About this Research Topic

Background

The computing resources of today, in combination with high-resolution individualized structural MRI scans, enable advanced forward modeling in a wide range of bioelectromagnetic applications targeting the brain. Forward solvers are used in combination with inverse methods to localize neuronal sources. These solvers have evolved from analytical approaches using a single or multi-layered spherical domain towards realistic modeling tools based, e.g., on the boundary and finite element methods (BEMs and FEMs). Modern FEM methods - using state-of-the-art high-resolution MRI, numerical solvers, and computing hardware - can handle high-resolution spatial discretization, and advanced sensor models, and incorporate tissue anisotropies. Similarly, inverse methods can also make use of individualized MRI to support an inference of the brain activity, e.g. through constraints on the placement of active neurons in the brain, or on connectivity between neuronal populations inferred for diffusion MRI tractography. Through reciprocity, similar approaches are also applicable to the modeling of electromagnetic brain stimulation. Integrating anatomical information, and advanced forward and inverse approaches will be crucial in the development of the next generation of software tools for Spatiotemporal analysis of whole-brain electrophysiology.

This research topic aims to discover new approaches to solve multi-modal electric and magnetic brain imaging problems in source localization and stimulation as well as in complementary modalities such as impedance tomography. In particular, our goal is to advance the development of unified solver approaches that can utilize the vast amount of volumetric information that is available today through high-resolution and high-contrast MRI scans and benefit from the complementary of the different modalities. This issue will focus on novel methods that make use of high-resolution MRI data in combination with novel approaches to modeling and inference applied to problems in source localization, modeling of brain stimulation, and impedance tomography. These methods might make use of machine learning, dynamic modeling, filtering techniques, or statistical inference.

The scope covers new forward and inverse methods and multi-modal studies motivated by the following examples.

(1) Integrating powerful volumetric forward simulation techniques with inverse approaches currently includes many open questions. These include, for example, the stability of a FEM-based source model inside a complex-structured head model with high contrasts inside, or the inverse effects that follow from using a realistic geometry.

(2) Numerical implementations, their performance, and experimental applications are welcome, for instance, in building an advanced inverse approach, e.g., a dynamic Bayesian solver or a Machine Learning scheme, which requires a solid interplay between different forward and inverse solver components.

(3) Improving the level of multi-modality in inverse modeling: for instance, optimized stimulation and source localization approaches, or complementary modalities, can be coupled in a straightforward manner, if a volumetric forward simulation is applied.

The scope includes (but is not limited to) the following invasive non-invasive multimodal neuroimaging techniques and their application:

Electroencephalography (EEG);
Magnetoencephalography (MEG);
Magnetic Resonance Imaging (MRI);
Invasive EEG sEEG, iEEG, ECOG
DBS, CCEPs and low-power stimulation
Neurotherapeutic approaches;
Transcranial Magnetic Stimulation (TMS);
Transcranial Electric Stimulation (TES);
Temporal Interference Stimulation (TIS).
Functional MRI, diffusion tensor imaging (DTI);
Combined EEG/MEG/fMRI
Functional Near-Infrared Spectroscopy (fNIRS);
Utilizing cutting-edge artificial intelligence;
Machine Learning;
Deep Learning;

We thus welcome contributions to new methods and implementations that impact the public through novelty, practicality, and easy human application.

Keywords: Forward Simulation, Inverse Problems, Multi-Modality, Reciprocity, Machine Learning Theory, Implementation, Application, Source Localization, EEG, MEG, sEEG, iEEG, Stimulation, TES/TMS, Tomography, EIT

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.

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