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Carbon dioxide removal (CDR) is the essential ‘net’ in net zero. However, a thriving CDR industry will not come into being without government intervention. As governments start to devise CDR support policies, this paper solicits the views of market participants in two of the most prominent CDR methods: bioenergy with carbon capture and storage (BECCS) and direct air carbon capture and storage (DACCS). We survey 47 BECCS and DACCS project developers and financiers active in Europe, conducting in-depth interviews with 27 of them to identify their key challenges and preferred policy interventions to address them. We find that participants prefer compliance markets, such as links to emissions trading systems, to generate demand but seek government support to cushion early market risks. They acknowledge the need for stringent monitoring and regulation to ensure environmental integrity. Bearing industry expectations in mind, policymakers face five key challenges in developing CDR: reaching scale, striking a balance with emissions cuts, safeguarding integrity, ensuring fairness and accelerating the speed of deployment.

Telomeres form protective caps at the ends of chromosomes, and their attrition is a marker of biological aging. Short telomeres are associated with an increased risk of neurological and psychiatric disorders including dementia. The mechanism underlying this risk is unclear, and may involve brain structure and function. However, the relationship between telomere length and neuroimaging markers is poorly characterized. Here we show that leucocyte telomere length (LTL) is associated with multi-modal MRI phenotypes in 31,661 UK Biobank participants. Longer LTL is associated with: i) larger global and subcortical grey matter volumes including the hippocampus, ii) lower T1-weighted grey-white tissue contrast in sensory cortices, iii) white-matter microstructure measures in corpus callosum and association fibres, iv) lower volume of white matter hyperintensities, and v) lower basal ganglia iron. Longer LTL was protective against certain related clinical manifestations, namely all-cause dementia (HR 0.93, 95% CI: 0.91-0.96), but not stroke or Parkinson's disease. LTL is associated with multiple MRI endophenotypes of neurodegenerative disease, suggesting a pathway by which longer LTL may confer protective against dementia.

BackgroundBrain iron deposition has been linked to several neurodegenerative conditions and reported in alcohol dependence. Whether iron accumulation occurs in moderate drinkers is unknown. Our objectives were to investigate evidence in support of causal relationships between alcohol consumption and brain iron levels and to examine whether higher brain iron represents a potential pathway to alcohol-related cognitive deficits.Methods and findingsObservational associations between brain iron markers and alcohol consumption (n = 20,729 UK Biobank participants) were compared with associations with genetically predicted alcohol intake and alcohol use disorder from 2-sample mendelian randomization (MR). Alcohol intake was self-reported via a touchscreen questionnaire at baseline (2006 to 2010). Participants with complete data were included. Multiorgan susceptibility-weighted magnetic resonance imaging (9.60 ± 1.10 years after baseline) was used to ascertain iron content of each brain region (quantitative susceptibility mapping (QSM) and T2*) and liver tissues (T2*), a marker of systemic iron. Main outcomes were susceptibility (χ) and T2*, measures used as indices of iron deposition. Brain regions of interest included putamen, caudate, hippocampi, thalami, and substantia nigra. Potential pathways to alcohol-related iron brain accumulation through elevated systemic iron stores (liver) were explored in causal mediation analysis. Cognition was assessed at the scan and in online follow-up (5.82 ± 0.86 years after baseline). Executive function was assessed with the trail-making test, fluid intelligence with puzzle tasks, and reaction time by a task based on the "Snap" card game. Mean age was 54.8 ± 7.4 years and 48.6% were female. Weekly alcohol consumption was 17.7 ± 15.9 units and never drinkers comprised 2.7% of the sample. Alcohol consumption was associated with markers of higher iron (χ) in putamen (β = 0.08 standard deviation (SD) [95% confidence interval (CI) 0.06 to 0.09], p < 0.001), caudate (β = 0.05 [0.04 to 0.07], p < 0.001), and substantia nigra (β = 0.03 [0.02 to 0.05], p < 0.001) and lower iron in the thalami (β = -0.06 [-0.07 to -0.04], p < 0.001). Quintile-based analyses found these associations in those consuming >7 units (56 g) alcohol weekly. MR analyses provided weak evidence these relationships are causal. Genetically predicted alcoholic drinks weekly positively associated with putamen and hippocampus susceptibility; however, these associations did not survive multiple testing corrections. Weak evidence for a causal relationship between genetically predicted alcohol use disorder and higher putamen susceptibility was observed; however, this was not robust to multiple comparisons correction. Genetically predicted alcohol use disorder was associated with serum iron and transferrin saturation. Elevated liver iron was observed at just >11 units (88 g) alcohol weekly c.f. ConclusionsTo the best of our knowledge, this study represents the largest investigation of moderate alcohol consumption and iron homeostasis to date. Alcohol consumption above 7 units weekly associated with higher brain iron. Iron accumulation represents a potential mechanism for alcohol-related cognitive decline.

BackgroundExposure and response prevention, a type of cognitive-behavioral therapy, is an effective first-line treatment for obsessive-compulsive disorder (OCD). Despite extensive evidence of the efficacy of exposure and response prevention (ERP) from clinical studies and in real-world samples, it is still underused as a treatment. This is likely due to the limits to access to care that include the availability of adequately trained therapists, as well as geographical location, time, and cost barriers. To address these, NOCD created a digital behavioral health treatment for OCD using ERP delivered via video teletherapy and with technology-assisted elements including app-based therapy tools and between-session therapist messaging. ObjectiveWe examined treatment outcomes in a large naturalistic sample of 3552 adults with a primary OCD diagnosis who received NOCD treatment. MethodsThe treatment model consisted of twice-weekly, live, face-to-face video teletherapy ERP for 3 weeks, followed by 6 weeks of once-weekly brief video teletherapy check-ins for 30 minutes. Assessments were conducted at baseline, at midpoint after completion of 3 weeks of twice-weekly sessions, and at the end of 6 weeks of brief check-ins (endpoint). Longitudinal assessments were also obtained at 3, 6, 9, and 12 months after endpoint. ResultsTreatment resulted in clinically and statistically significant improvements, with a 43.4% mean reduction in obsessive-compulsive symptoms (g=1.0; 95% CI 0.93 to 1.03) and a 62.9% response rate. Treatment also resulted in a 44.2% mean reduction in depression, a 47.8% mean reduction in anxiety, and a 37.3% mean reduction in stress symptoms. Quality of life improved by a mean of 22.7%. Reduction in OCD symptoms and response rates were similar for those with mild, moderate, or severe symptoms. The mean duration of treatment was 11.5 (SD 4.0) weeks, and the mean total therapist time was 10.6 (SD 1.1) hours. Improvements were maintained at 3, 6, 9, and 12 months. ConclusionsIn this sample, representing the largest reported treated cohort of patients with OCD to date, video teletherapy treatment demonstrated effectiveness in reducing obsessive-compulsive and comorbid symptoms and improved quality of life. Further, it achieved meaningful results in less than half the total therapist time compared with standard once-weekly outpatient treatment, an efficiency that represents substantial monetary and time savings. The effect size was large and similar to studies of in-person ERP. This technology-assisted remote treatment is readily accessible for patients, offering an advancement in the field in the dissemination of effective evidence-based care for OCD.

Heterozygous loss-of-function mutation of the calcium sensing-receptor (CaSR), causes familial hypocalciuric hypercalcemia type 1 (FHH1), a typically benign condition characterized by mild hypercalcemia. In contrast, homozygous mutation of this dimer-forming G-protein coupled receptor manifests as the lethal neonatal severe hyperparathyroidism (NSHPT). To investigate the mechanisms by which CaSR mutations lead to these distinct disease states, we engineered wild-type (WT) and an exon 5-deficient disease-causing mutation, and transfected expression constructs into human embryonic kidney (HEK) cells. WT protein was mainly membrane-expressed whereas the mutant CaSR protein (mCaSR) was confined to the cytoplasm. Co-expression of WT CaSR directed mCaSR to the cell membrane. In assays of CaSR function, increases in extracellular [Ca2+] ([Ca2+]o) increased intracellular [Ca2+] ([Ca2+]i) in cells expressing WT CaSR while the response was reduced in cells co-expressing mutant and WT receptor. Untransfected cells or those expressing mCaSR alone, showed minimal, equivalent responses to increased [Ca2+]o. Immunoprecipitation experiments confirmed an association between mutant and wild-type CaSR. The affinity of the WT CaSR for calcium was three times greater than that of the heterodimer. The maximal functional response to [Ca]o was dependent on localization of CaSR to the membrane level and independent of homo- or heterodimerizations. In summary, these results suggest that heterodimerization of WT and mCaSR receptors, rescues the trafficking defect of the mutant receptors and also reduces the affinity of the WT-mutant heterodimer for [Ca]o. In contrast, the homozygous mutants do not produce functional receptors on cell membrane. These data indicate how substantial differences between signaling of hetero- and homodimeric mutants may lead to profound differences in the severity of disease in heterozygous and homozygous carriers of these mutations.

IntroductionMagnetic resonance imaging (MRI) of the brain could be a key diagnostic and research tool for understanding the neuropsychiatric complications of COVID-19. For maximum impact, multi-modal MRI protocols will be needed to measure the effects of SARS-CoV-2 infection on the brain by diverse potentially pathogenic mechanisms, and with high reliability across multiple sites and scanner manufacturers. Here we describe the development of such a protocol, based upon the UK Biobank, and its validation with a travelling heads study. A multi-modal brain MRI protocol comprising sequences for T1-weighted MRI, T2-FLAIR, diffusion MRI (dMRI), resting-state functional MRI (fMRI), susceptibility-weighted imaging (swMRI), and arterial spin labelling (ASL), was defined in close approximation to prior UK Biobank (UKB) and C-MORE protocols for Siemens 3T systems. We iteratively defined a comparable set of sequences for General Electric (GE) 3T systems. To assess multi-site feasibility and between-site variability of this protocol, N = 8 healthy participants were each scanned at 4 UK sites: 3 using Siemens PRISMA scanners (Cambridge, Liverpool, Oxford) and 1 using a GE scanner (King's College London). Over 2,000 Imaging Derived Phenotypes (IDPs), measuring both data quality and regional image properties of interest, were automatically estimated by customised UKB image processing pipelines (S2 File). Components of variance and intra-class correlations (ICCs) were estimated for each IDP by linear mixed effects models and benchmarked by comparison to repeated measurements of the same IDPs from UKB participants. Intra-class correlations for many IDPs indicated good-to-excellent between-site reliability. Considering only data from the Siemens sites, between-site reliability generally matched the high levels of test-retest reliability of the same IDPs estimated in repeated, within-site, within-subject scans from UK Biobank. Inclusion of the GE site resulted in good-to-excellent reliability for many IDPs, although there were significant between-site differences in mean and scaling, and reduced ICCs, for some classes of IDP, especially T1 contrast and some dMRI-derived measures. We also identified high reliability of quantitative susceptibility mapping (QSM) IDPs derived from swMRI images, multi-network ICA-based IDPs from resting-state fMRI, and olfactory bulb structure IDPs from T1, T2-FLAIR and dMRI data.ConclusionThese results give confidence that large, multi-site MRI datasets can be collected reliably at different sites across the diverse range of MRI modalities and IDPs that could be mechanistically informative in COVID brain research. We discuss limitations of the study and strategies for further harmonisation of data collected from sites using scanners supplied by different manufacturers. These acquisition and analysis protocols are now in use for MRI assessments of post-COVID patients (N = 700) as part of the ongoing COVID-CNS study.

Despite the importance of carbon dioxide removal (CDR) in most climate change mitigation scenarios that limit warming to well below 2 °C, the study of CDR is still a nascent field with basic questions to be resolved. Crucially, it is not known how much CDR is currently deployed at a global scale, nor how that compares to mitigation scenario estimates. Here, we address this problem by developing an estimate of global current CDR activity. We draw on national greenhouse gas inventory data combined with CDR registries and commercial databases to estimate that global anthropogenic activity presently generates ∼1985 MtCO _2 yr ^−1 of atmospheric removals. Almost all of these—1983 MtCO _2 yr ^−1 —are removals from land-use, land-use change and forestry. Non-land-management CDR projects such as bioenergy with carbon capture and storage, direct air capture with carbon capture and storage and biochar remove only about 2 MtCO _2 yr ^−1 . We compare this estimate with Shared Socioeconomic Pathways projections of CDR deployed in ‘well-below 2°C’ mitigation pathways. In so doing we demonstrate current CDR deployment would need to grow exponentially to keep the world aligned with most ‘well-below 2°C’ scenarios, which see CDR deployment growing between 75% and 100% per year between 2020 and 2030, adding ∼300–2500 MtCO _2 in total CDR capacity. To conclude we discuss uncertainties related to our estimates, and suggest priorities for the future collection and management of CDR data, particularly related to the role of the land sink in generating CDR.

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

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

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

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

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.

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.

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.

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.

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 3 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 different.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.

Artefact 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 artefacts. It is important that only inter-subject variability due to the artefacts 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 X-noiseifier –FIX- cleanup with soft and aggressive options) on data acquired at 1.5T. 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 artefact 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.

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.

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.

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.

Independent component analysis (ICA) can identify covarying functional networks in the resting brain. Despite its relatively widespread use, the potential of the temporal information (unlike spatial information) obtained by ICA from resting state fMRI (RS-fMRI) data is not always fully utilized. In this study, we systematically investigated which features in ICA of resting-state fMRI relate to behaviour, with stop signal reaction time (SSRT) in a stop-signal task taken as a test case. We did this by correlating SSRT with the following three kinds of measure obtained from RS-fMRI data: (1) the amplitude of each resting state network (RSN) (evaluated by the standard deviation of the RSN timeseries), (2) the temporal correlation between every pair of RSN timeseries, and (3) the spatial map of each RSN. For multiple networks, we found significant correlations not only between SSRT and spatial maps, but also between SSRT and network activity amplitude. Most of these correlations are of functional interpretability. The temporal correlations between RSN pairs were of functional significance, but these correlations did not appear to be very sensitive to finding SSRT correlations. In addition, we also investigated the effects of the decomposition dimension, spatial smoothing and Z-transformation of the spatial maps, as well as the techniques for evaluating the temporal correlation between RSN timeseries. Overall, the temporal information acquired by ICA enabled us to investigate brain function from a complementary perspective to the information provided by spatial maps.

Echo planar imaging (EPI) is an MRI technique of particular value to neuroscience, with its use for virtually all functional MRI (fMRI) and diffusion imaging of fiber connections in the human brain. EPI generates a single 2D image in a fraction of a second; however, it requires 2-3 seconds to acquire multi-slice whole brain coverage for fMRI and even longer for diffusion imaging. Here we report on a large reduction in EPI whole brain scan time at 3 and 7 Tesla, without significantly sacrificing spatial resolution, and while gaining functional sensitivity. The multiplexed-EPI (M-EPI) pulse sequence combines two forms of multiplexing: temporal multiplexing (m) utilizing simultaneous echo refocused (SIR) EPI and spatial multiplexing (n) with multibanded RF pulses (MB) to achieve m×n images in an EPI echo train instead of the normal single image. This resulted in an unprecedented reduction in EPI scan time for whole brain fMRI performed at 3 Tesla, permitting TRs of 400 ms and 800 ms compared to a more conventional 2.5 sec TR, and 2-4 times reductions in scan time for HARDI imaging of neuronal fibertracks. The simultaneous SE refocusing of SIR imaging at 7 Tesla advantageously reduced SAR by using fewer RF refocusing pulses and by shifting fat signal out of the image plane so that fat suppression pulses were not required. In preliminary studies of resting state functional networks identified through independent component analysis, the 6-fold higher sampling rate increased the peak functional sensitivity by 60%. The novel M-EPI pulse sequence resulted in a significantly increased temporal resolution for whole brain fMRI, and as such, this new methodology can be used for studying non-stationarity in networks and generally for expanding and enriching the functional information.

An increased rate of brain atrophy is often observed in older subjects, in particular those who suffer from cognitive decline. Homocysteine is a risk factor for brain atrophy, cognitive impairment and dementia. Plasma concentrations of homocysteine can be lowered by dietary administration of B vitamins.To determine whether supplementation with B vitamins that lower levels of plasma total homocysteine can slow the rate of brain atrophy in subjects with mild cognitive impairment in a randomised controlled trial (VITACOG, ISRCTN 94410159).Single-center, randomized, double-blind controlled trial of high-dose folic acid, vitamins B(6) and B(12) in 271 individuals (of 646 screened) over 70 y old with mild cognitive impairment. A subset (187) volunteered to have cranial MRI scans at the start and finish of the study. Participants were randomly assigned to two groups of equal size, one treated with folic acid (0.8 mg/d), vitamin B(12) (0.5 mg/d) and vitamin B(6) (20 mg/d), the other with placebo; treatment was for 24 months. The main outcome measure was the change in the rate of atrophy of the whole brain assessed by serial volumetric MRI scans.A total of 168 participants (85 in active treatment group; 83 receiving placebo) completed the MRI section of the trial. The mean rate of brain atrophy per year was 0.76% [95% CI, 0.63-0.90] in the active treatment group and 1.08% [0.94-1.22] in the placebo group (P = 0.001). The treatment response was related to baseline homocysteine levels: the rate of atrophy in participants with homocysteine >13 µmol/L was 53% lower in the active treatment group (P = 0.001). A greater rate of atrophy was associated with a lower final cognitive test scores. There was no difference in serious adverse events according to treatment category.The accelerated rate of brain atrophy in elderly with mild cognitive impairment can be slowed by treatment with homocysteine-lowering B vitamins. Sixteen percent of those over 70 y old have mild cognitive impairment and half of these develop Alzheimer's disease. Since accelerated brain atrophy is a characteristic of subjects with mild cognitive impairment who convert to Alzheimer's disease, trials are needed to see if the same treatment will delay the development of Alzheimer's disease.Controlled-Trials.com ISRCTN94410159.

The last 15 years have witnessed a steady increase in the number of resting-state functional neuroimaging studies. The connectivity patterns of multiple functional, distributed, large-scale networks of brain dynamics have been recognised for their potential as useful tools in the domain of systems and other neurosciences. The application of functional connectivity methods to areas such as cognitive psychology, clinical diagnosis and treatment progression has yielded promising preliminary results, but is yet to be fully realised. This is due, in part, to an array of methodological and interpretative issues that remain to be resolved. We here present a review of the methods most commonly applied in this rapidly advancing field, such as seed-based correlation analysis and independent component analysis, along with examples of their use at the individual subject and group analysis levels and a discussion of practical and theoretical issues arising from this data ‘explosion’. We describe the similarities and differences across these varied statistical approaches to processing resting-state FMRI signals, and conclude that further technical optimisation and experimental refinement is required in order to fully delineate and characterise the gross complexity of the human neural functional architecture.

BackgroundNerve terminal invasion by an axonal spike activates voltage-gated channels, triggering calcium entry, vesicle fusion, and release of neurotransmitter. Ion channels activated at the terminal shape the presynaptic spike and so regulate the magnitude and duration of calcium entry. Consequently characterization of the functional properties of ion channels at nerve terminals is crucial to understand the regulation of transmitter release. Direct recordings from small neocortical nerve terminals have revealed that external [Ca(2+)] ([Ca(2+)](o)) indirectly regulates a non-selective cation channel (NSCC) in neocortical nerve terminals via an unknown [Ca(2+)](o) sensor. Here, we identify the first component in a presynaptic calcium signaling pathway.Methodology/principal findingsBy combining genetic and pharmacological approaches with direct patch-clamp recordings from small acutely isolated neocortical nerve terminals we identify the extracellular calcium sensor. Our results show that the calcium-sensing receptor (CaSR), a previously identified G-protein coupled receptor that is the mainstay in serum calcium homeostasis, is the extracellular calcium sensor in these acutely dissociated nerve terminals. The NSCC currents from reduced function mutant CaSR mice were less sensitive to changes in [Ca(2+)](o) than wild-type. Calindol, an allosteric CaSR agonist, reduced NSCC currents in direct terminal recordings in a dose-dependent and reversible manner. In contrast, glutamate and GABA did not affect the NSCC currents.Conclusions/significanceOur experiments identify CaSR as the first component in the [Ca(2+)](o) sensor-NSCC signaling pathway in neocortical terminals. Decreases in [Ca(2+)](o) will depress synaptic transmission because of the exquisite sensitivity of transmitter release to [Ca(2+)](o) following its entry via voltage-activated Ca(2+) channels. CaSR may detects such falls in [Ca(2+)](o) and increase action potential duration by increasing NSCC activity, thereby attenuating the impact of decreases in [Ca(2+)](o) on release probability. CaSR is positioned to detect the dynamic changes of [Ca(2+)](o) and provide presynaptic feedback that will alter brain excitability.

Caffeine stimulates calcium-induced calcium release (CICR) in many cell types. In neurons, caffeine stimulates CICR presynaptically and thus modulates neurotransmitter release.Using the whole-cell patch-clamp technique we found that caffeine (20 mM) reversibly increased the frequency and decreased the amplitude of miniature excitatory postsynaptic currents (mEPSCs) in neocortical neurons. The increase in mEPSC frequency is consistent with a presynaptic mechanism. Caffeine also reduced exogenously applied glutamate-activated currents, confirming a separate postsynaptic action. This inhibition developed in tens of milliseconds, consistent with block of channel currents. Caffeine (20 mM) did not reduce currents activated by exogenous NMDA, indicating that caffeine block is specific to non-NMDA type glutamate receptors.Caffeine-induced inhibition of mEPSC amplitude occurs through postsynaptic block of non-NMDA type ionotropic glutamate receptors. Caffeine thus has both pre and postsynaptic sites of action at excitatory synapses.