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Edited by: Tobias Alecio Mattei, Brain & Spine Center - InvisionHealth - Kenmore Mercy Hospital, USA

Reviewed by: Malte J. Rasch, Beijing Normal University, China

*Correspondence: Fatemeh Bakouie,

Shahriar Gharibzadeh,

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Multisensory Integration (MSI) is the study of how information coming from different sensory modalities, such as vision, audition and etc. are being integrated by the nervous system (Stein et al.,

Different researches have modeled MSI in a variety of ways. Computational methods, such as Kalman Filter (KF) and Bayesian Networks (BN) are used widely to model probabilistic functions of the nervous system including MSI (Van Der Kooij et al.,

Since BNs have not any assumption on accuracy of the input data, they have attracted much attention recently. A BN is a graphical model that represents probabilistic relationships among variables of interest. By using graphical models in conjunction with statistical techniques, several advantages for data analysis will be obtained: Firstly, because a BN represents conditional dependencies among all variables, it is able to handle situations where some data entries are missing. Secondly, the model can be used to learn causal relationships, so it can be used to understand a problem domain and to predict the consequences of intervention. Thirdly, because BNs have both causal and probabilistic semantics, they represent combining prior knowledge and data ideally (Heckerman,

Generally, there are three main inference tasks for BNs: inferring unobserved variables, parameter learning, and structure learning. They are used widely for modeling knowledge in computational biology, bioinformatics, etc. For example, a BN could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.

As it mentioned before, the brain needs using different resources of information altogether to be able to make a sound decision about a situation. In such cases BNs can be used to model brain's function in many studies (Seilheimer et al.,

However, it is obvious that the reliability of sensory modalities varies widely according to the context and in a BN the effect of one node on the other one can vary from one task or situation to another one. But it is clear that when we assume a node as a parent node for another one, this relation could not be changed and new experiences would not cause new links between separated nodes. The main weakness of the BN based models is the failure to address the way it uses to reconstruct the network, based on new observed experiences. Most studies in MSI modeling have only focused on one task in which the effective sensory resources are known before, therefore, the structure of the network is known too, and we only need to train the network. By contrast, when we want to model MSI, we should not restrain it only in some certain tasks but the model should instead be generalizable to other tasks. It means that the model should be more dynamic and task independent. In addition, it is clear that time has a great influence in our decision making and reasoning and unfortunately, BN fails to code the time directly (Mihajlovic and Petkovic,

We suggest that, MSI models will be more generalized if we use Dynamic Bayesian Networks (DBN) which describes a system that dynamically changes over time. In a BN that models the interactions between sensory modalities, the nodes are associated with activated sensory modalities and the edges represent the interactions among sensory modalities. Sensory modalities of a neural system including n sensory modalities are indexed in a set _{i}

BNs describe the PDF over the activation of sensory modalities, where the graphical structure provides an easy way to specify conditional interdependencies for a compact parameterization of the distribution. A BN defined by a structure _{i}_{i}_{i}:

DBNs extend BNs to incorporate temporal characteristics of the time-series _{i}

To avoid an explosion of the model complexity, one can assume that the temporal changes of activations of brain regions are stationary and first-order Markovian. This assumption provides a tractable causal model that explicitly takes into account the temporal dependencies of brain processes. When facing more complex temporal processes and connectivity patterns, higher-order and non-stationary Markov models can be used to overcome the complexity.

The connectivity structure between two consecutive data sampling is represented by the transition network, which renders the joint distribution of all possible trajectories of temporal processes. The structure of the DBN is obtained by unrolling the transition network over consecutive scans for all

In an overview, we here suggest that DBN may be a more useful method to model MSI in comparison to prior methods because of three reasons. Firstly, as DBN changes dynamically, initial structure of the network does not lead to an unreliable result and we can use the network in various kinds of studies (because this method is task-independent). Secondly, in cases which we are not sure about the relation and interaction between different sensory modalities, DBN output can help us to achieve a more accurate understanding about MSI processes. Moreover, there exist cyclic functional networks in the brain, such as cortico-subcortical loops which BNs are not capable to model. Unlike BN, DBN has the capability of modeling recurrent networks while still satisfying the acyclic constraint of the transition network (Rajapakse and Zhou,

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.