MEG and fMRI Fusion for Non-Linear Estimation of Neural and BOLD Signal Changes

Exploiting neurovascular coupling: a Bayesian sequential Monte Carlo approach applied to simulated EEG fNIRS data

OBJECTIVE

Electrical and hemodynamic brain activity are linked through the neurovascular coupling process and they can be simultaneously measured through integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Thanks to the lack of electro-optical interference, the two procedures can be easily combined and, whereas EEG provides electrophysiological information, fNIRS can provide measurements of two hemodynamic variables, such as oxygenated and deoxygenated hemoglobin. A Bayesian sequential Monte Carlo approach (particle filter, PF) was applied to simulated recordings of electrical and neurovascular mediated hemodynamic activity, and the advantages of a unified framework were shown.

APPROACH

Multiple neural activities and hemodynamic responses were simulated in the primary motor cortex of a subject brain. EEG and fNIRS recordings were obtained by means of forward models of volume conduction and light propagation through the head. A state space model of combined EEG and fNIRS data was built and its dynamic evolution was estimated through a Bayesian sequential Monte Carlo approach (PF).

MAIN RESULTS

We showed the feasibility of the procedure and the improvements in both electrical and hemodynamic brain activity reconstruction when using the PF on combined EEG and fNIRS measurements.

SIGNIFICANCE

The investigated procedure allows one to combine the information provided by the two methodologies, and, by taking advantage of a physical model of the coupling between electrical and hemodynamic response, to obtain a better estimate of brain activity evolution. Despite the high computational demand, application of such an approach to in vivo recordings could fully exploit the advantages of this combined brain imaging technology.

The Advents of Hybrid Imaging Modalities: A New Era in Neuroimaging Applications

Hybrid Imaging modalities have shown great potential in medical imaging and diagnosis. A more comprehensive and targeted view of neurological disorders can be achieved by blending the anatomical and functional perspectives through hybridization. With consistently improving technologies, there have been many developments in fused imaging techniques over the past few decades. This article provides an overview of various bimodal and trimodal hybrid imaging techniques being developed and explored for neuroimaging applications. Recent advancements and potentials are discussed for single photon emission computed tomography-computed tomography (SPECT-CT), positron emission tomography-CT (PET-CT), PET-magnetic resonance imaging (PET-MRI), electroencephalography-functional magnetic resonance imaging (EEG-fMRI), magnetoencephalography-fMRI (MEG-fMRI), EEG-near-infrared spectroscopy (EEG-NIRS), magnetic resonance-PET-EEG (MR-PET-EEG) and MR-PET-CT in the perspective of neuroimaging. A comparison of these hybrid approaches is provided on a single platform to analyze their performance on the basis of several common factors essential for imaging and analyzing neurological disorders and in vivo molecular processes. This article also provides an overview of recently developed advanced imaging technologies that are being hybridized with other imaging modalities and being explored as potential techniques for neuroscience. Novel approaches and clinical applications of hybrid neuroimaging are anticipated with inclusion of new technologies, better sensing capabilities, multimodal probes, and improved hybridization.

Overview of MEG

Magnetoencephalography (MEG) is a method to study electrical activity in the human brain by recording the neuromagnetic field outside the head. MEG, like electroencephalography (EEG), provides an excellent, millisecond-scale time resolution, and allows the estimation of the spatial distribution of the underlying activity, in favorable cases with a localization accuracy of a few millimeters. To detect the weak neuromagnetic signals, superconducting sensors, magnetically shielded rooms, and advanced signal processing techniques are used. The analysis and interpretation of MEG data typically involves comparisons between subject groups and experimental conditions using various spatial, temporal, and spectral measures of cortical activity and connectivity. The application of MEG to cognitive neuroscience studies is illustrated with studies of spoken language processing in subjects with normal and impaired reading ability. The mapping of spatiotemporal patterns of activity within networks of cortical areas can provide useful information about the functional architecture of the brain related to sensory and cognitive processing, including language, memory, attention, and perception.

EEG-fMRI Bayesian framework for neural activity estimation: a simulation study

OBJECTIVE

Due to the complementary nature of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), and given the possibility of simultaneous acquisition, the joint data analysis can afford a better understanding of the underlying neural activity estimation. In this simulation study we want to show the benefit of the joint EEG-fMRI neural activity estimation in a Bayesian framework.

APPROACH

We built a dynamic Bayesian framework in order to perform joint EEG-fMRI neural activity time course estimation. The neural activity is originated by a given brain area and detected by means of both measurement techniques. We have chosen a resting state neural activity situation to address the worst case in terms of the signal-to-noise ratio. To infer information by EEG and fMRI concurrently we used a tool belonging to the sequential Monte Carlo (SMC) methods: the particle filter (PF).

MAIN RESULTS

First, despite a high computational cost, we showed the feasibility of such an approach. Second, we obtained an improvement in neural activity reconstruction when using both EEG and fMRI measurements.

SIGNIFICANCE

The proposed simulation shows the improvements in neural activity reconstruction with EEG-fMRI simultaneous data. The application of such an approach to real data allows a better comprehension of the neural dynamics.

Bayesian networks in neuroscience: a survey

Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind-morphological, electrophysiological, -omics and neuroimaging-, thereby broadening the scope-molecular, cellular, structural, functional, cognitive and medical- of the brain aspects to be studied.

Sharing privacy-sensitive access to neuroimaging and genetics data: a review and preliminary validation

The growth of data sharing initiatives for neuroimaging and genomics represents an exciting opportunity to confront the “small N” problem that plagues contemporary neuroimaging studies while further understanding the role genetic markers play in the function of the brain. When it is possible, open data sharing provides the most benefits. However, some data cannot be shared at all due to privacy concerns and/or risk of re-identification. Sharing other data sets is hampered by the proliferation of complex data use agreements (DUAs) which preclude truly automated data mining. These DUAs arise because of concerns about the privacy and confidentiality for subjects; though many do permit direct access to data, they often require a cumbersome approval process that can take months. An alternative approach is to only share data derivatives such as statistical summaries—the challenges here are to reformulate computational methods to quantify the privacy risks associated with sharing the results of those computations. For example, a derived map of gray matter is often as identifiable as a fingerprint. Thus alternative approaches to accessing data are needed. This paper reviews the relevant literature on differential privacy, a framework for measuring and tracking privacy loss in these settings, and demonstrates the feasibility of using this framework to calculate statistics on data distributed at many sites while still providing privacy.

A selective review of multimodal fusion methods in schizophrenia

Schizophrenia (SZ) is one of the most cryptic and costly mental disorders in terms of human suffering and societal expenditure (van Os and Kapur, 2009). Though strong evidence for functional, structural, and genetic abnormalities associated with this disease exists, there is yet no replicable finding which has proven accurate enough to be useful in clinical decision making (Fornito et al., 2009), and its diagnosis relies primarily upon symptom assessment (Williams et al., 2010a). It is likely in part that the lack of consistent neuroimaging findings is because most models favor only one data type or do not combine data from different imaging modalities effectively, thus missing potentially important differences which are only partially detected by each modality (Calhoun et al., 2006a). It is becoming increasingly clear that multimodal fusion, a technique which takes advantage of the fact that each modality provides a limited view of the brain/gene and may uncover hidden relationships, is an important tool to help unravel the black box of schizophrenia. In this review paper, we survey a number of multimodal fusion applications which enable us to study the schizophrenia macro-connectome, including brain functional, structural, and genetic aspects and may help us understand the disorder in a more comprehensive and integrated manner. We also provide a table that characterizes these applications by the methods used and compare these methods in detail, especially for multivariate models, which may serve as a valuable reference that helps readers select an appropriate method based on a given research question.

A review of multivariate methods for multimodal fusion of brain imaging data

The development of various neuroimaging techniques is rapidly improving the measurements of brain function/structure. However, despite improvements in individual modalities, it is becoming increasingly clear that the most effective research approaches will utilize multi-modal fusion, which takes advantage of the fact that each modality provides a limited view of the brain. The goal of multi-modal fusion is to capitalize on the strength of each modality in a joint analysis, rather than a separate analysis of each. This is a more complicated endeavor that must be approached more carefully and efficient methods should be developed to draw generalized and valid conclusions from high dimensional data with a limited number of subjects. Numerous research efforts have been reported in the field based on various statistical approaches, e.g. independent component analysis (ICA), canonical correlation analysis (CCA) and partial least squares (PLS). In this review paper, we survey a number of multivariate methods appearing in previous multimodal fusion reports, mostly fMRI with other modality, which were performed with or without prior information. A table for comparing optimization assumptions, purpose of the analysis, the need of priors, dimension reduction strategies and input data types is provided, which may serve as a valuable reference that helps readers understand the trade-offs of the 7 methods comprehensively. Finally, we evaluate 3 representative methods via simulation and give some suggestions on how to select an appropriate method based on a given research.

Coupling electrophysiological and hemodynamic responses to errors

Performance errors are associated with distinct electrophysiological and hemodynamic signatures: a fronto-central error-related negativity (ERN) is seen in the event-related potentials and a network of activations including medio-frontal, parietal, and insular cortex is revealed by functional magnetic resonance imaging. We used simultaneous electroencephalography and functional magnetic resonance imaging (fMRI) to characterize the relationship between the electrophysiological and hemodynamic responses to errors. Participants performed a modified Flanker task. When analyzed independently, we found the ERN and hemodynamic activations in dorsal anterior cingulate cortex, superior frontal gyrus, precentral gyrus, inferior frontal gyrus, and inferior parietal lobule. fMRI-informed dipole modeling and joint independent component analysis (ICA) were used to couple electrophysiological and hemodynamic data. Both techniques revealed a temporal evolution of the areas found in the fMRI analysis, with the right hemisphere activations peaking before the left hemisphere. However, joint ICA added information, revealing a number of cortical and subcortical areas that had not been shown with parametric mapping. This technique also uncovered how these areas evolve over time. All together, these analyses provide a more detailed picture of the spatiotemporal dynamics of the processing of performance errors.

Effective connectivity analysis of fMRI and MEG data collected under identical paradigms

Estimation of effective connectivity, a measure of the influence among brain regions, can potentially reveal valuable information about organization of brain networks. Effective connectivity is usually evaluated from the functional data of a single modality. In this paper we show why that may lead to incorrect conclusions about effective connectivity. In this paper we use Bayesian networks to estimate connectivity on two different modalities. We analyze structures of estimated effective connectivity networks using aggregate statistics from the field of complex networks. Our study is conducted on functional MRI and magnetoencephalography data collected from the same subjects under identical paradigms. Results showed some similarities but also revealed some striking differences in the conclusions one would make on the fMRI data compared with the MEG data and are strongly supportive of the use of multiple modalities in order to gain a more complete picture of how the brain is organized given the limited information one modality is able to provide.