Integrated MEG/fMRI model validated using real auditory data

Modeling the BOLD correlates of competitive neural dynamics

Winner-take-all models are commonly used to model decision-making tasks where one outcome must be selected from several competing options. Related random walk and diffusion models have been used to explain such processes and apply them to psychometric and neurophysiological data. Recent model-based fMRI studies have sought to find the neural correlates of decision-making processes. However, due to the fact that hemodynamic responses likely reflect synaptic rather than spiking activity, the expected BOLD signature of winner-take-all circuits is not clear. A powerful way to integrate data from neurophysiology and brain imaging is by developing biologically plausible neural network models constrained and testable by neural and behavioral data, and then using Synthetic Brain Imaging - transforming the output of simulations with the model to make predictions testable against neuroimaging data. We developed a biologically realistic spiking winner-take-all model comprised of coupled excitatory and inhibitory neural populations. We varied the difficulty of a decision-making task by adjusting the contrast, or relative strength of inputs representing two response options. Synthetic brain imaging was used to estimate the BOLD response of the model and analyze its peak as a function of input contrast. We performed a parameter space analysis to determine values for which the model performs the task accurately, and given accurate performance, the distribution of the input contrast-BOLD response relationship. This underscores the need for models grounded in neurophysiological data for brain imaging analyses which attempt to localize the neural correlates of cognitive processes based on predicted BOLD responses.

Connectivity analysis of novelty process in habitual short sleepers

Neurophysiological processes underlying auditory memory and attention are impaired in habitually short sleepers. The aim of this study was to use dynamic causal modeling (DCM) to study the mechanisms of these impairments in short sleepers. Eight normal sleepers (total sleep time (TST)=7-8h) and nine habitual short sleepers (TST ≤ 6 h) participated. The time in bed was increased from habitual (≤ 6 h) to extended (~8.5h) for one week in the short sleep group. Event related potentials (ERPs) were collected using an auditory novelty task in "IGNORE" and "ATTEND" conditions. Fourteen DCM models were considered using different configurations of connections among the following six areas: left and right primary auditory cortices, superior temporal gyri (STG), and inferior temporal gyri (IFG). After fitting the ERPs to the 14 models (separately for the IGNORE and ATTEND conditions), the best model (across subjects) was chosen using the Bayesian model comparison. For both conditions, the connection from right-STG to right-IFG for normal sleepers was significantly greater than habitual short sleepers. This connection did not differ in habitual short sleepers before and after one week of extended sleep time. This connection for normal sleepers was not significantly greater than the habitual short sleepers after one week of extended sleep. These results show that the deficiency of novelty processing, seen in short sleepers, can be explained by the differences in connectivity of the pathway between frontal and temporal brain areas as compared to the normal sleepers. In addition, one week of extended time in bed was not enough to fully normalize this neuronal pathway between STG and IFG in short sleepers.

EEG-fMRI integration: a critical review of biophysical modeling and data analysis approaches

The diverse nature of cerebral activity, as measured using neuroimaging techniques, has been recognised long ago. It seems obvious that using single modality recordings can be limited when it comes to capturing its complex nature. Thus, it has been argued that moving to a multimodal approach will allow neuroscientists to better understand the dynamics and structure of this activity. This means that integrating information from different techniques, such as electroencephalography (EEG) and the blood oxygenated level dependent (BOLD) signal recorded with functional magnetic resonance imaging (fMRI), represents an important methodological challenge. In this work, we review the work that has been done thus far to derive EEG/fMRI integration approaches. This leads us to inspect the conditions under which such an integration approach could work or fail, and to disclose the types of scientific questions one could (and could not) hope to answer with it.

Development of a variational scheme for model inversion of multi-area model of brain. Part I: simulation evaluation

We previously developed an integrated model of the brain within a single cortical area for functional Magnetic Resonance Imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) using an extended neural mass model (ENMM). We then extended ENMM from a single-area to a multi-area model to develop a neural mass model of the entire brain. To this end, we derived a nonlinear state-space representation of the multi-area model. In Parts I and II of these two companion papers (henceforth called Part I and Part II), we develop and evaluate a variational Bayesian expectation maximization (VBEM) method to estimate parameters of multi-area ENMM (MEN) using E/MEG data. In Part I, we derive a state-space representation of MEN and use VBEM method for model inversion (parameter estimation). We evaluate and validate performance of VBEM method for model inversion of MEN using simulation studies in various signal-to-noise ratios. Details of VBEM method are presented in Part II. The proposed approach provides a useful technique for analyzing effective connectivity using non-invasive EEG and MEG methods.

Predicting stimulus-rate sensitivity of human somatosensory fMRI signals with MEG

With increasing stimulus rate (SR), cortical EEG and MEG responses typically decrease in amplitude whereas BOLD fMRI signals increase. To address this discrepancy, we predicted BOLD responses with squared MEG waveforms using a recently proposed energy-density model. Tactile stimuli were delivered to finger tips at SRs of 1, 4, or 10 Hz in successive 25-s blocks, and brain signals were detected from area 3b of the primary somatosensory cortex of nine healthy adults using a 306-channel whole-scalp neuromagnetometer and a 3-T fMRI magnet. The main MEG deflections decreased in amplitude as a function of SR, whereas the BOLD signals increased from 1- to 4-Hz SR, with no further change at 10 Hz. MEG energy densities, obtained over the whole stimulus train and convolved with different hemodynamic response functions, predicted both the shape and amplitude of the BOLD signals well, and incorporation of nonlinear terms into the model did not offer any further advantage. Thus, squared MEG waveforms obtained over the entire stimulus train provided an appropriate estimate of area 3b neuronal activity associated with the BOLD signal.