Zeynep Acar

EEGLAB, SIFT, NFT, BCILAB, and ERICA: new tools for advanced EEG processing

We describe a set of complementary EEG data collection and processing tools recently developed at the Swartz Center for Computational Neuroscience (SCCN) that connect to and extend the EEGLAB software environment, a freely available and readily extensible processing environment running under Matlab. The new tools include (1) a new and flexible EEGLAB STUDY design facility for framing and performing statistical analyses on data from multiple subjects; (2) a neuroelectromagnetic forward head modeling toolbox (NFT) for building realistic electrical head models from available data; (3) a source information flow toolbox (SIFT) for modeling ongoing or event-related effective connectivity between cortical areas; (4) a BCILAB toolbox for building online brain-computer interface (BCI) models from available data, and (5) an experimental real-time interactive control and analysis (ERICA) environment for real-time production and coordination of interactive, multimodal experiments.

Neuroelectromagnetic Forward Head Modeling Toolbox

This paper introduces a Neuroelectromagnetic Forward Head Modeling Toolbox (NFT) running under MATLAB (The Mathworks, Inc.) for generating realistic head models from available data (MRI and/or electrode locations) and for computing numerical solutions for the forward problem of electromagnetic source imaging. The NFT includes tools for segmenting scalp, skull, cerebrospinal fluid (CSF) and brain tissues from T1-weighted magnetic resonance (MR) images. The Boundary Element Method (BEM) is used for the numerical solution of the forward problem. After extracting segmented tissue volumes, surface BEM meshes can be generated. When a subject MR image is not available, a template head model can be warped to measured electrode locations to obtain an individualized head model. Toolbox functions may be called either from a graphic user interface compatible with EEGLAB (http://sccn.ucsd.edu/eeglab), or from the MATLAB command line. Function help messages and a user tutorial are included. The toolbox is freely available under the GNU Public License for noncommercial use and open source development.

Modeling cortical source dynamics and interactions during seizure

Mapping the dynamics and spatial topography of brain source processes critically involved in initiating and propagating seizure activity is critical for effective epilepsy diagnosis, intervention, and treatment. In this report we analyze neuronal dynamics before and during epileptic seizures using adaptive multivariate autoregressive (VAR) models applied to maximally-independent (ICA) sources of intracranial EEG (iEEG, ECoG) data recorded from subdural electrodes implanted in a human patient for evaluation of surgery for epilepsy. We visualize the spatial distribution of causal sources and sinks of ictal activity on the cortical surface (gyral and sulcal) using a novel combination of multivariate Granger-causal and graph-theoretic metrics combined with distributed source localization by Sparse Bayesian Learning applied to a multi-scale patch basis. This analysis reveals and visualizes distinct, seizure stage-dependent shifts in inter-component spatiotemporal dynamics and connectivity including the clinically-identified epileptic foci.

Simultaneous head tissue conductivity and EEG source location estimation.

Accurate electroencephalographic (EEG) source localization requires an electrical head model incorporating accurate geometries and conductivity values for the major head tissues. While consistent conductivity values have been reported for scalp, brain, and cerebrospinal fluid, measured brain-to-skull conductivity ratio (BSCR) estimates have varied between 8 and 80, likely reflecting both inter-subject and measurement method differences. In simulations, mis-estimation of skull conductivity can produce source localization errors as large as 3cm. Here, we describe an iterative gradient-based approach to Simultaneous tissue Conductivity And source Location Estimation (SCALE). The scalp projection maps used by SCALE are obtained from near-dipolar effective EEG sources found by adequate independent component analysis (ICA) decomposition of sufficient high-density EEG data. We applied SCALE to simulated scalp projections of 15cm(2)-scale cortical patch sources in an MR image-based electrical head model with simulated BSCR of 30. Initialized either with a BSCR of 80 or 20, SCALE estimated BSCR as 32.6. In Adaptive Mixture ICA (AMICA) decompositions of (45-min, 128-channel) EEG data from two young adults we identified sets of 13 independent components having near-dipolar scalp maps compatible with a single cortical source patch. Again initialized with either BSCR 80 or 25, SCALE gave BSCR estimates of 34 and 54 for the two subjects respectively. The ability to accurately estimate skull conductivity non-invasively from any well-recorded EEG data in combination with a stable and non-invasively acquired MR imaging-derived electrical head model could remove a critical barrier to using EEG as a sub-cm(2)-scale accurate 3-D functional cortical imaging modality.

Electrocortical source imaging of intracranial EEG data in epilepsy

Here we report first results of numerical methods for modeling the dynamic structure and evolution of epileptic seizure activity in an intracranial subdural electrode recording from a patient with partial refractory epilepsy. A 16-min dataset containing two seizures was decomposed using up to five competing adaptive mixture independent component analysis (AMICA) models. Multiple models modeled early or late ictal, or pre- or post-ictal periods in the data, respectively. To localize sources, a realistic Boundary Element Method (BEM) head model was constructed for the patient with custom open skull and plastic (non-conductive) electrode holder features. Source localization was performed using Sparse Bayesian Learning (SBL) on a dictionary of overlapping multi-scale cortical patches constructed from 80,130 dipoles in gray matter perpendicular to the cortical surface. Remaining mutual information among seizure-model AMICA components was dominated by two dependent component subspaces with largely contiguous source domains localized to superior frontal gyrus and precen-tral gyrus; these accounted for most of the ictal activity. Similar though much weaker dependent subspaces were also revealed in pre-ictal data by the associated AMICA model. Electrocortical source imaging appears promising both for clinical epilepsy research and for basic cognitive neuroscience research using volunteer patients who must undergo invasive monitoring for medical purposes.

Patch-basis electrocortical source imaging in epilepsy

In this study, we developed numerical methods for investigating the sources of epileptic activity from intracranial EEG recordings acquired from intracranial subdural electrodes (iEEG) in patients undergoing pre-surgical evaluation at the epilepsy center of the Mayo Clinic (Rochester, MN). The data were analyzed using independent component analysis (ICA), which identifies and isolates maximally independent signal components in multi-channel recordings. A realistic individual head model was constructed for a patient undergoing pre-surgical evaluation. Structural models of gray matter, white matter, CSF, skull, and scalp were extracted from pre-surgical MR and post-surgical CT images. The electromagnetic source localization forward problem was solved using the Boundary Element Method (BEM). Source localization was performed using the Sparse Bayesian Learning (SBL) algorithm. The multiscale patch-basis source space constructed for this purpose includes a large number of dipole elements on the cortical layer oriented perpendicular to the local cortical surface. These source dipoles are combined into overlapping multi-scalepatches. Using this approach, we were able to detect seizure activity on sulcal walls and on gyrus of the cortex.

Head modeling and cortical source localization in epilepsy

In this study, we developed numerical methods for investigating the dynamics of epilepsy from multi-scale EEG recordings acquired simultaneously from the scalp (sEEG) and intracranial subdural and/or depth electrodes (iEEG) in patients undergoing pre-surgical evaluation at the epilepsy center of the Mayo Clinic (Rochester, MN). The data are analyzed using independent component analysis (ICA), which identifies and isolates independent signal components from multi-channel recordings. A realistic individual head model was constructed for a patient undergoing pre-surgical evaluation. The forward problem of electro-magnetic source localization was solved using the Boundary Element Method (BEM). Using this approach, we investigated the relationships between noninvasive and invasive source localization of human electrical brain data sources. A difference of about 1 cm was observed between sources estimated from sEEG and iEEG measurements.

Cental macular thickness in patients with type 2 diabetes mellitus without clinical retinopathy

BackgroundAn increase in macular thickness due to fluid accumulation in the macula in patients with diabetes mellitus. Optical coherence tomography (OCT) has been shown to be highly reproducible in measuring macular thickness in normal individuals and diabetic patients. OCT can detect subtle changes of macular thickness. The aim of this study is to compare central macular thickness (CMT) of diabetic patients with type 2 diabetes without clinical retinopathy and normal controls, in order to assess possible increased macular thickness associated with diabetes mellitus.MethodsOptical coherence tomography (OCT) measurements were performed in 124 eyes of 62 subjects with diabetes mellitus without clinically retinopathy (study group: 39 female, 23 male, mean age: 55.06 ± 9.77 years) and in 120 eyes of 60 healthy subjects (control group: 35 female, 25 male, mean age: 55.78 ± 10.34 years). Blood biochemistry parameters were analyzed in all cases. The data for central macular thickness (at 1 mm) and the levels of the fasting plasma glucose and glycosylated hemoglobin (HbA1c) were compared in both groups.ResultsThe mean central macular thickness was 232.12 ±24.41 μm in the study group and 227.19 ± 29.94 μm in the control group.The mean HbA1c level was 8.92 ± 2.58% in the study group and 5.07 ± 0.70% in the control group (p=0.001). No statistically significant relationship was found between CMT, HbA1c, and fasting plasma glucose level in either group (p=0.05).ConclusionsCentral macular thickness was not significantly thicker in patients with type 2 diabetes without clinical retinopathy than in healthy subjects.

Neuroelectromagnetic Forward Modeling Toolbox

This paper introduces a Neuroelectromagnetic Forward Modeling Toolbox running under MATLAB (The Mathworks, Inc.) for generating realistic head models from available data (MRI and/or electrode locations) and for solving the forward problem of electro-magnetic source imaging numerically. The toolbox includes tools for segmenting scalp, skull, cerebrospinal fluid (CSF) and brain tissues from T1-weighted magnetic resonance (MR) images. After extracting the segmented tissue volumes, mesh generation can be performed using deformable models. When MR images are not available, it is possible to warp a template head model to measured electrode locations to obtain a better-fitting realistic model. The Boundary Element Method (BEM) is used for the numerical solution of the forward problem. Toolbox functions can be called from either a graphic user interface or from the command line. Function help messages and a tutorial are included. The toolbox is freely available under the GNU Public License for noncommercial use and open source development.

Meta Analysis of Studies about Breast Self Examination between 2000-2009 in Turkey

AIM The aim of this study was to analyze studies in Turkey about self-breast examination and produce conclusive, reliable and detailed basis for future studies. METHODS Studies performed between 2000 and 2009 (until the end of September) were retrieved from databases using breast cancer, breast examination, breast cancer screening and risk factors as key words. Fifty-nine studies were identified and 18 of them (15 journal articles and three theses) were used for the meta-analysis t. RESULTS Married women and women with a family history of breast cancer were found to perform self-breast examination more frequently than single women and women without a family history of breast cancer, respectively (OR=1.02 %CI 0.82-1.63; OR=1.16 %CI 0.82-1.63). According to the health belief model scales, women performing self-breast examination were determined to have 1.7 times higher susceptibility (OR=1.70), 1.34 times higher seriousness perception (OR=1.34), 3.32 times higher health motivation (OR=3.32), 5.21 times more self-efficacy/confidence (OR=5.21) and 2.56 times higher self-breast examination benefit perception (OR=2.56). CONCLUSION Nursing care models caused an increase in self-breast examination by women, and thus, it may be useful to organize and evaluate such health-related programs and consider women health perceptions.