Numerical aspects of spatio-temporal current density reconstruction from EEG-/MEG-data

Leveraging sound speed dynamics and generative deep learning for ray-based ocean acoustic tomography

A generative deep learning framework is introduced for ray-based ocean acoustic tomography (OAT), an inverse problem for estimating sound speed profiles (SSP) based on arrival-times measurements between multiple acoustic transducers, which is typically ill-posed. This framework relies on a robust low-dimensional parametrization of the expected SSP variations using a variational autoencoder and a linear dynamical model as further regularization. This framework was tested using SSP variations simulated by a regional ocean model with submesoscale permitting horizontal resolution and various transducer configurations spanning the upper ocean over short propagation ranges and was found to outperform conventional linear least squares formulations of OAT.

EEG dynamic source imaging using a regularized optimization with spatio-temporal constraints

One of the most important needs in neuroimaging is brain dynamic source imaging with high spatial and temporal resolution. EEG source imaging estimates the underlying sources from EEG recordings, which provides enhanced spatial resolution with intrinsically high temporal resolution. To ensure identifiability in the underdetermined source reconstruction problem, constraints on EEG sources are essential. This paper introduces a novel method for estimating source activities based on spatio-temporal constraints and a dynamic source imaging algorithm. The method enhances time resolution by incorporating temporal evolution of neural activity into a regularization function. Additionally, two spatial regularization constraints based on L 1 and L 2 norms are applied in the transformed domain to address both focal and spread neural activities, achieved through spatial gradient and Laplacian transform. Performance evaluation, conducted quantitatively using synthetic datasets, discusses the influence of parameters such as source extent, number of sources, correlation level, and SNR level on temporal and spatial metrics. Results demonstrate that the proposed method provides superior spatial and temporal reconstructions compared to state-of-the-art inverse solutions including STRAPS, sLORETA, SBL, dSPM, and MxNE. This improvement is attributed to the simultaneous integration of transformed spatial and temporal constraints. When applied to a real auditory ERP dataset, our algorithm accurately reconstructs brain source time series and locations, effectively identifying the origins of auditory evoked potentials. In conclusion, our proposed method with spatio-temporal constraints outperforms the state-of-the-art algorithms in estimating source distribution and time courses.

Cross-Modal Reorganization From Both Visual and Somatosensory Modalities in Cochlear Implanted Children and Its Relationship to Speech Perception

HYPOTHESIS

We hypothesized that children with cochlear implants (CIs) who demonstrate cross-modal reorganization by vision also demonstrate cross-modal reorganization by somatosensation and that these processes are interrelated and impact speech perception.

BACKGROUND

Cross-modal reorganization, which occurs when a deprived sensory modality's cortical resources are recruited by other intact modalities, has been proposed as a source of variability underlying speech perception in deaf children with CIs. Visual and somatosensory cross-modal reorganization of auditory cortex have been documented separately in CI children, but reorganization in these modalities has not been documented within the same subjects. Our goal was to examine the relationship between cross-modal reorganization from both visual and somatosensory modalities within a single group of CI children.

METHODS

We analyzed high-density electroencephalogram responses to visual and somatosensory stimuli and current density reconstruction of brain activity sources. Speech perception in noise testing was performed. Current density reconstruction patterns were analyzed within the entire subject group and across groups of CI children exhibiting good versus poor speech perception.

RESULTS

Positive correlations between visual and somatosensory cross-modal reorganization suggested that neuroplasticity in different sensory systems may be interrelated. Furthermore, CI children with good speech perception did not show recruitment of frontal or auditory cortices during visual processing, unlike CI children with poor speech perception.

CONCLUSION

Our results reflect changes in cortical resource allocation in pediatric CI users. Cross-modal recruitment of auditory and frontal cortices by vision, and cross-modal reorganization of auditory cortex by somatosensation, may underlie variability in speech and language outcomes in CI children.

A state-space modeling approach for localization of focal current sources from MEG

State-space modeling is a promising approach for current source reconstruction from magnetoencephalography (MEG) because it constrains the spatiotemporal behavior of inverse solutions in a flexible manner. However, state-space model-based source localization research remains underdeveloped; extraction of spatially focal current sources and handling of the high dimensionality of the distributed source model remain problematic. In this study, we propose a novel state-space model-based method that resolves these problems, extending our previous source localization method to include a temporal constraint by state-space modeling. To enable focal current reconstruction, we account for spatially inhomogeneous temporal dynamics by introducing dynamics model parameters that differ for each cortical position. The model parameters and the intensity of the current sources are jointly estimated according to a bayesian framework. We circumvent the high dimensionality of the problem by assuming prior distributions of the model parameters to reduce the sensitivity to unmodeled components, and by adopting variational bayesian inference to reduce the computational cost. Through simulation experiments and application to real MEG data, we have confirmed that our proposed method successfully reconstructs focal current activities, which evolve with their temporal dynamics.

Tracking cortical activity from M/EEG using graph cuts with spatiotemporal constraints

This work proposes to use magnetoencephalography (MEG) and electroencephalography (EEG) source imaging to provide cinematic representations of the temporal dynamics of cortical activations. Cortical activation maps, seen as images of the active brain, are scalar maps defined at the vertices of a triangulated cortical surface. They can be computed from M/EEG data using a linear inverse solver every millisecond. Taking as input these activation maps and exploiting both the graph structure of the cortical mesh and the high sampling rate of M/EEG recordings, neural activations are tracked over time using an efficient graph cut based algorithm. The method estimates the spatiotemporal support of the active brain regions. It consists in computing a minimum cut on a particularly designed weighted graph imposing spatiotemporal regularity constraints on the activation patterns. Each node of the graph is assigned a label (active or non active). The method works globally on the full time-period of interest, can cope with spatially extended active regions and allows the active domain to exhibit topology changes over time. The algorithm is illustrated and validated on synthetic data. Results of the method are provided on two MEG cognitive experiments in the visual and somatosensory cortices, demonstrating the ability of the algorithm to handle various types of data.

A distributed spatio-temporal EEG/MEG inverse solver

We propose a novel l(1)l(2)-norm inverse solver for estimating the sources of EEG/MEG signals. Based on the standard l(1)-norm inverse solvers, this sparse distributed inverse solver integrates the l(1)-norm spatial model with a temporal model of the source signals in order to avoid unstable activation patterns and "spiky" reconstructed signals often produced by the currently used sparse solvers. The joint spatio-temporal model leads to a cost function with an l(1)l(2)-norm regularizer whose minimization can be reduced to a convex second-order cone programming (SOCP) problem and efficiently solved using the interior-point method. The efficient computation of the SOCP problem allows us to implement permutation tests for estimating statistical significance of the inverse solution. Validation with simulated and human MEG data shows that the proposed solver yields source time course estimates qualitatively similar to those obtained through dipole fitting, but without the need to specify the number of dipole sources in advance. Furthermore, the l(1)l(2)-norm solver achieves fewer false positives and a better representation of the source locations than the conventional l(2) minimum-norm estimates.

Neural systems connecting interoceptive awareness and feelings

In many theories of emotions the representations of bodily responses play an important role for subjective feelings. We tested the hypothesis that the perception of bodily states is positively related to the experienced intensity of feelings as well as to the activity of first-order and second-order brain structures involved in the processing of feelings. Using a heartbeat perception task, subjects were separated into groups with either high or poor interoceptive awareness. During emotional picture presentation we measured high-density EEG and used spatiotemporal current density reconstruction to identify regions involved in both interoceptive awareness and emotion processing. We observed a positive relation between interoceptive awareness and the experienced intensity of emotions. Furthermore, the P300 amplitudes to pleasant and unpleasant pictures were enhanced for subjects with high interoceptive awareness. The source reconstruction revealed that interoceptive awareness is related to an enhanced activation in both first-order structures (insula, somatosensory cortices) and second-order structures (anterior cingulate, prefrontal cortices). We conclude that the perception of bodily states is a crucial determinant for the processing and the subjective experience of feelings.

Recursive penalized least squares solution for dynamical inverse problems of EEG generation

In the dynamical inverse problem of electroencephalogram (EEG) generation where a specific dynamics for the electrical current distribution is assumed, we can impose general spatiotemporal constraints onto the solution by casting the problem into a state space representation and assuming a specific class of parametric models for the dynamics. The Akaike Bayesian Information Criterion (ABIC), which is based on the Type II likelihood, was used to estimate the parameters and evaluate the model. In addition, dynamic low-resolution brain electromagnetic tomography (LORETA), a new approach for estimating the current distribution is introduced. A recursive penalized least squares (RPLS) step forms the main element of our implementation. To obtain improved inverse solutions, dynamic LORETA exploits both spatial and temporal information, whereas LORETA uses only spatial information. A considerable improvement in performance compared to LORETA was found when dynamic LORETA was applied to simulated EEG data, and the new method was applied also to clinical EEG data.