Eleonora Maggioni1, Maria Gabriella Tana2, Filippo Arrigoni3, Claudio Zucca3, Anna Maria Bianchi4. 1. 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. 2. 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. 3. Scientific Institute IRCCS E.Medea, Via Don Luigi Monza 20, 23842 Bosisio Parini, Lecco, Italy. 4. 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.
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. NEW METHOD: 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.
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. NEW METHOD: 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 epilepticpatients' 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: Pantea Moghimi; Anh The Dang; Quan Do; Theoden I Netoff; Kelvin O Lim; Gowtham Atluri Journal: J Neurophysiol Date: 2022-06-08 Impact factor: 2.974
Authors: André Zugman; Anita Harrewijn; Elise M Cardinale; Hannah Zwiebel; Gabrielle F Freitag; Katy E Werwath; Janna M Bas-Hoogendam; Nynke A Groenewold; Moji Aghajani; Kevin Hilbert; Narcis Cardoner; Daniel Porta-Casteràs; Savannah Gosnell; Ramiro Salas; Karina S Blair; James R Blair; Mira Z Hammoud; Mohammed Milad; Katie Burkhouse; K Luan Phan; Heidi K Schroeder; Jeffrey R Strawn; Katja Beesdo-Baum; Sophia I Thomopoulos; Hans J Grabe; Sandra Van der Auwera; Katharina Wittfeld; Jared A Nielsen; Randy Buckner; Jordan W Smoller; Benson Mwangi; Jair C Soares; Mon-Ju Wu; Giovana B Zunta-Soares; Andrea P Jackowski; Pedro M Pan; Giovanni A Salum; Michal Assaf; Gretchen J Diefenbach; Paolo Brambilla; Eleonora Maggioni; David Hofmann; Thomas Straube; Carmen Andreescu; Rachel Berta; Erica Tamburo; Rebecca Price; Gisele G Manfro; Hugo D Critchley; Elena Makovac; Matteo Mancini; Frances Meeten; Cristina Ottaviani; Federica Agosta; Elisa Canu; Camilla Cividini; Massimo Filippi; Milutin Kostić; Ana Munjiza; Courtney A Filippi; Ellen Leibenluft; Bianca A V Alberton; Nicholas L Balderston; Monique Ernst; Christian Grillon; Lilianne R Mujica-Parodi; Helena van Nieuwenhuizen; Gregory A Fonzo; Martin P Paulus; Murray B Stein; Raquel E Gur; Ruben C Gur; Antonia N Kaczkurkin; Bart Larsen; Theodore D Satterthwaite; Jennifer Harper; Michael Myers; Michael T Perino; Qiongru Yu; Chad M Sylvester; Dick J Veltman; Ulrike Lueken; Nic J A Van der Wee; Dan J Stein; Neda Jahanshad; Paul M Thompson; Daniel S Pine; Anderson M Winkler Journal: Hum Brain Mapp Date: 2020-06-29 Impact factor: 5.399