title: Capturing dynamic patterns of task-based functional connectivity with EEG.
abstract: A new approach to trace the dynamic patterns of task-based functional connectivity, by combining signal segmentation, dynamic time warping (DTW), and Quality Threshold (QT) clustering techniques, is presented. Electroencephalography (EEG) signals of 5 healthy subjects were recorded as they performed an auditory oddball and a visual modified oddball tasks. To capture the dynamic patterns of functional connectivity during the execution of each task, EEG signals are segmented into durations that correspond to the temporal windows of previously well-studied event-related potentials (ERPs). For each temporal window, DTW is employed to measure the functional similarities among channels. Unlike commonly used temporal similarity measures, such as cross correlation, DTW compares time series by taking into consideration that their alignment properties may vary in time. QT clustering analysis is then used to automatically identify the functionally connected regions in each temporal window. For each task, the proposed approach was able to establish a unique sequence of dynamic pattern (observed in all 5 subjects) for brain functional connectivity.
authors.affiliation: National Institutes of Health, NICHD, SAFB, Bethesda, MD, USA; School of Physics, Astronomy, and Computational Sciences, George Mason University, Fairfax, VA, USA; Center for Neuroscience and Regenerative Medicine at the Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
authors.forename: Nader
authors.initials: N
authors.lastname: Karamzadeh
authors.affiliation: Center for Functional and Molecular Imaging, Georgetown University, Washington, DC, USA.
authors.forename: Andrei
authors.initials: A
authors.lastname: Medvedev
authors.affiliation: National Institutes of Health, NICHD, SAFB, Bethesda, MD, USA; Department of Biomedical Engineering, University of California at Davis, Davis, CA, USA.
authors.forename: Afrouz
authors.initials: A
authors.lastname: Azari
authors.affiliation: National Institutes of Health, NICHD, SAFB, Bethesda, MD, USA. Electronic address: amir@helix.nih.gov.
authors.forename: Amir
authors.initials: A
authors.lastname: Gandjbakhche
authors.affiliation: National Institutes of Health, NICHD, SAFB, Bethesda, MD, USA; Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA; Center for Neuroscience and Regenerative Medicine at the Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
authors.forename: Laleh
authors.initials: L
authors.lastname: Najafizadeh
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D009434:Neural Pathways / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted
keywords: Clustering analysis; Electroencephalography (EEG); Functional connectivity