Frozen tears II

Russell, J. [Ed. & designer]. Including texts by Kathy Acker, Dominique Auch, Dennis Cooper, Trinie Dalton, Sue De Beer, Felix Ensslin, Dan Fox, Matthew Greene, Pierre Guyotat, Rachel Howe, Kevin Killian, Christopher Knowles, Gean Moreno, J.P. Munro, Paulina Olowska, Damon Packard, Allison Smith, Banks Violette, Benjamin Weissman, & Thom Wolf. 832 pages.

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.

Towards a model-based integration of co-registered electroencephalography/functional magnetic resonance imaging data with realistic neural population meshes

Brain activity can be measured with several non-invasive neuroimaging modalities, but each modality has inherent limitations with respect to resolution, contrast and interpretability. It is hoped that multimodal integration will address these limitations by using the complementary features of already available data. However, purely statistical integration can prove problematic owing to the disparate signal sources. As an alternative, we propose here an advanced neural population model implemented on an anatomically sound cortical mesh with freely adjustable connectivity, which features proper signal expression through a realistic head model for the electroencephalogram (EEG), as well as a haemodynamic model for functional magnetic resonance imaging based on blood oxygen level dependent contrast (fMRI BOLD). It hence allows simultaneous and realistic predictions of EEG and fMRI BOLD from the same underlying model of neural activity. As proof of principle, we investigate here the influence on simulated brain activity of strengthening visual connectivity. In the future we plan to fit multimodal data with this neural population model. This promises novel, model-based insights into the brain's activity in sleep, rest and task conditions.

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.

The expression of ferritin, lactoferrin, transferrin receptor and solute carrier family 11A1 in the host response to BCG-vaccination and Mycobacterium tuberculosis challenge

Iron is an essential cofactor for both mycobacterial growth during infection and for a successful protective immune response by the host. The immune response partly depends on the regulation of iron by the host, including the tight control of expression of the iron-storage protein, ferritin. BCG vaccination can protect against disease following Mycobacterium tuberculosis infection, but the mechanisms of protection remain unclear. To further explore these mechanisms, splenocytes from BCG-vaccinated guinea pigs were stimulated ex vivo with purified protein derivative from M. tuberculosis and a significant down-regulation of ferritin light- and heavy-chain was measured by reverse-transcription quantitative-PCR (P ≤0.05 and ≤0.01, respectively). The mechanisms of this down-regulation were shown to involve TNFα and nitric oxide. A more in depth analysis of the mRNA expression profiles, including genes involved in iron metabolism, was performed using a guinea pig specific immunological microarray following ex vivo infection with M. tuberculosis of splenocytes from BCG-vaccinated and naïve guinea pigs. M. tuberculosis infection induced a pro-inflammatory response in splenocytes from both groups, resulting in down-regulation of ferritin (P ≤0.05). In addition, lactoferrin (P ≤0.002), transferrin receptor (P ≤0.05) and solute carrier family 11A1 (P ≤0.05), were only significantly down-regulated after infection of the splenocytes from BCG-vaccinated animals. The results show that expression of iron-metabolism genes is tightly regulated as part of the host response to M. tuberculosis infection and that BCG-vaccination enhances the ability of the host to mount an iron-restriction response which may in turn help to combat invasion by mycobacteria.