Abstract
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.
Highlights
The human brain is capable of complex functions which are supported by its distinct spatiotemporal organization
Resting-State Networks Via ICA, we identified 24 resting-state networks (RSNs) that corresponded to networks previously reported by Allen et al (2011)
We propose a new aggregate measure–the Spectral Centroid–which represents the “center of gravity” of the full power spectrum of individual RSN time courses
Summary
The human brain is capable of complex functions which are supported by its distinct spatiotemporal organization. Human cortical regions are arranged in a characteristic sensorimotor-to-transmodal gradient, revealing gradual variation in structural features (Huntenburg et al, 2017), macroscopic connectivity (Margulies et al, 2016) and functional specialization (Huth et al, 2016) This spatial gradient is anchored in early sensory cortical areas such as the primary visual, somatomotor, and Spectral Centroid Grading Across RSNs auditory cortices and evolves toward higher-order transmodal areas in the parietal, temporal, and prefrontal cortex (for review see Huntenburg et al, 2018). Primary sensory areas were found to encode instantaneous, rapidly changing information whereas transmodal association areas were shown to encode information accumulated over a longer time Such temporal hierarchy of information integration during task relates to timescales of intrinsic (restingstate) cortical dynamics (Honey et al, 2012; Stephens et al, 2013; Murray et al, 2014). Numerous other studies in humans and primates further support the notion of a hierarchy of resting-state timescales across individual cortical regions (Baria et al, 2011; Chaudhuri et al, 2015; Cocchi et al, 2016)
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