#58109 | AsPredicted

'ABCD 2.0: Vigilance as a potential mechanism of cognitive resilience'
(AsPredicted #58109)


Created:       02/11/2021 01:46 PM (PT)

This is an anonymized version of the pre-registration.  It was created by the author(s) to use during peer-review.
A non-anonymized version (containing author names) should be made available by the authors when the work it supports is made public.

1) Have any data been collected for this study already?
It's complicated. We have already collected some data but explain in Question 8 why readers may consider this a valid pre-registration nevertheless.

2) What's the main question being asked or hypothesis being tested in this study?
The main focus of this pre-registration is on unpacking findings from our previous study (Ellwood-Lowe, Whitfield-Gabrieli, & Bunge, 2020). We will use a combination of data from ABCD’s baseline assessment (T0, ages 9-10) and the two-year follow-up (T2, ages 11-12).

1) Resting state coupling changes for LFPN-DMN over early adolescence: Based on prior findings from the literature, we predict that LFPN and DMN will generally become less coupled over early adolescence for children above poverty. We hypothesize that children below poverty with high test scores will show a similar trajectory to their higher income peers (H1). If this is a general developmental trend, children below poverty who had very low coupling at T0 (who tended to have lower cognitive test scores) would be expected to show the same pattern (H2a). Alternatively, if these children are on a delayed developmental trajectory, they may show increased coupling over this period, approaching the LFPN-DMN connectivity values that their higher-performing peers exhibited two years earlier (H2b).

2) Vigilance as a potential mechanism of resilience for children in poverty: We hypothesize that vigilance is a potential mechanism of resilience for children below (but not above) poverty, such that heightened vigilance helps to explain why higher LFPN-DMN connectivity is associated with better test performance for children living in poverty, whereas the association is negative for children above poverty (H3a). If this is the case, we would expect to see a positive association between self-reported vigilance and both cognitive test performance and LFPN-DMN connectivity for children below poverty – but a negative relation for children above poverty.

Alternatively, heightened vigilance may be maladaptive for cognitive test performance for all children, but low-performing children living below poverty are simply more likely to exhibit high levels of vigilance (H3b). In this case, we would predict a negative association between self-reported vigilance and both test performance and connectivity.

Because the available measure of vigilance is two self-report questions, the lack of an association (H30) would not allow us to definitively rule out the possibility that vigilance helps to explain our prior findings.

3) Describe the key dependent variable(s) specifying how they will be measured.
Cognitive test performance: NIH toolbox fluid ability composite, as provided by the ABCD data curators.

LFPN-DMN connectivity: Resting state connectivity (average strength of correlation across nodes within/between specific brain networks/nodes) between LFPN and DMN, as identified using the ABCD in-house processing at T0 and T2.

Vigilance: KSADS-5 Diagnostic Interview, summed score of two questions about hypervigilance past and present from the PTSD subscale at T0 (1=yes, 0=no, for a max of 2).

Poverty level: Binary above- or below-poverty measure, considering annual household income and number of people in home. Children will be considered to be in poverty if they are living in a household of 4 of less with a total income of less than $25,000, or a household of 5 or more with a total income of less than $35,000.

Additional variables for secondary analyses:

DMN-CON connectivity: Resting state connectivity (average strength of correlation across nodes within/between specific brain networks/nodes) between DMN and CON, as identified using the ABCD in-house processing at T0 and T2.

LFPN-CON connectivity: Resting state connectivity (average strength of correlation across nodes within/between specific brain networks/nodes) between LFPN and CON, as identified using the ABCD in-house processing at T0 and T2.

4) How many and which conditions will participants be assigned to?
None.

5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
For each set of analyses, we will run linear mixed effects models. We will start by including each of the following covariates: fixed effects of age, motion (mean framewise displacement), and sex; random intercepts for study site and family. We will drop covariates that do not contribute significantly to model fit (assessing significance using the Anova() function from the “car” package in R, for a Type 2 Anova test).

1) Resting state coupling changes over early adolescence.

a. Linear mixed effects model associating LFPN-DMN connectivity with timepoint (T0, T2), with an interaction between timepoint and poverty level.

2) Vigilance as a potential mechanism of resilience for children in poverty.
a. Linear mixed effects model associating vigilance at T0 with poverty level.
b. Linear mixed effects model associating cognitive test performance at T0 with vigilance at T0, with an interaction between vigilance and poverty level.
c. Linear mixed effects model associating vigilance at T0 with an interaction between LFPN-DMN connectivity and poverty level.

6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
For each of the relevant analyses, we will exclude children who are missing data for one or more of the variables. In addition, we will use ABCD’s usability guidelines to determine usability of fMRI data, as in our prior analyses.

7) How many observations will be collected or what will determine sample size?
No need to justify decision, but be precise about exactly how the number will be determined.

As many as usable at T0 and T2 according to the criteria outlined above.

8) Anything else you would like to pre-register?
(e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?)

Analyses involving Cingulo-opercular network (CON)

1) Resting state coupling changes for CON-DMN and CON-LFPN over early adolescence: Based on prior findings from the literature, these networks are expected to become increasingly decoupled during development. If this is a general phenomenon (Supplementary H1a), we would expect to find decreased connectivity for these networks for both children above and below poverty. However, because we found previously that children below poverty with higher test scores tended to have low DMN-CON connectivity at T0, these resilient children may not show a reduction in coupling with age (Supplementary H1b).

To explore these hypotheses, we will conduct the following secondary analyses:

a. Linear mixed effects model associating DMN-CON connectivity with timepoint (T0, T2), with an interaction between timepoint and poverty level.
b. Linear mixed effects model associating LFPN-CON connectivity with timepoint (T0, T2), with an interaction between timepoint and poverty level.

2) Differential interplay between three brain networks as a potential mechanism of resilience for children in poverty: The CON network has been heavily implicated in alerting the LFPN to external challenges, and has been theorized to switch mental states between internally and externally focused attention. If we find that self-reported vigilance is associated with cognitive test performance for children in poverty (Section 5, part 2a), we would perform exploratory analyses testing whether vigilance is associated with CON-DMN and/or CON-LFPN connectivity for these children. We do not have strong predictions regarding the directionality of these effects. Thus, if we were to observe a relation between vigilance and cognitive performance, we would perform the following analysis in addition to the one specified above:

Linear mixed effects model associating vigilance at T0 with an interaction between LFPN-DMN connectivity and poverty level, an interaction between DMN-CON and poverty level, and an interaction between LFPN-CON and poverty level.

To better characterize the data, we will also test whether vigilance is overall higher for children in poverty:

Linear mixed effects model associating vigilance at T0 with poverty level.

Data have already been collected but we have not yet accessed data from the KSADS, or any data from T2. (We have previously worked with LFPN-DMN connectivity at T0).

We are pre-registering a distinct set of questions with the same dataset concurrently; see pre-registration entitled, “Longitudinal predictors of cognitive wellbeing.”

####BUNDLE####
This pre-registration is part of a bundle of similar and/or related pre-registrations sharing at least one author. When a pre-registration in a bundle is shared with reviewers or made public, all of them are. Links to all other pre-registrations in the bundle are listed below:

#58114 - https://aspredicted.org/NTG_RRB - Title: 'Longitudinal predictors of cognitive well-being in children below poverty'