EEG source imaging of brain states using spatiotemporal regression

Relating measures of electroencephalography (EEG) back to the underlying sources is a long-standing inverse problem. Here we propose a new method to estimate the EEG sources of identified electrophysiological states that represent spontaneous activity, or are evoked by a stimulus, or caused by disease or disorder. Our method has the unique advantage of seamlessly integrating a statistical significance of the source estimate while efficiently eliminating artifacts (e.g., due to eye blinks, eye movements, bad electrodes). After determining the electrophysiological states in terms of stable topographies using established methods (e.g.: ICA, PCA, k-means, epoch average), we propose to estimate these states' time courses through spatial regression of a General Linear Model (GLM). These time courses are then used to find EEG sources that have a similar time-course (using temporal regression of a second GLM). We validate our method using both simulated and experimental data. Simulated data allows us to assess the difference between source maps obtained by the proposed method and those obtained by applying conventional source imaging of the state topographies. Moreover, we use data from 7 epileptic patients (9 distinct epileptic foci localized by intracranial EEG) and 2 healthy subjects performing an eyes-open/eyes-closed task to elicit activity in the alpha frequency range. Our results indicate that the proposed EEG source imaging method accurately localizes the sources for each of the electrical brain states. Furthermore, our method is particularly suited for estimating the sources of EEG resting states or otherwise weak spontaneous activity states, a problem not adequately solved before. (C) 2014 Elsevier Inc. All rights reserved.

Blood flow velocity field estimation via spatial regression with PDE penalization

Fabio Nobile

We propose an innovative method for the accurate estimation of surfaces and spatial fields when prior knowledge of the phenomenon under study is available. The prior knowledge included in the model derives from physics, physiology, or mechanics of the problem at hand, and is formalized in terms of a partial differential equation governing the phenomenon behavior, as well as conditions that the phenomenon has to satisfy at the boundary of the problem domain. The proposed models exploit advanced scientific computing techniques and specifically make use of the finite element method. The estimators have a penalized regression form and the usual inferential tools are derived. Both the pointwise and the areal data frameworks are considered. The driving application concerns the estimation of the blood flow velocity field in a section of a carotid artery, using data provided by echo-color Doppler. This applied problem arises within a research project that aims at studying atherosclerosis pathogenesis. Supplementary materials for this article are available online.

American Statistical Association2015

Planar microcoil-based microfluidic NMR probes

Pierre-André Besse, Giovanni Boero, Nico de Rooij, Klaus Ehrmann, Charles Massin, Radivoje Popovic, Franck Vincent

Microfabricated small-volume NMR probes consisting of electroplated planar microcoils integrated on a glass substrate with etched microfluidic channels are fabricated and tested. 1H NMR spectra are acquired at 300MHz with three different probes having observed sample volumes of respectively 30, 120, and 470nL. The achieved sensitivity enables acquisition of an 1H spectrum of 160μg sucrose in D2O, corresponding to a proof-of-concept for on-chip NMR spectroscopy. Increase of mass-sensitivity with coil diameter reduction is demonstrated experimentally for planar microcoils. Models that enable quantitative prediction of the signal-to-noise ratio and of the influence of microfluidic channel geometry on spectral resolution are presented and successfully compared to the experimental data. The main factor presently limiting sensitivity for high-resolution applications is identified as being probe-induced static magnetic field distortions. Finally, based on the presented model and measured data, future performance of planar microcoil-based microfluidic NMR probes is extrapolated and discussed. © 2003 Elsevier Science (USA). All rights reserved.

2003

Accelerating Range Queries For Brain Simulations

Anastasia Ailamaki, Laurynas Biveinis, Thomas Heinis, Henry Markram, Felix Schürmann, Farhan Tauheed

Neuroscientists increasingly use computational tools to build and simulate models of the brain. The amounts of data involved in these simulations are immense and the importance of their efficient management is primordial. One particular problem in analyzing this data is the scalable execution of range queries on spatial models of the brain. Known indexing approaches do not perform well, even on today's small models containing only few million densely packed spatial elements. The problem of current approaches is that with the increasing level of detail in the models, the overlap in the tree structure also increases, ultimately slowing down query execution. The neuroscientists' need to work with bigger and more importantly, with increasingly detailed (denser) models, motivates us to develop a new indexing approach. To this end we developed FLAT, a scalable indexing approach for dense data sets. We based the development of FLAT on the key observation that current approaches suffer from overlap in case of dense data sets. We hence designed FLAT as an approach with two phases, each independent of density. Our experimental results confirm that FLAT achieves independence from data set size as well as density and also outperforms R-Tree variants in terms of I/O overhead from a factor of two up to eight.

2012