Search results
Found 16111 matches for
Task induced modulation of neural oscillations in electrophysiological brain networks.
In recent years, one of the most important findings in systems neuroscience has been the identification of large scale distributed brain networks. These networks support healthy brain function and are perturbed in a number of neurological disorders (e.g. schizophrenia). Their study is therefore an important and evolving focus for neuroscience research. The majority of network studies are conducted using functional magnetic resonance imaging (fMRI) which relies on changes in blood oxygenation induced by neural activity. However recently, a small number of studies have begun to elucidate the electrical origin of fMRI networks by searching for correlations between neural oscillatory signals from spatially separate brain areas in magnetoencephalography (MEG) data. Here we advance this research area. We introduce two methodological extensions to previous independent component analysis (ICA) approaches to MEG network characterisation: 1) we show how to derive pan-spectral networks that combine independent components computed within individual frequency bands. 2) We show how to measure the temporal evolution of each network with millisecond temporal resolution. We apply our approach to ~10h of MEG data recorded in 28 experimental sessions during 3 separate cognitive tasks showing that a number of networks could be identified and were robust across time, task, subject and recording session. Further, we show that neural oscillations in those networks are modulated by memory load, and task relevance. This study furthers recent findings on electrodynamic brain networks and paves the way for future clinical studies in patients in which abnormal connectivity is thought to underlie core symptoms.
Minor structural abnormalities in the infant face disrupt neural processing: a unique window into early caregiving responses.
Infant faces elicit early, specific activity in the orbitofrontal cortex (OFC), a key cortical region for reward and affective processing. A test of the causal relationship between infant facial configuration and OFC activity is provided by naturally occurring disruptions to the face structure. One such disruption is cleft lip, a small change to one facial feature, shown to disrupt parenting. Using magnetoencephalography, we investigated neural responses to infant faces with cleft lip compared with typical infant and adult faces. We found activity in the right OFC at 140 ms in response to typical infant faces but diminished activity to infant faces with cleft lip or adult faces. Activity in the right fusiform face area was of similar magnitude for typical adult and infant faces but was significantly lower for infant faces with cleft lip. This is the first evidence that a minor change to the infant face can disrupt neural activity potentially implicated in caregiving.
Vascular territory image analysis using vessel encoded arterial spin labeling.
Arterial Spin Labeling (ASL) permits the non-invasive assessment of cerebral perfusion, by magnetically labeling all the blood flowing into the brain. Vessel encoded (VE) ASL extends this concept by introducing spatial modulations of the labeling procedure, resulting in different patterns of label applied to the blood from different vessels. Here a Bayesian inference solution to the analysis of VE-ASL is presented based on a description of the relative locations of labeled vessels and a probabilistic classification of brain tissue to vessel source. In simulation and on real data the method is shown to reliably determine vascular territories in the brain, including the case where the number of vessels exceeds the number of independent measurements.
Trial-type dependent frames of reference for value comparison.
A central question in cognitive neuroscience regards the means by which options are compared and decisions are resolved during value-guided choice. It is clear that several component processes are needed; these include identifying options, a value-based comparison, and implementation of actions to execute the decision. What is less clear is the temporal precedence and functional organisation of these component processes in the brain. Competing models of decision making have proposed that value comparison may occur in the space of alternative actions, or in the space of abstract goods. We hypothesized that the signals observed might in fact depend upon the framing of the decision. We recorded magnetoencephalographic data from humans performing value-guided choices in which two closely related trial types were interleaved. In the first trial type, each option was revealed separately, potentially causing subjects to estimate each action's value as it was revealed and perform comparison in action-space. In the second trial type, both options were presented simultaneously, potentially leading to comparison in abstract goods-space prior to commitment to a specific action. Distinct activity patterns (in distinct brain regions) on the two trial types demonstrated that the observed frame of reference used for decision making indeed differed, despite the information presented being formally identical, between the two trial types. This provides a potential reconciliation of conflicting accounts of value-guided choice.
Vessel-encoded dynamic magnetic resonance angiography using arterial spin labeling.
A new noninvasive MRI method for vessel-selective angiography is presented. The technique combines vessel-encoded pseudocontinuous arterial spin labeling with a two-dimensional dynamic angiographic readout and was used to image the cerebral arteries in healthy volunteers. Time-of-flight angiograms were also acquired prior to vessel-selective dynamic angiography acquisitions in axial, coronal, and/or sagittal planes, using a 3-T MRI scanner. The latter consisted of a vessel-encoded pseudocontinuous arterial spin labeling pulse train of 300 or 1000 ms followed by a two-dimensional thick-slab flow-compensated fast low-angle shot readout combined with a segmented Look-Locker sampling strategy (temporal resolution = 55 ms). Selective labeling was performed at the level of the neck to generate individual angiograms for both right and left internal carotid and vertebral arteries. Individual vessel angiograms were reconstructed using a bayesian inference method. The vessel-selective dynamic angiograms obtained were consistent with the time-of-flight images, and the longer of the two vessel-encoded pseudocontinuous arterial spin labeling pulse train durations tested (1000 ms) was found to give better distal vessel visibility. This technique provides highly selective angiograms quickly and noninvasively that could potentially be used in place of intra-arterial x-ray angiography for larger vessels.
RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI.
The trade-off between signal-to-noise ratio (SNR) and spatial specificity governs the choice of spatial resolution in magnetic resonance imaging (MRI); diffusion-weighted (DW) MRI is no exception. Images of lower resolution have higher signal to noise ratio, but also more partial volume artifacts. We present a data-fusion approach for tackling this trade-off by combining DW MRI data acquired both at high and low spatial resolution. We combine all data into a single Bayesian model to estimate the underlying fiber patterns and diffusion parameters. The proposed model, therefore, combines the benefits of each acquisition. We show that fiber crossings at the highest spatial resolution can be inferred more robustly and accurately using such a model compared to a simpler model that operates only on high-resolution data, when both approaches are matched for acquisition time.
Ensemble learning incorporating uncertain registration.
This paper proposes a novel approach for improving the accuracy of statistical prediction methods in spatially normalized analysis. This is achieved by incorporating registration uncertainty into an ensemble learning scheme. A probabilistic registration method is used to estimate a distribution of probable mappings between subject and atlas space. This allows the estimation of the distribution of spatially normalized feature data, e.g., grey matter probability maps. From this distribution, samples are drawn for use as training examples. This allows the creation of multiple predictors, which are subsequently combined using an ensemble learning approach. Furthermore, extra testing samples can be generated to measure the uncertainty of prediction. This is applied to separating subjects with Alzheimer's disease from normal controls using a linear support vector machine on a region of interest in magnetic resonance images of the brain. We show that our proposed method leads to an improvement in discrimination using voxel-based morphometry and deformation tensor-based morphometry over bootstrap aggregating, a common ensemble learning framework. The proposed approach also generates more reasonable soft-classification predictions than bootstrap aggregating. We expect that this approach could be applied to other statistical prediction tasks where registration is important.
Biomagnetic biomarkers for dementia: A pilot multicentre study with a recommended methodological framework for magnetoencephalography.
Introduction: An increasing number of studies are using magnetoencephalography (MEG) to study dementia. Here we define a common methodological framework for MEG resting-state acquisition and analysis to facilitate the pooling of data from different sites. Methods: Two groups of patients with mild cognitive impairment (MCI, n = 84) and healthy controls (n = 84) were combined from three sites, and site and group differences inspected in terms of power spectra and functional connectivity. Classification accuracy for MCI versus controls was compared across three different types of MEG analyses, and compared with classification based on structural MRI. Results: The spectral analyses confirmed frequency-specific differences in patients with MCI, both in power and connectivity patterns, with highest classification accuracy from connectivity. Critically, site acquisition differences did not dominate the results. Discussion: This work provides detailed protocols and analyses that are sensitive to cognitive impairment, and that will enable standardized data sharing to facilitate large-scale collaborative projects.
Localization of MEG human brain responses to retinotopic visual stimuli with contrasting source reconstruction approaches.
Magnetoencephalography (MEG) allows the physiological recording of human brain activity at high temporal resolution. However, spatial localization of the source of the MEG signal is an ill-posed problem as the signal alone cannot constrain a unique solution and additional prior assumptions must be enforced. An adequate source reconstruction method for investigating the human visual system should place the sources of early visual activity in known locations in the occipital cortex. We localized sources of retinotopic MEG signals from the human brain with contrasting reconstruction approaches (minimum norm, multiple sparse priors, and beamformer) and compared these to the visual retinotopic map obtained with fMRI in the same individuals. When reconstructing brain responses to visual stimuli that differed by angular position, we found reliable localization to the appropriate retinotopic visual field quadrant by a minimum norm approach and by beamforming. Retinotopic map eccentricity in accordance with the fMRI map could not consistently be localized using an annular stimulus with any reconstruction method, but confining eccentricity stimuli to one visual field quadrant resulted in significant improvement with the minimum norm. These results inform the application of source analysis approaches for future MEG studies of the visual system, and indicate some current limits on localization accuracy of MEG signals.
A Bayesian approach for spatially adaptive regularisation in non-rigid registration.
This paper introduces a novel method for inferring spatially varying regularisation in non-rigid registration. This is achieved through full Bayesian inference on a probabilistic registration model, where the prior on transformations is parametrised as a weighted mixture of spatially localised components. Such an approach has the advantage of allowing the registration to be more flexibly driven by the data than a more traditional global regularisation scheme, such as bending energy. The proposed method adaptively determines the influence of the prior in a local region. The importance of the prior may be reduced in areas where the data better supports deformations, or can enforce a stronger constraint in less informative areas. Consequently, the use of such a spatially adaptive prior may reduce the unwanted impact of regularisation on the inferred deformation field. This is especially important for applications such as tensor based morphometry, where the features of interest are directly derived from the deformation field. The proposed approach is demonstrated with application to tensor based morphometry analysis of subjects with Alzheimer's disease and healthy controls. The results show that using the proposed spatially adaptive prior leads to deformation fields that have a substantially lower average complexity, but which also provide more accurate localisation of statistical group differences.
Serotonin and social norms: tryptophan depletion impairs social comparison and leads to resource depletion in a multiplayer harvesting game.
How do people sustain resources for the benefit of individuals and communities and avoid the tragedy of the commons, in which shared resources become exhausted? In the present study, we examined the role of serotonin activity and social norms in the management of depletable resources. Healthy adults, alongside social partners, completed a multiplayer resource-dilemma game in which they repeatedly harvested from a partially replenishable monetary resource. Dietary tryptophan depletion, leading to reduced serotonin activity, was associated with aggressive harvesting strategies and disrupted use of the social norms given by distributions of other players' harvests. Tryptophan-depleted participants more frequently exhausted the resource completely and also accumulated fewer rewards than participants who were not tryptophan depleted. Our findings show that rank-based social comparisons are crucial to the management of depletable resources, and that serotonin mediates responses to social norms.
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.
The Neural Dynamics of Fronto-Parietal Networks in Childhood Revealed using Magnetoencephalography.
Our ability to hold information in mind is limited, requires a high degree of cognitive control, and is necessary for many subsequent cognitive processes. Children, in particular, are highly variable in how, trial-by-trial, they manage to recruit cognitive control in service of memory. Fronto-parietal networks, typically recruited under conditions where this cognitive control is needed, undergo protracted development. We explored, for the first time, whether dynamic changes in fronto-parietal activity could account for children's variability in tests of visual short-term memory (VSTM). We recorded oscillatory brain activity using magnetoencephalography (MEG) as 9- to 12-year-old children and adults performed a VSTM task. We combined temporal independent component analysis (ICA) with general linear modeling to test whether the strength of fronto-parietal activity correlated with VSTM performance on a trial-by-trial basis. In children, but not adults, slow frequency theta (4-7 Hz) activity within a right lateralized fronto-parietal network in anticipation of the memoranda predicted the accuracy with which those memory items were subsequently retrieved. These findings suggest that inconsistent use of anticipatory control mechanism contributes significantly to trial-to-trial variability in VSTM maintenance performance.
Modulation of hippocampal theta and hippocampal-prefrontal cortex function by a schizophrenia risk gene.
Hippocampal theta-band oscillations are thought to facilitate the co-ordination of brain activity across distributed networks, including between the hippocampus and prefrontal cortex (PFC). Impairments in hippocampus-PFC functional connectivity are implicated in schizophrenia and are associated with a polymorphism within the ZNF804A gene that shows a genome-wide significant association with schizophrenia. However, the mechanisms by which ZNF804A affects hippocampus-PFC connectivity are unknown. We used a multimodal imaging approach to investigate the impact of the ZNF804A polymorphism on hippocampal theta and hippocampal network coactivity. Healthy volunteers homozygous for the ZNF804A rs1344706 (A[risk]/C[nonrisk]) polymorphism were imaged at rest using both magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). A dual-regression approach was used to investigate coactivations between the hippocampal network and other brain regions for both modalities, focusing on the theta band in the case of MEG. We found a significant decrease in intrahippocampal theta (using MEG) and greater coactivation of the superior frontal gyrus with the hippocampal network (using fMRI) in risk versus nonrisk homozygotes. Furthermore, these measures showed a significant negative correlation. Our demonstration of an inverse relationship between hippocampal theta and hippocampus-PFC coactivation supports a role for hippocampal theta in coordinating hippocampal-prefrontal activity. The ZNF804A-related differences that we find in hippocampus-PFC coactivation are consistent with previously reported associations with functional connectivity and with these changes lying downstream of altered hippocampal theta. Changes in hippocampal-PFC co-ordination, driven by differences in oscillatory activity, may be one mechanism by which ZNF804A impacts on brain function and risk for psychosis.
Frontoparietal and Cingulo-opercular Networks Play Dissociable Roles in Control of Working Memory.
We used magnetoencephalography to characterize the spatiotemporal dynamics of cortical activity during top-down control of working memory (WM). fMRI studies have previously implicated both the frontoparietal and cingulo-opercular networks in control over WM, but their respective contributions are unclear. In our task, spatial cues indicating the relevant item in a WM array occurred either before the memory array or during the maintenance period, providing a direct comparison between prospective and retrospective control of WM. We found that in both cases a frontoparietal network activated following the cue, but following retrocues this activation was transient and was succeeded by a cingulo-opercular network activation. We also characterized the time course of top-down modulation of alpha activity in visual/parietal cortex. This modulation was transient following retrocues, occurring in parallel with the frontoparietal network activation. We suggest that the frontoparietal network is responsible for top-down modulation of activity in sensory cortex during both preparatory attention and orienting within memory. In contrast, the cingulo-opercular network plays a more downstream role in cognitive control, perhaps associated with output gating of memory.
Impaired corticomuscular and interhemispheric cortical beta oscillation coupling in amyotrophic lateral sclerosis.
OBJECTIVES: The neural activity of the primary motor cortex is variably synchronised with contralateral peripheral electromyographic signals, which is thought to facilitate long-range communication through the motor system. Such corticomuscular coherence (CMC) is typically observed in the beta-band (15-30 Hz) range during steady force production. We aimed to measure pathological alteration to CMC resulting from ALS. METHODS: CMC was appraised during a forearm grip task in 17 ALS patients contrasted against age-matched healthy controls. An exploratory comparison with a group of asymptomatic ALS gene carriers and neuropathy disease mimics was also undertaken. Neural signals were acquired by whole-head magnetoencephalography and localised via structural MRI to the motor cortices. RESULTS: During light voluntary muscular contraction, beta-band CMC was significantly reduced in ALS patients compared to healthy controls. Propagation of motoric beta rhythms across the cortical hemispheres was also shown to be impaired in ALS patients. CMC was preserved in the asymptomatic gene carrier and did not distinguish ALS patients from neuropathy mimics. CONCLUSION: Functional connectivity metrics reveal an ALS-related decrease in both corticomuscular and interhemispheric communication during bilateral grip force production. SIGNIFICANCE: MEG-derived beta oscillation coupling may be a potential biomarker of motor system dysfunction in ALS, against which to measure future therapeutic efficacy.