title: Factors affecting characterization and localization of interindividual differences in functional connectivity using MRI.
abstract: Much recent attention has been paid to quantifying anatomic and functional neuroimaging on the individual subject level. For optimal individual subject characterization, specific acquisition and analysis features need to be identified that maximize interindividual variability while concomitantly minimizing intra-subject variability. We delineate the effect of various acquisition parameters (length of acquisition, sampling frequency) and analysis methods (time course extraction, region of interest parcellation, and thresholding of connectivity-derived network graphs) on characterizing individual subject differentiation. We utilize a non-parametric statistical metric that quantifies the degree to which a parameter set allows this individual subject differentiation by both maximizing interindividual variance and minimizing intra-individual variance. We apply this metric to analysis of four publicly available test-retest resting-state fMRI (rs-fMRI) data sets. We find that for the question of maximizing individual differentiation, (i) for increasing sampling, there is a relative tradeoff between increased sampling frequency and increased acquisition time; (ii) for the sizes of the interrogated data sets, only 3-4 min of acquisition time was sufficient to maximally differentiate each subject with an algorithm that utilized no a priori information regarding subject identification; and (iii) brain regions that most contribute to this individual subject characterization lie in the default mode, attention, and executive control networks. These findings may guide optimal rs-fMRI experiment design and may elucidate the neural bases for subject-to-subject differences. Hum Brain Mapp 37:1986-1997, 2016. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland.
authors.forename: Raag D
authors.initials: RD
authors.lastname: Airan
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland.|Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
authors.forename: Joshua T
authors.initials: JT
authors.lastname: Vogelstein
authors.affiliation: Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland.
authors.forename: Jay J
authors.initials: JJ
authors.lastname: Pillai
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland.
authors.forename: Brian
authors.initials: B
authors.lastname: Caffo
authors.affiliation: Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland.|F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland.
authors.forename: James J
authors.initials: JJ
authors.lastname: Pekar
authors.affiliation: Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland.
authors.forename: Haris I
authors.initials: HI
authors.lastname: Sair
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D001931:Brain Mapping; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007206:Individuality; D008279:Magnetic Resonance Imaging; D009434:Neural Pathways; D018709:Statistics, Nonparametric
keywords: functional connectivity; resting state fMRI; subject-level differences