Sleep State Modulates Resting-State Functional Connectivity in Neonates

Lee, Chuen Wai; Blanco, Borja; Dempsey, Laurie A; Chalia, Maria; Hebden, Jeremy C.; Caballero-Gaudes, César; Austin, Topun; Cooper, Robert J.

doi:10.3389/fnins.2020.00347

Cited by 20 publications

(21 citation statements)

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“…Testing sleeping infants might have prevented us from detecting subtle differences in RSFC properties across experimental groups. 79 , 80 Nonetheless, previous studies assessing RSFC in infants have also been conducted with sleeping infants, irrespective of the imaging modality (i.e., fMRI or fNIRS), and were able to identify RSFC differences induced by prenatal and postnatal factors such as premature birth 25 or socioeconomic status. 28 Brain imaging techniques are particularly sensitive to motion-induced artifacts commonly observed in acquisitions on awake infants.…”

Section: Discussionmentioning

confidence: 99%

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Group-level cortical functional connectivity patterns using fNIRS: assessing the effect of bilingualism in young infants

et al. 2021

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. Significance: Early monolingual versus bilingual experience induces adaptations in the development of linguistic and cognitive processes, and it modulates functional activation patterns during the first months of life. Resting-state functional connectivity (RSFC) is a convenient approach to study the functional organization of the infant brain. RSFC can be measured in infants during natural sleep, and it allows to simultaneously investigate various functional systems. Adaptations have been observed in RSFC due to a lifelong bilingual experience. Investigating whether bilingualism-induced adaptations in RSFC begin to emerge early in development has important implications for our understanding of how the infant brain’s organization can be shaped by early environmental factors. Aims: We attempt to describe RSFC using functional near-infrared spectroscopy (fNIRS) and to examine whether it adapts to early monolingual versus bilingual environments. We also present an fNIRS data preprocessing and analysis pipeline that can be used to reliably characterize RSFC in development and to reduce false positives and flawed results interpretations. Methods: We measured spontaneous hemodynamic brain activity in a large cohort ( ) of 4-month-old monolingual and bilingual infants using fNIRS. We implemented group-level approaches based on independent component analysis to examine RSFC, while providing proper control for physiological confounds and multiple comparisons. Results: At the group level, we describe the functional organization of the 4-month-old infant brain in large-scale cortical networks. Unbiased group-level comparisons revealed no differences in RSFC between monolingual and bilingual infants at this age. Conclusions: High-quality fNIRS data provide a means to reliably describe RSFC patterns in the infant brain. The proposed group-level RSFC analyses allow to assess differences in RSFC across experimental conditions. An effect of early bilingual experience in RSFC was not observed, suggesting that adaptations might only emerge during explicit linguistic tasks, or at a later point in development.

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Section: Discussionmentioning

confidence: 99%

“… 21 However, assessing sleep state through behavioral and/or electrophysiological measures would have been desirable to provide a more accurate control. 80 …”

Section: Discussionmentioning

confidence: 99%

Group-level cortical functional connectivity patterns using fNIRS: assessing the effect of bilingualism in young infants

et al. 2021

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“…Second, resting fMRI studies of infants are conducted during sleep (without sedation), whereas studies in adults and children are more commonly done while participants are awake. Different sleep states may be associated with distinct connectivity patterns, yet sleep state is seldom assessed or controlled for in infant fMRI research ( Lee et al, 2020 ). Third, the increased water content of the infant brain, relative to a more mature brain, alters tissue contrast (e.g., T2-weighted images in infants provide clearer grey-white matter demarcation) and may make image processing more challenging, eroding measurement reliability.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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 sites. 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 scientists 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.

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“…The signal is corrected by removing the motion artifacts. 30 However, this method is not applicable when faced with the difficulty of intercepting long-time artifact-free data segments. 31 Some researchers have corrected the motion artifacts according to their characteristics or frequencies through methods, such as principal component analysis method (PCA) 32 and wavelet filtering (WAVE).…”

Section: Introductionmentioning

confidence: 99%

Motion artifact correction for resting-state neonatal functional near-infrared spectroscopy through adaptive estimation of physiological oscillation denoising

et al. 2022

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. Significance Functional near-infrared spectroscopy (fNIRS) for resting-state neonatal brain function evaluation provides assistance for pediatricians in diagnosis and monitoring treatment outcomes. Artifact contamination is an important challenge in the application of fNIRS in the neonatal population. Aim Our study aims to develop a correction algorithm that can effectively remove different types of artifacts from neonatal data. Approach In the study, we estimate the recognition threshold based on the amplitude characteristics of the signal and artifacts. After artifact recognition, Spline and Gaussian replacements are used separately to correct the artifacts. Various correction method recovery effects on simulated artifact and actual neonatal data are compared using the Pearson correlation ( ) and root mean square error ( RMSE ). Simulated data connectivity recovery is used to compare various method performances. Results The neonatal resting-state data corrected by our method showed better agreement with results by visual recognition and correction, and significant improvements ( , ; paired -test, ** ). Moreover, the method showed a higher degree of recovery of connectivity in simulated data. Conclusions The proposed algorithm corrects artifacts such as baseline shifts, spikes, and serial disturbances in neonatal fNIRS data quickly and more effectively. It can be used for preprocessing in clinical applications of neonatal fNIRS brain function detection.

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Sleep State Modulates Resting-State Functional Connectivity in Neonates

Cited by 20 publications

References 75 publications

Group-level cortical functional connectivity patterns using fNIRS: assessing the effect of bilingualism in young infants

Group-level cortical functional connectivity patterns using fNIRS: assessing the effect of bilingualism in young infants

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

Motion artifact correction for resting-state neonatal functional near-infrared spectroscopy through adaptive estimation of physiological oscillation denoising

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