Cortical Cross-Frequency Coupling Is Affected by in utero Exposure to Antidepressant Medication

Abstract

Up to five percent of human infants are exposed to maternal antidepressant medication by serotonin reuptake inhibitors (SRI) during pregnancy, yet the SRI effects on infants’ early neurodevelopment are not fully understood. Here, we studied how maternal SRI medication affects cortical frequency-specific and cross-frequency interactions estimated, respectively, by phase-phase correlations (PPC) and phase-amplitude coupling (PAC) in electroencephalographic (EEG) recordings. We examined the cortical activity in infants after fetal exposure to SRIs relative to a control group of infants without medical history of any kind. Our findings show that the sleep-related dynamics of PPC networks are selectively affected by in utero SRI exposure, however, those alterations do not correlate to later neurocognitive development as tested by neuropsychological evaluation at two years of age. In turn, phase-amplitude coupling was found to be suppressed in SRI infants across multiple distributed cortical regions and these effects were linked to their neurocognitive outcomes. Our results are compatible with the overall notion that in utero drug exposures may cause subtle, yet measurable changes in the brain structure and function. Our present findings are based on the measures of local and inter-areal neuronal interactions in the cortex which can be readily used across species, as well as between different scales of inspection: from the whole animals to in vitro preparations. Therefore, this work opens a framework to explore the cellular and molecular mechanisms underlying neurodevelopmental SRI effects at all translational levels.

Multiplex dynamic networks in the newborn brain disclose latent links with neurobehavioral phenotypes

“…Second, the mdFCN analysis pipeline guides the extraction/decomposition of networks by an automatic model‐order selection procedure to avoid undescriptive group‐level features (under‐fitting) or the inclusion of overt intersubject differences and noise (over‐fitting). Third, the proposed framework automatically finds a subset of the decomposed networks for reconstruction, which allows identifying FCNs with much stronger and wider relationships to neurobehavioral phenotypes than before (Tokariev et al, 2018 ; Tokariev et al, 2019 ; Tokariev et al, 2021 ; Tokariev et al, 2022 ; Videman et al, 2016 ; Yrjölä et al, 2021 ; Yrjölä et al, 2022 ), and demonstrates the wide‐scale effects of in utero drug exposure (Tokariev et al, 2021 ; Tokariev et al, 2022 ; Videman et al, 2016 ). Finally, it adopts multiplex network representations (Brookes et al, 2016 ; Buldú & Porter, 2018 ) and various graph metrics (De Domenico et al, 2016 ; Yu et al, 2017 ) to express structural multiplexity differences in the groups' mdFCNs (Mandke et al, 2018 ) at the edge, nodal, and layer levels, in a meaningful concise manner (Battiston et al, 2014 ; Battiston et al, 2017 ; Rubinov & Sporns, 2010 ).…”

“…The EEG signals were captured near term age (HC group: 42.2 ± 0.9 weeks; AED group: 42.1 ± 0.9 weeks) for about an hour that included both cardinal sleep states, active (AS), and quiet sleep (QS). Most subjects had clean enough EEG data for both sleep states ( N = 53/46 for the HC/AED group), whereas others had only one state (Tokariev et al, 2019 ; Tokariev et al, 2021 ; Tokariev et al, 2022 ). The infants' sleep state was identified, as described in detail before (André et al, 2010 ), visually by the standard combination of electrophysiological and behavioral measures.…”

“…The filtering process was achieved by means of cascaded zero‐phase low‐pass and high‐pass infinite impulse response filters. Moreover, cortical signals were estimated from the EEG using the symmetric boundary element method and the dynamic statistical parametric mapping for the forward and inverse modeling, respectively (Tokariev et al, 2018 ; Tokariev et al, 2021 ; Tokariev et al, 2022 ; Yrjölä et al, 2021 ). This process utilized a realistic three‐shell newborn head model that is comprised of manually segmented scalp, skull, and intracranial volumes (Tokariev et al, 2021 ), and the conductivities were set to 0.43 S/m for the scalp, 0.2 S/m for the skull, and 1.79 S/m for the other intracranial volume (Tokariev et al, 2018 ; Yrjölä et al, 2021 ).…”

“…A recently proposed alternative reduces the high dimensionality of FCNs through a multivariate decomposition to produce a low‐dimensional representation of highly resolved time‐frequency interdependencies (Mehrkanoon et al, 2014 ). Despite its apparent analytical success in decomposition, such approaches suffer from lacking interpretability backward, that is, they are not based on frequency‐ or coupling mode‐related networks that would allow intuitive interpretation in the context of prior infant studies (Omidvarnia et al, 2015 ; Tokariev et al, 2019 ; Tokariev et al, 2021 ; Tokariev et al, 2022 ; Yrjölä et al, 2021 ; Yrjölä et al, 2022 ). One plausible solution to this challenge is to extract latent networks from the cohort‐level FCNs by finding low‐rank approximations (Mahyari et al, 2017 ) to suppress random and subject‐specific fluctuations before the decomposition into subnetworks of lower entropy (Du et al, 2018 ; Mahyari et al, 2017 ).…”

“…There is a particular scientific and medical need to better understand the early life correlations of FCNs and neurobehavior. Most cortical FCNs are known to organize during late pregnancy and early postnatal life in an activity‐dependent manner (Oldham & Fornito, 2019 ), making early brain development exquisitely sensitive to a range of early life adversities, such as exposure to drug treatments (Tokariev et al, 2021 ; Tokariev et al, 2022 ; Videman et al, 2016 ), prematurity (Omidvarnia et al, 2015 ; Tokariev et al, 2018 ; Tokariev et al, 2019 ; Uchitel et al, 2022 ; Westende et al, 2020 ; Yrjölä et al, 2021 ; Yrjölä et al, 2022 ), or perinatal adversities (Abbas et al, 2021 ; Rocca et al, 2020 ). In this work, we propose a novel mdFCN analysis pipeline for objectively uncovering latent network structures from the spontaneous cortical activity in newborn infants (Figure 1 shows a graphical overview of the study).…”