How Behavior Shapes The Brain And The Brain Shapes Behavior: Insights From Memory Development

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Source memory improves substantially during childhood. This improvement is thought to be closely related to hippocampal maturation. As previous studies have mainly used cross-sectional designs to assess relations between source memory and hippocampal function, it remains unknown whether changes in the brain precede improvements in memory or vice versa. To address this gap, the current study used an accelerated longitudinal design (n = 200, 100 males) to follow 4- and 6-year-old human children for 3 years. We traced developmental changes in source memory and intrinsic hippocampal functional connectivity and assessed differences between the 4- and 6-year-old cohorts in the predictive relations between source memory changes and intrinsic hippocampal functional connectivity in the absence of a demanding task. Consistent with previous studies, there were age-related increases in source memory and intrinsic functional connectivity between the hippocampus and cortical regions known to be involved during memory encoding. Novel findings showed that changes in memory ability early in life predicted later connectivity between the hippocampus and cortical regions and that intrinsic hippocampal functional connectivity predicted later changes in source memory. These findings suggest that behavioral experience and brain development are interactive, bidirectional processes, such that experience shapes future changes in the brain and the brain shapes future changes in behavior. Results also suggest that both timing and location matter, as the observed effects depended on both children’s age and the specific brain ROIs. Together, these findings add critical insight into the interactive relations between cognitive processes and their underlying neurologic bases during development.

Keywords: accelerated longitudinal design; brain development; episodic memory; hippocampal functional connectivity; memory development; source memory

1Department of Curriculum and Learning Sciences, Zhejiang University, Zijingang Campus, Hangzhou, 310058, 2Department of Psychology,

The University of Maryland, College Park, Maryland 20742, and 3Children’s Hospital, Zhejiang University School of Medicine, National Clinical

Research Center for Child Health, Hangzhou, 310052

Significance Statement

Cross-sectional studies have shown that the ability to remember the contextual details of previous experiences (i.e., source memory) is related to hippocampal development in childhood. It is unknown whether hippocampal functional changes pre-cede improvements in memory or vice versa. By using an accelerated longitudinal design, we found that early source memory changes predicted later intrinsic hippocampal functional connectivity and that this connectivity predicted later source memory changes. These findings suggest that behavioral experience and brain development are interactive, bidirectional processes, such that experience shapes future changes in the brain and the brain shapes future behavioral changes. Moreover, these interactions varied as a function of children’s age and brain region, highlighting the importance of a developmental perspective when investigating brain-behavior interactions.

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Introduction

Source memory improves substantially during childhood (e.g., Riggins, 2014). Specifically, with age, children become better at reporting and retaining contextual details of life experiences (Bauer, 2007). This development is closely related to hippocampal maturation, as evidenced by age- and memory-related differences in hippocampal structure and function across development (see Ghetti and Bunge, 2012 for review; Sastre et al., 2016; Tang et al., 2018; Riggins et al., 2020). Previous studies have mainly used cross-sectional designs to assess relations between brain and memory development, which do not allow for investigating true developmental changes and may be influenced by confounding factors, such as cohort effects. Therefore, it remains unknown whether changes in the brain precede improvements in memory or vice versa. Additionally, previous studies have focused on either young children or school-aged children, which makes comparisons of different developmental periods difficult. To address these gaps, the current study used an accelerated longitudinal design to follow 4- and 6-year-old children for 3 years. This design allowed us to explore developmental changes in intrinsic hippocampal functional connectivity (iHFC) and to assess differences between the 4- and 6-year-old cohorts in the predictive relations between source memory and functional connectivity (see Fig. 1).

In adults, intrinsic functional connectivity is thought to reflect the brain’s functional architecture, which emerges as a result of task-elicited coactivation between brain regions (Fox and Raichle, 2007). Children’s intrinsic functional connectivity patterns are likely constructed in a similar manner; however, the long-term molding hypothesis has proposed that these connectivity patterns are shaped over time as a result of both maturation and experience (Gabard-Durnam et al., 2016). For example, by following 4- to 18-year-olds over 2 years, prospective analyses indicated that task-elicited amygdala functional connectivity predicted resting-state functional connectivity 2 years later, but not concurrently (Gabard-Durnam et al., 2016). These findings suggest associations between task-based brain activation and intrinsic functional connectivity in both children and adults; however, such associations may differ across development.

Empirical data also support bidirectional influences between the brain and behavior. First, previous studies support the notion that behavioral changes shape task-based and intrinsic functional connectivity (e.g., Jolles et al., 2016; Clark et al., 2017; Rosenberg-Lee et al., 2018). For example, in 8- to 9-yearold children, 8 weeks of math tutoring strengthened iHFC to intraparietal sulcus (Jolles et al., 2016). Second, empirical studies have shown that intrinsic functional connectivity can predict gains in cognitive abilities later in development (e.g., Hoeft et al., 2011; Supekar et al., 2013; Evans et al., 2015). For example, Supekar et al. (2013) found that iHFC measured before math tutoring predicted performance improvements after tutoring during middle childhood.

Based on these studies, we explored whether there were bidirectional influences between source memory changes and intrinsic functional connectivity from the hippocampus to brain regions reported to support encoding contextual information (Geng et al., 2019). We used an accelerated longitudinal design to assess the following: (a) age-related changes in concurrent relations between source memory and iHFC; (b) predictive relations between early source memory changes and later iHFC; and (c) predictive relations between early iHFC and later source memory changes (Fig. 1)

We hypothesized that early source memory gains would predict later iHFC because the impact of experience is thought to build up over time (Gabard-Durnam et al., 2016). Accumulating experiences with everyday memory activities were expected to drive developmental changes in iHFC during childhood. Additionally, because of greater plasticity early in development (e.g., Tottenham and Sheridan, 2010), we expected that memory changes would more robustly predict later connectivity in the younger versus older cohort.

As brain connectivity has been suggested to shape later behavior (e.g., Evans et al., 2015), we hypothesized that iHFC would predict gains in source memory abilities. Additionally, because the hippocampal function is more mature during middle versus early childhood (Geng et al., 2019), we hypothesized that connectivity at 6 years would more robustly predict future memory than connectivity at 4 years.

Materials and Methods

Participants Children were participants in a large study investigating memory and brain development in early childhood that used an accelerated longitudinal design (N = 200, 100 males) (Riggins et al., 2018; Geng et al., 2019). The first wave (W1) of the study included 4- to 8-year-old children. The 4- and 6-year-old children were invited back for two subsequent waves of testing (W2 and W3; Fig. 2). In total, there were three waves, and each wave included young and old cohorts (young cohort: W1 = 4 years, W2 = 5 years, W3 = 6 years; old cohort: W1 = 6 years, W2 = 7 years, W3 = 8 years). Table 1 shows the number of children who provided 3, 2, or 1 wave of data for final analyses in each cohort. The main reasons for the loss of neuroimaging data were that the children moved too much, fell asleep during the scan, refused to enter the scanner, or the families failed to follow up.

We measured children’s IQ at W1 by using the vocabulary and block design subtests from either the Wechsler Intelligence Scale for Children (Ed 4) (Wechsler, 2003) or the Wechsler Preschool and Primary Scale for Intelligence (Wechsler, 2012). All children included in the analyses had average to above-average estimated IQ. No difference was found between the young and old cohorts in scaled scores on the vocabulary (young: mean = 11.07, SD = 2.96; old: mean = 11.66, SD = 2.55; p = 0.33) or block design subtests (young: mean = 12.96, SD = 2.73; old: mean = 13.03, SD = 3.00; p = 0.92). Parents reported all participants to be healthy without any neurodevelopmental disorders, neurologic conditions, or psychiatric conditions. Additionally, parents reported 88.5% of children as righthanded, 6.8% as lefthanded, 3.7% as ambidextrous, and 1% as not able to be determined.

Experimental design Encoding. During the first visit, children learned novel facts (e.g., “A group of rhinos is called a crash”) from one of two different sources, a female adult (“Abby”) and a male-voiced puppet (“Henry”) via digital videos (Drummey and Newcombe, 2002; Riggins, 2014). Each source provided 6 facts for a total of 12 facts. Presentation of facts was blocked by source, where children first learned 6 facts from one source followed by 6 facts from the other source, and the order of blocks was randomized across participants. There were three lists of facts; each list consisted of unique facts that were similar across lists (e.g., “A group of kangaroos is called a mob” or “A group of goats is called a tribe”). These lists were randomly assigned across participants. Children were asked to pay attention to the facts as they would be tested on the facts the following week but were not told that they would be tested on the source of the facts. Children were asked whether they knew the facts before the experiment. Known facts were excluded and were replaced with additional novel facts from the list of the same source (but this rarely occurred). Each source had 8 possible facts to account for the possibility that children would know 1 or 2 of the facts. If a child knew 3 or more facts from one source, the total number of facts the child was tested on was reduced (but this was rare, n = 4).

Retrieval. During the second visit, children were tested on their memory for the facts and sources from the first visit. Children were asked to answer 22 trivia questions and to tell the experimenter where they had learned the answers to those trivia questions. They were told that they had learned some of the questions the week before from either “Abby” or “Henry,” some they might have learned outside the laboratory (e.g., from a teacher or parent), and some they may not know. The children learned 6 of the 22 facts presented from “Abby,” 6 from “Henry,” 5 were facts commonly known by children (e.g., “What color is the sky?”), and 5 were facts that children typically would not know (e.g., “What is the colored part of your eye called?”). Each list of 22 facts had two random presentation orders, and these orders were counterbalanced across participants. If children were unable to recall the source for a particular question, five multiple-choice options were given: parents, teacher, girl in the video, a puppet in the video, or just knew/guessed

Source memory was calculated as the proportion of questions for which children accurately recalled or recognized both the fact and the source out of the total number of facts learned (see the formula below). This measure of source memory is thought to reflect the binding of facts and sources, which is an important aspect of episodic memory (Miller et al., 2013; Cooper and Ritchey, 2020).

During the task-free scan, participant head motion was monitored in real-time. If a participant exhibited excessive head motion (.2 mm in any direction) during the first half of any run, the scan was restarted and the participant was reminded to stay as still as possible.

Imaging data preprocessing In the analyses, all 210 collected resting-state fMRI images were included, as the first four volumes were discarded before data collection because of the instability of the initial MR signal and participant adaptation. Preprocessing included the following steps. First, slice time correction, head motion correction, and smoothing were performed using DPABI 1.3 (Yan et al., 2016). An independent component analysis was then run on smoothed data using MELODIC, an FSL toolbox, to remove artifact-related components (Geng et al., 2019). After removing all artifact-related components, brain extraction, normalization, and filtering were conducted. Following the procedure suggested by Tillman et al. (2018), brain extraction on T1-weighted image was conducted separately in six toolboxes: the Advanced Normalization Tools, AFNI, FSL, BSE, ROBEX, and SPM8 to ensure high-quality data. The voxels extracted by at least four toolboxes were included in the brain mask. Advanced Normalization Tools were used to perform coregistration and normalization. Statistical analyses were conducted in AFNI (Cox, 1996). Temporal bandpass filtering (0.01-0.1 Hz) and spatial smoothing with a 5 mm FWHM Gaussian kernel were performed in AFNI on normalized data.

Individual seed regions (bilateral