Time-variant estimation of directed influences during Parkinsonian tremor.
The inference of interaction structures in multidimensional time series is a major challenge not only in neuroscience but in many fields of research. To gather information about the connectivity in a network from measured data, several parametric as well as non-parametric approaches have been proposed and widely examined. Today a lot of interest is focused on the evolution of the network connectivity in time which might contain information about ongoing tasks in the brain or possible dynamic dysfunctions. Therefore an extension of the current approaches towards time-resolved analysis techniques is desired. We present a parametric approach for time variant analysis, test its performance for simulated data, and apply it to real-world data.
Sommerlade
Linda
L
FDM, Freiburg Center for Data Analysis and Modeling, University of Freiburg, Eckerstr. 1, 79104 Freiburg, Germany. linda.sommerlade@fdm.uni-freiburg.de
Henschel
Kathrin
K
Wohlmuth
Johannes
J
Jachan
Michael
M
Amtage
Florian
F
Hellwig
Bernhard
B
Lücking
Carl Hermann
CH
Timmer
Jens
J
Schelter
Björn
B
D003198:Computer Simulation; D004569:Electroencephalography; D004576:Electromyography; D005542:Forearm / Q000503:physiopathology; D006801:Humans; D008959:Models, Neurological; D018482:Muscle, Skeletal / Q000503:physiopathology*; D009415:Nerve Net / Q000503:physiopathology; D009474:Neurons / Q000502:physiology; D010300:Parkinson Disease / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted; D020531:Subthalamic Nucleus / Q000503:physiopathology*; D013997:Time Factors; D014202:Tremor / Q000503:physiopathology*