Hinds, W. | Search bioRxiv

bioRxiv2020

Intra-session test-retest reliability of functional connectivity in infants

Resting functional MRI studies of the infant brain are increasingly becoming an important tool in developmental neuroscience. Whereas the test-retest reliability of functional connectivity (FC) measures derived from resting fMRI data have been characterized in the adult and child brain, similar assessments have not been conducted in infants. In this study, we examined the intra-session test-retest reliability of FC measures from 119 infant brain MRI scans from four neurodevelopmental studies. We investigated edge-level and subject-level reliability within one MRI session (between and within runs) measured by the Intraclass correlation coefficient (ICC). First, using an atlas-based approach, we examined whole-brain connectivity as well as connectivity within two common resting fMRI networks - the default mode network (DMN) and the sensorimotor network (SMN). Second, we examined the influence of run duration, study site, and scanning manufacturer (e.g., Philips and General Electric) on ICCs. Lastly, we tested spatial similarity using the Jaccard Index from networks derived from independent component analysis (ICA). Consistent with resting fMRI studies from adults, our findings indicated poor edge-level reliability (ICC = 0.14 - 0.18), but moderate-to-good subject-level intra-session reliability for whole-brain, DMN, and SMN connectivity (ICC = 0.40 - 0.78). We also found significant effects of run duration, site, and scanning manufacturer on reliability estimates. Some ICA-derived networks showed strong spatial reproducibility (e.g., DMN, SMN, and Visual Network), and were labelled based on their spatial similarity to analogous networks measured in adults. These networks were reproducibly found across different study studies. However, other ICA-networks (e.g. Executive Control Network) did not show strong spatial reproducibility, suggesting that the reliability and/or maturational course of functional connectivity may vary by network. In sum, our findings suggest that developmental scientist may be on safe ground examining the functional organization of some major neural networks (e.g. DMN and SMN), but judicious interpretation of functional connectivity is essential to its ongoing success. HighlightsO_LIInfant functional connectivity (FC) shows poor edge-level reliability (ICCs) C_LIO_LIHowever, subject-level infant FC estimates show good-to-excellent ICCs C_LIO_LISpatial reproducibility is better for some resting networks (DMN, SMN) than others (ECN) C_LIO_LIReliability estimates differ across study site and MRI scanner C_LIO_LIConclusion - Infant FC can be a reliable measurement, but judicious use is needed C_LI

bioinformatics↗

bioRxiv2019

Tonic resting-state hubness supports high-frequency activity defined verbal-memory encoding network in epilepsy

High-frequency gamma activity of verbal-memory encoding using invasive-electroencephalogram coupled has laid the foundation for numerous studies testing the integrity of memory in diseased populations. Yet, the functional connectivity characteristics of networks subserving these HFA-memory linkages remains uncertain. By integrating this electrophysiological biomarker of memory encoding from IEEG with resting-state BOLD fluctuations, we estimated the segregation and hubness of HFA-memory regions in drug-resistant epilepsy patients and matched healthy controls. HFA-memory regions express distinctly different hubness compared to neighboring regions in health and in epilepsy, and this hubness was more relevant than segregation in predicting verbal memory encoding. The HFA-memory network comprised regions from both the cognitive control and primary processing networks, validating that effective verbal-memory encoding requires multiple functions, and is not dominated by a central cognitive core. Our results demonstrate a tonic intrinsic set of functional connectivity, which provides the necessary conditions for effective, phasic, task-dependent memory encoding.\n\nHighlightsO_LIHigh frequency memory activity in IEEG corresponds to specific BOLD changes in resting-state data.\nC_LIO_LIHFA-memory regions had lower hubness relative to control brain nodes in both epilepsy patients and healthy controls.\nC_LIO_LIHFA-memory network displayed hubness and participation (interaction) values distinct from other cognitive networks.\nC_LIO_LIHFA-memory network shared regional membership and interacted with other cognitive networks for successful memory encoding.\nC_LIO_LIHFA-memory network hubness predicted both concurrent task (phasic) and baseline (tonic) verbal-memory encoding success.\nC_LI

neuroscience↗