56,980 research outputs found
EEG and MEG data analysis in SPM8.
SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools
Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling
Identifying a coupled dynamical system out of many plausible candidates, each
of which could serve as the underlying generator of some observed measurements,
is a profoundly ill posed problem that commonly arises when modelling real
world phenomena. In this review, we detail a set of statistical procedures for
inferring the structure of nonlinear coupled dynamical systems (structure
learning), which has proved useful in neuroscience research. A key focus here
is the comparison of competing models of (ie, hypotheses about) network
architectures and implicit coupling functions in terms of their Bayesian model
evidence. These methods are collectively referred to as dynamical casual
modelling (DCM). We focus on a relatively new approach that is proving
remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid
evaluation and comparison of models that differ in their network architecture.
We illustrate the usefulness of these techniques through modelling
neurovascular coupling (cellular pathways linking neuronal and vascular
systems), whose function is an active focus of research in neurobiology and the
imaging of coupled neuronal systems
Active inference and oculomotor pursuit: the dynamic causal modelling of eye movements.
This paper introduces a new paradigm that allows one to quantify the Bayesian beliefs evidenced by subjects during oculomotor pursuit. Subjects' eye tracking responses to a partially occluded sinusoidal target were recorded non-invasively and averaged. These response averages were then analysed using dynamic causal modelling (DCM). In DCM, observed responses are modelled using biologically plausible generative or forward models - usually biophysical models of neuronal activity
Network model of immune responses reveals key effectors to single and co-infection dynamics by a respiratory bacterium and a gastrointestinal helminth
Co-infections alter the host immune response but how the systemic and local processes at the site of infection interact is still unclear. The majority of studies on co-infections concentrate on one of the infecting species, an immune function or group of cells and often focus on the initial phase of the infection. Here, we used a combination of experiments and mathematical modelling to investigate the network of immune responses against single and co-infections with the respiratory bacterium Bordetella bronchiseptica and the gastrointestinal helminth Trichostrongylus retortaeformis. Our goal was to identify representative mediators and functions that could capture the essence of the host immune response as a whole, and to assess how their relative contribution dynamically changed over time and between single and co-infected individuals. Network-based discrete dynamic models of single infections were built using current knowledge of bacterial and helminth immunology; the two single infection models were combined into a co-infection model that was then verified by our empirical findings. Simulations showed that a T helper cell mediated antibody and neutrophil response led to phagocytosis and clearance of B. bronchiseptica from the lungs. This was consistent in single and co-infection with no significant delay induced by the helminth. In contrast, T. retortaeformis intensity decreased faster when co-infected with the bacterium. Simulations suggested that the robust recruitment of neutrophils in the co-infection, added to the activation of IgG and eosinophil driven reduction of larvae, which also played an important role in single infection, contributed to this fast clearance. Perturbation analysis of the models, through the knockout of individual nodes (immune cells), identified the cells critical to parasite persistence and clearance both in single and co-infections. Our integrated approach captured the within-host immuno-dynamics of bacteria-helminth infection and identified key components that can be crucial for explaining individual variability between single and co-infections in natural populations
Modes and models in disorders of consciousness science
The clinical assessment of non-communicative brain damaged patients is extremely difficult and there is a need for paraclinical diagnostic markers of the level of consciousness. In the last few years, progress within neuroimaging has led to a growing body of studies investigating vegetative state and minimally conscious state patients, which can be classified in two main approaches. Active neuroimaging paradigms search for a response to command without requiring a motor response. Passive neuroimaging paradigms investigate spontaneous brain activity and brain responses to external stimuli and aim at identifying neural correlates of consciousness. Other passive paradigms eschew neuroimaging in favour of behavioural markers which reliably distinguish conscious and unconscious conditions in healthy controls. In order to furnish accurate diagnostic criteria, a mechanistic explanation of how the brain gives rise to consciousness seems desirable. Mechanistic and theoretical approaches could also ultimately lead to a unification of passive and active paradigms in a coherent diagnostic approach. In this paper, we survey current passive and active paradigms available for diagnosis of residual consciousness in vegetative state and minimally conscious patients. We then review the current main theories of consciousness and see how they can apply in this context. Finally, we discuss some avenues for future research in this domai
Plant responses to abiotic stress: the chromatin context of transcriptional regulation
The ability of plants to cope with abiotic environmental stresses such as drought, salinity, heat, cold or flooding relies on flexible mechanisms for re-programming gene expression. Over recent years it has become apparent that transcriptional regulation needs to be understood within its structural context. Chromatin, the assembly of DNA with histone proteins, generates a local higher-order structure that impacts on the accessibility and effectiveness of the transcriptional machinery, as well as providing a hub for multiple protein interactions. Several studies have shown that chromatin features such as histone variants and post-translational histone modifications are altered by environmental stress, and they could therefore be primary stress targets that initiate transcriptional stress responses. Alternatively, they could act downstream of stress-induced transcription factors as an integral part of transcriptional activity. A few experimental studies have addressed this ‘chicken-and-egg’ problem in plants and other systems, but to date the causal relationship between dynamic chromatin changes and transcriptional responses under stress is still unclear. In this review we have collated the existing information on concurrent epigenetic and transcriptional responses of plants to abiotic stress, and we have assessed the evidence using a simple theoretical framework of causality scenarios
Imaging the spatial-temporal neuronal dynamics using dynamic causal modelling
Oscillatory brain activity is a ubiquitous feature of neuronal dynamics and
the synchronous discharge of neurons is believed to facilitate integration both
within functionally segregated brain areas and between areas engaged by the same
task. There is growing interest in investigating the neural oscillatory networks in
vivo. The aims of this thesis are to (1) develop an advanced method, Dynamic
Causal Modelling for Induced Responses (DCM for IR), for modelling the brain
network functions and (2) apply it to exploit the nonlinear coupling in the motor
system during hand grips and the functional asymmetries during face perception.
DCM for IR models the time-varying power over a range of
frequencies of coupled electromagnetic sources. The model parameters encode
coupling strength among areas and allows the differentiations between linear
(within frequency) and nonlinear (between-frequency) coupling. I applied DCM
for IR to show that, during hand grips, the nonlinear interactions among neuronal
sources in motor system are essential while intrinsic coupling (within source) is
very likely to be linear. Furthermore, the normal aging process alters both the
network architecture and the frequency contents in the motor network.
I then use the bilinear form of DCM for IR to model the experimental
manipulations as the modulatory effects. I use MEG data to demonstrate
functional asymmetries between forward and backward connections during face
perception: Specifically, high (gamma) frequencies in higher cortical areas
suppressed low (alpha) frequencies in lower areas. This finding provides direct
evidence for functional asymmetries that is consistent with anatomical and
physiological evidence from animal studies. Lastly, I generalize the bilinear form of DCM for IR to dissociate the induced responses from evoked ones in terms of
their functional role. The backward modulatory effect is expressed as induced, but
not evoked responses
A dual role for prediction error in associative learning
Confronted with a rich sensory environment, the brain must learn
statistical regularities across sensory domains to construct causal
models of the world. Here, we used functional magnetic resonance
imaging and dynamic causal modeling (DCM) to furnish neurophysiological
evidence that statistical associations are learnt, even when
task-irrelevant. Subjects performed an audio-visual target-detection
task while being exposed to distractor stimuli. Unknown to them,
auditory distractors predicted the presence or absence of subsequent
visual distractors. We modeled incidental learning of these associations
using a Rescorla--Wagner (RW) model. Activity in primary visual
cortex and putamen reflected learning-dependent surprise: these areas
responded progressively more to unpredicted, and progressively less
to predicted visual stimuli. Critically, this prediction-error response
was observed even when the absence of a visual stimulus was
surprising. We investigated the underlying mechanism by embedding
the RW model into a DCM to show that auditory to visual connectivity
changed significantly over time as a function of prediction error. Thus,
consistent with predictive coding models of perception, associative
learning is mediated by prediction-error dependent changes in connectivity.
These results posit a dual role for prediction-error in encoding
surprise and driving associative plasticity
Advancing functional connectivity research from association to causation
Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures
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