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1.  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.
PMCID: PMC4811949  PMID: 27065884
calcium sensing receptor; nervous system; synaptic transmission; action potentials; ion channels; calcium; excitability
2.  Classification of follicular lymphoma: the effect of computer aid on pathologists grading 
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.
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.
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.
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.
PMCID: PMC4696238  PMID: 26715518
Follicular lymphoma grading; HPF detection; HPF classification; Digital pathology
3.  k-t FASTER: Acceleration of Functional MRI Data Acquisition Using Low Rank Constraints 
Magnetic resonance in medicine  2014;74(2):353-364.
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).
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.
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.
PMCID: PMC4682483  PMID: 25168207
fMRI; k-t acceleration; compressed sensing; low-rank acceleration; matrix completion; resting state networks
4.  k-t FASTER: Acceleration of functional MRI data acquisition using low rank constraints 
Magnetic Resonance in Medicine  2014;74(2):353-364.
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).
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.
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. Magn Reson Med 74:353–364, 2015. © 2014 Wiley Periodicals, Inc.
PMCID: PMC4682483  PMID: 25168207
fMRI; k-t acceleration; compressed sensing; low-rank acceleration; matrix completion; resting state networks
5.  Multi-level block permutation 
Neuroimage  2015;123:253-268.
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.
•The presence of structured, non-independent data affects simple permutation testing.•Modelling full dependence obviated through definition of variance groups (minimal assumptions).•Implementation based on shuffling branches of a tree-like (hierarchical) structure.•Validity demonstrated with simulations, and exemplified with data from the HCP.
PMCID: PMC4644991  PMID: 26074200
Permutation inference; Multiple regression; General linear model; Repeated measurements
6.  MSM: a new flexible framework for Multimodal Surface Matching☆ 
NeuroImage  2014;100:414-426.
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 unconstrained by 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 datasets.
PMCID: PMC4190319  PMID: 24939340
Surface-based cortical registration; Multimodal; Functional Alignment; Discrete Optimisation
7.  Fast transient networks in spontaneous human brain activity 
eLife  2014;3:e01867.
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.
eLife digest
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.
PMCID: PMC3965210  PMID: 24668169
magnetoencephalography; resting state; connectivity; non-stationary; hidden Markov model; microstates; human
8.  ICA-based artefact and accelerated fMRI acquisition for improved Resting State Network imaging 
NeuroImage  2014;95:232-247.
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.
PMCID: PMC4154346  PMID: 24657355
functional magnetic resonance imaging (fMRI); resting-state; artefact removal; functional connectivity; multiband acceleration
9.  Primary Sinonasal Mucosal Melanoma with Aberrant Diffuse and Strong Desmin Reactivity: A Potential Diagnostic Pitfall! 
Head and Neck Pathology  2014;9(1):165-171.
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.
PMCID: PMC4382480  PMID: 24974197
Melanoma; Sinonasal mucosa; Desmin; Rhabdoid; Pitfalls
10.  Calcium regulation of spontaneous and asynchronous neurotransmitter release 
Cell calcium  2012;52(3-4):226-233.
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.
PMCID: PMC3433637  PMID: 22748761
11.  Ventral Striatum/Nucleus Accumbens Activation to Smoking-Related Pictorial Cues in Smokers and Nonsmokers: A Functional Magnetic Resonance Imaging Study 
Biological psychiatry  2005;58(6):488-494.
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.
PMCID: PMC4439461  PMID: 16023086
Nucleus accumbens; smoking; nicotine; tobacco; cue reactivity; fMRI
12.  Co-activation of multiple tightly-coupled calcium channels triggers spontaneous release of GABA 
Nature neuroscience  2012;15(9):1195-1197.
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.
PMCID: PMC3431448  PMID: 22842148
13.  Automatic Denoising of Functional MRI Data: Combining Independent Component Analysis and Hierarchical Fusion of Classifiers 
NeuroImage  2014;90:449-468.
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.
PMCID: PMC4019210  PMID: 24389422
14.  Large-scale Probabilistic Functional Modes from resting state fMRI 
Neuroimage  2015;109:217-231.
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.
PMCID: PMC4349633  PMID: 25598050
Resting state fMRI; Functional parcellation; Bayesian modelling; Subject variability; ICA
15.  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.
PMCID: PMC4531245  PMID: 26321937
functional magnetic resonance imaging; resting state; artifacts; functional connectivity; default mode network; Alzheimer's disease
16.  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.
PMCID: PMC4621866  PMID: 26578859
resting-state functional MRI; multi-center analysis; independent component analysis; dual regression; structured noise reduction
17.  Correction: Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging 
PLoS ONE  2011;6(9):10.1371/annotation/d9496d01-8c5d-4d24-8287-94449ada5064.
PMCID: PMC3182862
18.  Spontaneous glutamate release is independent of calcium influx and tonically activated by the calcium-sensing receptor 
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.
PMCID: PMC3097128  PMID: 21430159
19.  Correction: Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging 
PLoS ONE  2011;6(9):10.1371/annotation/5e4082fd-6d86-441f-b946-a6e87a22ea57.
PMCID: PMC3182257
20.  Evaluation of slice accelerations using multiband echo planar imaging at 3 Tesla 
NeuroImage  2013;83:10.1016/j.neuroimage.2013.07.055.
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.
PMCID: PMC3815955  PMID: 23899722
lipped CAIPI; leakage (L-) factor; g-factor; residual aliasing; spectral analysis; single-shot fMRI time series
21.  Functional connectomics from resting-state fMRI 
Trends in cognitive sciences  2013;17(12):666-682.
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.
PMCID: PMC4004765  PMID: 24238796
connectomics; resting-state fMRI; network modelling
22.  An anatomically comprehensive atlas of the adult human brain transcriptome 
Nature  2012;489(7416):391-399.
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.
PMCID: PMC4243026  PMID: 22996553
Neuroscience; Genetics; Genomics; Databases
23.  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.
•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.
PMCID: PMC4222599  PMID: 25151915
Intrusive memories; Trauma; Flashback; MVPA; Machine learning; Functional magnetic resonance imaging; Mental imagery
24.  Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project 
NeuroImage  2013;80:80-104.
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.
PMCID: PMC3740184  PMID: 23702417
25.  The WU-Minn Human Connectome Project: An Overview 
NeuroImage  2013;80:62-79.
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.
PMCID: PMC3724347  PMID: 23684880

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