, we first show how functional magnetic resonance imaging (fMRI) during an experimental analogue of trauma (a trauma film) via a prospective event-related design was able to capture an individual's later intrusive memories. Results showed widespread increases in brain activation at encoding when viewing a scene in the scanner that would later return as an intrusive memory in the real world. These fMRI results were replicated in a second study. While traditional mass univariate regression analysis highlighted an association between brain processing and symptomatology, this is not the same as prediction. Using MVPA and a machine learning classifier, it was possible to predict later intrusive memories across participants with 68% accuracy, and within a participant with 97% accuracy; i.e. the classifier could identify out of multiple scenes those that would later return as an intrusive memory. We also report here brain networks key in intrusive memory prediction. MVPA opens the possibility of decoding brain activity to reconstruct idiosyncratic cognitive events with relevance to understanding and predicting mental health symptoms.

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Why only some moments within a trauma intrude while others do not is unclear.

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Neuroimaging may provide further clues as to why this is the case.

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Multivariate pattern analysis, a recent neuroimaging analysis tool, was able to predict intrusive memories.

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Those brain networks involved in intrusive memory prediction are presented.

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Multivariate pattern analysis may inform future innovation in mental health.

Group-PCA for very large fMRI datasets

Increasingly-large datasets (for example, the resting-state fMRI data from the Human Connectome Project) are demanding analyses that are problematic because of the sheer scale of the aggregate data. We present two approaches for applying group-level PCA; both give a close approximation to the output of PCA applied to full concatenation of all individual datasets, while having very low memory requirements regardless of the number of datasets being combined. Across a range of realistic simulations, we find that in most situations, both methods are more accurate than current popular approaches for analysis of multi-subject resting-state fMRI studies. The group-PCA output can be used to feed into a range of further analyses that are then rendered practical, such as the estimation of group-averaged voxelwise connectivity, group-level parcellation, and group-ICA.

MSM: a new flexible framework for Multimodal Surface Matching

Surface-based cortical registration methods that are driven by geometrical features, such as folding, provide sub-optimal alignment of many functional areas due to variable correlation between cortical folding patterns and function. This has led to the proposal of new registration methods using features derived from functional and diffusion imaging. However, as yet there is no consensus over the best set of features for optimal alignment of brain function. In this paper we demonstrate the utility of a new Multimodal Surface Matching (MSM) algorithm capable of driving alignment using a wide variety of descriptors of brain architecture, function and connectivity. The versatility of the framework originates from adapting the discrete Markov Random Field (MRF) registration method to surface alignment. This has the benefit of being very flexible in the choice of a similarity measure and relatively insensitive to local minima. The method offers significant flexibility in the choice of feature set, and we demonstrate the advantages of this by performing registrations using univariate descriptors of surface curvature and myelination, multivariate feature sets derived from resting fMRI, and multimodal descriptors of surface curvature and myelination. We compare the results with two state of the art surface registration methods that use geometric features: FreeSurfer and Spherical Demons. In the future, the MSM technique will allow explorations into the best combinations of features and alignment strategies for inter-subject alignment of cortical functional areas for a wide range of neuroimaging data sets.

Study protocol: The Whitehall II imaging sub-study

BACKGROUND

The Whitehall II (WHII) study of British civil servants provides a unique source of longitudinal data to investigate key factors hypothesized to affect brain health and cognitive ageing. This paper introduces the multi-modal magnetic resonance imaging (MRI) protocol and cognitive assessment designed to investigate brain health in a random sample of 800 members of the WHII study.

METHODS/DESIGN

A total of 6035 civil servants participated in the WHII Phase 11 clinical examination in 2012-2013. A random sample of these participants was included in a sub-study comprising an MRI brain scan, a detailed clinical and cognitive assessment, and collection of blood and buccal mucosal samples for the characterisation of immune function and associated measures. Data collection for this sub-study started in 2012 and will be completed by 2016. The participants, for whom social and health records have been collected since 1985, were between 60-85 years of age at the time the MRI study started. Here, we describe the pre-specified clinical and cognitive assessment protocols, the state-of-the-art MRI sequences and latest pipelines for analyses of this sub-study.

DISCUSSION

The integration of cutting-edge MRI techniques, clinical and cognitive tests in combination with retrospective data on social, behavioural and biological variables during the preceding 25 years from a well-established longitudinal epidemiological study (WHII cohort) will provide a unique opportunity to examine brain structure and function in relation to age-related diseases and the modifiable and non-modifiable factors affecting resilience against and vulnerability to adverse brain changes.

Permutation inference for the general linear model

Permutation methods can provide exact control of false positives and allow the use of non-standard statistics, making only weak assumptions about the data. With the availability of fast and inexpensive computing, their main limitation would be some lack of flexibility to work with arbitrary experimental designs. In this paper we report on results on approximate permutation methods that are more flexible with respect to the experimental design and nuisance variables, and conduct detailed simulations to identify the best method for settings that are typical for imaging research scenarios. We present a generic framework for permutation inference for complex general linear models (GLMS) when the errors are exchangeable and/or have a symmetric distribution, and show that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios. We also demonstrate how the inference on GLM parameters, originally intended for independent data, can be used in certain special but useful cases in which independence is violated. Detailed examples of common neuroimaging applications are provided, as well as a complete algorithm - the "randomise" algorithm - for permutation inference with the GLM.

Local GABA concentration is related to network-level resting functional connectivity

Anatomically plausible networks of functionally inter-connected regions have been reliably demonstrated at rest, although the neurochemical basis of these ‘resting state networks’ is not well understood. In this study, we combined magnetic resonance spectroscopy (MRS) and resting state fMRI and demonstrated an inverse relationship between levels of the inhibitory neurotransmitter GABA within the primary motor cortex (M1) and the strength of functional connectivity across the resting motor network. This relationship was both neurochemically and anatomically specific. We then went on to show that anodal transcranial direct current stimulation (tDCS), an intervention previously shown to decrease GABA levels within M1, increased resting motor network connectivity. We therefore suggest that network-level functional connectivity within the motor system is related to the degree of inhibition in M1, a major node within the motor network, a finding in line with converging evidence from both simulation and empirical studies.

DOI:http://dx.doi.org/10.7554/eLife.01465.001

Even when your body is at rest, your brain remains active. Subjects lying in brain scanners without any specific task to perform show coordinated and reproducible patterns of brain activity. Areas of the brain with similar functions, such as those involved in vision or in movement, tend to increase or decrease their activity in sync, and these coordinated patterns are referred to as resting state networks.

The functions of these networks are unclear—they may support introspection, memory recall or planning for the future, or they may help to strengthen newly acquired skills by enabling the brain to replay previous learning episodes. There is evidence that resting state networks are altered in disorders such as Alzheimer’s disease, autism and schizophrenia, but little is known about how these changes arise or what they might mean.

Now, Stagg et al. have used a type of brain scan called magnetic resonance spectroscopy to gain insights into the mechanisms by which one particular network—the resting motor network—is generated. This network consists of areas involved in planning, monitoring and executing movements, and includes the primary motor cortex, which initiates movements by sending instructions to the spinal cord.

The levels of a chemical called GABA—a neurotransmitter molecule that tends to inhibit the activity of nerve cells—were measured in the primary motor cortex of young healthy volunteers as they lay idle in a scanner. GABA levels were negatively correlated with the amount of coordinated activity within the resting motor network. By contrast, no relation was seen between coordinated activity and the levels of the neurotransmitter glutamate, which tends to increase the activity of nerve cells. Furthermore, when a weak electric current was applied through the subjects’ scalp to their primary motor cortex—a technique previously shown to lower levels of GABA in the region—the resting motor network became stronger.

In addition to providing new information on how the rhythmic patterns of activity seen in the resting brain arise, the work of Stagg et al. contributes to the more general effort to understand the complex patterns of connections within the human brain.

DOI:http://dx.doi.org/10.7554/eLife.01465.002

Fast transient networks in spontaneous human brain activity

To provide an effective substrate for cognitive processes, functional brain networks should be able to reorganize and coordinate on a sub-second temporal scale. We used magnetoencephalography recordings of spontaneous activity to characterize whole-brain functional connectivity dynamics at high temporal resolution. Using a novel approach that identifies the points in time at which unique patterns of activity recur, we reveal transient (100–200 ms) brain states with spatial topographies similar to those of well-known resting state networks. By assessing temporal changes in the occurrence of these states, we demonstrate that within-network functional connectivity is underpinned by coordinated neuronal dynamics that fluctuate much more rapidly than has previously been shown. We further evaluate cross-network interactions, and show that anticorrelation between the default mode network and parietal regions of the dorsal attention network is consistent with an inability of the system to transition directly between two transient brain states.

DOI:http://dx.doi.org/10.7554/eLife.01867.001

When subjects lie motionless inside scanners without any particular task to perform, their brains show stereotyped patterns of activity across regions known as resting state networks. Each network consists of areas with a common function, such as the ‘motor’ network or the ‘visual’ network. The role of resting state networks is unclear, but these spontaneous activity patterns are altered in disorders including autism, schizophrenia, and Alzheimer’s disease.

One puzzling feature of resting state networks is that they seem to last for relatively long times. However, the majority of studies into resting state networks have used fMRI brain scans, in which changes in the level of oxygen in the blood are used as a proxy for the activity of a given brain region. Since changes in blood oxygen occur relatively slowly, the ability of fMRI to detect rapid changes in activity is limited: it is thus possible that the long-lived nature of resting state networks is an artefact of the use of fMRI.

Now, Baker et al. have used a different type of brain scan known as an MEG scan to show that the activity of resting state networks is shorter lived than previously thought. MEG scanners measure changes in the magnetic fields generated by electrical currents in the brain, which means that they can detect alterations in brain activity much more rapidly than fMRI.

MEG recordings from the brains of nine healthy subjects revealed that individual resting state networks were typically stable for only 100 ms to 200 ms. Moreover, transitions between different networks did not occur randomly; instead, certain networks were much more likely to become active after others. The work of Baker et al. suggests that the resting brain is constantly changing between different patterns of activity, which enables it to respond quickly to any given situation.

DOI:http://dx.doi.org/10.7554/eLife.01867.002

ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging

The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB's ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses.

The p66Shc adapter protein regulates the morphogenesis and epithelial maturation of fetal mouse lungs

Many signaling pathways are mediated by Shc adapter proteins that, in turn, are expressed as three isoforms with distinct functions. The p66(Shc) isoform antagonizes proliferation, regulates oxidative stress, and mediates apoptosis. It is highly expressed in the canalicular but not the later stages of mouse lung development, and its expression persists in bronchopulmonary dysplasia, a chronic disease associated with premature birth. These observations suggest that p66(Shc) has a developmental function. However, constitutive p66(Shc) deletion yields no morphological phenotype, and the structure of the Shc gene precludes its inducible deletion. To elucidate its function in lung development, we transfected p66(Shc) or nonsilencing small-interfering RNA (siRNA) into the epithelia of embryonic day 11 mouse lungs that were then cultured for 3 days and analyzed morphometrically. To assess cellular proliferation and epithelial differentiation, lung explants were immunostained and immunoblotted for p66(Shc), proliferating cell nuclear antigen (PCNA), the proximal airway differentiation antigens Clara cell 10-kDa protein (CC10) and thyroid transcription factor (TTF)-1, and the alveolar surfactant proteins (SP)-A, -B, and -C. Explants transfected with nonsilencing siRNA demonstrated specific epithelial uptake and normal morphological development relative to uninjected controls. In contrast, transfection with p66(Shc) siRNA significantly increased lumenal cross-sectional areas, decreased branching, and increased epithelial proliferation (P < 0.05 for all). Relative to controls, the expression of SP-B, SP-C, CC10, and TTF-1 was decreased by p66(Shc) knockdown. SP-A was not expressed in either control or treated lungs. These data suggest that p66(Shc) attenuates epithelial proliferation while promoting both distal and proximal epithelial maturation.

ICA model order selection of task co-activation networks

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders.

Functional connectomics from resting-state fMRI

Spontaneous fluctuations in activity in different parts of the brain can be used to study functional brain networks. We review the use of resting-state functional MRI (rfMRI) for the purpose of mapping the macroscopic functional connectome. After describing MRI acquisition and image-processing methods commonly used to generate data in a form amenable to connectomics network analysis, we discuss different approaches for estimating network structure from that data. Finally, we describe new possibilities resulting from the high-quality rfMRI data being generated by the Human Connectome Project and highlight some upcoming challenges in functional connectomics.