Accelerating Computation of DCM for ERP in MATLAB by External Function Calls to the GPU

A survey of brain network analysis by electroencephalographic signals

Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

Characterizing Network Search Algorithms Developed for Dynamic Causal Modeling

Dynamic causal modeling (DCM) is a widely used tool to estimate the effective connectivity of specified models of a brain network. Finding the model explaining measured data is one of the most important outstanding problems in Bayesian modeling. Using heuristic model search algorithms enables us to find an optimal model without having to define a model set a priori. However, the development of such methods is cumbersome in the case of large model-spaces. We aimed to utilize commonly used graph theoretical search algorithms for DCM to create a framework for characterizing them, and to investigate relevance of such methods for single-subject and group-level studies. Because of the enormous computational demand of DCM calculations, we separated the model estimation procedure from the search algorithm by providing a database containing the parameters of all models in a full model-space. For test data a publicly available fMRI dataset of 60 subjects was used. First, we reimplemented the deterministic bilinear DCM algorithm in the ReDCM R package, increasing computational speed during model estimation. Then, three network search algorithms have been adapted for DCM, and we demonstrated how modifications to these methods, based on DCM posterior parameter estimates, can enhance search performance. Comparison of the results are based on model evidence, structural similarities and the number of model estimations needed during search. An analytical approach using Bayesian model reduction (BMR) for efficient network discovery is already available for DCM. Comparing model search methods we found that topological algorithms often outperform analytical methods for single-subject analysis and achieve similar results for recovering common network properties of the winning model family, or set of models, obtained by multi-subject family-wise analysis. However, network search methods show their limitations in higher level statistical analysis of parametric empirical Bayes. Optimizing such linear modeling schemes the BMR methods are still considered the recommended approach. We envision the freely available database of estimated model-spaces to help further studies of the DCM model-space, and the ReDCM package to be a useful contribution for Bayesian inference within and beyond the field of neuroscience.

Effective connectivity of mental fatigue: Dynamic causal modeling of EEG data

BACKGROUND

Recognition of sources in the brain and their interaction with mental fatigue states are interesting subjects for researchers.

OBJECTIVE

The aim of this study was to investigate the mental fatigue effects on brain areas by dynamic casual modeling (DCM) parameters that are extracted from event-related potential (ERP) signals which were then estimated based on mental fatigue data with visual stimulation.

METHODS

ERP were recorded based on a Continuous Performance Task in four consecutive trials. Active regions and brain sources were extracted by a Multiple Sparse Priors algorithm.

RESULTS

Four models are proposed for DCM. The parameters and the structure of the best model were obtained by SPM software for ERP in each of the four trials.

CONCLUSION

The results illustrate that an increase of mental fatigue through trials leads to increased likelihood of choosing forward models.

Annealed Importance Sampling for Neural Mass Models

Neural Mass Models provide a compact description of the dynamical activity of cell populations in neocortical regions. Moreover, models of regional activity can be connected together into networks, and inferences made about the strength of connections, using M/EEG data and Bayesian inference. To date, however, Bayesian methods have been largely restricted to the Variational Laplace (VL) algorithm which assumes that the posterior distribution is Gaussian and finds model parameters that are only locally optimal. This paper explores the use of Annealed Importance Sampling (AIS) to address these restrictions. We implement AIS using proposals derived from Langevin Monte Carlo (LMC) which uses local gradient and curvature information for efficient exploration of parameter space. In terms of the estimation of Bayes factors, VL and AIS agree about which model is best but report different degrees of belief. Additionally, AIS finds better model parameters and we find evidence of non-Gaussianity in their posterior distribution.

The activity of populations of neurons in the human brain can be described using a set of differential equations known as a neural mass model. These models can then be connected to describe activity in multiple brain regions and, by fitting them to human brain imaging data, statistical inferences can be made about changes in macroscopic connectivity among brain regions. For example, the strength of a connection from one region to another may be more strongly engaged in a particular patient population or during a specific cognitive task. Current statistical inference approaches use a Bayesian algorithm based on principles of local optimization and the assumption that uncertainty about model parameters (e.g. connectivity), having seen the data, follows a Gaussian distribution. This paper evaluates current methods against a global Bayesian optimization algorithm and finds that the two approaches (local/global) agree about which model is best, but finds that the global approach produces better parameter estimates.

Neuronal correlates of a virtual-reality-based passive sensory P300 network

P300, a positive event-related potential (ERP) evoked at around 300 ms after stimulus, can be elicited using an active or passive oddball paradigm. Active P300 requires a person's intentional response, whereas passive P300 does not require an intentional response. Passive P300 has been used in incommunicative patients for consciousness detection and brain computer interface. Active and passive P300 differ in amplitude, but not in latency or scalp distribution. However, no study has addressed the mechanism underlying the production of passive P300. In particular, it remains unclear whether the passive P300 shares an identical active P300 generating network architecture when no response is required. This study aims to explore the hierarchical network of passive sensory P300 production using dynamic causal modelling (DCM) for ERP and a novel virtual reality (VR)-based passive oddball paradigm. Moreover, we investigated the causal relationship of this passive P300 network and the changes in connection strength to address the possible functional roles. A classical ERP analysis was performed to verify that the proposed VR-based game can reliably elicit passive P300. The DCM results suggested that the passive and active P300 share the same parietal-frontal neural network for attentional control and, underlying the passive network, the feed-forward modulation is stronger than the feed-back one. The functional role of this forward modulation may indicate the delivery of sensory information, automatic detection of differences, and stimulus-driven attentional processes involved in performing this passive task. To our best knowledge, this is the first study to address the passive P300 network. The results of this study may provide a reference for future clinical studies on addressing the network alternations under pathological states of incommunicative patients. However, caution is required when comparing patients' analytic results with this study. For example, the task presented here is not applicable to incommunicative patients.