Longitudinal associations between environmental unpredictability and transient states of dynamic functional connectivity
Creators: name: Hardi, Felicia
Publisher: name: OSF Registries
Year: 2024
Subjects: subject: Developmental Neuroscience
subject: Life Sciences
subject: Neuroscience and Neurobiology
subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology

description: RESEARCH QUESTIONS AND/OR THEORY. Identifying neural correlates of environmental unpredictability, a dimension of childhood adversity, has received increased attention in recent years (Gee, 2021). The developing brain encodes information by associating, regulating, and integrating signals from the immediate environment (Conway, 2020). Unpredictable environments, marked by a high degree of changes and fluctuations, could introduce greater volatility in environmental signals, thus influencing how and what environmental information is being encoded. Such unstable fluctuations could affect the development of neural circuits and functional specialization (Knudsen, 2004; Johnson, 2011). Prior studies have found that greater variations in functional connectivity were related to childhood adversity and psychiatric disorders (e.g., Belleau et al., 2022), suggesting that dynamic brain function could potentially reflect fluctuations in the early environment. However, whether these dynamic patterns of functional connectivity would relate to other measures of childhood instability (e.g., household instability and chaos) remains to be tested. Based on these theoretical frameworks, we sought to test two aims. In the first aim, we focus on the associations between indexes of childhood environmental unpredictability (early household instability, marked by frequent residential moves and paternal transitions within the first five years; and household chaos during middle to late childhood) and the variation in transient states of adolescent dynamic functional connectivity. In the second aim, we propose to investigate whether household instability and chaos are linked to greater stochasticity in functional connectivity by the novel application of sequence analysis (Liao et al., 2022) on resulting dynamic functional connectivity states. The findings of this study across two aims will enhance our understanding of neural development, highlighting how stable/unstable early-life environments become longitudinally and biologically expressed as stable/unstable brain function and development. Moreover, sequence analysis is a well-established method for genome sequencing and measuring instability in longitudinal and time-series data in the social sciences. Thus, the proposed novel application of sequence analysis on functional connectivity data will expand the repertoire of tools available to developmental neuroscientists to capture instability in functional connectivity patterns. 

HYPOTHESES. We theorized that the adolescent brain, in particular dynamic functional connectivity states, would reflect environmental unpredictability experienced in childhood. Thus, we hypothesized that increased household instability and chaos would relate to 1) a greater diversity of dynamic functional connectivity states and 2) increased stochasticity in dynamic functional connectivity states. 

STUDY DESIGN AND METHODS. The analysis will be conducted using data from the Study of Adolescent Neural Development (N=177), a subsample of the population-based longitudinal Future of Families and Child Wellbeing Study. Independent Variables are 1) Household instability at ages 0-5 (composite score consisting of a number of residential moves and changes in household composition and caregiver transitions; see Hardi et al., 2023) and 2) Household chaos at ages 9-15 in the shorter version of the CHAOS scale (Petrill et al., 2004). Dependent Variables are two metrics estimated using rs-fMRI data (see e.g., Goetschius et al., 2020; Tillem et al., 2023): 1) The number of distinct dynamic functional connectivity states (i.e., diversity) estimated using sliding window analysis and unsupervised clustering approach (Hutchison et al., 2013); 2) Turbulence metric of dynamic functional connectivity states to characterize stochasticity (i.e., diversity plus fluctuations over time) estimated by applying sequence analysis (Liao et al., 2022) on functional connectivity states. Covariates: sex at birth; ethnoracial identity; family income at birth; age during the scan; mean framewise displacement. 

KEY ANALYSES THAT WILL TEST THE HYPOTHESES AND/OR ANSWER THE RESEARCH QUESTION(S). We will first utilize sliding window analysis to examine dynamic functional connectivity over time and subsequently apply unsupervised clustering algorithms to identify transient states of dynamic functional connectivity. For the first aim, we will determine the diversity of states by examining the variation of states for each person. Parametric linear models will then be employed to investigate the relationship between environmental unpredictability and the diversity of dynamic functional connectivity states. For the second aim, we will build upon the first aim by employing sequence analysis to evaluate the stochasticity of functional states using the turbulence metric. This approach will enable us to capture the randomness, unpredictability, and fluctuations of dynamic functional connectivity states over time. Similarly, we will utilize parametric linear models to assess whether there is an association between environmental unpredictability and the stochasticity of dynamic functional connectivity states. 

CONCLUSIONS THAT WILL BE DRAWN GIVEN DIFFERENT RESULTS. For aim 1, significant positive/negative associations would suggest that unpredictability in the early environment is related to increases/decreases in the variations of adolescent functional connectivity patterns. For aim 2, significant positive/negative associations would suggest that early unpredictability is related to increases/decreases in the degree of fluctuations between specific functional states. If no associations were found, we would conclude that there could be other mechanisms through which instability in the childhood environment could influence neural development in the present sample. 

KEY REFERENCES. 1. Gee ( 2021), https://doi.org/10.1176/appi.ajp.2021.21090944. 2. Conway (2020), https://doi.org/ 10.1016/j.neubiorev.2020.01.032. 3. Knudsen (2014), /10.1162/0898929042304796. 4. Johnson (2011), https://doi.org/ /10.1016/j.dcn.2010.07.003. 5. Belleau et al. (2022), /10.1016/j.nicl.2022.103164. 6. Hardi et al. (2023), https://doi.org/ /10.1016/j.dcn.2023.101253. 7. Petrill et al (2004), /10.1016/j.intell.2004.06.010. 8. Goetschius et al. (2020), https://doi. org/10.1001/jamanetworkopen.2020.17850. 9. Tillem et al. (2023), doi.org/10.1093/scan/nsad026. 10. Hutchison et al (2014), doi.org/10.1016/j.neuroimage.2013.05.079. 11. Liao, et al. (2022), doi.org/10.1016/j.ssresearch.2022.102772.