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.
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.
magnetoencephalography; resting state; connectivity; non-stationary; hidden Markov model; microstates; human
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 were assessed using timeseries (amplitude and spectra), network matrix and spatial map analyses. For timeseries 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.
functional magnetic resonance imaging (fMRI); resting-state; artefact removal; functional connectivity; multiband acceleration
The broad morphologic spectrum, inherent immunophenotypic heterogeneity of malignant melanoma and its rarity in the sinonasal tract are major challenges in eliciting the correct diagnosis, which may lead to misclassification and inadequate medical management. Herein, we describe a single case of a 70 year-old male with sinonasal mucosal melanoma, exhibiting varying histologic phenotypes including small round blue cell morphology, epithelioid and focal rhabdoid morphology and strong, diffuse desmin immunoreactivity. These constellation of features initially prompted the diagnosis of rhabdomyosarcoma. The differential diagnosis in this anatomic area includes other malignant small round blue cell tumors of the sinonasal mucosa such as rhabdomyosarcoma, olfactory neuroblastoma, sinonasal undifferentiated carcinoma, and lymphoma. We reviewed precedent literature and further discuss the potential pitfalls to which pathologists may be prone.
Melanoma; Sinonasal mucosa; Desmin; Rhabdoid; Pitfalls
Converging evidence from several theories of the development of incentive-sensitization to smoking-related environmental stimuli suggests that the ventral striatum plays an important role in the processing of smoking-related cue reactivity.
Twenty-six healthy right-handed volunteers (14 smokers and 12 nonsmoking controls) underwent functional magnetic resonance imaging (fMRI) during which neutral and smoking-related images were presented. Region of interest analyses were performed within the ventral striatum/nucleus accumbens (VS/NAc) for the contrast between smoking-related (SR) and nonsmoking related neutral (N) cues.
Group activation for SR versus N cues was observed in smokers but not in nonsmokers in medial orbitofrontal cortex, superior frontal gyrus, anterior cingulate cortex, and posterior fusiform gyrus using whole-brain corrected Z thresholds and in the ventral VS/NAc using uncorrected Z-statistics (smokers Z = 3.2). Region of interest analysis of signal change within ventral VS/NAc demonstrated significantly greater activation to SR versus N cues in smokers than controls.
This is the first demonstration of greater VS/NAc activation in addicted smokers than nonsmokers presented with smoking-related cues using fMRI. Smokers, but not controls, demonstrated activation to SR versus N cues in a distributed reward signaling network consistent with cue reactivity studies of other drugs of abuse.
Nucleus accumbens; smoking; nicotine; tobacco; cue reactivity; fMRI
The molecular machinery underlying action potential-evoked, synchronous neurotransmitter release, has been intensely studied. It was presumed that two other forms of exocytosis- delayed (asynchronous) and spontaneous transmission, were mediated by the same voltage-activated Ca2+ channels (VACCs), intracellular Ca2+ sensors and vesicle pools. However, a recent explosion in the study of spontaneous and asynchronous release has shown these presumptions to be incorrect. Furthermore, the finding that different forms of synaptic transmission may mediate distinct physiological functions emphasizes the importance of identifying the mechanisms by which Ca2+ regulates spontaneous and asynchronous release. In this article we will briefly summarize new and published data on the role of Ca2+ in regulating spontaneous and asynchronous release at a number of different synapses. We will discuss how an increase of extracellular [Ca2+] increases spontaneous and asynchronous release, show that VACCs are involved at only some synapses, and identify regulatory roles for other ion channels and G protein-coupled receptors. In particular, we will focus on two novel pathways that play important roles in the regulation of non-synchronous release at two exemplary synapses: one modulated by the Ca2+-sensing receptor and the other by transient receptor potential cation channel sub-family V member 1.
Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject “at rest”). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing “signal” (brain activity) can be distinguished form the “noise” components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX (“FMRIB’s ICA-based X-noiseifier”), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different Classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data, to provide automated cleanup. On conventional resting-state fMRI (rfMRI) single-run datasets, FIX achieved about 95% overall accuracy. On high-quality rfMRI data from the Human Connectome Project, FIX achieves over 99% classification accuracy, and as a result is being used in the default rfMRI processing pipeline for generating HCP connectomes. FIX is publicly available as a plugin for FSL.
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.
•We introduce a probabilistic model for modes in resting state fMRI.•Our hierarchical model captures subject variability and haemodynamic effects.•We illustrate its performance on simulated data and rfMRI data from 200 subjects.•We demonstrate the ability of our method to infer spatio-temporally interacting modes.
Resting state fMRI; Functional parcellation; Bayesian modelling; Subject variability; ICA
Voltage-activated Ca2+ channels (VACCs) mediate Ca2+ influx to trigger action potential-evoked neurotransmitter release but the mechanism by which Ca2+ regulates spontaneous transmission is unclear. Here we show VACCs are the major physiological triggers for spontaneous release at murine neocortical inhibitory synapses. Moreover, despite the absence of a synchronizing action potential, we find that spontaneous fusion of a GABA-containing vesicle requires the activation of multiple tightly-coupled VACCs of variable type.
We evaluate residual aliasing among simultaneously excited and acquired slices in slice accelerated multiband (MB) echo planar imaging (EPI). No in-plane accelerations were used in order to maximize and evaluate achievable slice acceleration factors at 3 Tesla. We propose a novel leakage (L-) factor to quantify the effects of signal leakage between simultaneously acquired slices. With a standard 32-channel receiver coil at 3 Tesla, we demonstrate that slice acceleration factors of up to eight (MB = 8) with blipped controlled aliasing in parallel imaging (CAIPI), in the absence of in-plane accelerations, can be used routinely with acceptable image quality and integrity for whole brain imaging. Spectral analyses of single-shot fMRI time series demonstrate that temporal fluctuations due to both neuronal and physiological sources were distinguishable and comparable up to slice-acceleration factors of nine (MB = 9). The increased temporal efficiency could be employed to achieve, within a given acquisition period, higher spatial resolution, increased fMRI statistical power, multiple TEs, faster sampling of temporal events in a resting state fMRI time series, increased sampling of q-space in diffusion imaging, or more quiet time during a scan.
lipped CAIPI; leakage (L-) factor; g-factor; residual aliasing; spectral analysis; single-shot fMRI time series
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 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.
connectomics; resting-state fMRI; network modelling
Neuroanatomically precise, genome-wide maps of transcript distributions are critical resources to complement genomic sequence data and to correlate functional and genetic brain architecture. Here we describe the generation and analysis of a transcriptional atlas of the adult human brain, comprising extensive histological analysis and comprehensive microarray profiling of ~900 neuroanatomically precise subdivisions in two individuals. Transcriptional regulation varies enormously by anatomical location, with different regions and their constituent cell types displaying robust molecular signatures that are highly conserved between individuals. Analysis of differential gene expression and gene co-expression relationships demonstrates that brain-wide variation strongly reflects the distributions of major cell classes such as neurons, oligodendrocytes, astrocytes and microglia. Local neighbourhood relationships between fine anatomical subdivisions are associated with discrete neuronal subtypes and genes involved with synaptic transmission. The neocortex displays a relatively homogeneous transcriptional pattern, but with distinct features associated selectively with primary sensorimotor cortices and with enriched frontal lobe expression. Notably, the spatial topography of the neocortex is strongly reflected in its molecular topography— the closer two cortical regions, the more similar their transcriptomes. This freely accessible online data resource forms a high-resolution transcriptional baseline for neurogenetic studies of normal and abnormal human brain function.
Neuroscience; Genetics; Genomics; Databases
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.
•Why only some moments within a trauma intrude while others do not is unclear.•Neuroimaging may provide further clues as to why this is the case.•Multivariate pattern analysis, a recent neuroimaging analysis tool, was able to predict intrusive memories.•Those brain networks involved in intrusive memory prediction are presented.•Multivariate pattern analysis may inform future innovation in mental health.
Intrusive memories; Trauma; Flashback; MVPA; Machine learning; Functional magnetic resonance imaging; Mental imagery
The human connectome project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce ‘functional connectivity’; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3 Tesla, leading to whole brain coverage with 2 mm isotropic resolution in 0.7 second for fMRI, and 1.25 mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7 Tesla magnetic field are also presented, targeting higher resolution, specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields and power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure.
The Human Connectome Project consortium led by Washington University, University of Minnesota, and Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality. The first quarterly release of imaging and behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets freely accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings.
Resting-state functional magnetic resonance imaging (rfMRI) allows one to study functional connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI scanner, and taking advantage of the fact that functionally related brain regions spontaneously co-activate. rfMRI is one of the two primary data modalities being acquired for the Human Connectome Project (the other being diffusion MRI). A key objective is to generate a detailed in vivo mapping of functional connectivity in a large cohort of healthy adults (over 1,000 subjects), and to make these datasets freely available for use by the neuroimaging community. In each subject we acquire a total of one hour of whole-brain rfMRI data at 3 Tesla, with a spatial resolution of 2×2×2mm and a temporal resolution of 0.7s, capitalizing on recent developments in slice-accelerated echo-planar imaging. We will also scan a subset of the cohort at higher field strength and resolution. In this paper we outline the work behind, and rationale for, decisions taken regarding the rfMRI data acquisition protocol and pre-processing pipelines, and present some initial results showing data quality and example functional connectivity analyses.
Spontaneous release of glutamate is important for maintaining synaptic strength and controlling spike timing in the brain. Mechanisms regulating spontaneous exocytosis remain poorly understood. Extracellular calcium concentration ([Ca2+]o) regulates Ca2+ entry through voltage-activated calcium channels (VACCs) and consequently is a pivotal determinant of action potential-evoked vesicle fusion. Extracellular Ca2+ also enhances spontaneous release, but via unknown mechanisms. Here we report that external Ca2+ triggers spontaneous glutamate release more weakly than evoked release in mouse neocortical neurons. Blockade of VACCs has no effect on the spontaneous release rate or its dependence on [Ca2+]o. Intracellular [Ca2+] slowly increases in a minority of neurons following increases in [Ca2+]o. Furthermore, the enhancement of spontaneous release by extracellular calcium is insensitive to chelation of intracellular calcium by BAPTA. Activation of the calcium-sensing receptor (CaSR), a G-protein coupled receptor present in nerve terminals, by several specific agonists increased spontaneous glutamate release. The frequency of spontaneous synaptic transmission was decreased in CaSR mutant neurons. The concentration effect relationship for extracellular calcium regulation of spontaneous release was well described by a combination of CaSR-dependent and CaSR-independent mechanisms. Overall these results indicate that extracellular Ca2+ does not trigger spontaneous glutamate release by simply increasing calcium influx but stimulates CaSR and thereby promotes resting spontaneous glutamate release.
We propose a novel computational strategy to partition the cerebral cortex into disjoint, spatially contiguous and functionally homogeneous parcels. The approach exploits spatial dependency in the fluctuations observed with functional Magnetic Resonance Imaging (fMRI) during rest. Single subject parcellations are derived in a two stage procedure in which a set of (~1000 to 5000) stable seeds is grown into an initial detailed parcellation. This parcellation is then further clustered using a hierarchical approach that enforces spatial contiguity of the parcels.
A major challenge is the objective evaluation and comparison of different parcellation strategies; here, we use a range of different measures. Our single subject approach allows a subject-specific parcellation of the cortex, which shows high scan-to-scan reproducibility and whose borders delineate clear changes in functional connectivity. Another important measure, on which our approach performs well, is the overlap of parcels with task fMRI derived clusters. Connectivity-derived parcellation borders are less well matched to borders derived from cortical myelination and from cytoarchitectonic atlases, but this may reflect inherent differences in the data.
Resting state fMRI; Cortical parcellation; Connectomics
Administration of aminoglycoside antibiotics can precipitate sudden, profound bouts of weakness that have been attributed to block of presynaptic voltage-activated calcium channels (VACC) and failure of neuromuscular transmission. This serious adverse drug reaction is more likely in neuromuscular diseases such as myasthenia gravis. The relatively low affinity of VACC for aminoglycosides prompted us to explore alternative mechanisms. We hypothesized that the presynaptic Ca2+-sensing receptor (CaSR) may contribute to aminoglycoside-induced weakness due to its role in modulating synaptic transmission and its sensitivity to aminoglycosides in heterologous expression systems. We have previously shown that presynaptic CaSR controls a non-specific cation channel (NSCC) that regulates nerve terminal excitability and transmitter release. Using direct, electrophysiological recording, we report that neuronal VACCs are inhibited by neomycin (IC50 830 ± 110 μM) at a much lower affinity than CaSR-modulated NSCC currents recorded from acutely isolated presynaptic terminals (synaptosomes; IC50 20 ± 1 μM). Thus, at clinically relevant concentrations, aminoglycoside-induced weakness is likely precipitated by enhanced CaSR activation and subsequent decrease in terminal excitability rather than through direct inhibition of VACCs themselves.
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.
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.
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.
Epidemiology; Magnetic resonance imaging; Diffusion tensor imaging; White matter; Functional MRI; Connectome; Resting state brain networks; Neuropsychology; Dementia; Affective disorders
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.
•Permutation for the GLM in the presence of nuisance or non-independence.•A generalised statistic that performs well even under heteroscedasticity.•Permutation and/or sign-flipping, exchangeability blocks and variance groups.•The “randomise” algorithm, as well as various practical examples.
Permutation inference; Multiple regression; General linear model; Randomise
At excitatory synapses, decreases in cleft [Ca] arising from activity-dependent transmembrane Ca flux reduce the probability of subsequent transmitter release. Intense neural activity, induced by physiological and pathological stimuli, disturb the external microenvironment reducing extracellular [Ca] ([Ca]o) and thus may impair neurotransmission. Increases in [Ca]o activate the extracellular calcium sensing receptor (CaSR) which in turn inhibits non-selective cation channels (NSCC) at the majority of cortical nerve terminals. This pathway may modulate synaptic transmission by attenuating the impact of decreases in [Ca]o on synaptic transmission. Using patch-clamp recording from isolated cortical terminals, cortical neuronal pairs and isolated neuronal soma we examined the modulation of synaptic transmission by CaSR. Excitatory postsynaptic currents were increased on average by 88% in reduced affinity CaSR-mutant (CaSR−/−) neurons compared to wild-type. Variance-mean analysis indicates that the enhanced synaptic transmission was due largely to an increase in average probability of release (0.27 vs 0.46 for wild-type vs CaSR−/− pairs) with little change in quantal size (23 ± 4 pA vs 22 ± 4 pA) or number of release sites (11 vs 13). In addition, the CaSR agonist spermidine reduced synaptic transmission and increased paired-pulse depression at physiological [Ca]o. Spermidine did not affect quantal size, consistent with a presynaptic mechanism of action, nor did it affect voltage-activated Ca channel currents. In summary, reduced CaSR function enhanced synaptic transmission and CaSR stimulation had the opposite effect. Thus CaSR provides a mechanism that may compensate for the fall in release probability that accompanies decreases in [Ca]o .
calcium; GPCR; Depression; Synaptic plasticity; synaptic transmission; Synaptic vesicle release; Synaptosome; Extracellular; Receptor
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.
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.
magnetic resonance spectroscopy; GABA; resting state fMRI; human
The endogenous polyamines spermine, spermidine and putrescine are present at high concentrations inside neurons and can be released into the extracellular space where they have been shown to modulate ion channels. Here, we have examined polyamine modulation of voltage-activated Ca2+ channels (VACCs) and voltage-activated Na+ channels (VANCs) in rat superior cervical ganglion neurons using whole-cell voltage-clamp at physiological divalent concentrations. Polyamines inhibited VACCs in a concentration-dependent manner with IC50s for spermine, spermidine, and putrescine of 4.7 ± 0.7, 11.2 ± 1.4 and 90 ± 36 mM, respectively. Polyamines caused inhibition by shifting the VACC half-activation voltage (V0.5) to depolarized potentials and by reducing total VACC permeability. The shift was described by Gouy-Chapman-Stern theory with a surface charge density of 0.120 ± 0.005 e− nm−2 and a surface potential of −19 mV. Attenuation of spermidine and spermine inhibition of VACC at decreased pH was explained by H+ titration of surface charge. Polyamine-mediated effects also decreased at elevated pH due to the inhibitors having lower valence and being less effective at screening surface charge. Polyamines affected VANC currents indirectly by reducing TTX inhibition of VANCs at high pH. This may reflect surface charge induced decreases in the local TTX concentration or polyamine-TTX interactions. In conclusion, polyamines inhibit neuronal VACCs via complex interactions with extracellular H+ and Ca. Many of the observed effects can be explained by a model incorporating polyamine binding, H+ binding and surface charge screening.
polyamine; spermine; spermidine; putrescine; surface charge screening; surface potential; Gouy-Chapman; voltage-activated calcium channel; VACC; voltage-activated Na+ channels; VANC; superior cervical ganglion; neuron