Identification of large-scale networks in the brain using fMRI

• Published in: NeuroImage (Orlando, Fla.) Vol. 29; no. 4; pp. 1231 - 1243
• Main Authors: Bellec, Pierre, Perlbarg, Vincent, Jbabdi, Saâd, Pélégrini-Issac, Mélanie, Anton, Jean-Luc, Doyon, Julien, Benali, Habib
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.02.2006; Elsevier Limited; Elsevier
• Subjects: Adult; Bioengineering; Brain Mapping; Brain Mapping - methods; Cerebral Cortex; Cerebral Cortex - physiology; Computer Simulation; Dominance, Cerebral; Dominance, Cerebral - physiology; Economic models; Fourier Analysis; Functional connectivity; Functional MRI; Humans; Image Processing, Computer-Assisted; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional; Imaging, Three-Dimensional - methods; Large-scale network; Life Sciences; Magnetic Resonance Imaging; Magnetic Resonance Imaging - methods; Male; Mathematical Computing; Mental Processes; Mental Processes - physiology; Nerve N; Nerve Net - physiology; Neural networks; Neural Networks (Computer); Noise; Orientation - physiology; Principal components analysis; Psychomotor Performance - physiology; Reaction Time - physiology; Reproducibility of Results; Robust estimation; Serial Learning - physiology; Software; Spatial noise modeling; Statistics as Topic; Studies; Time series; Functional connectivity; Large-scale network; Functional MRI; Robust estimation; Spatial noise modeling
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2005.08.044

Cognition is thought to result from interactions within large-scale networks of brain regions. Here, we propose a method to identify these large-scale networks using functional magnetic resonance imaging (fMRI). Regions belonging to such networks are defined as sets of strongly interacting regions, each of which showing a homogeneous temporal activity. Our method of large-scale network identification (LSNI) proceeds by first detecting functionally homogeneous regions. The networks of functional interconnections are then found by comparing the correlations among these regions against a model of the correlations in the noise. To test the LSNI method, we first evaluated its specificity and sensitivity on synthetic data sets. Then, the method was applied to four real data sets with a block-designed motor task. The LSNI method correctly recovered the regions whose temporal activity was locked to the stimulus. In addition, it detected two other main networks highly reproducible across subjects, whose activity was dominated by slow fluctuations (0–0.1 Hz). One was located in medial and dorsal regions, and mostly overlapped the “default” network of the brain at rest [Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V., 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the U.S.A. 100, 253–258]; the other was composed of lateral frontal and posterior parietal regions. The LSNI method we propose allows to detect in an exploratory and systematic way all the regions and large-scale networks activated in the working brain.