title: Functional Connectivity in Frequency-Tagged Cortical Networks During Active Harm Avoidance.
abstract: Many behavioral and cognitive processes are grounded in widespread and dynamic communication between brain regions. Thus, the quantification of functional connectivity with high temporal resolution is highly desirable for capturing in vivo brain function. However, many of the commonly used measures of functional connectivity capture only linear signal dependence and are based entirely on relatively simple quantitative measures such as mean and variance. In this study, the authors used a recently developed algorithm, the generalized measure of association (GMA), to quantify dynamic changes in cortical connectivity using steady-state visual evoked potentials (ssVEPs) measured in the context of a conditioned behavioral avoidance task. GMA uses a nonparametric estimator of statistical dependence based on ranks that are efficient and capable of providing temporal precision roughly corresponding to the timing of cognitive acts (∼ 100-200 msec). Participants viewed simple gratings predicting the presence/absence of an aversive loud noise, co-occurring with peripheral cues indicating whether the loud noise could be avoided by means of a key press (active) or not (passive). For active compared with passive trials, heightened connectivity between visual and central areas was observed in time segments preceding and surrounding the avoidance cue. Viewing of the threat stimuli also led to greater initial connectivity between occipital and central regions, followed by heightened local coupling among visual regions surrounding the motor response. Local neural coupling within extended visual regions was sustained throughout major parts of the viewing epoch. These findings are discussed in a framework of flexible synchronization between cortical networks as a function of experience and active sensorimotor coupling.
authors.affiliation: 1 Computational NeuroEngineering Laboratory, Department of Electrical and Computer Engineering, University of Florida , Gainesville, Florida.
authors.forename: Mehrnaz Khodam
authors.initials: MK
authors.lastname: Hazrati
authors.affiliation: 2 Department of Psychology, State University of New York at Binghamton , Binghamton, New York.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Miskovic
authors.affiliation: 1 Computational NeuroEngineering Laboratory, Department of Electrical and Computer Engineering, University of Florida , Gainesville, Florida.
authors.forename: José C
authors.initials: JC
authors.lastname: Príncipe
authors.affiliation: 3 Center for the Study of Emotion and Attention, University of Florida , Gainesville, Florida.|4 Department of Psychology, University of Florida , Gainesville, Florida.
authors.forename: Andreas
authors.initials: A
authors.lastname: Keil
mesh_terms: D000293:Adolescent; D000465:Algorithms; D001362:Avoidance Learning / Q000502:physiology*; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000502:physiology*; D003214:Conditioning, Classical / Q000502:physiology; D003463:Cues; D004569:Electroencephalography; D004924:Escape Reaction / Q000502:physiology*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D009415:Nerve Net / Q000502:physiology*; D010775:Photic Stimulation; D011601:Psychophysics; D055815:Young Adult
keywords: avoidance conditioning; brain connectivity; generalized measure of association; visual cortex