title: Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures.
abstract: Characterizing the brain connectome using neuroimaging data and measures derived from graph theory emerged as a new approach that has been applied to brain maturation, cognitive function and neuropsychiatric disorders. For a broad application of this method especially for clinical populations and longitudinal studies, the reliability of this approach and its robustness to confounding factors need to be explored. Here we investigated test-retest reliability of graph metrics of functional networks derived from functional magnetic resonance imaging (fMRI) recorded in 33 healthy subjects during rest. We constructed undirected networks based on the Anatomic-Automatic-Labeling (AAL) atlas template and calculated several commonly used measures from the field of graph theory, focusing on the influence of different strategies for confound correction. For each subject, method and session we computed the following graph metrics: clustering coefficient, characteristic path length, local and global efficiency, assortativity, modularity, hierarchy and the small-worldness scalar. Reliability of each graph metric was assessed using the intraclass correlation coefficient (ICC). Overall ICCs ranged from low to high (0 to 0.763) depending on the method and metric. Methodologically, the use of a broader frequency band (0.008-0.15 Hz) yielded highest reliability indices (mean ICC=0.484), followed by the use of global regression (mean ICC=0.399). In general, the second order metrics (small-worldness, hierarchy, assortativity) studied here, tended to be more robust than first order metrics. In conclusion, our study provides methodological recommendations which allow the computation of sufficiently robust markers of network organization using graph metrics derived from fMRI data at rest.
authors: Central Institute of Mental Health, University of Heidelberg, J5, 68159 Mannheim, Germany.
authors: Urs
authors: U
authors: Braun
authors: Michael M
authors: MM
authors: Plichta
authors: Christine
authors: C
authors: Esslinger
authors: Carina
authors: C
authors: Sauer
authors: Leila
authors: L
authors: Haddad
authors: Oliver
authors: O
authors: Grimm
authors: Daniela
authors: D
authors: Mier
authors: Sebastian
authors: S
authors: Mohnke
authors: Andreas
authors: A
authors: Heinz
authors: Susanne
authors: S
authors: Erk
authors: Henrik
authors: H
authors: Walter
authors: Nina
authors: N
authors: Seiferth
authors: Peter
authors: P
authors: Kirsch
authors: Andreas
authors: A
authors: Meyer-Lindenberg
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009415:Nerve Net / Q000033:anatomy & histology / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012146:Rest / Q000502:physiology*; D012680:Sensitivity and Specificity; D055815:Young Adult