02948nas a2200445   4500008004100000022001400041245008900055210006900144260001200213300001000225490000900235520165000244653002501894653001201919653002201931653001001953653002001963653002701983653002702010653003802037653001102075653002602086653004002112653000902152653000902161653002302170653002902193653003602222653002502258653002902283653001702312653002202329100001702351700001702368700001102385700001602396700002502412700001702437856004802454       2013                        eng d  a1872-624000aNovel inter-hemispheric white matter connectivity in the BTBR mouse model of autism.0 aNovel interhemispheric white matter connectivity in the BTBR mou  c06/2013  a26-330 v15133 aAlterations 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.10aAnalysis of Variance10aAnimals10aAutistic Disorder10aBrain10aCorpus Callosum10aDisease Models, Animal10aElectroencephalography10aEnzyme-Linked Immunosorbent Assay10aFemale10aFunctional Laterality10aImage Processing, Computer-Assisted10aMale10aMice10aMice, Inbred C57BL10aMice, Neurologic Mutants10aMicrotubule-Associated Proteins10aMyelin Basic Protein10aNerve Fibers, Myelinated10aNeuroimaging10aSpectrum Analysis1 aMiller, V, M1 aGupta, Disha1 aNeu, N1 aCotroneo, A1 aBoulay, Chadwick, B.1 aSeegal, R, F  uhttp://www.ncbi.nlm.nih.gov/pubmed/2357070701698nas a2200265   4500008004100000022001400041245008600055210006900141260001200210300000900222490000900231520087600240653001501116653002801131653003301159653002701192653001301219653001101232653003501243653003101278653003201309100002601341700001701367856004801384       2009                        eng d  a1557-170X00aSeizure prediction for epilepsy using a multi-stage phase synchrony based system.0 aSeizure prediction for epilepsy using a multistage phase synchro  c09/2009  a25-80 v20093 aSeizure 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.10aAlgorithms10aArtificial Intelligence10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aHumans10aPattern Recognition, Automated10aReproducibility of Results10aSensitivity and Specificity1 aJames, Christopher, J1 aGupta, Disha  uhttp://www.ncbi.nlm.nih.gov/pubmed/1996510401751nas a2200229   4500008004100000022001400041245012300055210006900178260001200247300001100259490000900270520101400279653001501293653002701308653001101335653003001346653001301376653004101389100001701430700002601447856004801473       2007                        eng d  a1557-170X00aNarrowband vs. broadband phase synchronization analysis applied to independent components of ictal and interictal EEG.0 aNarrowband vs broadband phase synchronization analysis applied t  c08/2007  a3864-70 v20073 aThis 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.10aAlgorithms10aElectroencephalography10aHumans10aPredictive Value of Tests10aSeizures10aSignal Processing, Computer-Assisted1 aGupta, Disha1 aJames, Christopher, J  uhttp://www.ncbi.nlm.nih.gov/pubmed/1800284202201nas a2200289   4500008004100000022001400041245012000055210006900175260000900244300001100253490001200264520126500276653001501541653002801556653003301584653002701617653001301644653001101657653003501668653003301703653003101736653003201767100002601799700002101825700001701846856004801863       2007                        eng d  a1557-170X00aSpace-time ICA versus Ensemble ICA for ictal EEG analysis with component differentiation via Lempel-Ziv complexity.0 aSpacetime ICA versus Ensemble ICA for ictal EEG analysis with co  c2007  a5473-60 v08/20073 aIn 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.10aAlgorithms10aArtificial Intelligence10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aHumans10aPattern Recognition, Automated10aPrincipal Component Analysis10aReproducibility of Results10aSensitivity and Specificity1 aJames, Christopher, J1 aAbásolo, Daniel1 aGupta, Disha  uhttp://www.ncbi.nlm.nih.gov/pubmed/18003250