Grading of Frequency Spectral Centroid Across Resting-State Networks

• Main Authors: Anja Ries, Catie Chang, Sarah Glim, Chun Meng, Christian Sorg, Afra Wohlschläger
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2018-10-01
• Series: Frontiers in Human Neuroscience
• Subjects: resting-state; fMRI; resting-state networks; spectral centroid; frequency-based grading; major depressive disorder
• Online Access: https://www.frontiersin.org/article/10.3389/fnhum.2018.00436/full

Ongoing, slowly fluctuating brain activity is organized in resting-state networks (RSNs) of spatially coherent fluctuations. Beyond spatial coherence, RSN activity is governed in a frequency-specific manner. The more detailed architecture of frequency spectra across RSNs is, however, poorly understood. Here we propose a novel measure–the Spectral Centroid (SC)–which represents the center of gravity of the full power spectrum of RSN signal fluctuations. We examine whether spectral underpinnings of network fluctuations are distinct across RSNs. We hypothesize that spectral content differs across networks in a consistent way, thus, the aggregate representation–SC–systematically differs across RSNs. We therefore test for a significant grading (i.e., ordering) of SC across RSNs in healthy subjects. Moreover, we hypothesize that such grading is biologically significant by demonstrating its RSN-specific change through brain disease, namely major depressive disorder. Our results yield a highly organized grading of SC across RSNs in 820 healthy subjects. This ordering was largely replicated in an independent dataset of 25 healthy subjects, pointing toward the validity and consistency of found SC grading across RSNs. Furthermore, we demonstrated the biological relevance of SC grading, as the SC of the salience network–a RSN well known to be implicated in depression–was specifically increased in patients compared to healthy controls. In summary, results provide evidence for a distinct grading of spectra across RSNs, which is sensitive to major depression.