TY - JOUR T1 - Novel inter-hemispheric white matter connectivity in the BTBR mouse model of autism. JF - Brain Res Y1 - 2013 A1 - Miller, V M A1 - Disha Gupta A1 - Neu, N A1 - Cotroneo, A A1 - Chadwick B. Boulay A1 - Seegal, R F KW - Analysis of Variance KW - Animals KW - Autistic Disorder KW - Brain KW - Corpus Callosum KW - Disease Models, Animal KW - Electroencephalography KW - Enzyme-Linked Immunosorbent Assay KW - Female KW - Functional Laterality KW - Image Processing, Computer-Assisted KW - Male KW - Mice KW - Mice, Inbred C57BL KW - Mice, Neurologic Mutants KW - Microtubule-Associated Proteins KW - Myelin Basic Protein KW - Nerve Fibers, Myelinated KW - Neuroimaging KW - Spectrum Analysis AB - Alterations in the volume, density, connectivity and functional activation of white matter tracts are reported in some individuals with autism and may contribute to their abnormal behaviors. The BTBR (BTBR T+tf/J) inbred strain of mouse, is used to model facets of autism because they develop low social behaviors, stereotypical and immune changes similar to those found in people with autism. Previously, it was thought a total absence of corpus callosal interhemispheric connective tissues in the BTBR mice may underlie their abnormal behaviors. However, postnatal lesions of the corpus callosum do not precipitate social behavioral problems in other strains of mice suggesting a flaw in this theory. In this study we used digital pathological methods to compare subcortical white matter connective tracts in the BTBR strain of mice with those found in the C57Bl/6 mouse and those reported in a standardized mouse brain atlas. We report, for the first time, a novel connective subcortical interhemispheric bridge of tissue in the posterior, but not anterior, cerebrum of the BTBR mouse. These novel connective tissues are comprised of myelinated fibers, with reduced myelin basic protein levels (MBP) compared to levels in the C57Bl/6 mouse. We used electrophysiological analysis and found increased inter-hemispheric connectivity in the posterior hemispheres of the BTBR strain compared with the anterior hemispheres. The conduction velocity was slower than that reported in normal mice. This study shows there is novel abnormal interhemispheric connectivity in the BTBR strain of mice, which may contribute to their behavioral abnormalities. VL - 1513 UR - http://www.ncbi.nlm.nih.gov/pubmed/23570707 ER - TY - JOUR T1 - Seizure prediction for epilepsy using a multi-stage phase synchrony based system. JF - Conf Proc IEEE Eng Med Biol Soc Y1 - 2009 A1 - Christopher J James A1 - Disha Gupta KW - Algorithms KW - Artificial Intelligence KW - Diagnosis, Computer-Assisted KW - Electroencephalography KW - Epilepsy KW - Humans KW - Pattern Recognition, Automated KW - Reproducibility of Results KW - Sensitivity and Specificity AB - Seizure onset prediction in epilepsy is a challenge which is under investigation using many and varied signal processing techniques. Here we present a multi-stage phase synchrony based system that brings to bear the advantages of many techniques in each substage. The 1(st) stage of the system unmixes continuous long-term (2-4 days) multichannel scalp EEG using spatially constrained Independent Component Analysis and estimates the long term significant phase synchrony dynamics of narrowband (2-8 Hz and 8-14 Hz) seizure components. It then projects multidimensional features onto a 2-D map using Neuroscale and evaluates the probability of predictive events using Gaussian Mixture Models. We show the possibility of seizure onset prediction within a prediction window of 35-65 minutes with a sensitivity of 65-100% and specificity of 65-80% across epileptic patients. VL - 2009 UR - http://www.ncbi.nlm.nih.gov/pubmed/19965104 ER - TY - JOUR T1 - Narrowband vs. broadband phase synchronization analysis applied to independent components of ictal and interictal EEG. JF - Conf Proc IEEE Eng Med Biol Soc Y1 - 2007 A1 - Disha Gupta A1 - Christopher J James KW - Algorithms KW - Electroencephalography KW - Humans KW - Predictive Value of Tests KW - Seizures KW - Signal Processing, Computer-Assisted AB - This paper presents a comparison of the use of broadband and narrow band signals for phase synchronization analysis as applied to Independent Components of ictal and interictal scalp EEG in the context of seizure onset detection and prediction. Narrow band analysis for phase synchronization is found to be better performed in the present context than the broad band signal analysis. It has been observed that the phase synchronization of Independent Components in a narrow band (particularly the Gamma band) shows a prominent trend of increasing and decreasing synchronization at seizure onset near the epileptogenic area (spatially). This information is not always found to be consistent in analysis with the raw EEG signals, which may show spurious synchronization happening due to volume conduction effects. These observations lead us to believe that tracking changes in phase synchronization of narrow band activity, on continuous data records will be of great value in the context of seizure prediction. VL - 2007 UR - http://www.ncbi.nlm.nih.gov/pubmed/18002842 ER - TY - JOUR T1 - Space-time ICA versus Ensemble ICA for ictal EEG analysis with component differentiation via Lempel-Ziv complexity. JF - Conf Proc IEEE Eng Med Biol Soc Y1 - 2007 A1 - Christopher J James A1 - Abásolo, Daniel A1 - Disha Gupta KW - Algorithms KW - Artificial Intelligence KW - Diagnosis, Computer-Assisted KW - Electroencephalography KW - Epilepsy KW - Humans KW - Pattern Recognition, Automated KW - Principal Component Analysis KW - Reproducibility of Results KW - Sensitivity and Specificity AB - In this proof-of-principle study we analyzed intracranial electroencephalogram recordings in patients with intractable focal epilepsy. We contrast two implementations of Independent Component Analysis (ICA) - Ensemble (or spatial) ICA (E-ICA) and Space-Time ICA (ST-ICA) in separating out the ictal components underlying the measurements. In each case we assess the outputs of the ICA algorithms by means of a non-linear method known as the Lempel-Ziv (LZ) complexity. LZ complexity quantifies the complexity of a time series and is well suited to the analysis of non-stationary biomedical signals of short length. Our results show that for small numbers of intracranial recordings, standard E-ICA results in marginal improvements in the separation as measured by the LZ complexity changes. ST-ICA using just 2 recording channels both near and far from the epileptic focus result in more distinct ictal components--although at this stage there is a subjective element to the separation process for ST-ICA. Our results are promising showing that it is possible to extract meaningful information from just 2 recording electrodes through ST-ICA, even if they are not directly over the seizure focus. This work is being further expanded for seizure onset analysis. VL - 08/2007 UR - http://www.ncbi.nlm.nih.gov/pubmed/18003250 ER -