5,907 research outputs found
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Neural masses and fields in dynamic causal modeling
Dynamic causal modeling (DCM) provides a framework for the analysis of effective connectivity among neuronal subpopulations that subtend invasive (electrocorticograms and local field potentials) and non-invasive (electroencephalography and magnetoencephalography) electrophysiological responses. This paper reviews the suite of neuronal population models including neural masses, fields and conductance-based models that are used in DCM. These models are expressed in terms of sets of differential equations that allow one to model the synaptic underpinnings of connectivity. We describe early developments using neural mass models, where convolution-based dynamics are used to generate responses in laminar-specific populations of excitatory and inhibitory cells. We show that these models, though resting on only two simple transforms, can recapitulate the characteristics of both evoked and spectral responses observed empirically. Using an identical neuronal architecture, we show that a set of conductance based modelsâthat consider the dynamics of specific ion-channelsâpresent a richer space of responses; owing to non-linear interactions between conductances and membrane potentials. We propose that conductance-based models may be more appropriate when spectra present with multiple resonances. Finally, we outline a third class of models, where each neuronal subpopulation is treated as a field; in other words, as a manifold on the cortical surface. By explicitly accounting for the spatial propagation of cortical activity through partial differential equations (PDEs), we show that the topology of connectivityâthrough local lateral interactions among cortical layersâmay be inferred, even in the absence of spatially resolved data. We also show that these models allow for a detailed analysis of structureâfunction relationships in the cortex. Our review highlights the relationship among these models and how the hypothesis asked of empirical data suggests an appropriate model class
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
Tensor Analysis and Fusion of Multimodal Brain Images
Current high-throughput data acquisition technologies probe dynamical systems
with different imaging modalities, generating massive data sets at different
spatial and temporal resolutions posing challenging problems in multimodal data
fusion. A case in point is the attempt to parse out the brain structures and
networks that underpin human cognitive processes by analysis of different
neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the
multimodal, multi-scale nature of neuroimaging data is well reflected by a
multi-way (tensor) structure where the underlying processes can be summarized
by a relatively small number of components or "atoms". We introduce
Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network
notation in order to analyze these models. These diagrams not only clarify
matrix and tensor EEG and fMRI time/frequency analysis and inverse problems,
but also help understand multimodal fusion via Multiway Partial Least Squares
and Coupled Matrix-Tensor Factorization. We show here, for the first time, that
Granger causal analysis of brain networks is a tensor regression problem, thus
allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI
recordings shows the potential of the methods and suggests their use in other
scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE
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Neural masses and fields: modeling the dynamics of brain activity
This technical note introduces a conductance-based neural field model that combines biologically realistic synaptic dynamicsâbased on transmembrane currentsâwith neural field equations, describing the propagation of spikes over the cortical surface. This model allows for fairly realistic inter-and intra-laminar intrinsic connections that underlie spatiotemporal neuronal dynamics. We focus on the response functions of expected neuronal states (such as depolarization) that generate observed electrophysiological signals (like LFP recordings and EEG). These response functions characterize the model's transfer functions and implicit spectral responses to (uncorrelated) input. Our main finding is that both the evoked responses (impulse response functions) and induced responses (transfer functions) show qualitative differences depending upon whether one uses a neural mass or field model. Furthermore, there are differences between the equivalent convolution and conductance models. Overall, all models reproduce a characteristic increase in frequency, when inhibition was increased by increasing the rate constants of inhibitory populations. However, convolution and conductance-based models showed qualitatively different changes in power, with convolution models showing decreases with increasing inhibition, while conductance models show the opposite effect. These differences suggest that conductance based field models may be important in empirical studies of cortical gain control or pharmacological manipulations
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Working Memory Load Modulates Neuronal Coupling
There is a severe limitation in the number of items that can be held in working memory. However, the neurophysiological limits remain unknown. We asked whether the capacity limit might be explained by differences in neuronal coupling. We developed a theoretical model based on Predictive Coding and used it to analyze Cross Spectral Density data from the prefrontal cortex (PFC), frontal eye fields (FEF), and lateral intraparietal area (LIP). Monkeys performed a change detection task. The number of objects that had to be remembered (memory load) was varied (1â3 objects in the same visual hemifield). Changes in memory load changed the connectivity in the PFCâFEFâLIP network. Feedback (top-down) coupling broke down when the number of objects exceeded cognitive capacity. Thus, impaired behavioral performance coincided with a break-down of Prediction signals. This provides new insights into the neuronal underpinnings of cognitive capacity and how coupling in a distributed working memory network is affected by memory load
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Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields
This article describes the first application of a generic (empirical) Bayesian analysis of betweenâsubject effects in the dynamic causal modeling (DCM) of electrophysiological (MEG) data. It shows that (i) nonâinvasive (MEG) data can be used to characterize subjectâspecific differences in cortical microcircuitry and (ii) presents a validation of DCM with neural fields that exploits intersubject variability in gamma oscillations. We find that intersubject variability in visually induced gamma responses reflects changes in the excitationâinhibition balance in a canonical cortical circuit. Crucially, this variability can be explained by subjectâspecific differences in intrinsic connections to and from inhibitory interneurons that form a pyramidalâinterneuron gamma network. Our approach uses Bayesian model reduction to evaluate the evidence for (large sets of) nested modelsâand optimize the corresponding connectivity estimates at the within and betweenâsubject level. We also consider Bayesian crossâvalidation to obtain predictive estimates for gammaâresponse phenotypes, using a leaveâoneâout procedure
Markov Blankets in the Brain
Recent characterisations of self-organising systems depend upon the presence
of a Markov blanket: a statistical boundary that mediates the interactions
between what is inside of and outside of a system. We leverage this idea to
provide an analysis of partitions in neuronal systems. This is applicable to
brain architectures at multiple scales, enabling partitions into single
neurons, brain regions, and brain-wide networks. This treatment is based upon
the canonical micro-circuitry used in empirical studies of effective
connectivity, so as to speak directly to practical applications. This depends
upon the dynamic coupling between functional units, whose form recapitulates
that of a Markov blanket at each level. The nuance afforded by partitioning
neural systems in this way highlights certain limitations of modular
perspectives of brain function that only consider a single level of
description.Comment: 25 pages, 5 figures, 1 table, Glossar
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