title: A deconvolution-based approach to identifying large-scale effective connectivity.
abstract: Rapid, robust computation of effective connectivity between neural regions is an important next step in characterizing the brain's organization, particularly in the resting state. However, recent work has called into question the value of causal inference computed directly from BOLD, demonstrating that valid inferences require transformation of the BOLD signal into its underlying neural events as necessary for accurate causal inference. In this work we develop an approach for effective connectivity estimation directly from deconvolution-based features and estimates of inter-regional communication lag. We then test, in both simulation as well as whole-brain fMRI BOLD signal, the viability of this approach. Our results show that deconvolution precision and network size play outsized roles in effective connectivity estimation performance. Idealized simulation conditions allow for statistically significant effective connectivity estimation of networks of up to four hundred regions-of-interest (ROIs). Under simulation of realistic recording conditions and deconvolution performance, however, our result indicates that effective connectivity is viable in networks containing up to approximately sixty ROIs. We then validated the ability for the proposed method to reliably detect effective connectivity in whole-brain fMRI signal parcellated into networks of viable size.
authors.affiliation: Department of Computer Science, University of Arkansas at Little Rock (UALR), 2801 S. University Ave., Little Rock, AR, USA 72204. Electronic address: kabush@ualr.edu.
authors.forename: Keith
authors.initials: K
authors.lastname: Bush
authors.affiliation: Department of Computer Science, University of Arkansas at Little Rock (UALR), 2801 S. University Ave., Little Rock, AR, USA 72204. Electronic address: sxzhou@ualr.edu.
authors.forename: Suijian
authors.initials: S
authors.lastname: Zhou
authors.affiliation: Brain Imaging Research Center, University of Arkansas for Medical Sciences (UAMS), 4301 W. Markham St., Little Rock, AR, USA 72205. Electronic address: jcisler@uams.edu.
authors.forename: Josh
authors.initials: J
authors.lastname: Cisler
authors.affiliation: Department of Health Outcomes and Policy, University of Florida (UF), 1329 SW 16th St., Gainesville, FL, USA 32608. Electronic address: bianjiang@ufl.edu.
authors.forename: Jiang
authors.initials: J
authors.lastname: Bian
authors.affiliation: Department of Computer Science, University of Arkansas at Little Rock (UALR), 2801 S. University Ave., Little Rock, AR, USA 72204. Electronic address: oxhazaroglu@ualr.edu.
authors.forename: Onder
authors.initials: O
authors.lastname: Hazaroglu
authors.affiliation: Department of Computer Science, University of Arkansas at Little Rock (UALR), 2801 S. University Ave., Little Rock, AR, USA 72204. Electronic address: kxgillispie@ualr.edu.
authors.forename: Keenan
authors.initials: K
authors.lastname: Gillispie
authors.affiliation: Department of Computer Science, University of Arkansas at Little Rock (UALR), 2801 S. University Ave., Little Rock, AR, USA 72204. Electronic address: kxyoshigoe@ualr.edu.
authors.forename: Kenji
authors.initials: K
authors.lastname: Yoshigoe
authors.affiliation: Brain Imaging Research Center, University of Arkansas for Medical Sciences (UAMS), 4301 W. Markham St., Little Rock, AR, USA 72205. Electronic address: cdkilts@uams.edu.
authors.forename: Clint
authors.initials: C
authors.lastname: Kilts
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009434:Neural Pathways / Q000033:anatomy & histology; D012016:Reference Values
keywords: BOLD; Deconvolution; Effective connectivity; Imaging analysis; fMRI