About this Research Topic
The study of brain connectivity is one of the main challenges when studying the active brain. Brain connectivity is operationally defined as the estimation of the relation between brain areas (Regions of interest, ROIs, or Volumes of interest, VOIs); these relations are established when a specific cognitive task is being solved or when resting. In order to estimate the connectivity different brain signals can be used, each of which has different neurofucntional properties. One of signals showing great ability to represent the active brain is the BOLD signal, obtained when using functional magnetic resonance imaging (fMRI). What is registered is the modification of the magnetic field that takes place due to the increment of the presence of oxygen is certain brain areas when these are activated during a cognitive task. When the brain is resting, the signal shows the basal state used as a reference.
In order to estimate the connectivity networks, the value of the BOLD signal is isolated, throughout the recording period, in these voxels that anatomically or statistically present significant activation as compared to resting. In each grouping of voxels, a value for this ROI is estimated via dimensionality reduction techniques and the connectivity networks are tentatively established. Currently, there are two main models for estimation of connectivity: one based on structural equations models (SEM) one estimating the effective connectivity via dynamic causal models (DCM). Both models reflect different perspectives on the neurobiological bases of the network, but there are also some similarities in their mathematical and statistical properties.
The effect of instrumental and methodological variables on the networks is not known, but they affect the replication of results from different studies, making the networks estimated from equivalent cognitive tasks not comparable. Variables such as the type of design (e.g., comparing groups or tasks), the estimation technique or the number of ROIs are potentially relevant for the estimated networks obtained as a result. Specifically, the effect of these methodological variables has to be compared when using SEM versus DCM. Moreover, it has to be considered that the degree of fit of the networks estimated to the data is assessed via traditional statistical models without incorporating indicators which are specific for complex networks.
The aim of this proposition is to evaluate the SEM and DCM approach, the effect of instrumentation and methodological variables on the networks estimated. Additionally, real data will be presented (fMRI) and, in this way; cross validation of the simulated results would be possible. Moreover, new indicators will be proposed for studying network connectivity and for giving information on basic aspects such as its temporal stability, its internal variability, centrality or brain symmetry. In this way, we would have available a set of methodological tools for correctly estimating connectivity networks via either SEM or DCM, ensuring their applied use, given that the connectivity networks are crucial for estimating the active brain in general and, specifically, in neurorehabilitation process, for brain plasticity and for cognitive reorganization.
In order to estimate the connectivity networks, the value of the BOLD signal is isolated, throughout the recording period, in these voxels that anatomically or statistically present significant activation as compared to resting. In each grouping of voxels, a value for this ROI is estimated via dimensionality reduction techniques and the connectivity networks are tentatively established. Currently, there are two main models for estimation of connectivity: one based on structural equations models (SEM) one estimating the effective connectivity via dynamic causal models (DCM). Both models reflect different perspectives on the neurobiological bases of the network, but there are also some similarities in their mathematical and statistical properties.
The effect of instrumental and methodological variables on the networks is not known, but they affect the replication of results from different studies, making the networks estimated from equivalent cognitive tasks not comparable. Variables such as the type of design (e.g., comparing groups or tasks), the estimation technique or the number of ROIs are potentially relevant for the estimated networks obtained as a result. Specifically, the effect of these methodological variables has to be compared when using SEM versus DCM. Moreover, it has to be considered that the degree of fit of the networks estimated to the data is assessed via traditional statistical models without incorporating indicators which are specific for complex networks.
The aim of this proposition is to evaluate the SEM and DCM approach, the effect of instrumentation and methodological variables on the networks estimated. Additionally, real data will be presented (fMRI) and, in this way; cross validation of the simulated results would be possible. Moreover, new indicators will be proposed for studying network connectivity and for giving information on basic aspects such as its temporal stability, its internal variability, centrality or brain symmetry. In this way, we would have available a set of methodological tools for correctly estimating connectivity networks via either SEM or DCM, ensuring their applied use, given that the connectivity networks are crucial for estimating the active brain in general and, specifically, in neurorehabilitation process, for brain plasticity and for cognitive reorganization.
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
