Alex Ossadtchi

Automated interictal spike detection and source localization in magnetoencephalography using independent components analysis and spatio-temporal clustering

OBJECTIVE Magnetoencephalography (MEG) dipole localization of epileptic spikes is useful in epilepsy surgery for mapping the extent of abnormal cortex and to focus intracranial electrodes. Visually analyzing large amounts of data produces fatigue and error. Most automated techniques are based on matching of interictal spike templates or predictive filtering of the data and do not explicitly include source localization as part of the analysis. This leads to poor sensitivity versus specificity characteristics. We describe a fully automated method that combines time-series analysis with source localization to detect clusters of focal neuronal current generators within the brain that produce interictal spike activity. METHODS We first use an ICA (independent components analysis) method to decompose the multichannel MEG data and identify those components that exhibit spike-like characteristics. From these detected spikes we then find those whose spatial topographies across the array are consistent with focal neural sources, and determine the foci of equivalent current dipoles and their associated time courses. We then perform a clustering of the localized dipoles based on distance metrics that takes into consideration both their locations and time courses. The final step of refinement consists of retaining only those clusters that are statistically significant. The average locations and time series from significant clusters comprise the final output of our method. RESULTS AND SIGNIFICANCE Data were processed from 4 patients with partial focal epilepsy. In all three subjects for whom surgical resection was performed, clusters were found in the vicinity of the resectioned area. CONCLUSIONS The presented procedure is promising and likely to be useful to the physician as a more sensitive, automated and objective method to help in the localization of the interictal spike zone of intractable partial seizures. The final output can be visually verified by neurologists in terms of both the location and distribution of the dipole clusters and their associated time series. Due to the clinical relevance and demonstrated promise of this method, further investigation of this approach is warranted.

Genes regulated by learning in the hippocampus

The enduring changes in long‐term memory probably depend on regulation of gene expression in the hippocampus. To seek genes regulated by learning, we used microarray technology to compare hippocampal gene expression in mice undergoing training in the Morris water maze and control mice forced to swim for the same period in the absence of a hidden platform. ANOVA was employed to prioritize genes for further study, and three genes were confirmed by real‐time PCR as being regulated during learning. One of the genes was the α subunit of the platelet‐derived growth factor receptor (Pdgfra); another showed homology to DnaJ and cAMP response element‐binding protein 2 (CREB2); and a third was novel. These genes may provide useful insights into the molecular mechanisms of hippocampal learning.

Finding new candidate genes for learning and memory

The genetic mechanisms underlying learning and memory remain mysterious, but many of the genes are likely to be expressed in the hippocampus, a region pivotal to this process. We used a 9,000 gene microarray to examine differences in hippocampal gene expression between two F1 hybrid mouse strains that perform well on the Morris water maze and two inbred strains that perform poorly. This resulted in identification of 27 differentially expressed genes, which could be used to place the F1 hybrid and inbred strains into separate clusters based on singular value decomposition. Most of the genes have unknown function, but those with known functions may provide clues to the molecular mechanisms of learning. Using multiple strains to narrow down the number of candidate genes should be a useful general approach to genome‐wide studies of behavioral and other complex traits.

Hidden Markov modelling of spike propagation from interictal MEG data

For patients with partial epilepsy, automatic spike detection techniques applied to interictal MEG data often discover several potentially epileptogenic brain regions. An important determination in treatment planning is which of these detected regions are most likely to be the primary sources of epileptogenic activity. Analysis of the patterns of propagation activity between the detected regions may allow for detection of these primary epileptic foci. We describe the use of hidden Markov models (HMM) for estimation of the propagation patterns between several spiking regions from interictal MEG data. Analysis of the estimated transition probability matrix allows us to make inferences regarding the propagation pattern of the abnormal activity and determine the most likely region of its origin. The proposed HMM paradigm allows for a simple incorporation of the spike detector specificity and sensitivity characteristics. We develop bounds on performance for the case of perfect detection. We also apply the technique to simulated data sets in order to study the robustness of the method to the non-ideal specificity-sensitivity characteristics of the event detectors and compare results with the lower bounds. Our study demonstrates robustness of the proposed technique to event detection errors. We conclude with an example of the application of this method to a single patient.

High-resolution voxelation mapping of human and rodent brain gene expression

Voxelation allows high-throughput acquisition of multiple volumetric images of brain gene expression, similar to those obtained from biomedical imaging systems. To obtain these images, the method employs analysis of spatially registered voxels (cubes). For creation of high-resolution maps using voxelation, relatively small voxel sizes are necessary and instruments will be required for semiautomated harvesting of such voxels. Here, we describe two devices that allow spatially registered harvesting of voxels from the human and rodent brain, giving linear resolutions of 3.3 and 1 mm, respectively. Gene expression patterns obtained using these devices showed good agreement with known expression patterns. The voxelation instruments and their future iterations represent a valuable approach to the genome scale acquisition of gene expression patterns in the human and rodent brain.

Statistical Analysis of Multiplex Brain Gene Expression Images

Analysis of variance (ANOVA) was employed to investigate 9,000 gene expression patterns from brains of both normal mice and mice with a pharmacological model of Parkinsons disease (PD). The data set was obtained using voxelation, a method that allows high-throughput acquisition of 3D gene expression patterns through analysis of spatially registered voxels (cubes). This method produces multiple volumetric maps of gene expression analogous to the images reconstructed in biomedical imaging systems. The ANOVA model was compared to the results from singular value decomposition (SVD) by using the first 42 singular vectors of the data matrix, a number equal to the rank of the ANOVA model. The ANOVA was also compared to the results from non-parametric statistics. Lastly, images were obtained for a subset of genes that emerged from the ANOVA as significant. The results suggest that ANOVA will be a valuable framework for insights into the large number of gene expression patterns obtained from voxelation.

Automated interictal spike detection and source localization in MEG using ICA and spatial-temporal clustering

MEG dipole localization of epileptic spikes is useful in epilepsy surgery for mapping the extent of abnormal cortex and to focus intracranial electrodes. Visually analyzing large amounts of data produces fatigue and error. Most existing methods are based on matching of interictal spike templates or predictive filtering of the data and do not explicitly include source localization as part of the analysis. We describe a fully automated method that combines time-series analysis with source localization to detect clusters of focal generators within the brain that produce interictal spike activity. We first use an ICA (Independent Component Analysis) method to decompose the multichannel MEG data and identify those components that exhibit spikelike characteristics. From these detected spikes we then find those whose spatial topographies across the array are consistent with focal neural sources and determine the foci of equivalent current dipoles and their associated time courses. Finally we perform a clustering of the sources based on distance metrics that takes into consideration both their locations and time courses. Tight clusters of equivalent current dipoles with a fit of greater than 95% are considered to be the reliably determined sources and are the final output of our detection scheme.