Realistic mean field forward predictions for the integration of co-registered EEG/fMRI

Brain activity can be measured non-invasively with functional imaging techniques. Each pixel in such an image represents a neural mass of about 105 to 107 neurons. Mean field models (MFMs) approximate their activity by averaging out neural variability while retaining salient underlying features, like neurotransmitter kinetics. However, MFMs incorporating the regional variability, realistic geometry and connectivity of cortex have so far appeared intractable. This lack of biological realism has led to a focus on gross temporal features of the EEG. We address these impediments and showcase a "proof of principle" forward prediction of co-registered EEG/fMRI for a full-size human cortex in a realistic head model with anatomical connectivity, see figure 1. MFMs usually assume homogeneous neural masses, isotropic long-range connectivity and simplistic signal expression to allow rapid computation with partial differential equations. But these approximations are insufficient in particular for the high spatial resolution obtained with fMRI, since different cortical areas vary in their architectonic and dynamical properties, have complex connectivity, and can contribute non-trivially to the measured signal. Our code instead supports the local variation of model parameters and freely chosen connectivity for many thousand triangulation nodes spanning a cortical surface extracted from structural MRI. This allows the introduction of realistic anatomical and physiological parameters for cortical areas and their connectivity, including both intra- and inter-area connections. Proper cortical folding and conduction through a realistic head model is then added to obtain accurate signal expression for a comparison to experimental data. To showcase the synergy of these computational developments, we predict simultaneously EEG and fMRI BOLD responses by adding an established model for neurovascular coupling and convolving "Balloon-Windkessel" hemodynamics. We also incorporate regional connectivity extracted from the CoCoMac database [1]. Importantly, these extensions can be easily adapted according to future insights and data. Furthermore, while our own simulation is based on one specific MFM [2], the computational framework is general and can be applied to models favored by the user. Finally, we provide a brief outlook on improving the integration of multi-modal imaging data through iterative fits of a single underlying MFM in this realistic simulation framework.

Connecting mean field models of neural activity to EEG and fMRI data

Progress in functional neuroimaging of the brain increasingly relies on the integration of data from complementary imaging modalities in order to improve spatiotemporal resolution and interpretability. However, the usefulness of merely statistical combinations is limited, since neural signal sources differ between modalities and are related non-trivially. We demonstrate here that a mean field model of brain activity can simultaneously predict EEG and fMRI BOLD with proper signal generation and expression. Simulations are shown using a realistic head model based on structural MRI, which includes both dense short-range background connectivity and long-range specific connectivity between brain regions. The distribution of modeled neural masses is comparable to the spatial resolution of fMRI BOLD, and the temporal resolution of the modeled dynamics, importantly including activity conduction, matches the fastest known EEG phenomena. The creation of a cortical mean field model with anatomically sound geometry, extensive connectivity, and proper signal expression is an important first step towards the model-based integration of multimodal neuroimages.

Dynamic causal modeling applied to fMRI data shows high reliability

Sensitivity, specificity, and reproducibility are vital to interpret neuroscientific results from functional magnetic resonance imaging (fMRI) experiments. Here we examine the scan–rescan reliability of the percent signal change (PSC) and parameters estimated using Dynamic Causal Modeling (DCM) in scans taken in the same scan session, less than 5 min apart. We find fair to good reliability of PSC in regions that are involved with the task, and fair to excellent reliability with DCM. Also, the DCM analysis uncovers group differences that were not present in the analysis of PSC, which implies that DCM may be more sensitive to the nuances of signal changes in fMRI data.

Efficient modeling and inference for event-related fMRI data

Event-related functional magnetic resonance imaging (efMRI) has emerged as a powerful technique for detecting brains' responses to presented stimuli. A primary goal in efMRI data analysis is to estimate the Hemodynamic Response Function (HRF) and to locate activated regions in human brains when specific tasks are performed. This paper develops new methodologies that are important improvements not only to parametric but also to nonparametric estimation and hypothesis testing of the HRF. First, an effective and computationally fast scheme for estimating the error covariance matrix for efMRI is proposed. Second, methodologies for estimation and hypothesis testing of the HRF are developed. Simulations support the effectiveness of our proposed methods. When applied to an efMRI dataset from an emotional control study, our method reveals more meaningful findings than the popular methods offered by AFNI and FSL. (C) 2008 Elsevier B.V. All rights reserved.

The neural basis of maternal responsiveness to infants: an fMRI study

Using fMRI, we examined the neural correlates of maternal responsiveness. Ten healthy mothers viewed alternating blocks of video: (i) 40 s of their own infant; (ii) 20 s of a neutral video; (iii) 40 s of an unknown infant and (iv) 20 s of neutral video, repeated 4 times. Predominant BOLD signal change to the contrast of infants minus neutral stimulus occurred in bilateral visual processing regions BA minus neutral stimulus occurred in bilateral visual processing regions (BA 38), left amygdala and visual cortex (BA 19), and to the unknown infant minus own infant contrast in bilateral orbitofrontal cortex (BA 10,47) and medial prefrontal cortex (BA 8). These findings suggest that amygdala and temporal pole may be key sites in mediating a mother's response to her infant and reaffirms their importance in face emotion processing and social behaviour.

fMRI delineation of working memory for emotional prosody in the brain: commonalities with the lexico-semantic emotion network

Decoding emotional prosody is crucial for successful social interactions, and continuous monitoring of emotional intent via prosody requires working memory. It has been proposed by Ross and others that emotional prosody cognitions in the right hemisphere are organized in an analogous fashion to propositional language functions in the left hemisphere. This study aimed to test the applicability of this model in the context of prefrontal cortex working memory functions. BOLD response data were therefore collected during performance of two emotional working memory tasks by participants undergoing fMRI. In the prosody task, participants identified the emotion conveyed in pre-recorded sentences, and working memory load was manipulated in the style of an N-back task. In the matched lexico-semantic task, participants identified the emotion conveyed by sentence content. Block-design neuroimaging data were analyzed parametrically with SPM5. At first, working memory for emotional prosody appeared to be right-lateralized in the PFC, however, further analyses revealed that it shared much bilateral prefrontal functional neuroanatomy with working memory for lexico-semantic emotion. Supplementary separate analyses of males and females suggested that these language functions were less bilateral in females, but their inclusion did not alter the direction of laterality. It is concluded that Ross et al.'s model is not applicable to prefrontal cortex working memory functions, that evidence that working memory cannot be subdivided in prefrontal cortex according to material type is increased, and that incidental working memory demands may explain the frontal lobe involvement in emotional prosody comprehension as revealed by neuroimaging studies. (c) 2007 Elsevier Inc. All rights reserved.

Motor brain regions are involved in the encoding of delayed intentions: a fMRI study

In studies of prospective memory, recall of the content of delayed intentions is normally excellent, probably because they contain actions that have to be enacted at a later time. Action words encoded for later enactment are more accessible from memory than those encoded for later verbal report [Freeman, J.E., and Ellis, J.A. 2003a. The representation of delayed intentions: A prospective subject-performed task? Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 976-992.]. As this higher assessibility is lost when the intended actions have to be enacted during encoding, or when a motor interference task is introduced concurrent to intention encoding, Freeman and Ellis suggested that the advantage of to-be-enacted actions is due to additional preparatory motor operations during encoding. Accordingly, in a fMRI study with 10 healthy young participants, we investigated whether motor brain regions are differentially activated during verbal encoding of actions for later enactment with the right hand in contrast to verbal encoding of actions for later verbal report. We included an additional condition of verbal encoding of abstract verbs for later verbal report to investigate whether the semantic motor information inherent in action verbs in contrast to abstract verbs activates motor brain regions different from those involved in the verbal encoding of actions for later enactment. Differential activation for the verbal encoding of to-be-enacted actions in contrast to to-be-reported actions was found in brain regions known to be involved in covert motor preparation for hand movements, i.e. the postcentral gyrus, the precuneus, the dorsal and ventral premotor cortex, the posterior middle temporal gyrus and the inferior parietal lobule. There was no overlap between these brain regions and those differentially activated during the verbal encoding of actions in contrast to abstract verbs for later verbal report. Consequently, the results of this fMRI study suggest the presence of preparatory motor operations during the encoding of delayed intentions requiring a future motor response, which cannot be attributed to semantic information inherent to action verbs. (c) 2006 Elsevier B.V. All rights reserved.

Robustness of mutual information to inter-subject variability for automatic artefact removal from EEG

The externally recorded electroencephalogram (EEG) is contaminated with signals that do not originate from the brain, collectively known as artefacts. Thus, EEG signals must be cleaned prior to any further analysis. In particular, if the EEG is to be used in online applications such as Brain-Computer Interfaces (BCIs) the removal of artefacts must be performed in an automatic manner. This paper investigates the robustness of Mutual Information based features to inter-subject variability for use in an automatic artefact removal system. The system is based on the separation of EEG recordings into independent components using a temporal ICA method, RADICAL, and the utilisation of a Support Vector Machine for classification of the components into EEG and artefact signals. High accuracy and robustness to inter-subject variability is achieved.