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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.

The conservation of sleep across all animal species suggests that sleep serves a vital function. We here report that sleep has a critical function in ensuring metabolic homeostasis. Using real-time assessments of tetramethylammonium diffusion and two-photon imaging in live mice, we show that natural sleep or anesthesia are associated with a 60% increase in the interstitial space, resulting in a striking increase in convective exchange of cerebrospinal fluid with interstitial fluid. In turn, convective fluxes of interstitial fluid increased the rate of ß-amyloid clearance during sleep. Thus, the restorative function of sleep may be a consequence of the enhanced removal of potentially neurotoxic waste products that accumulate in the awake central nervous system.

Prefrontal cortex is thought to have a fundamental role in flexible, context-dependent behaviour, but the exact nature of the computations underlying this role remains largely unknown. In particular, individual prefrontal neurons often generate remarkably complex responses that defy deep understanding of their contribution to behaviour. Here we study prefrontal cortex activity in macaque monkeys trained to flexibly select and integrate noisy sensory inputs towards a choice. We find that the observed complexity and functional roles of single neurons are readily understood in the framework of a dynamical process unfolding at the level of the population. The population dynamics can be reproduced by a trained recurrent neural network, which suggests a previously unknown mechanism for selection and integration of task-relevant inputs. This mechanism indicates that selection and integration are two aspects of a single dynamical process unfolding within the same prefrontal circuits, and potentially provides a novel, general framework for understanding context-dependent computations. This study shows that in monkeys making context-dependent decisions, task-relevant and task-irrelevant signals are confusingly intermixed in single units of the prefrontal cortex, but are readily understood in the framework of a dynamical process unfolding at the level of the population; a recurrently connected neural network model reproduces key features of the data and suggests a novel mechanism for selection and integration of task-relevant evidence towards a decision. Selective integration of task-related sensory inputs The neurons of the primate prefrontal cortex represent multiple aspects of sensory stimuli and have task-dependent, time-varying responses. How these complex responses represent relevant aspects of their inputs or contribute to behaviour in different contexts remains unclear. Valerio Mante et al . show here that when monkeys perform a context-dependent sensorimotor task, task-relevant and task-irrelevant signals are intermingled in single units of the prefrontal cortex, but are readily understood in the framework of a dynamical process unfolding at the level of the population. A recurrently connected neural network model reproduces key features of the data and suggests a novel mechanism for selection and integration of task-relevant evidence towards a decision.

Animal behaviour arises from computations in neuronal circuits, but our understanding of these computations has been frustrated by the lack of detailed synaptic connection maps, or connectomes. For example, despite intensive investigations over half a century, the neuronal implementation of local motion detection in the insect visual system remains elusive. Here we develop a semi-automated pipeline using electron microscopy to reconstruct a connectome, containing 379 neurons and 8,637 chemical synaptic contacts, within the Drosophila optic medulla. By matching reconstructed neurons to examples from light microscopy, we assigned neurons to cell types and assembled a connectome of the repeating module of the medulla. Within this module, we identified cell types constituting a motion detection circuit, and showed that the connections onto individual motion-sensitive neurons in this circuit were consistent with their direction selectivity. Our results identify cellular targets for future functional investigations, and demonstrate that connectomes can provide key insights into neuronal computations. Reconstruction of a connectome within the fruitfly visual medulla, containing more than 300 neurons and over 8,000 chemical synapses, reveals a candidate motion detection circuit; such a circuit operates by combining displaced visual inputs, an operation consistent with correlation based motion detection. Visual system connectomics — from insects to mammals Three papers in this issue of Nature use the retina as a model for mapping neuronal circuits from the level of individual synaptic contacts to the long-range scale of dendritic interactions. Helmstaedter et al . used electron microscopy to map a mammalian retinal circuit of close to a thousand neurons. The work reveals a new type of retinal bipolar neuron and suggests functional mechanisms for known visual computations. The other two groups study the detection of visual motion in the Drosophila visual system — a classic neural computation model. Takemura et al . used semi-automated electron microscopy to reconstruct the basic connectome (8,637 chemical synapses among 379 neurons) of Drosophila 's optic medulla. Their results reveal a candidate motion detection circuit with a wiring plan consistent with direction selectivity. Maisak et al . used calcium imaging to show that T4 and T5 neurons are divided into specific subpopulations responding to motion in four cardinal directions, and are specific to 'ON' versus 'OFF' edges, respectively.

Chemical exchange saturation transfer (CEST) is a magnetization transfer (MT) technique to indirectly detect pools of exchangeable protons through the water signal. CEST MRI has focused predominantly on signals from exchangeable protons downfield (higher frequency) from water in the CEST spectrum. Low power radiofrequency (RF) pulses can slowly saturate protons with minimal interference of conventional semi-solid based MT contrast (MTC). When doing so, saturation-transfer signals are revealed upfield from water, which is the frequency range of non-exchangeable aliphatic and olefinic protons. The visibility of such signals indicates the presence of a relayed transfer mechanism to the water signal, while their finite width reflects that these signals are likely due to mobile solutes. It is shown here in protein phantoms and the human brain that these signals build up slower than conventional CEST, at a rate typical for intramolecular nuclear Overhauser enhancement (NOE) effects in mobile macromolecules such as proteins/peptides and lipids. These NOE-based saturation transfer signals show a pH dependence, suggesting that this process is the inverse of the well-known exchange-relayed NOEs in high resolution NMR protein studies, thus a relayed-NOE CEST process. When studying 6 normal volunteers with a low-power pulsed CEST approach, the relayed-NOE CEST effect was about twice as large as the CEST effects downfield and larger in white matter than gray matter. This NOE contrast upfield from water provides a way to study mobile macromolecules in tissue. First data on a tumor patient show reduction in both relayed NOE and CEST amide proton signals leading to an increase in magnetization transfer ratio asymmetry, providing insight into previously reported amide proton transfer (APT) effects in tumors.

With the advent of new analysis methods in neuroimaging that involve independent component analysis (ICA) and dynamic causal modelling (DCM), investigations have focused on measuring both the activity and connectivity of specific brain networks. In this study we combined DCM with spatial ICA to investigate network switching in the brain. Using time courses determined by ICA in our dynamic causal models, we focused on the dynamics of switching between the default mode network (DMN), the network which is active when the brain is not engaging in a specific task, and the central executive network (CEN), which is active when the brain is engaging in a task requiring attention. Previous work using Granger causality methods has shown that regions of the brain which respond to the degree of subjective salience of a stimulus, the salience network, are responsible for switching between the DMN and the CEN (Sridharan et al., 2008). In this work we apply DCM to ICA time courses representing these networks in resting state data. In order to test the repeatability of our work we applied this to two independent datasets. This work confirms that the salience network drives the switching between default mode and central executive networks and that our novel technique is repeatable. •DCM and spatial ICA can be combined to study the connectivity between networks.•The result was replicated in two independent datasets, demonstrating repeatability.•Our result confirms previous work on the connectivity between networks.•This work has a lot of potential applications to ageing and patient data.•The technique can be easily applied to commonly acquired resting state data.

Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10s after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects. •Motion-related signal changes are varied and can persist >10s after motion ceases.•Such signal changes are often shared across almost all brain voxels.•Within-subject correction strategies can eliminate motion-related group differences.•Examines the linearity of motion's influence on resting state correlations

Constrained spherical deconvolution (CSD) has become one of the most widely used methods to extract white matter (WM) fibre orientation information from diffusion-weighted MRI (DW-MRI) data, overcoming the crossing fibre limitations inherent in the diffusion tensor model. It is routinely used to obtain high quality fibre orientation distribution function (fODF) estimates and fibre tractograms and is increasingly used to obtain apparent fibre density (AFD) measures. Unfortunately, CSD typically only supports data acquired on a single shell in q-space. With multi-shell data becoming more and more prevalent, there is a growing need for CSD to fully support such data. Furthermore, CSD can only provide high quality fODF estimates in voxels containing WM only. In voxels containing other tissue types such as grey matter (GM) and cerebrospinal fluid (CSF), the WM response function may no longer be appropriate and spherical deconvolution produces unreliable, noisy fODF estimates. The aim of this study is to incorporate support for multi-shell data into the CSD approach as well as to exploit the unique b-value dependencies of the different tissue types to estimate a multi-tissue ODF. The resulting approach is dubbed multi-shell, multi-tissue CSD (MSMT-CSD) and is compared to the state-of-the-art single-shell, single-tissue CSD (SSST-CSD) approach. Using both simulations and real data, we show that MSMT-CSD can produce reliable WM/GM/CSF volume fraction maps, directly from the DW data, whereas SSST-CSD has a tendency to overestimate the WM volume in voxels containing GM and/or CSF. In addition, compared to SSST-CSD, MSMT-CSD can substantially increase the precision of the fODF fibre orientations and reduce the presence of spurious fODF peaks in voxels containing GM and/or CSF. Both effects translate into more reliable AFD measures and tractography results with MSMT-CSD compared to SSST-CSD. •Constrained spherical deconvolution is extended to support multi-shell DW data.•We use the unique b-value dependency of each tissue to estimate a multi-tissue ODF.•We obtain reliable WM/GM/CSF volume fraction maps directly from the DW data.•We obtain more precise WM fibre orientation estimates at the tissue interfaces.•This leads to more accurate apparent fibre density and more reliable fibre tracking.

Single-neuron activity in the prefrontal cortex (PFC) is tuned to mixtures of multiple task-related aspects. Such mixed selectivity is highly heterogeneous, seemingly disordered and therefore difficult to interpret. We analysed the neural activity recorded in monkeys during an object sequence memory task to identify a role of mixed selectivity in subserving the cognitive functions ascribed to the PFC. We show that mixed selectivity neurons encode distributed information about all task-relevant aspects. Each aspect can be decoded from the population of neurons even when single-cell selectivity to that aspect is eliminated. Moreover, mixed selectivity offers a significant computational advantage over specialized responses in terms of the repertoire of input–output functions implementable by readout neurons. This advantage originates from the highly diverse nonlinear selectivity to mixtures of task-relevant variables, a signature of high-dimensional neural representations. Crucially, this dimensionality is predictive of animal behaviour as it collapses in error trials. Our findings recommend a shift of focus for future studies from neurons that have easily interpretable response tuning to the widely observed, but rarely analysed, mixed selectivity neurons. When an animal is performing a cognitive task, individual neurons in the prefrontal cortex show a mixture of responses that is often difficult to decipher and interpret; here new computational methods to decode and extract rich sets of information from these neural responses are revealed and demonstrate how this mixed selectivity offers a computational advantage over specialized cells. Non-specialist neurons in cognition When an animal performs a cognitive task, individual neurons in the prefrontal cortex are often 'tuned' to various aspects related to the behaviour. The resulting mixture of responses is often difficult to decipher. This study of neural activity in monkeys performing an object sequence memory task was designed to establish whether the predominance of mixed selectivity neurons in the prefrontal cortex is critical to the function being performed. The results suggest that neurons with mixed selectivity contain as much information as those that are highly specialized in encoding a single task-relevant aspect. And mixed selectivity neurons actually offer a significant computational advantage over specialized cells in some respects. The new computational methods developed for this work to extract rich sets of information from recorded neural activity should make it easier to study the widely observed but rarely analysed mixed selectivity neurons.

Fluorescent calcium sensors are widely used to image neural activity. Using structure-based mutagenesis and neuron-based screening, we developed a family of ultrasensitive protein calcium sensors (GCaMP6) that outperformed other sensors in cultured neurons and in zebrafish, flies and mice in vivo . In layer 2/3 pyramidal neurons of the mouse visual cortex, GCaMP6 reliably detected single action potentials in neuronal somata and orientation-tuned synaptic calcium transients in individual dendritic spines. The orientation tuning of structurally persistent spines was largely stable over timescales of weeks. Orientation tuning averaged across spine populations predicted the tuning of their parent cell. Although the somata of GABAergic neurons showed little orientation tuning, their dendrites included highly tuned dendritic segments (5–40-µm long). GCaMP6 sensors thus provide new windows into the organization and dynamics of neural circuits over multiple spatial and temporal scales. Sensitive protein sensors of calcium have been created; these new tools are shown to report neural activity in cultured neurons, flies and zebrafish and can detect single action potentials and synaptic activation in the mouse visual cortex in vivo . A new sensor for neural activity Genetically encoded calcium sensors have brought neuronal recording to the tiny brains of invertebrates, but the methodology has lagged behind classical electrophysiology in vertebrates. Now Douglas Kim and colleagues have used selective mutagenesis to engineer a new ultrasensitive probe, GCaMP6, demonstrating improved spatial and temporal resolution in vivo , from flies to zebrafish. In addition, in mouse visual cortex GCaMP6 can reliably detect single action potentials and single-spine orientation tuning. GCaMP6 sensors can be used to image large groups of neurons as well as tiny synaptic compartments over multiple imaging sessions separated by months, offering a flexible new tool for brain research and calcium signalling studies.