title: Constructing fMRI connectivity networks: a whole brain functional parcellation method for node definition.
abstract: BACKGROUND
  Functional Magnetic Resonance Imaging (fMRI) is used for exploring brain functionality, and recently it was applied for mapping the brain connection patterns. To give a meaningful neurobiological interpretation to the connectivity network, it is fundamental to properly define the network framework. In particular, the choice of the network nodes may affect the final connectivity results and the consequent interpretation.
  
  
  METHODS
  We introduce a novel method for the intra subject topological characterization of the nodes of fMRI brain networks, based on a whole brain parcellation scheme. The proposed whole brain parcellation algorithm divides the brain into clusters that are homogeneous from the anatomical and functional point of view, each of which constitutes a node. The functional parcellation described is based on the Tononi's cluster index, which measures instantaneous correlation in terms of intrinsic and extrinsic statistical dependencies.
  
  
  RESULTS
  The method performance and reliability were first tested on simulated data, then on a real fMRI dataset acquired on healthy subjects during visual stimulation. Finally, the proposed algorithm was applied to epileptic patients' fMRI data recorded during seizures, to verify its usefulness as preparatory step for effective connectivity analysis. For each patient, the nodes of the network involved in ictal activity were defined according to the proposed parcellation scheme and Granger Causality Analysis (GCA) was applied to infer effective connectivity.
  
  
  CONCLUSIONS
  We showed that the algorithm 1) performed well on simulated data, 2) was able to produce reliable inter subjects results and 3) led to a detailed definition of the effective connectivity pattern.
authors.affiliation: Department of Electronics Information and Bioengineering (DEIB), Politecnico di Milano, P.za Leonardo da Vinci, 32, 20133 Milan, Italy; Scientific Institute IRCCS E.Medea, Via Don Luigi Monza 20, 23842 Bosisio Parini, Lecco, Italy.
authors.forename: Eleonora
authors.initials: E
authors.lastname: Maggioni
authors.affiliation: Department of Electronics Information and Bioengineering (DEIB), Politecnico di Milano, P.za Leonardo da Vinci, 32, 20133 Milan, Italy; BIND - Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio", Chieti, Italy; Department of Medicine and Aging Science, University "G. d'Annunzio", Chieti, Italy.
authors.forename: Maria Gabriella
authors.initials: MG
authors.lastname: Tana
authors.affiliation: Scientific Institute IRCCS E.Medea, Via Don Luigi Monza 20, 23842 Bosisio Parini, Lecco, Italy.
authors.forename: Filippo
authors.initials: F
authors.lastname: Arrigoni
authors.affiliation: Scientific Institute IRCCS E.Medea, Via Don Luigi Monza 20, 23842 Bosisio Parini, Lecco, Italy.
authors.forename: Claudio
authors.initials: C
authors.lastname: Zucca
authors.affiliation: Department of Electronics Information and Bioengineering (DEIB), Politecnico di Milano, P.za Leonardo da Vinci, 32, 20133 Milan, Italy. Electronic address: annamaria.bianchi@polimi.it.
authors.forename: Anna Maria
authors.initials: AM
authors.lastname: Bianchi
mesh_terms: D000328:Adult; D001921:Brain / Q000098:blood supply*; D001931:Brain Mapping; D003198:Computer Simulation; D066264:Datasets as Topic / Q000706:statistics & numerical data; D004569:Electroencephalography; D004827:Epilepsy / Q000473:pathology / Q000503:physiopathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008959:Models, Neurological; D009415:Nerve Net / Q000098:blood supply*; D010100:Oxygen / Q000097:blood
chemical_list: D010100:Oxygen
keywords: Brain connectivity; Functional clustering; Whole brain parcellation; fMRI; fMRI time series