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Developmental Cognitive Neuroscience 59 (2023) 101192
Available online 23 December 2022
1878-9293/© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).
Distinct multivariate structural brain proles are related to variations in
short- and long-delay memory consolidation across children and
young adults
Iryna Schommartz
a
,
g
,
*
, Philip F. Lembcke
b
, Francesco Pupillo
a
, Henriette Schuetz
b
,
Nina Wald de Chamorro
c
,
d
, Martin Bauer
b
, Angela M. Kaindl
c
,
d
,
e
, Claudia Buss
b
,
f
,
1
,
Yee Lee Shing
a
,
g
,
1
,
*
a
Department of Psychology, Goethe University Frankfurt, Frankfurt, Germany
b
Charit´
e – Universit¨
atsmedizin Berlin, Corporate Member of Freie Universit¨
at Berlin and Humboldt-Universit¨
at zu Berlin, Department of Medical Psychology, Berlin,
Germany
c
Charit´
e – Universit¨
atsmedizin Berlin, Department of Pediatric Neurology, Berlin, Germany
d
Charit´
e – Universit¨
atsmedizin Berlin, Center for Chronically Sick Children, Berlin, Germany
e
Charit´
e – Universit¨
atsmedizin Berlin, Institute of Cell, and Neurobiology, Berlin, Germany
f
Development, Health and Disease Research Program, Department of Pediatrics, University of California Irvine, USA
g
Center for Individual Development and Adaptive Education of Children at Risk (IDeA), Frankfurt, Germany
ARTICLE INFO
Keywords:
Episodic memory
Object-scene associations
Memory consolidation
Hippocampal subelds
Prefrontal cortex
Neocortex
Partial least square correlation
ABSTRACT
From early to middle childhood, brain regions that underlie memory consolidation undergo profound matura-
tional changes. However, there is little empirical investigation that directly relates age-related differences in
brain structural measures to memory consolidation processes. The present study examined memory consolidation
of intentionally studied object-location associations after one night of sleep (short delay) and after two weeks
(long delay) in normally developing 5-to-7-year-old children (n =50) and young adults (n =39). Behavioural
differences in memory retention rate were related to structural brain measures. Our results showed that children,
in comparison to young adults, retained correctly learnt object-location associations less robustly over short and
long delay. Moreover, using partial least squares correlation method, a unique multivariate prole comprised of
specic neocortical (prefrontal, parietal, and occipital), cerebellar, and hippocampal head and subeld structures
in the body was found to be associated with variation in short-delay memory retention. A different multivariate
prole comprised of a reduced set of brain structures, mainly consisting of neocortical (prefrontal, parietal, and
occipital), hippocampal head, and selective hippocampal subeld structures (CA1–2 and subiculum) was asso-
ciated with variation in long-delay memory retention. Taken together, the results suggest that multivariate
structural pattern of unique sets of brain regions are related to variations in short- and long-delay memory
consolidation across children and young adults.
1. Introduction
1.1. Memory consolidation across development
Humans have an impressive capacity to store and retrieve memories
of past experiences, consisting of unique temporal-spatial features, for
years and even decades (Squire et al., 2015; Tulving, 2002). This is made
possible due to memory consolidation, a dynamic and complex process
between encoding and retrieval through which acquired memory traces
become long-lasting (Axmacher and Rasch, 2017; Dudai, 2012; Mosco-
vitch and Gilboa, 2021). This process is referred to as systems consoli-
dation and entails reorganization of memory traces across brain systems,
including brain regions that initially encode the memory to potentially
additional cortical regions for integrating the new memory (Dudai,
2012). In this process, previously successfully encoded memories may
become stabilized, but also the availability of initially poor memories
* Correspondence to: Department of Psychology, Goethe University Frankfurt, Theodor-W.-Adorno-Platz, 6, Frankfurt 60323, Germany.
E-mail addresses: schommartz@psych.uni-frankfurt.de (I. Schommartz), shing@psych.uni-frankfurt.de (Y.L. Shing).
1
Yee Lee Shing and Claudia Buss should be considered joint senior author.
Contents lists available at ScienceDirect
Developmental Cognitive Neuroscience
journal homepage: www.elsevier.com/locate/dcn
https://doi.org/10.1016/j.dcn.2022.101192
Received 6 July 2022; Received in revised form 12 December 2022; Accepted 21 December 2022
Developmental Cognitive Neuroscience 59 (2023) 101192
2
may become enhanced (e.g., Ellenbogen et al., 2006). In this study we
focused on the former aspect. Behaviourally, examining memory
retention rates after initial successful encoding is one way to measure
the effectiveness of memory stabilization across time and is assessed as
an outcome during retrieval.
From an ontogenetic perspective, the ability to retrieve long-term
episodic memories emerges with the offset of childhood amnesia, i.e.,
the inability to recollect early life events, around four to seven years of
age (Alberini and Travaglia, 2017; Bauer, 2007; Scarf et al., 2013; Tustin
and Hayne, 2010). From there on, successful retrieval of complex
memory representations starts to steadily increase (Drummey and
Newcombe, 2002; Riggins, 2014; Sluzenski et al., 2006), suggesting
improvement of memory consolidation processes. In many nations, this
age range is associated with the transition from kindergarten to school
and is accompanied by dramatic increases in learning and knowledge
accumulation, advancing cognitive functioning (Brod et al., 2017;
McKay et al., 2022) and potentially also memory consolidation (cf.
Nolden et al., 2021). However, little is known about the ability to
consolidate memories over short and longer time in children who are
about to start the school and face the necessity to retain plethora of
newly acquired information.
Although much is known about how memory representations are
encoded and retrieved in childhood, memory retention across longer
consolidation periods is much less researched and may progress with
different temporal dynamics in children who are about to start the
school in comparison to adults (Peiffer et al., 2020a; Wang et al., 2018;
Wilhelm et al., 2008). For instance, it has been shown that short-delay
memory consolidation rate (i.e., measured after one night of sleep) is
comparable between children aged 6–8 years and young adults for
word-pair associates (Wilhelm et al., 2008). On the other hand, it was
also shown that in children and adolescents, successful retrieval of
events over a longer time (e.g., one week) increases with increasing age
(Østby et al., 2012). However, no study to date has directly compared
memory consolidation of complex representations over short (i.e., one
day) and long delays (i.e., weeks), and examined how maturational
differences in brain structures between children and adults may account
for potential age-related differences in memory consolidation. There-
fore, in this study, we compared the retention rate of 5- to 7-year-old
children and adults for learned object-location associations over one
night as well as two weeks after encoding. As it is not possible to directly
observed consolidation processes, we resorted to retention rate as an
indirect measure of memory consolidation that describes stabilization of
memories into long-term form over different time periods (Axmacher
and Rasch, 2017; Dudai, 2012) beyond initial successful encoding. We
also kept the retrieval procedure comparable and stable for the entire
study, reducing the demands on retrieval processes to reveal differences
in memory stabilization during the consolidation period. Furthermore,
we examined to what extent differences in retention rate are associated
with multivariate patterns of structural measures of brain regions that
are known to support memory consolidation.
1.2. Neural correlates of memory consolidation across development
Middle childhood is characterized by profound changes in cortical
and subcortical brain regions related to mnemonic processes (Ghetti and
Bunge, 2012; Ghetti and Fandakova, 2020; Lenroot and Giedd, 2006;
Ofen, 2012; Ofen et al., 2007; Shing et al., 2010). For instance, the
hippocampus, which is associated with the binding of event features into
a coherent representation, reaches its relative maturity in late child-
hood/adolescence, depending on the subelds (Keresztes et al., 2017,
2022; Lee et al., 2014; Shing et al., 2008, 2010; Sluzenski et al., 2006).
On the other hand, prefrontal brain regions, show protracted maturation
into late adolescence/young adulthood (Gogtay et al., 2004; Muftuler
et al., 2012; Sousa et al., 2018; Uda et al., 2015). This includes (i) the
ventrolateral prefrontal cortex (vlPFC) and the orbitofrontal cortex
(OFC) associated with strategy use that benet memory formation and
retrieval (Badre and Wagner, 2007; Kuhl et al., 2012; Østby et al., 2012),
and (ii) the ventromedial prefrontal cortex (Brod and Shing, 2018; van
Kesteren et al., 2012) and the rostral medial prefrontal cortex (Mella
et al., 2021) that are important for schema-integration processes that
benet long-term consolidation. Similar, posterior parietal cortex (PPC),
particularly its ventral part – precuneus, and lateral occipital cortex
(LOC) (Nishimura et al., 2015; Simmonds et al., 2017) show more pro-
tracted development. PPC was shown to be involved in successful
recollection of items with precise contextual details (DeMaster and
Ghetti, 2013) and LOC was found to be associated with the reinstate-
ment of object-related information upon retrieval (Grill-Spector et al.,
2001; Karanian and Slotnick, 2015) and neural specicity of scene
representation at retrieval in 8–15 years old children (Fandakova et al.,
2019).
Beyond neocortical regions, entorhinal cortex (EC) being an input-
output-hub for hippocampus-neocortical interactions plays a crucial
role in memory trace strengthening (Reagh and Yassa, 2014;
Takehara-Nishiuchi, 2014) and its structural maturity was related to
memory performance (Daugherty et al., 2017; Keresztes et al., 2017 ).
Parahippocampal gyrus (PHC) also supports spatial context-related
associative recollection (Davachi et al., 2003; Milton et al., 2011; Ran-
ganath and Ritchey, 2012) and was found to relate to subsequent
memory recollection and long-term memory improvements in
middle-late childhood (Ghetti et al., 2010). Finally, the cerebellum
showed increased activation during retrieval of long-term episodic
memories (Andreasen et al., 1999) and prefrontal-cerebellar circuits
were also found to be related to declarative memory processes (Vecchi
and Gatti, 2020), associative learning and recognition (Steinlin, 2007;
Timmann et al., 2010).
In an exceptional study that examined consolidation and its brain
correlates in participants aged 8–19 years, Østby et al. (2012) showed
that a thinner OFC was associated with higher short-delay (30 min)
recall, while larger hippocampal volumes were related to higher mem-
ory retention rates (1-week/30-min ratio) in a visuospatial task. These
ndings indicate that extended developmental trajectories of the
neocortical regions and the hippocampus may affect the memory
consolidation processes over short and long delay differentially in
children, beyond their effects on encoding or retrieval. However, no
other studies have directly compared short vs. long delay memory
consolidation in children, particularly in the younger age range, and
related these with structural brain measures.
1.3. The current study
In this study, we examined the consolidation of well-learnt object-
location associations across short delay (after one night of sleep) and
long delay (a 2-week-period) between learning and retrieval, comparing
5-to-7-year-old children to young adults, who served as a reference
group with a mature memory consolidation system. Memory retention
rates were measured as an indirect index of memory consolidation,
particularly the stabilization of initially encoded information, by keep-
ing encoding comparable and retrieval demands low across all time
points. We hypothesized no differences in short-delay memory consoli-
dation between children and young adults (Peiffer et al., 2020a; Wang
et al., 2018), but less robust long-delay consolidation in children in
comparison to young adults (Ghetti and Bunge, 2012; Lebel et al., 2012;
Shing et al., 2010). Furthermore, we applied a partial least squares
correlation analysis (PLSC) to map behavioural memory consolidation
measures (i.e., retention rate) onto multiple structural
regions-of-interest (ROIs) reported previously to be involved in memory
processes. This powerful statistical technique allowed us to overcome
shortcomings of univariate approaches in light of highly correlated and
interconnected brain ROIs and to identify brain proles comprised of
structural brain measures that are, in a multivariate pattern, associated
with for short- and long-delay memory consolidation, respectively
(Nestor et al., 2002). We hypothesized that a brain prole comprised of
I. Schommartz et al.
Developmental Cognitive Neuroscience 59 (2023) 101192
3
medial temporal lobe (MTL), cerebellar and neocortical (i.e. prefrontal,
parietal, occipital) regions would be associated with variations in
short-delay memory consolidation. This is because the availability of
detail-rich representation of associative memories and the use of
schema-integration and strategic control over memory should be bene-
cial. We also expected that a unique brain prole comprised of
neocortical (i.e. prefrontal, parietal) and cerebellar brain regions would
be associated with variations in long-delay memory consolidation, due
to the importance of strategic control over memory traces with decaying
perceptual representations.
2. Methods
2.1. Participants
For the recruitment of children, 4000 general research invitation
letters were sent to randomly selected families with 5-to-7-year-old
children in Berlin, Germany, of whom 110 families expressed interest
in participation. After screening, 63 typically developing children were
recruited to participate in the study. 46 young adults were recruited to
participate in the study through advertisement in the newspaper, on the
university campus, and through word-of-mouth.
All participants had normal vision with or without correction, no
history of psychological or neurological disorders or head trauma and
were term-born (i.e., born after 37 weeks of pregnancy). We included
only children and young adults with at least average IQ >85. Thirteen
children were excluded due to incomplete task execution and missing
data (n =6) or technical issues during data acquisition (n =7). Seven
young adult participants were excluded due to incomplete task execu-
tion and missing data (n =5), and identication as an extreme outlier (n
=2) based on interquartile range for learning and consolidation
behavioural measures (IQR; above Q3 +3xIQR or below Q1 – 3xIQR
(Hawkins, 1980)). The excluded participants were comparable in terms
of age, sex, and socio-economic status to the nal sample. In summary,
the nal sample size consisted of 50 typically developing children (20
female, mean age: 6.37 years, age range: 5.5 – 7 years), and 39 young
adults (20 female, mean age: 25.44 years, age range: 21.3 – 30.8 years;
Table 1). Structural T1-weighted brain images for volumetric assess-
ments of cortical and subcortical brain regions as well as high-resolution
structural hippocampal scans were acquired in all children and adults.
After considering quality check (see details below) and technical errors,
46 children and 39 adults provided usable T1 images, and 46 children
and 35 adults provided usable high-resolution hippocampal scan.
All participants or their legal guardians gave written informed con-
sent prior to participation. The study was approved by the ethics com-
mittee of the Goethe University Frankfurt am Main (approval E 145/18).
The participants were compensated for participation in the experiment
with 100 Euro.
2.2. Measures
2.2.1. Object-location associations task
The stimuli for the object-location associations task were chosen
based on the curriculum in social studies and science for the rst and
second grade of the German primary school (see similar procedure in
Brod and Shing, 2019). Sixty different semantic themes (e.g., forest,
farm, etc.) were chosen according to the ratings provided by four pri-
mary school teachers that assessed the familiarity of rst graders with
the topics. For each semantic theme, four scene pictures were combined
with four thematically congruent object pictures, resulting in four
unique object-location associations (see Fig. 1 for an example). We
identied 18 possible areas in the scenes to place the objects, one of
which was assigned to each object-location association (for more
detailed information see Supplementary Methods section). We pre-
sented the task using Psychtoolbox-3 (Kleiner et al., 2007) software in
Matlab 9.5, R2018b (MATLAB, 2018).
The task consisted of three phases as following (see Fig. 1):
(i) Initial encoding phase (Day 0). For a set of 60 object-location
pairs, participants saw the object followed by the same object
superimposed on the scene at a particular location. Participants
were instructed to remember 60 object-location pairs in total,
memorizing the exact location of the object within the scene by
elaboration (e.g., creating a story or making a “mental photo” of
the scene), as such elaborative encoding strategies aid the
recollection of the information (Craik and Tulving, 1975);
(ii) Learning phase (Day 0). Participants learned the correct location
of the object within the scene during adaptively repeated
retrieval-encoding cycles (minimum two cycles, maximum four
cycles). They had to choose the correct location out of three
choices and received feedback for their response. After the
feedback, the correct object-location associations were shown
again. The cycles ended when participants provided correct re-
sponses to 83% of the trials or the maximum fourth cycle was
reached. In this way we minimize encoding-related differences in
memory traces.
(iii) Retrieval phase (Day 1 and 14). Participants had to choose the
correct location of the object in the scene out of three options
without feedback. It is worth pointing out that for retrieval we
opted for a 3-alternative forced choice instead of a free-recall
procedure, which would place higher demand on retrieval (e.g.,
the need for strategic search in memory). By reducing the demand
on retrieval processes (and the age differences therein), we think
this helps to reveal differences in consolidation processes.
Importantly, we kept the retrieval procedure comparable and
stable for the entire study, making sure that the process of
retrieval was well-trained for all participants. Note: The retrieval
phase was carried out inside the MRI scanner with a functional
sequence, of which the data is not included as we focused here on
characterizing the retention rate behaviourally, both in terms of
group comparison and relations to structural integrity.
2.2.2. Assessment of demographic and cognitive covariates
In addition, IQ scores were assessed using the German version of the
“Kaufman Assessment Battery for Children – Second Edition” ( K-ABC II;
Kaufman and Kaufman, 2015) in children and the “Wechsler Adult In-
telligence Scale – Fourth Edition ( WAIS-IV; Wechsler, 2015) in young
adults. General socio-demographic questionnaires to assess
Table 1
Sample characteristics by age group (children, young adults) showing compa-
rability between groups.
Children (CH; N
=50)
Young adults
(YA; N =39)
Group effect
(CH vs YA)
M SD M SD p-
value
w
2
Demographic measures
Age 6.37 0.37 25.44 2.66 * ** 0.97
Sex (M/F) 30/20 19/20 - -
IQ Score 116.18 13.75 108.15 12.02 * ** 0.08
Socioeconomical Status
Income – Mother 3.6 1.8 - - - -
ISCED – Mother 6.1 1.5 4.1 1.8 * ** -
Education Years -
Mother
19.7 4.2 - - - -
Notes. Income is based on a 1–7 Scale (1 =less than 15.000
€
, 7 =more than
100.000
€
). CH =children; YA =young adults; ISCED =International Standard
Classication of Education 2011 (1 =primary education; 8 =doctoral level;
UNESCO Institute for Statistics, 2012); IQ =Intelligence Quotient based K-ABC
(Kaufman and Kaufman, 2015) for children and WAIS-IV (Wechsler, 2012) for
young adults; M =mean; SD =standard deviation; w
2
=omega squared; *p <
.05; * * <0.01, * ** <0.001 (signicant difference); ns: non-signicant
difference.
I. Schommartz et al.
Developmental Cognitive Neuroscience 59 (2023) 101192
4
socio-demographic characteristics of the participants were applied as
well.
2.3. Experimental procedure
Testing took place across three days (see Fig. 2). On Day 0, the
experimental procedure began with a short training to familiarize par-
ticipants with the object-location associations task. Participants had to
learn 60 object-location associations. The experimental task started with
the initial encoding of the rst 30 object-location associations. This was
followed by a brief distraction task in which participants listened to and
had to recall a string of numbers. This was followed by the learning
phase with retrieval-encoding cycles until the 83% criterion was reached
(or maximum of four cycles was reached). This procedure was done with
the aim to minimize variances attributed to encoding, so that the com-
parison of subsequent memory consolidation could be made with
starting points as similar as possible. After a short break, the same
procedure was repeated with the other half of 30 object-location asso-
ciations. On Day 1, short delay retrieval was conducted. Participants had
to retrieve 30 object-scene associations learnt on Day 0. On Day 14, long
delay retrieval was conducted. Participants had to retrieve another 30
object-scene associations learnt on Day 0.
2.4. Magnetic resonance imaging
MR images were acquired on a 3-T SIEMENS PRISMA scanner
(Siemens Medical Solutions, Erlangen, Germany) using a 64-channel
head coil. An MPRAGE (magnetization prepared rapid gradient echo)
T1-weighted sequence was applied with the following parameters: time
repetition/time echo/time to inversion/Flip Angle =2500 ms/2.9 ms/
1070 ms/8◦, matrix 256 ×256, eld of view =256. Each scan took
6 min 38 s. Each volume consisted of 176 sagittal slices with voxel sizes
1.0 ×1.0 ×1.0 mm.
2.5. Data analysis
2.5.1. Structural MRI data processing
2.5.1.1. Subcortical volumetric measures. Subcortical volumetric mea-
sures were derived using the anatomical pipeline of fMRIprep (version
20.2.1; Esteban et al., 2019), based on Nipype 1.5.1 (Gorgolewski et al.,
2011). Here, brain tissue segmentation of cerebrospinal uid,
white-matter, and grey-matter was performed on the brain-extracted
T1w scans using FAST (FSL 5.0.9; Zhang et al., 2001). Brain surfaces
were reconstructed using recon-all (FreeSurfer 6.0.1; Dale et al., 1999).
Volume-based spatial normalization to two standard spaces was
Fig. 1. Experimental Task. (A) Initial Encoding Phase. Participants were instructed to remember 60 object-location pairs in total, memorizing the exact location of the
object within the scene by creating a story or making a “mental photo” of the scene. (B) Learning Phase. Participants had to choose the correct location out of three
choices and received feedback for their response. After the feedback, the correct object-location association was shown again. (C) Retrieval Phase. Participants had to
choose the correct location of the object in the scene out of three options without feedback.
Fig. 2. Experimental Procedure. The testing took place
across three days. (i) On Day 0 participants had to learn 60
object-location associations. (ii) On Day 1 (short delay) the
retrieval of 30 association pairs learnt on Day 0 was con-
ducted. (iii) On Day 14 (long delay) the retrieval of another
30 association pairs learnt on Day 0 was conducted. Note
that on Day 1 and 14, new sets of object-location pairs were
learned to serve as baseline for fMRI analysis. Data on these
newly learned pairs was not included here.
I. Schommartz et al.
Developmental Cognitive Neuroscience 59 (2023) 101192
5
performed through nonlinear registration with antsRegistration (ANTs
2.3.3), using brain-extracted versions of both T1w reference and the
T1w template. Intracranial volume (ICV) was derived by the
surfaced-based measures.
2.5.1.2. Cortical thickness measures. Cortical thickness measures were
derived using the ABCD-HCP pipeline (Feczko et al., 2021) (https://gi-
thub.com/DCAN-Labs/abcd-hcp-pipeline). The anatomical part of the
pipeline includes three stages (refer to Supplementary Materials for
more details). Cortical thickness measures were calculated from the
distance between the reconstructed white matter and grey matter sur-
faces as well as from the reconstructed grey matter surface and cere-
brospinal uid boundaries. Structural data from one child participant
was excluded due to poor quality assessed with the Qoala-T tool
(Klapwijk et al., 2019).
2.5.1.3. Segmentation of hippocampal subelds. To delineate regions
within the hippocampus, the Automated Segmentation of Hippocampal
Subelds (ASHS) software (Yushkevich et al., 2015) was used, with a
lifespan atlas created from manual segmentations (Bender et al., 2018).
The approach used has been shown to be reliable and valid for delin-
eating hippocampal subelds in a pediatric sample, including
6–14-year-old children (Bender et al., 2018). ASHS uses a multi-atlas
method, integrating data extracted from segmentations of several
hippocampi. Three regions of the hippocampal subelds within the
hippocampal body were identied: the subiculum, Cornu ammoni re-
gions 1 and 2 (CA1–2), and a region including CA3 and the dentate gyrus
(CA3-DG). The CA3 and DG were not separated because even with
high-resolution images the validity of their separation is not conrmed
yet (Wisse et al., 2017). Presubiculum, subiculum, and parasubiculum
were aggregated into the "Subiculum" subeld, and CA1 and CA2 were
also collapsed into one single region for the same reason. Hippocampal
head was segmented in a similar fashion after ranging the hippocampus
to separate the head and the body. Within the hippocampal head, CA1,
CA2, CA3, Subiculum and DG subelds were identied. The ranged
subelds were summed to obtain the overall volume of the hippocampal
head. These was done because the validity of the segmentation in the
head is questionable (Wisse et al., 2017). The entorhinal cortex was also
delineated on 6 consecutive slices anterior to the hippocampal body
(Keresztes et al., 2020). Ranging of the hippocampal body was carried
out following recent progress made towards the development of a
standardized procedure for nding valid landmarks (Olsen et al., 2019).
2.5.1.4. Structural ROIs. The selection of structural ROIs was based on
the review of the developmental literature on memory consolidation and
related retrieval processes including structural MRI studies (Østby et al.,
2012), which identied the involvement of OFC and hippocampus, as
well as functional MRI studies (Andreasen et al., 1999; Davachi et al.,
2003; DeMaster and Ghetti, 2013; Fandakova et al., 2019; Grill-Spector
et al., 2001; Karanian and Slotnick, 2015; Kuhl et al., 2012; Mella et al.,
2021; Milton et al., 2011; Nishimura et al., 2015; Ranganath and
Ritchey, 2012; Reagh and Yassa, 2014; Simmonds et al., 2017; Steinlin,
2007; Timmann et al., 2010; van Kesteren et al., 2012), which identied
the involvement of vmPFC, vlPFC, rostral medial PFC, precuneus, LOC,
EC, cerebellum, PHG in memory retrieval. Based on the ndings that
hippocampal subelds along the long axis may follow different devel-
opmental trajectories (Botdorf et al., 2022; Keresztes et al., 2017, 2022;
Riggins et al., 2018) and be differentially involved in memory delays
(Atucha et al., 2021; Poppenk et al., 2013) and underlying mnemonic
processes of encoding and retention (Botdorf et al., 2022), EC and hip-
pocampal body subeld volumes and hippocampal head volume were
also included as separate ROIs. The following corresponding regions of
interest (ROI) were identied according to Desikan-Killiany atlas
(Desikan et al., 2006) for the (i) volumetric output: cerebellar cortex, EC,
hippocampal head, and hippocampal body subelds volumes (i.e.,
DG-CA3, Subiculum (Sub), and CA1–2); (ii) cortical thickness output:
inferior frontal cortex (IFG; comprised of pars opercularis, pars trian-
gularis and pars orbitalis), medial OFC, lateral OFC, rostral middle
frontal cortex, praecuneus, superior parietal cortex, inferior parietal
cortex, parahippocampal cortex, and lateral occipital cortex. As we did
not have specic hypotheses based on prior research related to lateral-
ization, these ROIs were collapsed across hemispheres for all following
analyses (Dick et al., 2022). To control for differences in head size, brain
volumes were adjusted for intracranial volume (ICV) using a covariance
approach (Clifford et al., 1989; Raz et al., 2005; Voevodskaya et al.,
2014). Cortical thickness was not adjusted for head size because cortical
thickness and head size are not associated (Barnes et al., 2010; Mills
et al., 2016).
2.5.2. Behavioural data analysis
The analyses of all behavioural measures were performed with R
packages (Version 4.0.4, R Core Team, 2021) in R Studio 1.4.1106
(RStudio, Inc.). Throughout the analyses, p-value signicance levels
were set to a <0.05. We conducted a linear mixed-effect model for
memory measures (accuracy dened as percentage of correct responses)
using the lmer function from the lme4 package in R (Bates et al., 2015)
and lmerTest (Kuznetsova et al., 2017). The linear mixed effect model
was calculated with maximum-likelihood estimation and Subject as
random intercept to account for between-subject variability in memory
accuracy. As xed factors, we included the within-subject factor of
Session (short delay and long delay relative on correctly recalled items
on Day 0) and the between-subject factor of Group (children and young
adults). In addition, IQ, Sex, and Handedness (Kang et al., 2017; Willems
et al., 2014) were added as covariates into the model. The main effects
were followed up with Sidak post-hoc multiple comparisons. For group
differences in memory measures, we conducted one-way independent
analysis of variance (ANOVA). In case of violated assumptions of ho-
mogeneity of variances, a Games-Howell test was performed (Lee and
Lee, 2018). The effect size estimation was performed with omega
squared (w
2
) as a less biased estimate for reporting practical signicance
of observed effects (Finch and French, 2012; Okada, 2013; Troncoso
Skidmore and Thompson, 2013). To determine the amount of variance
explained by the model, we used partR2 package in R (Stoffel et al.,
2020) with bootstrapping to calculate condence intervals.
2.5.3. PLSC (partial least square correlation): linking brain structures to
behavioural measures
We applied a multivariate Partial Least Square Correlation (PLSC)
method (Abdi and Williams, 2013; Keresztes et al., 2017; Krishnan et al.,
2011; McIntosh et al., 1996 ) to investigate the multivariate associations
between predened ROIs and variations in short- and long-delay
memory retention rate, both within and across both age groups. We
opted for memory retention rates as an indirect measure for memory
consolidation, because while we cannot entirely rule out variations in
retrieval, by keeping the procedure the same across time we believe that
the retention rates largely reect differences in the consolidation pro-
cess. We extracted a latent brain prole that maximally shares common
variance with either short-delay or long-delay variations in memory
retention rates, in which a large part of the variance is driven by
age-related differences in memory retention rates. We postulated that
this multivariate approach is better suited than univariate assessment of
the relation of different ROIs to memory consolidation due to their
interconnectedness and interdependence (see e.g.,Genon et al., 2022).
First, we calculated a between subject correlation matrix between (i)
a n x 16 matrix of volumetric or cortical thickness measures of all ROIs
and (ii) an n-vector representing a continuous measure of either short-
delay or long-delay retention rates (RR): R =CORR (RR, ROIs). All
measures entering the correlation were normalized. Singular value
decomposition was used to decompose this correlation: R =USV into
singular vectors U and V or saliences (Abdi and Williams, 2013;
Krishnan et al., 2011). The left singular vector represents the short- or
I. Schommartz et al.
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6
long-delay retention rates weights (U), the right singular vector of ROI
weights (V) that represents the characteristic of brain structures that
best represent R, and S is a diagonal matrix of singular values.
After that PLSC searches for a single estimable latent variable (LV)
nding pairs of latent vectors with maximal covariance in a least-
squares sense that represent the association between retention rates
and ROI characteristics. Hence, LV represents distinct proles of ROIs
that have the strongest relation to either short- or long-delay retention
rates. In addition, we calculated for each subject a single value, so-called
within-person “short-delay brain score” and “long-delay brain score”,
which are summary units of within-person robust expression of the
dened LV’s prole. For this purpose, the model-derived vector of ROI
weights (V) was multiplied by within-person vector of estimates of ROI
structural measures.
We ran 5000 permutation tests to obtain a p-value to identify the
generalizable vector of saliences or a LV and to assess whether the
identied association is signicant. Further, we identied the stability of
within-LV weights through the subsequent bootstrapping on 5000
bootstrap resamples of the data and obtained a bootstrap ratio (BSRs) by
dividing each ROI’s salience by its bootstrapped standard error. The
BSRs are akin to Z-scores and considered to be normalized estimates of
robustness (Keresztes et al., 2017), therefore when values are large-
r/smaller than ±1.96 (a <0.05) their corresponding saliences are
treated as signicantly stable. Due to a single analytic step in multi-
variate statistical assessment using PLSC, no correction for multiple
comparisons across all ROIs is necessary (McIntosh and Lobaugh, 2004).
Considering that more robust brain-wide association are observed in
multivariate vs univariate methods (Marek et al., 2022), several merit
can be highlighted for the application of PLSC to identify the relation-
ship between specic MRI structural measures of brain anatomy and
memory consolidation measures across age groups. The measures of
brain anatomy are highly correlated and interconnected, specically HC
subelds, which may otherwise lead to statistical multicollinearity and
redundancy, potentially reducing the statistical power to reveal
neural-behavioural relationships when applying canonical statistical
methods. Addressing these shortcomings, PLSC provides a statistically
powerful technique which allowed us to map memory consolidation
scores onto predened multiple structural MRI ROIs (Nestor et al.,
2002). It is important to note that our approach targets how brain
structures are related to variations (or individual differences) in memory
consolidations, and not of their involvement in consolidation processes
in a within-person sense.
3. Results
3.1. Behavioural results
3.1.1. Learning accuracy on day 0
In the following we rst tested for group-related differences on nal
learning accuracy of object-location associations during the learning
phase on Day 0. To reach at least the set criterion of 83% correct re-
sponses, children needed signicantly more learning-retrieval cycles on
average (mean =2.58, SD =0.70, range: 2–4), in comparison to young
adults who needed only 2 cycles as revealed by the Games-Howell test, b
= − 0.58, p <.0001, 95% CI [−0.77 – −0.38]. Next, nal learning
accuracy dened as percentage of correctly retrieved locations of the
objects within the scenes was separately calculated for items to be
retrieved on day 1 and day 14 as they differed between participants (see
Fig. 3A). The Games-Howell tests revealed no difference in nal memory
accuracy (i) between short delay (mean =90.40; SD =6.51) and long
delay items (mean =90.13; SD =5.51) in children (see Table 2), b
= − 0.003, p =.823, 95% CI [−0.03 –0.02]; ii) and between short delay
Fig. 3. Final learning accuracy and memory consolidation. (A) Final learning accuracy shows the percentage of correct responses for short delay items and long delay
items after learning was completed in children and young adults. There was no difference in nal learning accuracy for short and long delay item in either age group.
Children needed between two to four learning-retrieval cycles to reach the criterion of 83% correct responses; while young adults need on average two cycles; (B)
Memory consolidation over the course of two weeks, operationalized as percentage of correctly retrieved object-location associations on day 1 for items that were
correctly retrieved on day 0 (after one night of sleep) for short delay, and percentage of correctly retrieved object-location associations on day 14 that were correctly
retrieved on day 0 (after two weeks) for long delay. Error bars indicate model-based standard error. * p<.05. * * p<.01. * ** p<.001(signicant difference), ns:
non-signicant difference. P-values use Sidak correction for multiple comparisons.
Table 2
Descriptive statistics of memory performance based on percentage of correct
answers by age groups.
Session Children Young Adults
M SD M SD
Retrieval
Day 0 short delay 90.40 6.51 97.18 3.55
Day 0 long delay 90.13 5.51 98.46 2.74
Day 1* 83.37 11.72 94.62 5.90
Day 14 +61.43 12.38 76.16 12.03
Notes. M =mean; SD =standard deviation. * Relative to only correct items on
Day 0.
I. Schommartz et al.
Developmental Cognitive Neuroscience 59 (2023) 101192
7
(mean =97.18; SD =3.55) and long delay items (mean =98.46; SD =
2.74) in young adults, b =0.013, p =.078, 95% CI [−0.001 –0.02]. In
addition, the Games-Howell test revealed a signicantly higher per-
centage of correctly retrieved locations of the objects within the scenes
in young adults in comparison to children, b =0.076, p <.0001, 95% CI
[0.06 –0.09]. Hence, despite our training-to-criterion procedure, young
adults showed better memory performance than children at the end of
the training. Observed groups differences in the nal learning perfor-
mance were considered in the subsequent modelling approach, which
concentrated only on the items that were correctly retrieved during nal
learning performance.
3.1.2. Memory retention across time
In the following, we examined change in memory performance for
correctly remembered items on day 0 across time, particularly testing for
group differences in short- and long-delay memory retention (see
Fig. 3B). The linear mixed-effects model for retrieval accuracies of
learned object-location pairs explained a signicant amount of variance,
R
2
=.74, 95% CI [0.70 –0.79]. We observed a signicant main effects of
Session, F
(2178)
=342.05, p <.0001, w
2
=.79, Group, F
(1,89)
=51.46,
p<.0001, w
2
=.36, and Session x Group interaction, F
(2178)
=20.24,
p<.0001, w
2
=.18 (Supplementary Table S2 for a full overview). To
interpret the interaction, we examined short delay and long delay
separately (see Fig. 3C). For short delay, the model revealed a signi-
cantly steeper slope of accuracy decline from day 0 to day 1 in children
in comparison to young adults (b =11.25, t
(182)
=4.60, 95% CI [4.5 –
18.0], p <.0001), indicating less robust short-delay memory consoli-
dation in children compared to young adults. Model-based Sidak post
hoc comparisons for short delay revealed that (i) retrieval accuracy after
one night of sleep declined signicantly in children (b =16.63, t
(182)
=10.26, 95% CI [12.2 – 21.1], p <.0001), and in adults (b =5.38, t
(182)
=2.93, 95% CI [0.3 – 10.4], p =.029). For long delay, the model also
revealed signicantly steeper slope of accuracy decline from day 0 to
day 14 in children in comparison to young adults (b =14.73, t
(182)
=6.02, 95% CI [7.9 – 21], p <.0001), indicating less robust long-delay
memory consolidation in children. Sidak post hoc tests revealed a sig-
nicant decline in long-delay retention rates in both groups (all
p<.0001; Fig. 3B). Taken together both age groups showed a decline in
memory performance over time, however, compared to young adults,
children showed a steeper slope of memory decline for both short and
long delay.
3.2. Brain-behavioural relationships
3.2.1. Unique multivariate brains proles are associated with short and
long delay memory consolidation
As the next step, we applied PLSC to identify unique brain proles of
structural brain measures in relation to either short- or long-delay
memory consolidation for the items that were correctly learnt on day
0, estimating the brain-behaviour pairings that covary together within
and across age groups. The cross-correlational matrix was reduced to a
set of single latent variables (LV) or saliences. Within age groups, we
could not identify a single reliable LV to reliably represent brain-
behaviour association (all p ≥.50), due to the narrow age range
within the groups and little age-related structural variability in all ROIs
within the groups (Supplementary Table S3 and Fig. S5 for a full over-
view). In the following the results across age groups will be presented.
First, for short delay, we obtained a single composite score that captures
individual differences in brain structures across both age groups in
relation to memory retention rates, referred to in the following as “short-
delay brain score”. With permutation test of signicance, we identied a
single reliable LV (p =.0002) that optimally represents an association
between a prole of ROIs and short delay retention rates (r =0.44).
Using BSR that expresses the consistency with which the salience of a
particular ROI is non-zero across subjects, we identied several stable
components within the multivariate prole (see Fig. 4 A): a positive
short-delay retention rates association with ROI volumes of the cere-
bellum (BSR =2.78, r =0.27), hippocampal head (BSR =5.95,
r=0.44), ERC (BSR =2.39, r =0.27), and all hippocampal body sub-
elds: Sub (BSR =2.83, r =0.28), DG-CA3 (BSR =2.78, r =0.25),
CA1–2 (BSR =3.01 r =0.28); a negative short-delay RETENTION
RATES association with cortical thickness measures in parsopercularis
part of the IFG (BSR = − 5.79, r = − 0.42), parsorbitalis part of the IFG
(BSR = − 3.84, r = − 0.33), parstriangularis part of the IFG (BSR =
−3.12, r = − 0.31), the lateral orbitofrontal cortex (BSR = − 4.78,
r= − 0.38), the medial orbitofrontal cortex (BSR = − 5.87, r = − 0.43),
the rostromedial cortex (BSR = − 5.43, r = − 0.40), the precuneus (BSR
= − 3.97, r = − 0.35), the superior parietal cortex (BSR = − 5.01,
r= − 0.36), the inferior parietal cortex (BSR = − 4.65, r = − 0.35), and
the lateral occipital cortex (BSR = − 3.29, r = − 0.32). Taken together,
these stable components of the LV express the amount of information
common to short-delay retention rates across both age groups and
multivariate pattern of ROIs in specic neocortical, cerebellar, and
hippocampal structures (see Fig. 4B).
Second, with long delay, we obtained a single composite score that
captures individual differences in brain structures across both age
groups that relates to memory retention rates, referred to in the
following as “long delay brain score”. We identied a single reliable LV
(p =.0004) that optimally represents an association between predened
ROIs and long-delay retention rates (r =0.394). BSR identied several
stable components within the multivariate prole (see Fig. 4 C): a pos-
itive long-delay retention rates associations with ROI volumes of hip-
pocampal subelds: hippocampal head (BSR =, r =0.33), Sub (BSR =
2.40, r =0.27), CA1–2 (BSR =2.22 r =0.28); a negative long-delay
retention rates association with cortical thickness measures in the par-
sopercularis part of the IFG (BSR = − 3.33, r = − 0.30), the parsorbitalis
part of the IFG (BSR = − 3.48, r = − 0.32), the parstriangularis part of
the IFG (BSR = − 3.27, r = − 0.33), the lateral orbitofrontal cortex (BSR
= − 3.69, r = − 0.33), the medial orbitofrontal cortex (BSR = − 5.32,
r= − 0.45), the rostromedial cortex (BSR = − 5.13, r = − 0.44), the
precuneus (BSR = − 3.77, r = − 0.34), the superior parietal cortex (BSR
= − 3.82, r = − 0.33), the inferior parietal cortex (BSR = − 3.11,
r= − 0.30), and the lateral occipital cortex (BSR = − 2.04, r = − 0.22).
These stable components of the LV express the amount of information
common to long-delay retention rates across both age groups and
multivariate pattern in neocortical and hippocampal ROIs. In contrast to
short delay, cerebellar and ECR volumes as well as DG-CA3 hippocampal
volumes do not contribute reliably to long-delay retention rates. The
identied LVs account for a moderate portion of brain-behaviour
covariance (short delay: 44%, long delay: 39%). Of note is that not all
included ROIs contributed signicantly to the covariance with memory
performance, indicating specicity within proles of brain ROIs with
relation to either short- or long-delay memory retention rates (see
Fig. 4D).
In addition, when testing for age differences in the brain scores, t-test
revealed that the short-delay brain score, t
(160)
= − 6.621, p <.0001, as
well as the long delay brain score, t
(160)
= − 6.624, p ≤.0001, were
signicantly higher in young adults in comparison to children. This
suggests that while the brain proles were identied pulling across both
age groups, there are age differences in the derived brain score, in
parallel to age differences in behavioural memory consolidation
measures.
4. Discussion
In the present study, we investigated memory consolidation of
correctly learned object-location associations (through adaptive and
intentional learning) after short delay (with one night of sleep) and long
delay (after 2 weeks), comparing memory retention rates in 5-to-7-year-
old children and young adults. We found that: (i) children, in compar-
ison to young adults, showed lower retention rates of correctly learnt
associations both after one night of sleep and after a 2-week-period; (ii)
I. Schommartz et al.
Developmental Cognitive Neuroscience 59 (2023) 101192
8
Fig. 4. Multivariate proles of brain structures associated with variations in memory consolidation. A) Short Delay Brain Saliences. Brain saliences or latent variables
weights for each ROI were incorporated in the analysis to create one latent variable that expresses a composite short-delay brain score. Stability of salience elements
is dened by Z-scores (depicted as red line: a value larger/smaller than ±1.96 is treated as reliably robust at (a <0.05). B) Association between Short Delay Brain Score
and Short Delay Memory Retention Rate. Brain short delay score or a latent variable plotted against short delay retention rate. There were signicant age-related
differences in short delay brain score, paralleling age-related differences in short delay retention rate. C) Long Delay Brain Saliences. Brain saliences or latent vari-
ables weights for each ROI were incorporated in the analysis to create one latent variable that expresses a composite long-delay brain score. Stability of salience
elements is dened by Z-scores (depicted as red line: a value larger/smaller than ±1.96 is treated as reliably robust at (a <0.05). D) Association between Long Delay
Brain Score and Long Delay Retention Rate. Brain long delay score or a latent variable plotted against short delay retention rate. There were signicant age-related
differences in long delay brain score, paralleling age-related differences in short delay retention rate. Note: IFG – inferior frontal gyrus; OP – parsopercularis; OR
– parsorbitalis; TR – parstriangularis; lOFC – lateral orbitofrontal cortex; mOFC – medial orbitofrontal cortex; rmPFC – rostromedial cortex; PREC – precuneus; SP –
superior parietal cortex; IP – inferior parietal cortex; PHG – parahippocampal cortex; LOC – lateral occipital cortex; CE – cerebellum; HC-head – hippocampal head;
EC – entorhinal cortex; SUB* – Subiculum; DG-CA3 * – dental gyrus and CA3; CA1–2 * – CA1 and CA2 subelds of hippocampus. * Subelds of hippocampus body.
For group differences for each structural ROI (Houston et al., 2013), please refer to Table S8 in Supplementary Materials.
I. Schommartz et al.
Developmental Cognitive Neuroscience 59 (2023) 101192
9
applying multivariate PLSC analysis with structural high-resolution MRI
data, we identied a) a stable brain prole comprised of neocortical
(prefrontal, parietal, and occipital), cerebellar, and MTL (i.e., hippo-
campal head, all hippocampal body subelds and EC) structures that is
associated with variations in short-delay memory retention rates across
both age groups; b) a stable brain prole comprised of more specic
neocortical (prefrontal, parietal and occipital), and MTL (i.e., hippo-
campal head, subiculum and CA1–2 hippocampal body subelds)
structures that is associated with variations in long-delay memory
retention rates across both age groups. Moreover, we observed that the
identied scores of short- and long-delay brain proles were lower in
children in comparison to young adults. Thus, extending the conven-
tional univariate analyses, our approach suggests that individual dif-
ferences in short- and long-delay memory consolidation, which contain
signicant age-related variations, are associated with neural proles
comprised of distinct structural brain regions that are unique for short
and long delays. In the following, we discuss each nding in detail.
4.1. Short- and long-delay memory retention
4.1.1. Less robust short- and long-delay memory retention in children in
comparison to young adults
Children showed steeper accuracy percentage change and thus lower
short and long-delay retention rates in comparison to young adults,
indicating reduced retained memory of prior-knowledge-dependent
complex associative information across time. On the one hand, our
result is not in line with the ndings of higher short-delay memory
consolidation (i.e., after one night of sleep) for incidental learning
episodic tasks in 7–12-years-old children in comparison to young adults
(Peiffer et al., 2020a; Wang et al., 2018). These studies suggested that
higher proportion of slow wave sleep in children in comparison to adults
may contribute to possible age-related consolidation benets. On the
other hand, Wilhelm et al. (2008) did not nd a benecial effect of sleep
in 6–8-years-old children in comparison to young adults, showing a
comparable consolidation performance after one night of sleep for
declarative visuo-spatial and word-pairs associations. The mixed results
may be attributed to the nature of memory measures. Namely, Wang
et al. (2018) employed item memory and Peiffer et al. (2020a) employed
associative memory measures with stimuli that were not related to any
prior knowledge. This might have precluded the possibility of semantic
elaboration in conceiving prior-knowledge-based associations, as was
utilized in our study and Wilhelm et al. (2008). Activated prior knowl-
edge boosts memory consolidation for associative representations
(Fern´
andez and Morris, 2018; Tse et al., 2011). However, when
pre-existing knowledge representations are less extensive or limited in
children, it may counteract benecial effects of sleep in children (Gau-
dreau et al., 2001; Ohayon et al., 2004) on the consolidation of newly
correctly learnt associations. Therefore, lower short term retention rates
in children, compared to adults, after one night of sleep in our study may
be attributed to their lower level of prior knowledge for the stimuli. In
similar fashion, superior long-delay memory retention rates in young
adults may also be attributed to their more extensive world knowledge
and, therefore, more elaborate schemas, ensuring better accessible
memory representations in long term (Craik and Lockhart, 1972; Shing
et al., 2010; van Kesteren et al., 2012).
On a related note, an adaptive, strategic learning of object-location
associations that resulted in high level of nal performance through
repeated testing and encoding was applied in this study. It is worth
noting that no pure measurement for consolidation was conducted, as
this process happens ofine after Day 0 learning. Essentially the con-
sequences of consolidation at Day 1 and Day 14 retrieval were assessed.
In view of this limitation, we opted for a 3-alternative forced choice
procedure during retrieval to reduce the demand on retrieval processes
(e.g., the need for strategic search in free recall). Adding to this, the
retrieval procedure was kept comparable and stable over time, making
sure that the process of retrieval was well-trained for all participants.
Therefore, while we cannot entirely rule out variations in retrieval, by
keeping the procedure the same across time we believe that our
behavioral outcome largely revealed differences in consolidation pro-
cess (particularly, stabilization of encoded memories). In this way, in
comparison to previous studies that aimed at episodic memory retrieval
after one-shot encoding without mnemonic strategy use, our work shed
some light on memory consolidation of well-studied information.
Considering age differences in the nal learning performance, we
concentrated our analysis only on the retention of correctly learnt as-
sociation. Our ndings indicate that the potential benecial effect of
sleep for children (as reported in some studies) in comparison to adults
for incidentally learned information may not apply to elaboratively
learned information. This is potentially because such information is
easier to be strengthened and integrated into the more extensive, well-
connected network of knowledge in adults through sleep. It could also
be remembered more easily by adults through more efcient strategic
control of memories (e.g., self-generated cues) upon retrieval (Fanda-
kova et al., 2017; Shing et al., 2008). In other words, the advantage of
deliberate, repeated intentional learning, in comparison to accidental
episodic learning, is more pronounced in adults in comparison to chil-
dren (McDaniel and Masson, 1977), relying on the ability to utilize
adequate learning operations (Eagle and Leiter, 1964). Taken together,
our ndings provide novel empirical evidence showing that, in the case
of intentional encoding, 5-to-7-year-old children show less robust short-
and long-delay memory consolidation of correctly learned
object-location associations compared to young adults.
4.1.2. Short- and long-delay memory retention is related to differential
proles of structural brain measures across both age groups
Based on the memory consolidation literature that postulates dif-
ferential time-related neural reorganization of memory traces depend-
ing on the nature of stimuli (Moscovitch and Gilboa, 2021; Sekeres et al.,
2017) and of learning (Gilboa and Marlatte, 2017), we expected to
identify differential brain proles that would reect detail-rich mem-
ories in short delay and perceptually decayed mnemonic representations
in long delay, which increase the relative importance of strategic con-
trol/elaboration for memory. Somewhat in line with our expectations,
we identied a stable multivariate prole of short-delay memory
consolidation comprised of neocortical (i.e., prefrontal, parietal, and
occipital), cerebellar, and MTL (specically, hippocampal head, hippo-
campal body subelds and EC) structures across age groups. The iden-
tied brain prole related to variations in long-delay memory retention
rates is with a reduced number of brain regions, comprising of mostly
neocortical regions of prefrontal, parietal and occipital cortex, as well as
hippocampal head and two specic hippocampal subelds, namely the
subiculum and CA1–2.
First, our results extend previous univariate ndings on the relations
between brain measures and mnemonic processes in developmental
cohorts. For example, studies showed that extended developmental
trajectories of hippocampal head (Botdorf et al., 2022; Poppenk et al.,
2013), hippocampal body subelds and EC (Canada et al., 2019; Ker-
esztes et al., 2017), cerebellar (Sussman et al., 2016), prefrontal (Bauer
et al., 2019; Botdorf and Riggins, 2018; Mills et al., 2016, 2016;
Schlichting and Preston, 2015; Sousa et al., 2018; Sowell et al., 2001),
parietal and occipital (DeMaster and Ghetti, 2013; Hebscher et al., 2019;
Himmer et al., 2019; Karanian and Slotnick, 2015) regions are related to
age-related differences in encoding and retrieval of memories. The brain
proles identied in our study extend the literature, showing that
multivariate proles comprised of these structural brain measures can
also be related to memory retention across short and long delays. In
particular, thinner medial OFC, IFG, rmPFC, LOC, PPC regions and
larger volumes of hippocampal head, hippocampal body subelds, cer-
ebellum und EC are associated with higher memory retention rates after
one night of sleep. The long delay brain prole shows that thinner IFG,
OFC, rmPFC, PPC, and LOC as well as larger volumes of subiculum and
hippocampal head and CA1–2 hippocampal body subelds are
I. Schommartz et al.
Developmental Cognitive Neuroscience 59 (2023) 101192
10
associated with higher memory retention rates over two weeks. Notably,
the directionality in these regions is in line with existing ndings on
developmental trajectories of brain morphology, i.e. thicker cortex and
smaller MTL/cerebellum in 6–7-year-old children compared to adults
(see Hedman et al., 2012 for an overview). This corresponds to the
age-related and expected volumetric increase in cerebellum and hip-
pocampus on the one hand, and cortical thinning in neocortical areas on
the other hand. The derived brain scores also showed signicant age
difference, paralleling age-related differences in short- and long-delay
retention rates. Taken together, our interpretation is that the brain
proles identied with PLSC may partly underlie children’s worse short-
and long-delay consolidation compared to adults.
The distinctiveness of short- and long-delay brain proles may be
attributed as expected to time-related decay of detail-rich mnemonic
representation. As our task required utilization of mnemonic strategies
using prior knowledge to form vivid memories of object-location asso-
ciations, we expected that the stabilization of memory traces for
correctly learnt associations would depend on strategic elaborations
based on prior knowledge and controlled processing. This should be the
case both after one night of sleep as well as over longer time. On the
other hand, detail-rich and strong mnemonic representation may be
more prominent for short-delay than long-delay consolidation. The
involvement of EC, hippocampal body subelds, LOC and cerebellum in
the short-delay prole is in agreement with our hypothesis, as these
brain structures are important for perceptual vividness and precision of
memory representations (DeMaster and Ghetti, 2013; Fandakova et al.,
2019; Grill-Spector et al., 2001; Karanian and Slotnick, 2015; Keresztes
et al., 2017). There tends to be a decay of memory precision over longer
time, which may explain why such regions as EC and DG-CA3 and cer-
ebellum are no longer associated with variation in retention rates after
two weeks. This is in line with Østby et al. (2012), who showed that
variation in hippocampal volume was related to memory retention rate
after one week and Fjell et al. (2019), who showed that memory
retention rate over extended period of around 10 days was related to
hippocampal and lateral prefrontal cortex structure. We did not ex-
pected, however, any associations of more ne-grained body intra-
hippocampal structures with retention rate after more extended
consolidation time of two weeks and due to associative nature of our
task and lack of any developmental nding with this regard (Moscovitch
and Gilboa, 2021). However, our nding of subiculum and CA1–2 hip-
pocampal body subelds being associated with age-related variations in
long delay retention rates converges with recent evidence that in mice
CA1 is necessary for long-delay consolidation of very remote memories
or retrieval of gist memory, while CA3 is required for retrieval of precise
memories recent in time (Atucha et al., 2021). In addition, Barry et al.
(2021) showed a positive relationship between pre/parasubiculum
volume and autobiographical memory over time, showing its role in the
robustness of remote memory over time. Hippocampal head was asso-
ciated with retention rate variations for both short and long delay. This
is in line with the previous ndings showing that hippocampal head
involvement in source memory (Riggins et al., 2015) and in binding of
semantic information into conceptual and gist-like representations
(Patterson et al., 2007; van Kesteren et al., 2010), irrespective of
memory age (Poppenk et al., 2013). As our task required both source
memory and integration of semantic knowledge to form object-location
associations, it may explain the role of hippocampal head in retention of
across consolidation. In relation to this subiculum of hippocampal body
(Keresztes et al., 2022), CA1 hippocampal body subelds (Riggins et al.,
2018), and hippocampal head (Botdorf et al., 2022; Riggins et al., 2018;
Schlichting et al., 2017) was recently shown to undergo profound
volumetric increase in middle childhood, indicating that the age-related
increase in structural volume in these regions go hand in hand with
improvement in long-delay memory consolidation.
Prefrontal (lateral and medial PFC), parietal and occipital brain
structures, on the other hand, were associated with both short- and long-
delay memory retention rates. With the decay of memory precision, the
relative importance of these regions became even stronger. MOFC and
rmPFC are associated with schema-integration (Brod and Shing, 2018;
Mella et al., 2021; van Kesteren et al., 2012), while IFG and lOFC are
associated with strategic elaboration and control over memories (Badre
et al., 2005; Fjell et al., 2019; Kuhl et al., 2012; Østby et al., 2012).
Presumably individuals with better prole in these regions could form
memory representations in a controlled way, particularly by using prior
knowledge for elaboration, leading to better memory performance both
in short and long delay. This is in line with ndings that age-related
decrease in PFC volumes is related to increasing strategy use in
cross-sectional sample of 5–25 year old participants (Yu et al., 2018).
Furthermore, parietal regions such as PPC is found to be important for
reinstating neural representation of visuo-spatial association (Brodt
et al., 2016; DeMaster and Ghetti, 2013; Himmer et al., 2019; Takashima
et al., 2007). In line with this, successful recollection of items with
precise contextual details is found to be related to PPC in childhood
cohort (DeMaster and Ghetti, 2013). Also LOC as constituent of both
short and long delay brain prole is in line with its association with
reinstatement of item-specic information upon retrieval (Grill-Spector
et al., 2001; Karanian and Slotnick, 2015) and neural specicity of scene
representation at retrieval (Fandakova et al., 2019), as out task despite
decay of precision required associative location memory for both delays.
Taken together, age-related differences in neocortical parietal and pre-
frontal brain regions, which are important for creating and accessing
elaborative memory traces that are long lasting, may underlie children’s
steeper decline in memory retention.
Finally, contrary to our expectation, PHC was not associated with
memory retention rates at all. Despite PHG’s involvement in spatial
context-related associative recollection (Ghetti et al., 2010; Ranganath
and Ritchey, 2012), it is not involved in variations in short-delay
memory consolidation of detail-rich visuo-spatial associations. Simi-
larly, our ndings show that, despite cerebellar involvement in declar-
ative memory processes (Vecchi and Gatti, 2020), associative semantic
memory for words (Gatti et al., 2021) and retrieval of long-term episodic
memory (Andreasen et al., 1999), its structural volume is not linked to
variations in long-delay memory consolidation within long-delay brain
prole.
5. Limitations
Several limitations of the current study should be noted. First,
despite our procedure of learning to the criterion to maximize compa-
rability of retention rates, we observed group differences in initial
memory performance. Future studies may incorporate individualized
item-based approach of learning to criteria, excluding correctly
remembered items from further learning cycles to ensure faster learning
and lessening the overall task workload (Karpicke and Roedigeriii, 2007;
McDermott and Zerr, 2019; Zerr et al., 2018). Alternatively, the task
workload could be increased for the adults to ensure similar initial nal
learning performance. Second, we did not nd reliable brain prole that
relate to memory retention within the children and adult groups,
respectively. This may be due to the narrow age range and restricted
variation within each group, as our main questions call for maximizing
between-group differences. Future studies may either extend the age
range or increase sample sizes to create subgroups of high- and
low-performers, allowing a clustering approach to look at differentiated
neural proles of variations in short- and long-delay memory consoli-
dation. Third, the current ndings concentrate mainly on associative
memory of schema-congruent information. To investigate the benecial
effect of prior knowledge in memory consolidation, future studies
should investigate how violations of knowledge, namely
schema-incongruent information, may impact the learning of associative
information and their subsequent consolidation in short- and long-delay
memory and how this effect may differ throughout development.
Fourth, we do not report sleep-related measures that may have an
impact on memory consolidation. Finally, our sample of children was
I. Schommartz et al.
Developmental Cognitive Neuroscience 59 (2023) 101192
11
positively biased in IQ and mother’s education, in comparison to young
adults. The former may just be because different IQ tests were used for
children (WAIS-IV) and adults (K-ABC). The latter reects generational
difference in level of education. Nevertheless, the difference between
children and adults in these aspects should be noted, as they may limit
the generalizability of our results.
6. Conclusions
In this study, we provided novel empirical evidence that 5-to-7-year-
old children were worse in retaining intentionally learned object-
location associations in comparison to young adults after one night of
sleep and over an extended period of two weeks, suggesting less effective
memory consolidation through stabilization. We could identify distinct
stable multivariate proles comprised of specic memory-related brain
regions that explain variation in either short- or long-delay memory
consolidation. The brain regions involved support the notion that
perceptually rich, vivid memory traces are important for variations in
short delay, while controlled and elaboration processing are important
for variations in both types of delay. As memory consolidation shows
strong relation to age and the identied brain proles showed signi-
cant age differences, together the ndings indicate that age-related
differences in memory consolidation may be associated with specic
maturational processes of distinct anatomically interconnected brain
regions.
CRediT authorship contribution statement
Y.L.S, C.B., A.M.K secured funding. I.S and Y.L.S, C.B., A.M.K
contributed to conception and design of the study. I.S., H.S., N.W.-C.,
and P.L. performed data collection and data curation. I.S., P.L., and F.
P performed the statistical analysis. IS wrote the rst draft of the
manuscript, P.L., M.B. and F.P. wrote sections of the manuscript. All
authors contributed to manuscript revision, read, and approved the
submitted version.
Data Availability
The datasets generated and analysed during the current study are
available from the open science framework OSF under the following
link: https://osf.io/qx2wp/.
Acknowledgements
We thank all the children and parents who participated in the study.
This work was supported by the Deutsche Forschungsgemeinschaft
(DFG; German Research Foundation, Project-ID 327654276, SFB 1315,
“Mechanisms and Disturbances in Memory Consolidation: from Synap-
ses to Systems”). The work of YLS was also supported by the European
Union (ERC-2018-StG-PIVOTAL-758898) and the Hessian Ministry of
Higher Education, Science, Research and Art (Excellence Program,
project ’The Adaptive Mind’). The work of AMK was also supported by
the Einstein Stiftung.
Conict of interest disclosure
We have no known conict of interest to disclose.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the
online version at doi:10.1016/j.dcn.2022.101192.
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