research paper about childhood memory

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New perspectives on childhood memory: introduction to the special issue

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1 a Department of Human Development , Cornell University , Ithaca , NY , USA.
2 b Department of Psychology , Koç University , Istanbul , Turkey.
PMID: 30384823
DOI: 10.1080/09658211.2018.1537119

This special issue brings together the scholarship that contributes diverse new perspectives on childhood amnesia - the scarcity of memories for very early life events. The topics of the studies reported in the special issue range from memories of infants and young children for recent and distant life events, to mother-child conversations about memories for extended lifetime periods, and to retrospective recollections of early childhood in adolescents and adults. The methodological approaches are diverse and theoretical insights rich. The findings together show that childhood amnesia is a complex and malleable phenomenon and that the waning of childhood amnesia and the development of autobiographical memory are shaped by a variety of interactive social and cognitive factors. This collective body of work will facilitate discussion and deepen our understanding of the dynamics that influence the accessibility, content, accuracy, and phenomenological qualities of memories from early childhood.

Keywords: Childhood amnesia; autobiographical memory; early memory development; forgetting; infantile amnesia.

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Current Theories On Early Childhood Memories – A Systematic Review Of The Literature

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Children’s Memory: A Primer for Understanding Behavior

Published: 05 August 2006
Volume 33 , pages 405–411, ( 2006 )

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Elaine S. Barry 1

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This paper provides the opportunity to understand children’s behavior from a memory viewpoint. For the last three decades, cognitive developmentalists have been asking the question, “what develops in children’s memory?” Four answers to this question are presented, complete with explanations, examples, and possible applications where appropriate. The purpose of the paper is to provide early childhood educators and other practitioners who work with children a different lens through which to view children’s behavior. The memory view is compatible with current best practices in early childhood education, and may provide practitioners an additional viewpoint from which to draw when implementing developmentally appropriate practice.

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Foundations of theory of mind and its development in early childhood

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Barry, E.S. Children’s Memory: A Primer for Understanding Behavior. Early Childhood Educ J 33 , 405–411 (2006). https://doi.org/10.1007/s10643-006-0073-3

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Received : 17 January 2006

Accepted : 29 March 2006

Published : 05 August 2006

Issue Date : June 2006

DOI : https://doi.org/10.1007/s10643-006-0073-3

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ORIGINAL RESEARCH article

The fate of childhood memories: children postdated their earliest memories as they grew older.

$\r\nQi Wang*$

1 Department of Human Development, Cornell University, Ithaca, NY, USA
2 Department of Psychology, Memorial University of Newfoundland, St. John’s, NF, Canada

Childhood amnesia has been attributed to the inaccessibility of early memories as children grow older. We propose that systematic biases in the age estimates of memories may play a role. A group of 4- to 9-year-olds children were followed for 8 years, recalling and dating their earliest childhood memories at three time points. Although children retained many of the memories over time, their age estimates of these memories shifted forward in time, to later ages. The magnitude of postdating was especially sizable for earlier memories and younger children such that some memories were dated more than a year later than originally. As a result, the boundary of childhood amnesia increased with age. These findings shed light on childhood amnesia and the fate of early memories. They further suggest that generally accepted estimates for people’s age of earliest memory may be wrong, which has far-reaching implications.

Introduction

Most people can remember their childhood experiences from about 3 or 4 years of age but not earlier, a phenomenon commonly termed childhood amnesia ( Pillemer and White, 1989 ; Bauer, 2007 ; Peterson, 2012 ). Although retrospective research on adults’ earliest childhood memories is abundant, prospective research on children’s earliest childhood memories is relatively scarce. The developmental data are critical, however, in unraveling the mechanisms that produce childhood amnesia and further identifying factors responsible for early memory development. In particular, what happened to the memories for events that occurred in the first years of life?

One commonly held theoretical view is that early memories are destined to become inaccessible or forgotten as children get older and that this eventually results in childhood amnesia ( Bauer, 2007 ; Peterson, 2012 ). In support of this view, cross-sectional studies of children’s childhood recollections have observed an increase in the age of earliest memory with age, whereby older children and adolescents recall their earliest memories from later ages than do younger children ( Peterson et al., 2005 , 2009 ; Jack et al., 2009 ; Tustin and Hayne, 2010 ). Existing prospective research has also shown that early memories of children exhibit a constant rate of forgetting characterized by the exponential function, which results in a shrinking pool of memories available for later retrieval ( Bauer and Larkina, 2014 ).

However, not all early memories are lost to recollection over the course of development. Given that much of the memory faculty has been in place by preschool age and that young preschoolers are often able to recall events occurring months or even years ago ( Nelson and Fivush, 2004 ; Bauer, 2007 ; Peterson, 2012 ), it is possible that some of the early memories may remain accessible as children grow older. Indeed, Peterson et al. (2011) observed in a longitudinal study of earliest memories that 43.6% of preschool through teenage children produced overlapping memories between two interviews spanning across a 2-year period. This finding is critical: it suggests that childhood amnesia may not be a mere result of an obscured period of life and that there may be other explanations.

To explore the possibility, Wang and Peterson (2014) conducted two prospective studies in which they asked 4- to 13-year-olds children to recall and date their earliest memories at two time points, with a 1-year or 2-year interval. Consistent with the earlier observation ( Peterson et al., 2011 ), they found that many memories remained accessible over time. However, children postdated these memories to significantly older ages as time went by, especially memories from earlier years of life. Thus, although children continued to remember many of the same events as their earliest memories, the location in time of the memories shifted to an older age. Wang and Peterson (2014) suggest that this may eventually result in a period of childhood “amnesia” from which no memories are dated, instead of no memories able to be recalled.

These findings are in line with general research on memory dating. Studies have shown that when people recall and date distant memories from their lives, they often make telescoping errors: They postdate the memories as if the events have happened more recently than they actually have, which resembles the situation where an object appears closer in distance when viewed through a telescope ( Loftus and Marburger, 1983 ; Rubin and Baddeley, 1989 ; Janssen et al., 2006 ). Telescoping has been explained in terms of the smaller or less complete retention for distant memories, which are then dated with less precision than more recent events ( Huttenlocher et al., 1988 ; Rubin and Baddeley, 1989 ). Conceivably, childhood memories may be particularly prone to telescoping errors given their decreased retention with elapsed time ( Pillemer and White, 1989 ; Bauer, 2007 ), and children may be particularly vulnerable to telescoping errors due to their limited knowledge of time and memory dating strategies ( Friedman, 2005 ; Wang et al., 2010 ; Pathman et al., 2013 ; Pathman and Ghetti, 2014 ). Although studies with different age groups have demonstrated the malleability of earliest memories ( Wang et al., 2004 , 2010 ; Wang, 2006 ; Peterson et al., 2011 ; Kingo et al., 2013 ), the studies by Wang and Peterson (2014) are the first to identify systematic telescoping errors over time in the dating of earliest childhood memories.

Thus, the prospective studies by Wang and Peterson (2014) provide the initial evidence for an alternative explanation for childhood amnesia. Nevertheless, given the limited 1-year and 2-year intervals between interviews, the findings are inconclusive. Will children continue to remember and postdate their memories after a prolonged period of time? Will the memory age estimates become stabilized at some point over the course of development? We investigated these intriguing questions in the present study. In the sample of 125 children of Wang and Peterson (2014) , we were able to locate 37 children 8 years later after the initial interview. Thus, we were able to follow this small group of 4–5, 6–7, and 8–9-year-olds children for 8 years, examining their recall and dating of their earliest memories at three time points: an initial interview, a 2-year follow-up, and an 8-year follow-up. We expected that children would continue postdating their memories with elapsed time. On the other hand, we expected that as children grew older, memory age estimates might become part of their memory or personal “knowledge” (e.g., “I was three and a half when my parents took me to Paris the first time”) and thus stabilized. This, coupled with increasing memory retention and memory dating strategies ( Friedman, 2005 ; Bauer, 2007 ; Pathman et al., 2013 ; Pathman and Ghetti, 2014 ), might result in a decrease in the magnitude of postdating among older children and for older memories.

Materials and Methods

Ethical statement.

The Interdisciplinary Committee on Ethics in Human Research, Memorial University of Newfoundland, Canada approved the study. Parents were asked if they would give permission for their children to participate, and children were asked to give informed assent.

Participants

The sample consisted of 37 children who were interviewed three times about their earliest memories over the course of 8 years. At the initial interview, the children included 13 4- to 5-year-olds (seven girls, M = 5.04 years, SD = 0.66; referred to as the “youngest group” hereafter), 12 6- to 7-year-olds (three girls, M = 6.88 years, SD = 0.66; referred to as the “middle group”), 12 8- to 9-year-olds (five girls, M = 8.94 years, SD = 0.48; referred to as the “oldest group”). At the 2-year follow up, the mean age was 7.80, 9.08, and 11.26 years ( SDs = 0.93, 0.77, and 0.48) for the youngest, middle, and oldest groups, respectively. At the 8-year follow up, the mean age was 14.34, 15.60, and 16.17 ( SDs = 1.52, 2.23, and 1.16) for the three groups, respectively. The children were from primarily White, middle-class families in Newfoundland, Canada and were part of a larger study investigating children’s memory development. Parents gave permission for their children to participate and children gave informed assent.

A female experimenter interviewed children at home. She asked children to think of their three earliest memories. General prompts such as “What else do you remember about that?” were used to probe children to give as much information as possible. Following each memory, children were asked how old they were when the memory event took place, followed by questions to help them narrow down their age estimate into a particular month or small range of months: “How old were you when this happened?” “Do you remember what time of year it was?” “Was it summer or winter?” “Was it near your birthday/Christmas/Halloween?” If children specified a range of months (e.g., “The summer when I was 3”), the midpoint of that range was used.

Two years after the initial interview, children were interviewed again in an identical procedure during which their three earliest memories were elicited. Children first recalled three memories spontaneously, which yielded a mixture of “initial” (i.e., memories recalled at the initial interview) and “new” memories (i.e., memories recalled for the first time at the 2-year interview). To facilitate children’s recall, a cued-recall procedure followed if children failed to spontaneously produce any of the three “initial” memories they recalled 2 years previously. A synopsis of each of the memories was read to them that contained critical information about the event (e.g., “One time someone tripped you at school and you broke the pot that you had just made.”). After each memory was read, children were asked whether this memory ever happened to them and, if they recognized the memory, they were asked to recall and date the memory. To ensure that children were not simply confirming the cued events, three synopses of “lure” events (i.e., memories recalled by other children) were also read to them. Children invariably identified lures as having never happened to them.

Then, 8 years after the initial interview children were interviewed again identically to their prior interviews. They were first asked to recall and date their three earliest memories. If they failed to spontaneously produce any of the “initial” or “new” memories, a cued-recall procedure followed as in the 2-year interview.

Results and Discussion

Among the 37 children, 32 (86.5%; nine from the youngest group, 11 from the middle group, and 12 from the oldest group) recalled and dated at least one “initial” memory at both the 2-year and 8-year interviews ( N = 29) or at one of the interviews ( N = 3). This resulted in a total of 73 “initial” memories being recalled and dated at later time points, on average 2.28 memories per child (16 by the youngest group, 26 by the middle group, and 31 by the oldest group, whereby the youngest group recalled fewer initial memories than did the two older groups at marginal significance, F (2,29) = 2.94, p = 0.07, η p 2 = 0.17). At the 8-year interview, 30 out of the 37 children (81.1%; six from the youngest group, 12 from the middle group, and 12 from the oldest group) recalled and dated at least one “new” memory that they produced 6 years ago at the 2-year interview. This resulted in a total of 55 “new” memories being recalled and dated at the two follow-up interviews, on average 1.83 memories per child (10 by the youngest group, 24 by the middle group, and 21 by the oldest group, whereby the mean number did not differ significantly across groups, F (2,27) = 0.51, p = 0.61, η p 2 = 0.04). The “initial” memories ( M = 42.96 months, SD = 21.60) were significantly earlier than the “new” memories ( M = 55.69 months, SD = 22.30) at the first time when they were recalled, F (1,120) = 12.67, p = 0.0005, η p 2 = 0.08.

Subsequent analyses focused on the age estimates of the “initial” and “new” memories at different time points. Preliminary analyses showed no systematic gender differences, so gender was not considered further. The variability in the timing of follow-up interviews across children did not affect the pattern of results. In line with our previous findings ( Wang and Peterson, 2014 ), spontaneous (24%) and cued memories (76%) showed identical patterns and were pooled together in analysis. Given the small sample size, we included results with p- values close to 0.10. We emphasize the importance of considering effect sizes to appraise the strength of the evidence, which, unlike p- values, are not subject to the influence of sample sizes ( Rosenthal and Rosnow, 1991 ).

Initial Memories

We examined the age estimates of the initial memories across the three time points, with memory as the unit of analysis. Based on prior findings that memory events that occurred before 48 months were particularly prone to postdating ( Wang et al., 2010 ; Wang and Peterson, 2014 ), we examined children’s memories initially dated before (52%) and after (48%) 48 months separately. We conducted a 3 (age group) × 3 (time point) × 2 (initial memory age: before or after 48 months) mixed model analysis on age estimates using SAS PROC MIXED program ( Singer, 1998 ), with age group being a between-subject factor, time point and initial memory age being within-subject factors, and subject being a random factor. There was no significant 3-way interaction ( p = 0.97), which was then excluded from the final model.

There were main effects of time point, F (2,151) = 14.81, p < 0.0001, Δ R 2 = 0.19, and initial memory age, F (1,151) = 89.59, p < 0.0001, Δ R 2 = 0.29, qualified by an Age group × Time, F (4,151) = 3.58, p = 0.008, Δ R 2 = 0.06, and an Age group × Initial memory age interaction, F (2,151) = 2.95, p = 0.057, Δ R 2 = 0.03. Further analyses were conducted with memories from before and after 48 months, separately. As shown in Figure 1 , across all age groups, memories occurring before 48 months were generally postdated at the follow-up interviews, F (2,70) = 13.70, p < 0.0001, Δ R 2 = 0.31. This was particularly true for the youngest group, F (2,21) = 7.91, p = 0.003, Δ R 2 = 0.26, relative to the middle group, F (2,27) = 2.15, p = 0.14, Δ R 2 = 0.05, or the oldest group, F (2,22) = 5.35, p = 0.01, Δ R 2 = 0.26. Memories occurring after 48 months also showed an effect of time point, F (2,69) = 3.19, p = 0.05, Δ R 2 = 0.04, which appeared to be driven solely by the youngest group who tended to postdate memories over time, F (2,4) = 4.03, p = 0.11, Δ R 2 = 0.48.

FIGURE 1. Age of “initial” earliest memories dated at three time points as a function of age group and initial memory age. Error bars represent standard errors of the means.

To further test the effects of the initial memory age and the initial child age on the magnitude of postdating, we conducted regression analyses with memory age and child age at the initial interview (both being continuous variables) as predictors and the change in memory age at a subsequent interview (i.e., age estimates at the 2- or 8-year interview – age estimates at the initial interview) as the outcome variable, including subject in the models as a random factor. The initial memory age, t = –2.47, B = –0.35, p = 0.02, and the initial child age, t = –2.00, B = –0.33, p = 0.05, both negatively predicted the magnitude of postdating at the 8-year interview. A similar but non-significant trend also appeared at the 2-year interview for both memory age, t = –1.45, B = –0.12, p = 0.15, and child age, t = –1.31, B = –0.14, p = 0.20. Thus, confirming the findings from the mixed model analysis, earlier memories were postdated to a greater extent than later memories regardless of children’s age, especially as time further elapsed. As well, younger children postdated their memories to a greater extent than did older children, after an extended interval period.

In summary, childhood memories, especially those from earlier years and those of younger children, were subject to postdating over time. As a result, the average age of the very first memories children recalled increased from 35.81 months at the initial interview to 39.96 months 2 years later and to 52.54 months 8 years later. Thus, the boundary of childhood amnesia shifted substantially forward in time over the course of development. This may partially explain why younger children tend to provide earlier childhood memories than older children and adults ( Peterson et al., 2005 ; Jack et al., 2009 ; Tustin and Hayne, 2010 ). It is important to further stress that the actual events being recalled by the children did not shift forward in time – the same events were recalled across the interviews – only children’s dating of them. In other words, the shift forward of the boundary of childhood amnesia is at least in part an artifact of systematic changes in memory dating.

Given that memories from the earlier years of life and those of preschool children are often retained with lesser quality and coherence than more recent memories and memories of older children and adults ( Bauer, 2007 ; Pathman and Ghetti, 2014 ), they were particularly vulnerable to dating errors, consistent with previous findings ( Friedman, 2005 ; Wang et al., 2010 ; Pathman et al., 2013 ; Wang and Peterson, 2014 ). In contrast, the memory age estimates by older children and of later memories seemed to be stabilized over time. This may reflect better memory retention and thus less postdating among older children and for more recent memories. In addition, as children grow older, dating information of earliest memories may be encoded as part of their memory or personal knowledge, which then remains stable thereafter.

New Memories

Next, we examined the age estimates of the new memories first recalled and dated at the 2-year interview and again at the 8-year interview, with memory as the unit of analysis. Memories first dated before (29%) and after 48 months (71%) were examined separately. We conducted a 3 (age group) × 2 (time point) × 2 (initial memory age: before or after 48 months) mixed model analysis on age estimates using SAS PROC MIXED program ( Singer, 1998 ), with age group being a between-subject factor, time point and initial memory age being within-subject factors, and subject being a random factor. The 3-way interaction was not significant ( p = 0.91) and then excluded from the final model.

A main effect of initial memory age emerged, F (1,73) = 37.36, p < 0.0001, Δ R 2 = 0.37, qualified by a Time x Initial memory age interaction, F (1,73) = 4.23, p = 0.04, Δ R 2 = 0.04. As shown in Figure 2 , across all age groups, memories occurring before 48 months tended to be postdated between the 2-year and 8-year interviews, F (1,15) = 2.80, p = 0.12, Δ R 2 = 0.07, whereas memories from after 48 months were not postdated, F (1,47) = 0.14, p = 0.71, Δ R 2 = 0.02. As a result, the age differences between memories before and after 48 months decreased by the 8-year interview.

FIGURE 2. Age of “new” earliest memories dated at the 2-year and 8-year interviews as a function of age group and initial memory age. Error bars represent standard errors of the means.

We further conducted a regression analysis to test the effects of memory age and child age at the 2-year interview on the change in memory age by the 8-year interview (i.e., age estimates at the 8-year interview – age estimates at the 2-year interview), including subject in the model as a random factor. Memory age at the 2-year interview negatively predicted the magnitude of postdating at the 8-year interview, t = –2.76, B = –0.27, p = 0.008. Thus, earlier new memories were postdated to a greater extent than later new memories as time went by, independent of children’s age. Children’s age was not a significant predictor of the magnitude of postdating.

Thus, following a 6-year interval, children’s memories were postdated such that the average age of the earliest new memories children recalled shifted later in time, from 49.57 to 54.90 months. Like the initial memories, earlier new memories were particularly prone to postdating whereas later memories remained relatively stable in age estimates over time. Interestingly, there was no age difference among children in the magnitude of postdating for new memories. Because new memories were considerably older than initial memories and were first recalled at the 2-year follow-up when children were all in their middle childhood or beyond, children of different age groups might not differ in their levels of retention ( Bauer, 2007 ; Wang et al., 2014 ) and therefore showed similar levels of postdating.

General Discussion

This prospective study investigated children’s recall and dating of earliest childhood memories at multiple time points over an extended period of time. In spite of the small sample, the effect sizes were comparable with previous studies ( Peterson et al., 2011 ; Wang and Peterson, 2014 ). The cross-sectional longitudinal design allowed us to simultaneously examine the effects of age at encoding, retention interval, and age of children on memory dating. The findings showed that although children continued to remember many of the memories they recalled 8 years ago, they postdated the memories, especially the earlier ones, to considerably later ages as time passed. The memory age estimates seemed to be stabilized among older children and for older memories. The pattern of findings is consistent with both memories recalled at the initial interview (i.e., initial memories) and those newly recalled at the 2-year interview (i.e., new memories). The study further extends Wang and Peterson’s (2014) findings by showing that earliest memories continued to be postdated many years following the previous recalls and that the magnitude of postdating was smaller for older children and older memories. Perhaps over the course of development, the age estimates may eventually be integrated as part of the memory or personal “knowledge” so that later in retrospect we all “know” when our earliest memories took place.

We would like to emphasize our key finding: that young children continued the process of re-dating their memories for several years after the recalled events actually occurred. By the time children were 8 years older than initially, the age of estimated event occurrence was more than a year later. The magnitude of this re-dating is astonishing. This suggests that our accepted knowledge and wisdom (and our textbooks) may be wrong. If the average age of earliest memory identified in current research is 3.5 years and there is systematic mis-dating by a year or more, then people’s earliest memories may actually date from when they were 2-year-olds.

Note that we do not assume that memories were dated with absolute accuracy at the initial interview. It is the postdating of the same memories over time that is of interest. Indeed, children might have already made telescoping errors the first time they were interviewed for the memories. As shown in Wang et al. (2010) , children postdated early memories compared with their parents, and adult studies have shown that memories from the beginning of a life period (e.g., childhood as in the current study) tend to show telescoping errors of postdating ( Loftus and Marburger, 1983 ; Rubin and Baddeley, 1989 ). If the children in the current study were already making telescoping errors from the start, the magnitude of actual memory dating errors might be even larger than what we observed at the follow-up interviews. In addition, it is unlikely that children’s age estimates became more accurate over time, given that dating accuracy declines with retention interval in both children and adults ( Janssen et al., 2006 ; Friedman et al., 2011 ).

The present study spanned across 8 years. It yielded critical findings about the fate of early childhood memories, which have far-reaching implications. Again, we emphasize that the time of occurrence of the events being recalled by the children in this study did not shift forward in time. Rather, children’s dating of those memories shifted. Thus, as we suggested before ( Wang and Peterson, 2014 ), people’s earliest memories may be earlier than they think. Prior reviews of the childhood amnesia literature have suggested that the average age of earliest memories among Western Europeans and North Americans is 3.5 years of age (e.g., Rubin, 2000 ). We suggest that the average age of earliest memories is probably earlier than that, and that distortions in memory dating may have led to erroneous conclusions about when our earliest memories occurred.

Author Contributions

QW analyzed the data and drafted the manuscript. CP designed the study, supervised data collection and worked on the manuscript.

This research was supported by Grant 513-02 from the Natural Sciences and Engineering Research Council of Canada to CP; and by Grant BCS-0721171 from the National Science Foundation to QW.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgment

We thank Penny Voutier for her assistance, and the participating children who made the study possible.

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Keywords : childhood amnesia, postdating, earliest memory, memory age estimate, prospective study, telescoping

Citation: Wang Q and Peterson C (2016) The Fate of Childhood Memories: Children Postdated Their Earliest Memories as They Grew Older. Front. Psychol. 6:2038. doi: 10.3389/fpsyg.2015.02038

Received: 26 October 2015; Accepted: 21 December 2015; Published: 12 January 2016.

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Copyright © 2016 Wang and Peterson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Qi Wang, [email protected]

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> The Development of Memory

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The development of memory.

Published online by Cambridge University Press: 01 January 1998

This article reviews recent advances in understanding the changes in memory function that take place during the childhood years. Development of the following aspects of memory are considered: short-term memory, comprising phonological memory, visuospatial memory, and executive function; autobiographical memory; episodic memory, including eyewitness memory; and metamemory. Each of these aspects of memory function shows substantial qualitative change from infancy, through the preschool period, to the early school years. Beyond about 7 years of age, however, memory function appears adult-like in organisation and strategies, and shows only a gradual quantitative improvement through to early adolescence.

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Volume 39, Issue 1
Susan E. Gathercole (a1)
DOI: https://doi.org/10.1017/S0021963097001753

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Dev Cogn Neurosci
v.59; 2023 Feb

Childhood trauma and brain structure in children and adolescents

Matthew peverill.

a University of Wisconsin, Department of Psychiatry, 6001 Research Park Blvd., Madison, WI 53719, USA

b Harvard University, Department of Psychology, William James Hall, 10th Floor, 33 Kirkland St., Cambridge, MA 02138, USA

Maya L. Rosen

c Smith College, Program in Neuroscience, Clark Science Center, 44 College Ln, Northampton, MA 01063, USA

Lucy A. Lurie

d University of North Carolina, Chapel Hill, Department of Psychology, 235 E Cameron Ave., NC 27599, USA

Kelly A. Sambrook

Margaret a. sheridan, katie a. mclaughlin, associated data.

Data will be made available on request.

The dimensional model of adversity proposes that experiences of threat and deprivation have distinct neurodevelopmental consequences. We examined these dimensions, separately and jointly, with brain structure in a sample of 149 youth aged 8–17—half recruited based on exposure to threat-related experiences. We predicted that greater threat would be uniquely associated with reduced cortical thickness and surface area in brain regions associated with salience processing including ventromedial prefrontal cortex (vmPFC), anterior cingulate cortex (ACC), and insula, and that deprivation experiences would be uniquely associated with reductions in cortical thickness and surface area in frontoparietal areas associated with cognitive control. As predicted, greater threat was associated with thinner cortex in a network including areas involved in salience processing (anterior insula, vmPFC), and smaller amygdala volume (particularly in younger participants), after controlling for deprivation. Contrary to our hypotheses, threat was also associated with thinning in the frontoparietal control network. However, these associations were reduced following control for deprivation. No associations were found between deprivation and brain structure. This examination of deprivation and threat concurrently in the same sample provided further evidence that threat-related experiences influence the structure of the developing brain independent of deprivation.

• Threat-related experiences were associated with thinner cortex in multiple areas.
• Threat-related thinning spanned salience, default, and somatomotor networks.
• This thinning was robust to control for deprivation experiences.
• Younger, threat-exposed participants had smaller amygdala volumes.

1. Introduction

Increasing evidence suggests that adverse childhood experiences are associated with numerous aspects of neural development, including brain structure ( Hart and Rubia, 2012 ; McLaughlin, Weissman et al., 2019 ; Teicher et al., 2016 ). In particular, experiences of adversity have been associated with reduced cortical thickness and surface area, as well as reduced volume of sub-cortical structures including the amygdala and hippocampus in children and adolescents ( Colich et al., 2020 , Gold et al., 2016 , Kelly et al., 2015 , Lim et al., 2014 , McLaughlin et al., 2016 , McLaughlin et al., 2019 ). We have proposed that threat and deprivation are dissociable dimensions of adverse environmental experiences that have neurodevelopmental consequences which are at least partially distinct ( McLaughlin et al., 2014 , Sheridan and McLaughlin, 2014 ). However, prior studies of childhood adversity have been limited in their ability to jointly examine these dimensions of experience in relation to brain structure. In the current study, we separately test the associations of continuous measures of threat and deprivation with brain structure in a large sample of youth with a broad variety of adversity experiences.

The dimensional model of adversity suggests that adverse childhood experiences reflect underlying dimensions of environmental experience that are shared by numerous types of adversity. In particular, this model distinguishes between two central dimensions of adverse environmental experiences: threat and deprivation. Threat is defined as experiences that involve harm or threat of harm to the physical integrity of the child and includes experiences involving interpersonal violence such as physical abuse, sexual abuse, witnessing domestic violence, or encountering community violence. Deprivation is defined as a reduction in expected social and cognitive inputs from the environment and includes experiences such as physical neglect, emotional neglect, institutional rearing, parental separation, and lack of cognitive stimulation. The dimensional model improves upon single-exposure and cumulative risk approaches by accounting for the co-occurrence of adverse experiences without assuming that all experiences influence neurodevelopment through the same underlying mechanisms (See McLaughlin et al., 2014 , McLaughlin et al., 2021 ; Sheridan and McLaughlin, 2014 for review).

The dimensional model posits that experiences of threat will have the largest associations with brain systems involved in emotional processing and the detection of salience and threat ( McLaughlin et al., 2014 , McLaughlin et al., 2019 ). Extensive evidence shows that threatening experiences in childhood are associated with shifts in information processing that facilitate the rapid identification of threat, including greater perceptual sensitivity and attention biases to threat along with hostile attribution biases ( Dodge et al., 1995 , Pollak et al., 2000 , Pollak and Sinha, 2002 ). Children who have experienced violence also exhibit heightened emotional reactivity to threat cues ( Heleniak et al., 2016 , Jenness et al., 2021 , McCrory et al., 2011 ), altered fear learning ( DeCross et al., 2022 , Machlin et al., 2019 , McLaughlin et al., 2016 ), and changes in learning and memory in the presence of threat cues ( Lambert et al., 2019 , Lambert et al., 2017 ). Importantly, these changes are likely adaptive for young people in threatening environments such that they may help them mobilize defensive responses to promote safety. However, these changes may result in over-identification of threat in safe contexts, leading to over-generalization of fear responses and psychopathology ( Keding and Herringa, 2016 , Marusak et al., 2015 , Pine et al., 2005 ). We would thus expect that experiences of threat would be associated with changes in brain systems involved in processing salience and threat cues. This includes the salience network—encompassing the anterior insula, dorsal anterior cingulate, and the amygdala, and a network referred to in cortical network discovery studies as the limbic network (e.g., Yeo et al., 2011 )—including the lateral and medial orbitofrontal cortex (OFC), ventromedial prefrontal cortex (vmPFC), and temporal pole. Indeed, previous studies focused on the impact of child abuse—an experience involving a high degree of threat—on brain structure have found evidence of reduced volume and/or thickness in salience processing regions including medial OFC, vmPFC, and temporal pole as well as reduced amygdala and hippocampus volume ( Colich et al., 2020 , Gold et al., 2016 , Kelly et al., 2015 , Lim et al., 2014 , McLaughlin et al., 2016 ). A recent systematic review of over 100 studies of childhood adversity and neural development confirmed that exposure to threat, but not deprivation, was associated with reduced volume of amygdala and vmPFC as well as heightened activity in the salience network in response to negative emotional stimuli ( McLaughlin et al., 2019 ).

In contrast, experiences of deprivation—such as neglect, institutional rearing, and low levels of cognitive stimulation—are associated with reduced performance on tasks of attention and executive function ( Finn et al., 2017 , Sheridan et al., 2012 , Spielberg et al., 2015 ). Correspondingly, the dimensional model posits that the frontoparietal control network, which supports executive function ( Corbetta and Shulman, 2002 , Curtis and D’Esposito, 2003 ) may be particularly impacted by experiences of deprivation. Indeed, prior studies on adverse experience characterized by deprivation, including institutional rearing ( Herzberg et al., 2018 , Hodel et al., 2015 , McLaughlin et al., 2014 ), neglect (physical and emotional; Edmiston et al., 2011 ), and low cognitive stimulation ( Rosen et al., 2018 ) have found evidence of reduced cortical thickness and volume in lateral prefrontal and parietal cortex (see McLaughlin et al., 2019 for review).

Previous studies have demonstrated that threat and deprivation are separately associated with brain structure differences in distinct neural systems. However, prior work has predominantly recruited samples of children based on exposure to either threat or deprivation without assessing or controlling for the other. As argued elsewhere ( McLaughlin et al., 2021 ) the strongest evidence for distinct influences of these dimensions of adversity on neural development would come from documentation of associations of one dimension of adversity with brain structure while controlling for the other dimension of adversity. Here, in a large sample of children and adolescents aged 8–16 years, we investigate the associations of threat and deprivation experiences with cortical thickness and surface area as well as amygdala and hippocampus volume. We hypothesize that threat will be associated with reduced thickness and surface area of cortical regions in the salience and limbic network and decreased volume of the amygdala and hippocampus. Additionally, we hypothesize that deprivation will be associated with reduced cortical thickness and surface area in the frontoparietal control network. Finally, we hypothesize that brain structure differences associated with threat will persist after controlling for deprivation and those associated with deprivation will persist after controlling for threat.

2.1. Sample

Data were drawn from a larger study of youth examining maltreatment and emotion regulation in Seattle, WA between January 2015 and June 2017. A total of 161 youth participated in the MRI visit that serves as the basis of this paper. The MRI sample was comprised of children who were exposed to maltreatment, and a control sample of participants matched to each maltreatment-exposed participant on age, sex, and handedness. Exclusion criteria included IQ< 80, pervasive developmental disorder, active psychotic symptoms, mania, substance abuse, MRI contraindications (e.g., braces), or safety concerns as measured reported by caregivers during screening or assessed during the study visits. Brain structure data from 12 participants was excluded after data collection due to motion-related artifacts in their structural scan (see MRI pre-processing). Recruitment was targeted at identifying children with maltreatment experiences (see supplement for further recruitment details). See Table 1 , Table 2 for socio-demographic characteristics of the sample with comparison to the Seattle population.

Sample Description.

	mean	sd.	min	max
Age	12.64	2.67	8.03	17.25
Income-to-Needs Ratio	3.73	2.78	0.10	10.35
Threat Count	1.39	1.41	0	4.00
Deprivation Count	0.92	1.05	0	4.00
VEX-R Violence Experiences	3.58	2.66	0	10.00

Sample Categorical Descriptors. a: Census data from the National Center on Educational Statistics (2015) – see supplement for detail. b: Many participants identified with more than one race/ethnicity descriptor.

		Sample (n = 149)		With Exclusions (n = 161)		Census Comparison
Sex	Female	72	48.3%	77	47.8%	48.7%
	Male	77	51.7%	84	52.2%	51.3%
Parent Ed.	High School or Less	19	12.8%	26	16.1%	11.1%
(highest)	Some College (no degree)	18	12.1%	22	13.7%	10.4%
	College Degree	34	22.8%	34	21.1%	41.7%
	Post-Graduate Degree	52	34.9%	52	32.3%	37.0%
	Did not report	26	17.4%	27	16.8%	N/A
Race/Ethnicity	White	116	77.9%	123	76.4%	73.3%
	Black	33	22.1%	39	24.2%	16.5%
	Native American	13	8.7%	16	9.9%	2.6%
	Asian	19	12.8%	21	13.0%	20.1%
	Pacific Islander	0	0.0%	1	0.6%	1.2%
	Latinx	19	12.8%	20	12.4%	9.4%
	Biracial	9	6.0%	9	5.6%	16.0%
	Other	14	9.4%	14	8.7%	4.2%

The Institutional Review Board at the University of Washington approved all procedures. Legal guardians provided written informed consent; children provided written assent.

2.2. Measures

2.2.1. threat experiences.

A multi-informant, multi-method approach was used for assessing children’s experiences of threat. A composite threat score used in prior work (e.g., Sumner et al., 2019 ) was computed based on children’s experiences of physical abuse, sexual abuse, domestic violence, emotional abuse, and other forms of interpersonal violence. Caregivers and youth reported on physical abuse, sexual abuse, and domestic violence on the UCLA PTSD Reaction Index (PTSD-RI; Steinberg et al., 2013 ). Youth reported on experiences of physical abuse, sexual abuse, witnessing domestic violence, and emotional abuse on the Childhood Experiences of Care and Abuse Interview (CECA; Bifulco et al., 1994 ). Experiences of physical, sexual, and emotional abuse were also considered present if children scored above a validated threshold on the respective subscales from Childhood Trauma Questionnaire (CTQ; Bernstein et al., 1997 ; Walker, Gelfand et al., 1999 ). Domestic violence was also considered present if youth endorsed witnessing violence directed at a caregiver on the Violence Exposure Scale for Children Revised (VEX-R; Fox and Leavitt, 1995 ) or PTSD-RI. The number of different types of other witnessed or experienced interpersonal violence experiences (e.g., experiences of violence in the school or community) were measured based on youth report on the VEX-R. Finally, caregivers reported on their child’s experiences of physical abuse, sexual abuse, and domestic violence on the Juvenile Victimization Questionnaire (JVQ; Finkelhor et al., 2005 ). The final threat composite was computed by summing dichotomous scores for exposure to physical abuse, sexual abuse, domestic violence, and emotional abuse with the standardized interpersonal violence score from the VEX-R (See Supplement for example items and scoring). Inter-rater reliability was good for child and caregiver maltreatment reports (82.0% agreement; kappa=0.62).

2.2.2. Deprivation experiences

A composite score reflecting youths’ experiences of physical or emotional neglect, food insecurity, or low levels of cognitive stimulation in the home was computed based on a similar multi-reporter, multi-method approach utilizing youth and caregiver report on several self-report and interview measures ( Bifulco et al., 1994 , Blumberg et al., 1999 , Mott, 2004 , Walker et al., 1999 ). Physical neglect was assessed based on youth report on the physical neglect subscale from the CTQ. Experiences of emotional neglect were scored as present based on youth-report on the emotional needs subscale of the CECA. Food insecurity was assessed using caregiver report on four questions from the short form of the U.S. Department of Agriculture’s Food Security Scale, a validated measurement of food insecurity ( Blumberg et al., 1999 ). These items have been previously used in epidemiological surveys of youth psychopathology (e.g. Kessler et al., 2009 ). Cognitive stimulation was assessed using the short form of the Home Observation for Measurement of the Environment (HOME-SF; Mott, 2004 ). The HOME-SF has slightly different versions for children aged 6–9 and 10–15 years, with 16 items that are identical across these age ranges. We used only the 16 questions that are present in the HOME-SF for both younger and older children. The measure was scored using the cut-offs used in the original HOME, and following prior work (e.g., Sumner et al., 2019 ). Participants scoring under a 12 were considered to have been exposed to low levels of cognitive stimulation. The final deprivation composite was computed by summing the dichotomous scores for physical neglect, emotional neglect, food insecurity, and cognitive deprivation (See Supplement for example items and scoring).

2.2.3. Socioeconomic Status (SES)

Household income was assessed by parent report using questions adapted from the U.S. Census Bureau Current Population Survey (U.S. U.S. Census Bureau, 2015 ). The income-to-needs ratio was calculated by dividing this approximate continuous income by the 2018 federal poverty line for a family of the reported size, such that a value less than one indicating that a family was living below the poverty line (e.g., $25,465 for a two parent, two child household). This value was then log transformed and used as the index of family SES, which was included as a control variable in all models.

2.3. Image acquisition and pre-processing

Scanning was performed on a 3 T Phillips Achieva scanner at the University of Washington Integrated Brain Imaging Center using a 32-channel head coil. T1-weighted MPRAGE volumes were acquired (repetition time = 2530 ms, TE=3.5 ms, flip angle=7°, FOV=256 ×256, 176 slices, in-plane voxel size=1 mm 3 ).

Standard procedures, including cortical surface reconstruction, cortical and subcortical segmentation, and estimation of cortical thickness and surface area, were conducted using the FreeSurfer image analysis suite (Version 5.3; http://surfer.nmr.mgh.harvard.edu). The boundaries between grey and white matter and between grey matter and the pial surface were carefully inspected for each subject by at least two investigators and edited to ensure accuracy.

2.4. Whole brain analysis

In order to examine whether the associations between childhood adversity and neurodevelopmental outcomes were general or specific to particular dimensions of adverse experience, we estimated three linear models, first examining the association of threat and deprivation experiences with cortical thickness and surface area separately, and then examining the effect of each dimension while controlling for the other in order to examine their potential unique contributions to cortical thickness and surface area. This approach has been recommended ( McLaughlin et al., 2021 ) and implemented in previous research ( Sumner et al., 2019 ). All models included participants’ sex, age, and income-to-needs ratio as covariates. A final model was constructed including the interaction of threat and deprivation, respectively, with age. Cluster-wise correction was performed using a permutation approach implementing the Ter Braak approximation to correct for design non-orthogonality, as parametric approaches to cluster-wise correction have been shown to produce inflated false-positive rates ( Greve and Fischl, 2018 ). Cluster forming and family-wise error rate were set at.05 (see Supplement for analysis code).

2.5. Subcortical analysis

Amygdala and hippocampus volume were extracted from the subcortical segmentation in Freesurfer ( Fischl et al., 2002 ). Given that there is no consistent pattern of lateralization in findings of the relation between childhood violence exposure and hippocampus or amygdala volume ( Hanson et al., 2015 , McLaughlin et al., 2016 , McLaughlin et al., 2019 ), right and left volumes were summed to create bilateral volumes. Sensitivity analyses examining left and right subcortical volumes separately are presented in the supplement. Similar to the whole brain analysis, each subcortical region of interest was first separately regressed against the threat and deprivation composite scores, controlling for age, sex, total intra-cranial volume, and income-to-needs ratio. Then, threat and deprivation were entered as regressors together with the same covariates to examine any potential differential associations with subcortical volume. A final set of models additionally included the interaction of threat and deprivation, respectively, with age. Linear modeling was performed in the r package ‘lavaan’ and missing data from MRI exclusion was accounted for using full-information maximum-likelihood estimation (FIML; Rosseel, 2012 ).

Relative distribution of threat and deprivation composite scores are shown in Fig. 1 . Bivariate correlations between study variables are shown in Table 3 .

Distribution of threat and deprivation experience composite scores (r = 0.7). Deprivation scores have been jittered for legibility.

Bivariate Correlations. * : < 0.05; **: < 0.01; *** : < 0.001.

	1	2	3	4	5	6
1. Age
2. Sex	0.14
3. Income-to-Needs (log)	0.02	0.12
4. Threat Composite	0.09	0.04	-0.54 ***
5. Deprivation Composite	0.09	0.00	-0.50 ***	0.70 ***
6. Hippocampal Volume	0.03	-0.35 ***	0.18 *	-0.22 **	-0.13
7. Amygdala Volume	0.10	-0.38 ***	0.09	-0.24 **	-0.12	0.76 ***

3.1. Dimensions of adversity and cortical structure

3.1.1. cortical thickness.

Greater experiences of threat were associated with thinner cortex in numerous regions, after controlling for sex, age, and income-to-needs ratio (see Fig. 2 ). Higher threat scores were associated with thinner cortex in regions comprising the salience (bilateral insula), limbic (right vmPFC), somatomotor (bilateral post-central gyrus, parietal operculum, and left precentral gyrus), default (bilateral middle and superior temporal cortex, left parahippocampal cortex, and right inferior frontal gyrus), visual (fusiform gyrus), and frontoparietal (bilateral superior frontal gyrus [SFG], middle frontal gyrus [MFG], superior and inferior parietal cortex, precuneus, and left posterior cingulate) networks.

A: Map of p-values where cortex thickness was negatively associated with threat experiences, without control for deprivation. B: Map of p-values where cortex thickness was negatively associated with threat experiences, after controlling for deprivation. C: Concordance between A and B. D: map of p-values where cortical surface area was negatively associated with threat experiences after controlling for deprivation.

Deprivation was not associated with differences in cortical thickness after controlling for sex, age, and income-to-needs ratio. In a sensitivity test, this null finding remained when deprivation was modeled without control for the income-to-needs ratio.

In a combined model, greater threat experiences continued to be associated with reduced thickness across numerous cortical regions after controlling for co-occurring deprivation experiences (see Fig. 2 ). Compared to the threat-only model, thinning associated with greater threat experiences (controlling for deprivation) was attenuated in the frontoparietal network, with reduced extent in bilateral MFG and left SFG and no associations with right SFG, bilateral precuneus, left posterior cingulate, as well as the default network, with reduced extent in superior temporal cortex and no association with left inferior temporal gyrus. Associations between threat and cortical thinning were otherwise similar with and without control for deprivation experiences.

3.1.2. Cortical surface area

Threat experiences were associated with reduced surface area in portions of the right middle temporal gyrus and superior temporal sulcus after controlling for deprivation (see Fig. 2 ). No other associations of cortical surface area with threat or deprivation experiences were found in any model.

3.1.3. Adversity and age-related change in cortical structure

No associations were observed between age and either threat or deprivation in relation to cortical thickness or surface area. We additionally found no evidence for developmental differences in the associations of threat or deprivation experiences with cortical thickness or surface area when using pubertal stage as the metric of development instead of age (see supplement for details).

3.2. Dimensions of adversity and sub-cortical volume

Greater threat experiences were associated with reduced volume of the amygdala ( ß = −0.18, p = .021, 95% CI [−0.34, −0.03]), but not hippocampus ( ß = −0.05, p = .481, 95% CI [−0.21,0.10]), after controlling for age, sex, income-to-needs ratio, and intra-cranial volume. Deprivation experiences were not associated with either amygdala ( ß = −0.06, p = .441, 95% CI [−0.22,0.10]) or hippocampal ( ß =0.01, p = .935, 95% CI [−0.14,0.15]) volume (see supplement Fig. 2 ).

In a model where both composite scores were entered, threat experiences continued to be associated with smaller amygdala volume ( ß = −0.23, p = .021, 95% CI [−0.42, −0.03]), but not hippocampal volume ( ß = −0.08, p = .390, 95% CI [−0.27,0.10]. Deprivation was not associated with either amygdala ( ß =0.07, p = .459, 95% CI [−0.12,0.26]) or hippocampal ( ß =0.05, p = .579, 95% CI [−0.13,0.23]) volume (see Table 4 ).

Fully Specified Sub-Cortical Models; * : < 0.05; **: < 0.01; *** : < 0.001.

Model	Model Term	β	se	CI (lower)	(upper)	p
Hippocampal Volume (mm3)	Intercept	3.90***	0.94	2.01	5.71	< 0.001
	Deprivation	0.05	0.09	-0.13	0.23	0.579
	Threat	-0.08	0.10	-0.27	0.10	0.390
	Age	-0.07	0.07	-0.21	0.06	0.287
	Female	-0.05	0.08	-0.21	0.11	0.563
	Intra-cranial volume	0.57***	0.08	0.42	0.72	< 0.001
	Income-to-Needs	0.11	0.08	-0.05	0.28	0.181
Amygdala Volume (mm3)	Intercept	2.97**	0.98	1.05	4.88	0.002
	Deprivation	0.06	0.10	-0.13	0.25	0.551
	Threat × Age	0.18*	0.08	0.03	0.34	0.022
	Threat	-0.24*	0.10	-0.43	-0.05	0.014
	Age	-0.05	0.08	-0.21	0.12	0.578
	Female	-0.15	0.08	-0.32	0.01	0.065
	Intra-cranial volume	0.42***	0.08	0.26	0.58	< 0.001
	Income-to-Needs	-0.04	0.09	-0.21	0.13	0.637

3.2.1. Adversity and age-related changes in subcortical volume

Age interacted with threat to predict amygdala volume, such that younger participants with higher levels of threat showed smaller amygdala volume, but older participants did not show threat-related differences in amygdala volume ( ß =0.18, p = .022, 95% CI [0.03,0.34]; see Fig. 3 , Table 4 ). Fit indices of this interaction model were superior to the model without an age interaction (χ 2 (1) = 5.11, p = .024; see supplement for detail). The interaction of age with threat on hippocampal volume was marginal ( ß =0.15, p = .056, 95% CI [0,0.29]), and the pattern was in the same direction as amygdala with threat associated with smaller hippocampal volume among children but not adolescents (see Supplement Fig. 3 ).

Age and Threat interact to predict Amygdala Volume (visualized using InterActive; McCabe et al., 2018 ).

4. Discussion

The present study examined associations between experiences of childhood adversity involving threat and deprivation—independently and jointly—with cortical thickness and subcortical volume. Greater experiences of early-life threat were associated with numerous structural differences, including in brain regions typically recruited during salience processing, perception, and self-reflection. Structural differences in salience processing areas included thinning in bilateral anterior insula and right vmPFC, as well as reduced volume in the amygdala among younger, but not older, participants. Cortical regions involved in perception that were associated with threat-related experiences included the ventral visual stream (right fusiform and inferior temporal cortex), primary and secondary somatosensory cortex (left post-central gyrus and bilateral parietal operculum), and auditory processing areas (bilateral superior temporal cortex). Several regions of the default network were thinner and/or had reduced surface area in children with greater threat-related experiences, including medial PFC and lateral temporal cortex. We additionally observed thinning associated with threat in the frontoparietal network including bilateral middle frontal gyrus, precuneus, superior frontal gyrus, left parietal cortex, and inferior pre-central gyrus. After controlling for deprivation, similar associations between threat and cortical thickness were observed, but with reduced extent primarily in frontoparietal regions, including middle and superior frontal gyrus, as well as reductions in associations of threat with cortical surface area in the right middle temporal cortex and superior temporal sulcus. We did not find an association of deprivation experiences with brain structure with or without control for threat.

The associations we observed between threat experiences and structural differences in the salience processing and limbic networks were consistent with our hypotheses and previous literature. These networks signals the affective salience of stimuli to the rest of the brain in order to organize rapid responses to the environment ( Fusar-Poli, 2009 , Öhman, 2005 ). Structural changes in these networks have been extensively documented in children with threat-related experiences ( Gold et al., 2016 , Kelly et al., 2013 , Lim et al., 2014 , McLaughlin et al., 2016 , McLaughlin et al., 2019 ). Behavioral studies have demonstrated that youth who have experienced violence prioritize threat-related information during information processing ( Pollak and Sinha, 2002 , Pollak and Tolley-Schell, 2003 ), suggesting heightened sensitivity of these brain networks to environmental cues that could signify the presence of threat. Future research should explore whether the structural changes we observed mediate the elevated neural and behavioral responses to threat commonly observed in children who have experienced violence (see McLaughlin et al., 2020 ; McLaughlin and Lambert, 2017 for reviews). Given that cortical thinning in these networks is typically observed during adolescence, the thinning we observed in anterior insula and medial PFC may represent accelerated development of key regions involved in salience and emotional processing ( Callaghan and Tottenham, 2016 , Colich et al., 2020 ). Alternatively, thinning in these networks may simply reflect circuit refinement (i.e. synaptic pruning and/or increased myelination) related to more frequent utilization of these regions ( Tau and Peterson, 2010 ). Further research will be needed to investigate the development of these differences in longitudinal samples and associations with relevant emotional and behavioral processes.

We also observed smaller amygdala volume associated with greater experiences of threat, although this was restricted to the children in our sample and not the adolescents. Smaller amygdala volume in children who have experienced violence is consistent with prior literature (see McLaughlin et al., 2019 ; Teicher and Samson, 2013 for review), and complements our findings of threat-related thinning in the related salience and limbic networks. However, the age-related differences we observed in the association of threat with amygdala volume differ from prior work in longitudinal samples. In typically developing samples, amygdala volume increases throughout childhood until late adolescence/early adulthood ( Russell et al., 2021 , Wierenga et al., 2014 ). While the association of adversity with amygdala development over time is a subject of ongoing investigation, Whittle et al. (2013) found that during the transition from early to mid-adolescence, youth with more severe child maltreatment experiences showed equivalent or increased baseline amygdala volumes but slower growth in amygdala volume relative to youth with less severe maltreatment experiences. In our sample, most participants experienced violence relatively early in development (prior to the age of 8 years), which might have contributed to the differences in amygdala volume we observed in childhood here. It is unclear why these differences were not present in the adolescents in our sample. Importantly, the present study was cross-sectional, which limits its ability to investigate change in brain structure across age. Further longitudinal work is needed to explore how threat experiences relate to structural changes in the amygdala across development.

Youth with more experiences of threat also showed thinner cortex in a broad network of regions associated with perception across multiple sensory modalities. These findings align with previous studies reporting structural changes in areas of the brain implicated in perception among individuals with a history of threat-related adversity. For example, reductions in thickness of visual processing areas have been reported in young adults who witnessed domestic violence as children ( Tomoda et al., 2012 ), and reductions in somatosensory cortex thickness have been observed among adults with previous exposure to sexual abuse ( Heim et al., 2013 ) as well as maltreated children ( Kelly et al., 2015 ). Functionally, perceptual networks play an important role in salience processing. In a meta-analysis of neural responses to affective stimuli, Satpute et al. (2015) demonstrated that affect inductions utilizing unpleasant stimuli are typically accompanied by enhanced neural activity in salience processing regions (e.g. amygdala, hippocampus, and anterior insula) as well as primary and secondary sensory processing areas, as compared to neutral stimuli, according to the sensory modality in which the affective stimuli were administered (e.g. ventral visual stream for unpleasant images, superior temporal cortex for unpleasant sounds). Given the role of perception networks in salience processing, the thinning we observed in these areas could reflect similar developmental processes as that observed in the core salience network. However, such a conclusion is premature, and further research will be needed to discern the causes and functional consequences of structural differences in these areas associated with threat. Future research should additionally explore whether thinning in somatosensory areas contributes to the reduced emotional awareness often observed in youth who have experienced violence ( Weissman et al., 2019 , Weissman et al., 2020 ), as somatosensory cortex has been shown to play a role in interoception and emotion awareness ( Damasio et al., 2000 , Kropf et al., 2018 , Liddell et al., 2005 , Straube and Miltner, 2011 ).

Youth with greater numbers of threat experiences also showed thinning and reduced surface area in the default network, including medial PFC and lateral temporal cortex. These regions are frequently recruited during tasks involving self-reflection and social cognition (e.g., Dixon et al., 2017 ; Hein and Knight, 2008 ). Threat-related thinning in these regions parallels recent findings of functional differences in these regions that are associated with experiences of threat, but not deprivation ( Weissman et al., 2022 ). Future work should explore whether these differences mediate previously observed social information processing differences in children who have experienced violence ( McLaughlin et al., 2020 ), such as reduced theory of mind performance ( Deen et al., 2015 , Heleniak and McLaughlin, 2020 ).

Finally, threat was associated with cortical thinning and reduced surface area in the frontoparietal control network ( Yeo et al., 2011 ). The thinning we observed in this network contradicted our hypothesis that thickness in these areas would be uniquely associated with deprivation. Importantly, association between threat experiences and thinning in PFC was reduced substantially when controlling for deprivation experiences, suggesting that at least some of the thinning observed was not specific to threat-related experiences.

Outside the frontoparietal network, the structural differences we observed in threat-exposed youth remained after controlling for co-occurring deprivation, suggesting that our results reflected distinct developmental processes related to threat-related experiences rather than stress or childhood adversity in general. This interpretation is consistent with prior work showing that threat experiences may have effects on salience processing that are distinct from other forms of adversity ( Lambert et al., 2017 , Machlin et al., 2019 , Sheridan et al., 2019 , Weissman et al., 2022 ).

We did not find evidence of associations between brain structure and deprivation. Associations between deprivation and cortical structure have been observed in samples exposed to severe deprivation experiences including institutional rearing ( Bick and Nelson, 2016 , Herzberg et al., 2018 , Hodel et al., 2015 ) and neglect ( Edmiston et al., 2011 ). Although we measured neglect experiences, it may be that the deprivation experiences among children in our sample were not severe enough to have led to similar cortical changes. However, prior work on cognitive stimulation has observed thinning in the frontoparietal network in children who experienced reductions in cognitive stimulation in the normative range ( Rosen et al., 2018 ).

Although this study had many strengths, limitations of the study design and sample should be considered in interpreting these results. The study was cross-sectional by design and these correlational findings cannot be used to make inferences about causation or developmental differences over time. Our interpretations as to the function of observed structural changes are speculative and will need to be tested in future work. We did not find evidence for any of our hypotheses on the association of brain structure with deprivation. Additionally, we predicted that deprivation would be specifically associated with thinning in the frontoparietal control network but instead found an association between threat and thinning in these regions. Moreover, we have previously proposed that cognitive deprivation may drive the reduced cortical thickness and surface area in the ventral visual stream that has been observed among low-SES children ( Noble et al., 2015 , Rosen et al., 2018 ), but instead found an association between these regions and experiences of threat. Importantly, our recruitment strategies specifically targeted threat-exposed youth. Consequently, threat and deprivation were significantly co-occurring in our sample, and threat-related experiences were more common than deprivation-related experiences. Additionally, we had fewer measures of deprivation than threat. Our deprivation composite score was sensitive to several indices of deprivation (i.e., physical and cognitive deprivation) that may have different associations with brain structure. These limitations reduced our power to detect deprivation-related associations. Furthermore, recent work has suggested that very large samples are required to reliably identify associations between cortical thickness and phenotypic variables with adequate control for false positives ( Marek et al., 2022 ). Although this is among the largest samples examining associations of trauma with neural structure in children to date, it is small by the standards identified in Marek and colleagues (2022). Small sample sizes have undoubtedly contributed to heterogeneity in reported associations between dimensions of adversity and brain structure ( McLaughlin et al., 2019 ). Future studies including large samples and a wide range of exposure histories should be conducted to further explore the associations between experiences of threat and deprivation with brain structure. Because of the difficulty of recruiting such a sample, data-pooling efforts across institutions are underway to facilitate these analyses ( McLaughlin et al., 2019 ).

5. Conclusions

We observed that exposure to threatening experiences, but not experiences of deprivation, was associated with thinner cortex in youth exposed to threat in numerous cortical regions involved in salience processing, self-reflection, and perceptual processing, as well as smaller subcortical volume in the amygdala (among younger participants). These results provide further evidence that childhood trauma has pervasive influences on structural brain development. Structural differences in salience processing areas may contribute to changes in behavior in children exposed to threatening experiences, such as enhanced sensitivity and reactivity to threatening stimuli. Further research should evaluate this possibility, as enhanced sensitivity to threat may contribute to the increased risk for psychopathology experienced by children who have experienced violence.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research was supported by the National Institutes of Health [grant numbers R01-MH103291, R01-MH106482, K99-HD099203].

Appendix A Supplementary data associated with this article can be found in the online version at doi:10.1016/j.dcn.2022.101180 .

Appendix A. Supplementary material

Supplementary material.

Data availability

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New perspectives on childhood memory: introduction to the special issue
This special issue of Memory is devoted to research that brings together new perspectives on childhood memory. Since the time Freud (1905 / 1953) noted the phenomenon of childhood amnesia - the scarcity of memories for very early life events - the fascination with childhood memory has persisted both in popular culture and among memory ...
The Development of Children's Early Memory Skills
A multi-task battery tapping nonverbal memory and language skills was used to assess 60 children at 18, 24, and 30 months. Analyses focused on the degree to which language, working memory, and deliberate memory skills were linked concurrently to children's Elicited Imitation performance, and whether the patterns of association varied across the different ages.
(PDF) Remembering earliest childhood memories
Chinese children, the parallel age of first memory was 38.0 months, 41.5 months, and 52.8. months old. Thus, first memories on average were 6 months, 10 months, and 22 months later in. equivalent ...
(PDF) The Development of Children's Memory
Abstract. The development of memory has been studied for more than a century and is one of the most active areas of research in cognitive development. In this article, we first describe historical ...
The Sweet Spot: When Children's Developing Abilities, Brains, and
Specifically, their long-term memory, working memory, cognitive control, and attentional capacities undergo great change throughout early childhood. Turning to children's memories first, much work has shown that both long-term and working memory abilities develop greatly throughout childhood (despite the idiosyncratic instances reviewed above).
The Development of Implicit Memory From Infancy to Childhood: On
Memory is crucial for the acquisition of the tremendous amount of knowledge and skills infant and children acquire in the first years of life. A wealth of research over the last decades documented the impressive development of memory abilities from infancy to childhood (see, e.g., Hayne, Scarf, & Imuta, 2015).
New perspectives on childhood memory: introduction to the ...
Abstract. This special issue brings together the scholarship that contributes diverse new perspectives on childhood amnesia - the scarcity of memories for very early life events. The topics of the studies reported in the special issue range from memories of infants and young children for recent and distant life events, to mother-child ...
The development of children's memory.
The development of memory has been studied for more than a century and is one of the most active areas of research in cognitive development. In this article, we first describe historical developments in research on children's memory, focusing on systematic studies that began in the late 1960s. We then describe three important new lines of inquiry: short‐ and long‐term memory development in ...
Childhood Memory: An Update from the Cognitive Neuroscience ...
With adult participants it is reasonable to simply ask for freely recalled childhood memories to study events from childhood. For example, there are structured interviews , such as the Autobiographical Memory Interview (Kopelman, Wilson, & Baddeley, 1989) in which participants are asked to provide autobiographical memories and semantic information for different epochs over the life-span.
Current Theories On Early Childhood Memories
The present research was an examination of the onset of childhood amnesia and how it relates to maternal narrative style, an important determinant of autobiographical memory development.
Are your earliest childhood memories still lurking in your mind ...
Toddlers like 18-month-old Hilda struggle to remember events in context, such as where a toy is hidden, for more than a few months. New research suggests such memory lapses play an important role in brain development. Stefanie Loos. A version of this story appeared in Science, Vol 383, Issue 6688. Sarah Crespi, Sara Reardon.
Children's Memory: A Primer for Understanding Behavior
This paper provides the opportunity to understand children's behavior from a memory viewpoint. For the last three decades, cognitive developmentalists have been asking the question, "what develops in children's memory?" Four answers to this question are presented, complete with explanations, examples, and possible applications where appropriate. The purpose of the paper is to provide ...
Remembering Early Childhood: How Much, How, and Why (or Why Not)
In this article, we consider recent research on three questions about people's memories for their early childhood: whether childhood amnesia is a real phenomenon, whether implicit memories survive when explicit memories do not, and why early episodic memories are sketchy. The research leads us to form three conclusions. First, we argue that ...
New perspectives on childhood memory: introduction to the special issue
This special issue of Memory is devoted to research that brings together new perspectives on childhood memory. Since the time Freud (1905/1953) noted the phenomenon of childhood amnesia the scarcity of memories for very. -. early life events the fascination with childhood memory. -. has persisted both in popular culture and among memory ...
The Fate of Childhood Memories: Children Postdated Their Earliest
Introduction. Most people can remember their childhood experiences from about 3 or 4 years of age but not earlier, a phenomenon commonly termed childhood amnesia (Pillemer and White, 1989; Bauer, 2007; Peterson, 2012).Although retrospective research on adults' earliest childhood memories is abundant, prospective research on children's earliest childhood memories is relatively scarce.
Cognitive neuroscience perspective on memory: overview and summary
Abstract. This paper explores memory from a cognitive neuroscience perspective and examines associated neural mechanisms. It examines the different types of memory: working, declarative, and non-declarative, and the brain regions involved in each type. The paper highlights the role of different brain regions, such as the prefrontal cortex in ...
The Development of Memory
This article reviews recent advances in understanding the changes in memory function that take place during the childhood years. Development of the following aspects of memory are considered: short-term memory, comprising phonological memory, visuospatial memory, and executive function; autobiographical memory; episodic memory, including eyewitness memory; and metamemory.
The Development of Working Memory
Fig. 1. Simulations of a dynamic field model showing an increase in working memory (WM) capacity over development from infancy (left column) through childhood (middle column) and into adulthood (right column) as the strength of neural interactions is increased. The graphs in the top row (a, d, g) show how activation (z -axis) evolves through ...
What is remembered about early childhood events?
Almost 100 years ago, Freud identified infantile or childhood amnesia, the difficulty that most adults have remembering events from their first years of life. Recent research in cognitive psychology has in fact demonstrated a paucity of verbal memories of early life experiences. Although Freud believed that childhood memories are repressed ...
PDF The role of working memory in childhood education: Five questions and
crucial for optimal learning and development. There is considerable research on several theoretical aspects of working memory. Far less research has explored the application of such theory in order to understand how children perform in educational settings and to support and improve their academic performance. In this paper, five key aspects
Research Paper 'Black Holes' in memory: Childhood autobiographical
Introduction. Nelson and Fivush (2004) discuss autobiographical memory as a continually developing system that gradually emerges during very early childhood. Their theory is that it grows and expands in the context of other developmental issues in life (such as language, self-actualization, etc.), and in this way, such a view of the creation of autobiographical memory would also account for ...
Childhood trauma and brain structure in children and adolescents
Increasing evidence suggests that adverse childhood experiences are associated with numerous aspects of neural development, including brain structure (Hart and Rubia, 2012; McLaughlin, Weissman et al., 2019; Teicher et al., 2016). In particular, experiences of adversity have been associated with reduced cortical thickness and surface area, as ...
Retrieving and Modifying Traumatic Memories: Recent Research Relevant
The purpose of this article is to review recent research that is relevant to three controversies concerning memory for trauma. First, we briefly review the debate about recovered memories of childhood sexual abuse, summarizing a third interpretation distinct from both the repression and false-memory accounts.